euglipa

February 12-13rd 2014 The 4th Annual Basic Science International Conference

Batu, East Java, Indonesia | 1

BIOLOGY


2 | Batu, East Java, Indonesia



Abstract— Rust disease is a major disease on soybean,

wide spreading, existing in almost all soybean producer countries, and causing yield losses up to 80%. The objec-tive was to evaluate the resistance of soybean lines to rust disease. The research was conducted in the greenhouse of Indonesian Legume and Tuber Crops Research Institute, from March to June 2013. Design was randomized com-pletely block design with three replications. Materials were 10 soybean lines and five check varieties. Inoculating rust spores was carried out at 3 weeks after planting by using spore suspension (104/ml density) derived from infected soybean leaf. Inoculation was performed by spraying the spore suspension on the first leaves at 16.00-18.00. Obser-vations rust disease resistance based on the system of In-ternational Working Group on Soybean Rust. Results showed that there was no soybean line identified as resis-tant line. Of a total ten soybean lines, seven soybean lines (Tgm/Anj-908, Tgm/Anj-909, Tgm/Anj-910, Tgm/Anj-919, Tgm/Anj-932, Tgm/Anj-957, and Tgm/Anj-995 were iden-tified as moderately resistant, two soybean lines (Tgm/Anj-933, and Tgm/Anj-991) were susceptible, and one soybean line (Tgm/Anj-931) was susceptible. The three check varie-ties (Wilis, Tanggamus and Anjasmoro) were moderately resistant, while two check varieties (Grobogan and Argo-mulyo) were moderately susceptible.

Keywords— soybean lines, resistance, rust disease.

I. INTRODUCTION

ASED on the seed size, soybean seeds are classified as small seed, medium seed and large seed. Usually, medium and small seeds are used for soy sprouts,

soy sauce, tofu and soy milk ingredients, while large seeds is used for tempeh ingredient. However, the main usage is for tempeh ingredient as well as tofu. There-fore, one of the Iletri’s soybean breeding programs is directed to develop large seeded soybean variety.

In developing soybean variety, the resistance of the new variety to rust disease should be tested to provide supporting data for release variety; because rust disease is one of the major soybean disease in Indonesia. Rust disease also known as the "Asian soybean rust", caused by the fungus Phakopsora pachyrhizi. Rust disease is widespread in the area of soybean production centers in the world and causing significant yield loss. Distribution of rust disease started from Japan and East Asia in 1902, entered into Southeast Asia (Indonesia) and Australia in 1914, while in 1950 has reached India, and in 1994 en-

tered Hawaii. Then it entered South Africa in 1920 and has reached Uganda in 1996. In the years 2001 - 2002 rust disease infestation appeared in South America, and in 2004 had spread out to the north reaching the United States [1]. Yield losses can reach more than 85% if suit-able environment for disease development [2]. Further-more, it is stated that there are four types of genes that responsible for controlling rust disease resistance in soybean, i.e. Rpp1 , Rpp2 , Rpp3, and Rpp4 [2].

One of the techniques for controlling soybean rust disease is to grow resistant varieties. There are several soybean varieties having rust disease resistance that have been released. The resistance to rust disease is not durable and someday the resistance will be broken be-cause the fungus P. pachyrhizi can mutate to be new races. Therefore, developing new of superior that resis-tant to rust disease is still needed. The purpose of the study is to evaluate the resistance of large seeded soy-bean lines to rust diseases.

II. MATERIALS AND METHOD

The study was conducted in Indonesian Legume and Tuber Crops Institute, in the dry season (March-June 2013). The design was randomized completely block, with three replications. The materials research were 10 soybean lines (Tgm/Anj 908, Tgm/Anj-909, Tgm/Anj-910, Tgm/Anj-919, Tgm/Anj-931, Tgm/Anj-932 Tgm/Anj-933, TGM / Anj-957, Tgm/Anj-991, Tgm/Anj-995) and four check varieties (Wilis, Tanggamus, Anjasmoro, Grobogan, Argomulyo).

Soybean seeds were grown in plastic polybag (Φ = 15cm), two plants per polybag. At one month old, plants were inoculated with rust disease by spraying spore sus-pension (104/ml) to soybean leaves. Spore suspension was originated of rust-infected leaves from inoculum plantation sources. The day before inoculation, infected leaves were taken to be incubated at 100% humidity conditions in the laboratory. After 24 hours, the spores were taken by using a brush, and then the spores were diluted. The spore suspension was homogenized by us-ing Tween 20, two drops per liter. To avoid pest attack-ing, spraying insecticides (carbofuran, sipemetrin, cyha-lothrin) was carried out several times alternately.

Intensity of rust disease was observed in all tested plants at 7 and 9 weeks after planting by IWGSR (Inter-national Working Group on Soybean Rust) method [3] (Table 1).

Resistance Evaluation of Large Seeded Soybean Lines to Rust Disease

Sumartini 1 and Heru Kuswantoro 1*) 1) Indonesian Legume and Tuber Crops Research Institute

Indonesian Agency for Agricultural Research and Development *) Corresponding author: [email protected]

B



In addition to the intensity of rust disease, observation was also carried out on yield component, such as plant height, number of branches per plant, shoot fresh weight, shoot dry weight, number of filled and unfilled pods per plant, and seed weight.

III. RESULTS AND DISCUSSION

The results showed that until week 9th, position of

plant disease was in the middle to upper canopy, or 2 to 3 score. The intensity of rust disease scores ranged 3-4, or the number of pustules from 8 to more than 16 pustules per cm2. It means that the intensity was mild to high, and many pustules had spores. Of the 10 genotypes

contained, there was no resistant genotype, seven genotypes were moderately resistant (MR), two genotypes were moderately susceptible, and one genotype was susceptible (Table 2). In this experiment, the three checks varieties (Wilis, Tanggamus and An-jasmoro) were classified as moderately resistance. This results were similar to the varieties description [4]. However, Argomulyo was moderately susceptible, dif-ferent to the description that Argomulyo is tolerant to rust disease. Similar results also reported by investigators from Nigeria using simpler methods based solely on the intensity of rust disease on a scale 1-5. With this method they can classify resistance of soybean genotypes to rust disease. Of the 28 tested genotypes, most of the genotypes were susceptible to moderatly resistance. However, they found seven resistant genotypes [5]. Beside, they found dominant genes controlling rust disease found resistance in three different loci. Investigators from the United States classified resistance to soybean rust disease into four types of Phakopsora pachyrhizi fungi isolates and the symptoms by two types of symptoms i.e. reddish-brown (RB) and TAN. Of 34 tested genotypes, 28 genotypes were included as TAN type with many sporulating and six genotypes were included as RB (reddish-brown) [6].

Plant height is an important character because it has a positive correlation to grain yield [7]. Beside, plant height had indirect effect on grain yield through number of branches per plant, number of pods per plant, number of grain per plant and 100 grains weight [8]. Plant height of the tested genotypes were classified as normal. However, three genotypes had abnormal plant height, i.e. Tgm/Anj-995, Tgm/Anj-919 and Tgm/Anj-931

(Table 3). The three genotypes had different response to rust disease; where Tgm/Anj-995 and Tgm/Anj-919 were moderately resistant and Tgm/Anj-931 was susceptible. In this case, the rust disease did not cause plant height character. Probably, other unknown

TABLE II SCORES AND RESISTANCE CRITERIA OF LARGE SEEDED SOYBEAN GENOTYPES TO RUST DISEASE

Genotypes Pustul posi-tion

Number of pustul/cm2

Disease intensitas

Spore exis-tence

Score Resistance criteria

Tgm/Anj-908 2 14 3 3 233 Moderately resistance




Tgm/Anj-931 3 18 4 3 343 Susceptible


Tgm/Anj-933 3 11 3 3 333 Moderately suscepti-ble


Tgm/Anj-991 3 10 3 3 333 Moderately suscepti-ble


Wilis 2 12 3 3 233 Moderately resistance

Tanggamus 2 10 3 2 232 Moderately resistance

Anjasmoro 2 10 3 3 233 Moderately resistance

Grobogan 3 16 3 3 333 Moderately suscepti-ble Moderately suscepti-

TABLE I DETERMINATION OF RESISTANCE CRITERIA TO RUST DISEASE

Position of the disease on the plant 1 : 1/3 of lower part of the plant

2 : 2/3 of middle part of the plant

3 : 1/3 of upper part of the plant

Disease intensity 1 : no pustule

2 : mild ( 1 – 8 pustul/cm2 )

3 : medium ( 9 – 16 pustul/cm2 )

4 : high ( > 16 pustul/cm2 )

Resistance criteria Imun : Score 111

Resistant : Score 122, 123, 132, 133, 222. 223

Moderately resistant : Score 142, 143, 232, 233, 242, 243, 322, 323

Moderately susceptible : Score 332, 333

Susceptible : Score 343



environmental factors affected the plant height performance in this study.

The performance of fresh weight and dry weight of plants were similar to plant height performance. Plant with having higher fresh weight and dry weight also had higher plant height, and vice versa. The three shortest lines (Tgm/Anj-995, Tgm/Anj-919 and Tgm/Anj-931) also had the lowest fresh weight and dry weight of plants (Table 3). The low value of fresh weight and dry weight may be caused by inability of the short plant to develop larger or more organs than high/normal plants. Plant dry weight described photosynthate which produced by the plant. The photosynthate is partitioned into plant organs, and it leads the magnitude of grain yield.

The number of pods determine grain yield because grain yield is the total photosynthate partitioned into seeds, and the magnitude of grain yield was also

determined by the number of seeds [9], and the number of seeds is determined by the number of pods. The number of pods per plant varied among tested genotypes, where Tgm/Anj-995 showed the lowest number of filled pods and Tgm/Anj-909 showed the highest number of filled pods. Similar to fresh weight and dry weight of plants, soybean lines with lower plant height also showed lower number of pods (Table 4). The number of pods per plant and the number of reproductive nodes have a positive correlation [10]. It is because the formed organs (pods) are larger and more on normally growing plants. In contrast to the number of pods, number of unfilled pods did not seem related to plant height (Table 4). Unfilled pod is a pod that cannot be formed completely, because the lack of photosynthate for pod developing in the end of seed filling period. In addition, the number of unfilled pods is determined by genetic factors, where the broad sense heritability of this character was high (93.1%) [11].

The most number of branches per plant was indicated by the check variety of Anjasmoro, while the lowest by Tgm/Anj-995. Soybean lines with low plant height were still able to produce number of branches as much as normally growing genotypes (Table 4). Branch is a large plant organ where the development of this organ requires extremely high energy. Therefore the number of branches was relatively similar between the tested genotypes.

Grain yield is not an independent character [7], but a

complex character where the expression is determined by genetic and environmental factors [12]. The highest grain yield was showed by Tgm/Anj-991 while the lowest by Tgm/Anj-995 (Table 4). Both of these lines had different responses to rust disease, where Tgm/Anj-991 was moderately susceptible and Tgm/Anj-995 was

moderately resistant. In this study, grain yield was not affected by the response to rust disease. Grain yield is a plant characters that affected by other yield components,

TABLE III PLANT HEIGHT, SHOOT FRESH WEIGHT AND SHOOT DRY WEIGHT OF LARGE

SEEDS SOYBEAN. ILETRI GREENHOUSE, DRY SEASON 2013

Genotypes Plant height

(cm) Shoot fresh weight (g)

Shoot dry weight (g)

Tgm/Anj-908 68.17 a

15.34 abcd 8.567 abc

Tgm/Anj-909 63.83 ab 17.76ab 10.22 ab

Tgm/Anj-910 52.11 bc 18.75ab 9.753 ab

Tgm/Anj-919 43.94 c 9.40 cde 6.653 c

Tgm/Anj-931 44.62 c 8.66 de 5.400 c

Tgm/Anj-932 56.61 abc 13.98 bcd 8.453 abc

Tgm/Anj-933 67.17 a 18.78 ab 9.180 abc

Tgm/Anj-957 67.89 a 19.54 ab 9.610 ab

Tgm/Anj-991 62.06 ab 21.14 a 11.01 a

Tgm/Anj-995 18.44 d 3.03 f 1.117 d

Wilis 61.56 ab 16.79 ab 8.787 abc

Tanggamus 56.44 abc 16.59 ab 8.770 abc

Anjasmoro 62.39 ab 19.14 ab 9.733 ab

Grobogan 52.11 bc 15.95 abc 9.150 abc Argomulyo 65.39 a 14.25 bcd 7.957 bc

TABLE IV NUMBER OF FILLED AND UNFILLED PODS, NUMBER OF BRANCHES, AND GRAIN YIELD PER PLANT OF LARGE SOYBEAN SEEDS. ILETRI GREENHOUSE, DRY

SEASON 2013

Genotypes Number of filled pods per

plant Number of unfilled pods

per plant Number of branches per

plant Grain yield per plant (g)

Tgm/Anj-908 30.11 bcd 1.05 cd 2.17 cd 3.96 ab

Tgm/Anj-909 40.89 a 1.00 cd 2.55 bc 3.84 ab

Tgm/Anj-910 31.06 bcd 1.33 bcd 2.17 cd 3.98 ab

Tgm/Anj-919 14.05 e 2.28 ab 2.22 cd 2.67 c

Tgm/Anj-931 14.95 e 0.83 cd 2.06 cd 2.56 c

Tgm/Anj-932 27.44 cd 1.11 cd 2.61 bc 3.59 abc

Tgm/Anj-933 29.39 bcd 0.83 cd 1.94 cd 3.35 abc

Tgm/Anj-957 32.22 bc 1.06 cd 2.06 cd 3.30 abc

Tgm/Anj-991 29.22 bcd 0.95 cd 2.16 cd 4.28 a

Tgm/Anj-995 0.167 f 0.33 d 0.22 e 0.01 d

Wilis 33.56 abc 1.17 cd 3.28 ab 3.08 bc

Tanggamus 28.33 cd 1.44 abc 2.28 cd 3.34 abc

Anjasmoro 36.61 ab 1.22 cd 3.56 a 3.85 ab Grobogan 27.95 cd 2.39 a 2.00 cd 2.93 bc Argomulyo 23.94 d 1.28 bcd 1.61 d 3.39 abc



especially the number of pods and seed size. There is a positive correlation between grain yield and 100 seeds weight [13], [14]. Contribution of yield components to grain yield may a direct effect or an indirect effect, and varies depending on the environmental conditions [15], [16].

IV. CONCLUSION

Of the 10 soybean genotypes, there was no resistant genotype, seven genotype were moderately resistant (Tgm/Anj-908, Tgm/Anj-909, Tgm/Anj-910, Tgm/Anj-919, Tgm/Anj-932, Tgm/Anj -957, and Tgm/Anj-995), two genotypes were moderately susceptible (Tgm/Anj-933, and Tgm/Anj-991) and one genotype was susceptible (Tgm/Anj-931). Rust disease infestation did not affect yield and yield components.

REFERENCES

[1] R. M. Miles, R. D. Frederick, and G. L. Hartman. 2003. Soybean Rust : Is The US Soybean Crop at Risk? [http://www.apsnet. org/online/feature/rust/]

[2] G. L. Hartman, M. R. Miles, and R. D. Frederick, 2005. Breeding for resistance to soybean rust. Plant Disease 89(6): 664-666. The American Phytopathological Society. USA.

[3] Shanmugasundaram. 1977. The International working group on soybean rust and Its proposed soybean rust rating system. Work shop on rust of soybean. The problem and research needs. Manila, Philippines. 28 Feb – 4 March 1977.

[4] Balitkabi. 2009. Deskripsi Varietas Unggul Kacang-kacangan dan Umbi-umbian. Balai Penelitian Tanaman Kacang-kacangan dan Umbi-umbian. 175 Hlm.

[5] G. A. Iwo, M. A. Ittah, and E. O. Osai. 2012. Source of genetics of resistance to soybean rust Phakopsora pachyrhizi (H. Sydow & Sydow) in Nigeria. Journal of Agricultural Science 4(10). Canadian Centre of Science and Education.

[6] C. Paul, C. B. Hill, and G. L. Hartman. 2011. Comparisons of visual rust assessments and DNA levels of Phakopsora pachyrhizi in soybean genotype varying in rust resistant. Plant Disease 95:1007-1012. The American Phytopathological Society. USA.

[7] M. F. A. Malik, M. Ashraf, A. S. Qureshi and M. R. Khan. 2011. Investigation and comparison of some morphological traits of the soybean populations using cluster analysis. Pak. J. Bot. 43: 1249-1255.

[8] M. El. M . El-Badawy,. and S. A. S. Mehasen, 2012. Correlation and Path Coefficient Analysis for Yield and Yield Components of Soybean Genotypes Under Different Planting Density. Asian Journal of Crop Science, 4: 150-158.

[9] Harmida. 2010. Respons pertumbuhan galur harapan kedelai (Glycine max (L.) Merril) pada lahan masam. Jurnal Penelitian Sains 13: 13209-13248

[10] T. Machikowa, and P. Laosuwan. 2011. Path coefficient analysis for yield of early maturing soybean. Songklanakarin J. Sci. Technol. 33:365-368.

[11] G. Sahay, B. K. Sarma and A. A. Durai. 2005. Genetic variabiu-ty and interrelationship in f2 segregating generation of soybean Glycine max (L) Merril in mid-altitude ofmeghalaya. Agric. Sci. Digest, 25 (2) : 107 – 110.

[12] A. Sudaric, and M. Vrataric. 2002. Variability and interrelation-ships of grain quantity and quality characteristics in soybean. Die Bodenkultur 53: 137-142.

[13] M. Arshad, N. Ali and A. Ghafoor. 2006. Character correlation and path coefficient in soybean Glycine max (L.) Merrill. Pak. J. Bot. 38: 121-130.

[14] M. Showkat and S. D. Tyagi. 2010. Correlation and path coeffi-cient analysis of some quantitative traits in soybean (Glycine max L. Merrill.). Research Journal of Agricultural Sciences 1:102-106.

[15] R. A. Ball, R. W. McNew, E. D. Vories, T. C. Keisling, and L. C. Purcell. 2001. Path analyses of population density effects on short-season soybean yield. Agron. J. 93:187–195.

[16] S. Kobraee and K. Shamsi. 2011. Evaluation of soybean yield under drought stress by path analysis. Australian Journal of Ba-sic and Applied Sci. 5:890-895.



Abstract— Research about the relation of leaf characte-

ristics in some trees species to the accumulation of sulfate and nitrate has been done. The purpose of this research is to find out the leaf characteristics that related to the sulfate and nitrate accumulation. This research is using survey method and correlation analysis. The plants that used in this research were dadap merah (Erythrina crista-galli), suren (Toona sureni), bungur (Lagerstroemia speciosa), ki acret (Spathodea campanulata), kayu afrika (Maesopsis eminii) dan mahagoni (Swietenia mahagoni). The results show that bungur has the largest leaf surface and dadap merah has the thickest leaf. Mahagoni has the highest value of stomatal density and kayu afrika has the largest size of stomata. Dadap merah has the highest value of total chlo-rophyll content. The highest value of sulfate accumulation was in ki acret and the highest value of nitrate accumula-tion was in bungur. The leaf width has the positive correla-tion with sulfate accumulation. The leaf width, leaf thick-ness, stomatal size and chlorophyll content have the posi-tive correlation with nitrate accumulation.

Keywords— tree, leaf characteristics, sulfate, nitrate, pollutant accumulation

I. INTRODUCTION

IR pollution is the introduction of pollutants into the atmosphere that can cause disruption and dis-comfort to living organisms and also cause envi-

ronmental damages[1]. Air pollution emissions are re-leased naturally from smokes of forest or volcanic erup-tion but mostly cause due to anthropogenic sources[2,3]

such as human activities, economic development, in-creasing population, use of transport, industrial devel-opment and higher level of energy consumption[1,4].

Sulfur oxides (SOx) are one of the gaseous pollutants. SOx pollution is mainly caused by sulfur dioxide (SO2) and sulfur trioxide (SO3). Most of SO2 gases in the at-mosphere are the result of human activities that comes from burning fuel, such as coal, charcoal, wood and the results of industrial processes[5]. SOx pollution can cause human respiratory disease[6] and can cause tissue dam-age (leaf necrosis), even on the higher expose of SO2 can cause cholorosis to the plant[29].

Nitrogen oxides (NOx) can be found as nitrogen mo-noxides (NO) and nitrogen dioxides (NO2), but NO2 are the most common gases that found as air pollutant. The

primary sources of NOx pollutants are motor vehicle emissions, electric tools and human activities such as industrial, trading and the result of the household burn-ing[7]. Chronic exposure of NOx can cause immune sys-tem decrease and toxicity of NO2 can cause health prob-lems, especially lungs problem to human[8]. NO absorp-tion by plant can cause leaf flecks and higher concentra-tion of NO2 exposures can cause leaf necrosis[9].

Plants are one of the living organisms that receive a lot exposure from air pollution[4]. Most of pollutants enter the plant trough leaf and can cause some damages, although the plant still has a defense mechanism to mi-nimize the damages. The mechanism is conducted through the movement of opening and closing the sto-mata and also detoxification process[10].

Plants like trees have the potency to reduce pollutants by providing large leaf area to accumulate pollu-tants[7,11]. Trees provide a leaf surface onto which par-ticles are deposited and gases like SO2 and NO2 are re-moved[12]. Plant ability in absorbing gaseous pollutants was determined by some factors such as morphology, anatomy and physiology of the leaf. The absorption of gaseous pollutants process is mainly occurs through stomata[7]. The stomatal density affects the potential of leaf to absorb pollutants[3]. Moreover, morphological characters such as leaf surface and leaf thickness affect the absorption of pollutant too[13]. Chlorophyll content of the leaf may change due to air pollution[11].

Park is a green area which has a function to reduce air pollutant concentration. The vegetation at the park is effective to adsorb and absorb pollutants that come from motor vehicles emissions[14]. Front park area of Padjad-jaran University is areas that close to Jatinangor – Su-medang highway road. The relation between plant leaf characteristic with the absorption of SO2 and NO2 can be discovered by calculate the accumulation of sulfate and nitrate concentration then analyzed with correlation analysis. The plants selected for this research are the trees that exist in the front park area and are tolerant species, especially SO2 and NO2 pollutant, based on the study literature.

II. MATERIAL AND METHODS

Study area : The area of this study was in front park

Leaf Characteristics of Some Trees Species in Accumulating SO2 and NO2 Pollutant in Park

Area of Padjadjaran University Jatinangor Fauziah Hafidha *), Mohamad Nurzaman,

Nurullia Fitriani, and Teguh Husodo Department of Biology, Padjadjaran University, Sumedang, Indonesia

*) Corresponding author: [email protected]

A



area of Padjadjaran University near the Jatinangor – Sumedang highway road and located on 06º55’55.7”S and 107º46’21.7”E. This park location is near Jatinan-gor – Sumedang highway road and Arboretum Unpad.. The trees that are used for this study is the trees that exist on the park, near the road and have the height around 1 – 3 m and also based on literature have the ability to absorb SO2 and NO2 pollutant. Collected sample : Leaf were collected from the study area in the morning around 08.30 WIB. The taken leaves were mature leaves and relative have the same size. Temperature and light intensity were also meas-ured when the leaves were taken. Leaf characteristics : Leaf areas were measured using leaf area meter and the leaf thicknesses were measured by micrometer screw. Stomata were observed under light microscope with 400x magnificent. Total chloro-phyll content were measured using chlorophyll meter. Sulfate and nitrate accumulation : Sulfate accumula-tion were measured by forming BaSO4 and then calcu-lating SO4 concentration. Nitrate accumulation were measured by calculating total N of leaf and then con-verted to NO3. SO2 and NO2 concentration measurement : SO2 con-centration were measured using the absorption method with absorption solution of TCM. NO2 concentration were measured using the absorption method with ab-sorption solution of Saltzman-Griess. Analysis of data : The data were analyzed using corre-lation analysis on Microsoft Excel.

III. RESULT AND DISCUSSION

Study area : The average of light intensity in study area were 805,7 x 100 lux and the average temperature on site were 27,2 – 28,5ºC. Average pH soils in the location were 5.4 to 6.8. Leaf characteristic : The data of leaf characteristic can be seen on Table 1. All the leaves had a dark green color on the upper side and lighter green on the bottom side. Bungur has the largest leaf surface with 130,20 cm2 and suren has the smallest leaf surface with 34,31 cm2. Da-dap merah has the thickest leaf with 0,23 mm and suren has the thinnest leaf with 0,07 mm. Leaf thickness is one of internal factors that affect the transpiration process to lowering plant temperature[16].

Stomatal type on each plant was anomocytic type, ex-cept dadap merah which has parasitic type. Stomata of all studied species were only found on the bottom side of leaves. Stomata were found more numerous on the bottom side although can be found on both sides of leaves[17]. This is an adaptation to reduce rate of transpi-ration by plant, because 90% of the transpiration occurs trough stomata. Beside play a role in transpiration process, stomata were also having a function for gases exchange, like CO2, in the physiological process that related to photosynthesis[18].

TABLE 1 LEAF CHARACTERISTIC

Mahagoni has the highest value of stomatal density with 289,9/mm2 and dadap merah has the lowest value of stomatal density with 105,3/mm2. Kayu afrika has the highest value of stomatal size with 29µm and mahagoni has the lowest value of stomatal size with 12,1µm. Sto-matal density of each plant can be different according to the type of the plant[17]. The result shows that all the plants have low stomatal density. Stomatal density cate-gorized as low if the density is 200/mm2 or less[3]. This low value may be related to plant adaptation toward dryness. Some plants reduce the size and number of stomata to adapt a dry environment[18].

Total chlorophyll content was measured using chloro-phyll meter. Leaf plant which has the highest chloro-phyll content is dadap merah with 41,5 cci and suren leaf has the lowest with 13,5 cci. Dadap merah leaf has darker green color, have a fairly broad leaf surface and thicker than suren leaf which has thinner leaf, lighter color and smaller leaf surface. Chlorophyll is one of substantial pigment that used by plant to absorbs light for photosynthesis process. Leaf color is closely related to the chlorophyll content on leaf plant. Leaf plant with normal green color relatively has higher chlorophyll than the leaf with yellow or light green color[19]. Sulfate and nitrate accumulation : Sulfate and nitrate accumulation can be seen in Table 2. The highest value of sulfate accumulation was found in ki acret leaf with 15,0834 mg and the lowest value was found in kayu afrika leaf with sulfate content around 0,0467 mg. Sulfur (S) is secondary nutrients and generally required for optimum growth around 0,1% - 0,5% of the dry weight of plants. The structures of proteins in plants are largely determined by the S group, such as methionine and cysteine amino acids. S is also known as an essential nutrient for the production of chlorophyll which is close-ly related to photosynthesis process[20]. Sulfur is ab-sorbed by plants in the form of SO4 and stored in the cytosol[21]. Varying value of sulfate accumulation in leaf can be caused by genetic factors and environmental fac-tors around plants[22].

The highest value of nitrate accumulation was found in bungur leaf with 87,844 mg and the lowest value was found in mahagoni with 2,982 mg. Around 5 – 6% ni-trogen needs for the plants are come from NO2 absorp-tion from the atmosphere. Nitrogen especially needed for supporting the vegetative growth, such as leaf growth rate[22]. Most of absorbed NO2 from the atmos-phere remain in the leaves and only small portion were

Characteristic Species Dadap merah

Suren Bungur Ki acret Kayu afrika

Mahagoni

Leaf surface (cm2)

99,59 34,31 130,20 94,11 36,32 73,09

Leaf thick-ness (mm)

0,23 0,07 0,21 0,12 0,13 0,19

Stomatal type

Para-si-tic

Anomo-cy-tic

Anomo-cy-tic

Anomo-cy-tic

Anomo-cy-tic

Anomo-cy-tic

Stomatal density (/mm2)

105,3 288,95 210,2 122,2 142,3 289,9

Stomatal size (µm)

21,5 19,2 26 21,8 29 12,1

Total chlorophyll content (cci)

41,5 13,5 32,8 27,7 39,4 28,6



distributed to stems and roots[7].NO2 gas which entering the leaf through stomata will react with H2O and form nitric acid (H2NO3)

[22]. Nitric acid will ionized to H+ and nitrate, so nitrate will be assimilated and turn into amino acid which is needed by plant[8].

TABLE 2

SULFATE AND NITRATE ACCUMULATION

Correlation between accumulation of sulfate and nitrate with leaf characteristic : The result of this study showed that sulfate accumulation on leaf is related to some leaf characters such as leaf area, leaf thickness and stomatal density. There was a positive correlation between sulfate accumulations with leaf surface with a correlation coefficient 0,2935 (Fig 1). Sulfate accumula-tion has negative correlation with leaf thickness with a correlation coefficient -0,2734. Leaf thickness related to tissue thickness and gas become relatively difficult to enter the tissue leaf, so it causes the absorption of the gas is relatively small[8].

The correlation between sulfate accumulation with stomatal density and chlorophyll content showed a nega-tive correlation with a correlation coefficient -0,2707 and -0,2789. Accumulation of air pollutant in leaf can affect the chlorophyll content[11]. This showed that greater accumulation sulfate can cause decreasing of leaf chlorophyll content.

Fig. 1. Relation between sulfate accumulation with leaf area (r = 0,2935).

Ki acret leaf show greater sulfate uptake and accumu-lation can be caused by a relatively large leaf surface with low leaf thickness and the value of stomatal density which is not too high, compared to the others studied species. Suren leaf which has the thinnest leaf among all studied species can’t accumulate sulfate as much as ki acret because suren has relatively small leaf area that is 34,31 cm2 and also the higher value of stomatal density which is around 288,95/mm2.

Sulfate absorption by leaf, beside affected by plant characteristic such as leaf area, leaf thickness and sto-matal density, can also influenced by genetic trait of each plant. Each plant has a different transporter gene

for sulfate uptake and transport in plants.

Fig. 2. Relation between nitrate accumulation with leaf area (r = 0,7303) and between nitrate accumulation with leaf thickness (r = 0,4909).

The result showed that nitrate accumulation was re-

lated to some leaf characteristics such as leaf area, leaf thickness, stomatal density and size. Figure 2 shows that nitrate accumulation has a positive correlation with leaf area and leaf thickness. The correlation coefficient be-tween nitrate accumulation with leaf thickness is 0,4909. This positive correlation is contrary to Patra[8] and Nu-grahani et al.[7] which were reported that nitrate accumu-lation has negative correlation with leaf thickness. This difference may be caused by the absorption of NO2 which not only depend on leaf thickness but also depend on other factors such as leaf area and stomatal size. The research of Patra[8] showed that there was a plant which has relatively thick leaf were also accumulate high ni-trate.

The total chlorophyll content has positive correlation with nitrate accumulation with correlation coefficient around 0,334 (Fig 3). Chlorophyll content will be influ-enced by the pollutant accumulation on leaf[11]. Chloro-phyll synthesis is also influenced by several factors, such as light, carbohydrate, water, temperature, genetic factors and some elements such as nitrogen, magnesium, iron, manganese, sulfur, iron and oxygen[24]. Nitrogen (N) is closely related to chlorophyll synthesis[17], also for synthesis protein and enzymes such as rubisco[25]. Hen-driyani and Setiari[25] reported that N is main factors to forming chlorophyll.

The result showed that nitrate accumulation has a negative correlation with stomatal density. Patra[8] re-ported that the high value of stomatal density will cause a high concentration of nitrate accumulation, but Nugra-hani et al.[7] reported that there was no correlation be-tween stomatal density and nitrate accumulation. This difference suggests that stomatal density is not the only one factor that affected nitrate accumulation on leaf. showed that there is a positive correlation between ni-trate accumulations with the size of stomata (r = 0,5331). This suggests that the greater the value of sto-matal size tend to cause greater accumulation of nitrate on leaf. SO2 and NO2 concentration : SO2 gas concentration in the air are one of factors that directly affect sulfate ac-cumulation in leaf[21]. The results of ambient air quality measurements showed that the concentration of SO2 gas at the study site was not detected, so it can be catego-rized as an uncontaminated area. The very small concen-tration of sulfate accumulation on leaf may be due to the good quality of ambient air for SO2 in the study area.

Species Leaf weight

(g)

Sulfate (%)

Sulfate (mg)

Nitrate (%)

Nitrate (mg)

Dadap me-rah

2,58 0,0914 2,3581 2,91 75,08

Suren 0,58 0,5868 3,4034 5,75 33,35 Bungur 3,16 0,0993 3,1379 2,78 87,844 Ki acret 1,67 0,9032 15,083

4 2,45 40,915

Kayu afrika 0,57 0,0082 0,0467 4,29 24,453 Mahagoni 1,42 0,2242 3,1863 0,21 2,982



The concentration of SO2 gas was not detected in the study area can be caused due to the distance of SO2 main pollutant sources, such as coal industry, not close to the area. Concentration of SO2 is mainly influenced by coal combustion emissions[21], coal industry, oil and biomass burning[2]. Yanismai[26] reported that great reac-tions of SO2 can also cause the concentration of SO2 in the atmosphere become not detected.

Fig. 3. Relation between nitrate accumulation with the chlorophyll content (r = 0,3340).

The results of ambient air quality measurements

showed that the concentrations of NO2 gas at the study site was 49.64 µg/Nm3/hour and it categorized as good because the concentration of NO2 were lower than the standard quality which is 400 µg/Nm3/hour. High con-centration of NO2 is generally found in areas with the high transport activity[22]. Yanismai[26] reported that there is tendency of increasing concentrations of NO2 along with the increasing number of vehicles.

The number of vehicles that pass through the campus road and in front of Jatinangor – Sumedang highway can be seen in Table 3. The differences between NO2 gas concentration in campus road and highway road were in line with the research done by Yanismai[26] which re-ported that there is an increase in NO2 concentrations with increasing number of vehicles.

TABLE 3

THE NUMBER OF VEHICLES Location Numbers of ve-

hicles (/hour) NO2 gas concentra-tion (µg/Nm3/hour)

Campus road 671 24,41 Highway road 2828 74,67

The wind speed, air temperature and humidity are

some of the meteorological factors that could affect the concentration of gaseous pollutant in the atmosphere[27]. According to Subaid[28], wind speed can affect spreading and mixing process of air pollutants. Higher wind speed can cause the increasing of spreading process of air pol-lutants from the sources.

Istantinova et al.[27] reported that the wind speed has a negative correlation to the concentration of gaseous pol-lutant in the atmosphere. According to Yanismai[26], high wind speed can cause levels of gaseous pollutant like NO2 are distributed to other locations, so the con-centration of NO2 in the location that close to the source is reduced. The measurement of wind speed at the study site ranged from 0.53 to 3.02 m/sec and it categorized as normal. The wind speed was relatively normal, but the low concentration of SO2 and NO2 can cause gases with a low concentration were dispersed to other locations by wind.

The humidity at the location when conducted air sam-pling was around 35 – 41,35%. Istantinova et al.[27] re-ported that the higher humidity will cause the decreasing concentration of SO2 in the atmosphere. Temperature measurement was 28.8 to 30ºC and categorized as rela-tively normal. Air temperature has the positive correla-tion with the concentration of gaseous pollutants in the atmosphere. The increasing of air temperature will cause to the increasing of gaseous pollutants too [28,27].

IV. CONCLUSION

1) The results are bungur (Lagerstoemia speciosa) has the largest leaf surface with 130,20 cm2 and dadap merah (Erythrina crista-galli) has the thickest leaf with 0,23 mm. Mahagoni (Swietenia mahagoni) has the highest value of stomatal density with 289,9/mm2 and kayu afrika (Maesopsis eminii) has the largest size of stomata with 29µm. Dadap me-rah (Erythrina crista-galli) has the highest value of total chlorophyll content with 41,5 cci.

2) The highest value of sulfate accumulation was in ki acret (Spathodea campanulata) with 15,0834 mg and the highest value of nitrate accumulation was in bungur (Lagerstoemia speciosa) with 87,848 mg.

3) The leaf width has the positive correlation with sul-fate accumulation. The leaf width, leaf thickness, stomatal size and chlorophyll content has the posi-tive correlation with nitrate accumulation.

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Abstract—Pathogenicity of fusants Bacillus thuringiensis

isolates against cabbage head caterpillar larvae, Crocido-lomia binotalis Z.was examined. Of the three Bt. fusants (F28, F31 and F33) were tested against C. binotalis, the second and third instar larvae. Pathogenicity of each iso-late Bt. fusants varied for the second and third instar lar-vae. Among the 3 tested Bt. fusants. F28, F31 and F33 iso-lates showed that the Bt. F28 was the most pathogenic to the second instar larvae. The mortality of the second instar larvae were from 3,33 to 29.66 per cent recorded at 24 h after the treatment. While the Bt. fusant F31 recorded 4,17 per cent and the Bt. fusant F33 registered 2,50 per cent mortality of the second instar larvae. The Bt. fusant F 28 was the most pathogenic to the second instar larvae with the value LC50 and LC90 were 2,74 x1011

and 6,10 x 1013 respectively. However the LC50 and LC90 valued of Bt. fusant F31 and F33 were 1,03 x 10 17 and 2,14 x 10 23, and 2.33 x 1018 and 1,47x10 2 respectively. Therefore, the Bt. fusant F33 was not pathogenic to the second instar larvae. At 72 h of the treated time, the Bt. fusant F 28 was the most pathogenic to the third instar larvae. The percentage of mortality of the larvae caused by Bt. fusant F28 was 29,17 per cent, while the Bt. fusant F31 registered 13,33, per cent mortality whereas Bt. fusant F33 showed 10 per cent mortality. The value LC50 and LC90 of Bt. fusant F28 to the third instar larvae were 7,06 x108 and 4,98 x 109, whereas Bt. fusant F31 , and F33 respectively were 3,12 x 10 29 and 6,4 x 10 46, whereas Bt. fusant F33 was 8,74 x 1024 and 1,20 x10 42. In conclusion, all Bt. fusant strains of F28, F31, and F33 were pathogenic to C. Binotalis larvae. The third instar larvae was more susceptible than the second instar larva

Keywords—Crocidolomia binotalis, Bacillus thuringien-

sis, fusants, Biological control

I. INTRODUCTION

HE Cabagge head caterpillar Crocidolomia binota-lis, Zeller (Lepidoptera: Pyralidae), is a serious insect pest especially of cruciferous vegetables is

widely distributed in tropical and sub tropical region as South and Southeast Asia, Australia, South Africa, Tan-zania and the Pacific Islands [1]. In Indonesia, C. bino-talis and Plutella xylostella together may caused the

yield loss up to 100 per cent, if suitable control for the insects is not undertaken, especially in the dry season, [2] The life history parameters and its biology has re-ported by [3]. Chemicals are the frontline defense for control of insect pests. The sole reliance on pesticides is however cause many problems. Resurgence of insect pests, development of resistance of residue of toxic chemicals in food stuffs are the major problems [4]. Biological control approach appears to be the best alter-native to chemical control which are practical, effective, economical and safe. Bacillus thuringiensis (Berliner), a rod shaped gram positive entomopathogenic bacterium is abundant in soil [5]. B. thuringiensis is the most suc-cessful commercial biocontrol agent against insect pests more than 50th years [6]-[7]. B. huringiensis is an aero-bic spore former is well known for its ability to produce crystal proteins during sporulation [8]-[9]-[10]. The crystal protein designated as delta endotoxin is toxic to many insect larvae, such as lepidopterans, coleopterans and dipteran larvae [10]-[11]. Since 2005, more than 200 crystal protein gen (cry gens) have been identified and classified to 44 different families [12].

II. MATERIALS AND METHODS The research was carried out in 2013. Cabbage head

caterpillar collected from Cabagge crops in Kopeng Magelang, Central Java, Indonesia. The insect was mass reared in the insectary of Laboratory of Entomology, Faculty of Biology, University of Gadjah Mada, Indone-sia.The larvae collected from the infested fields of cab-bage were reared separately on cabbage leaves raised in green house under insecticide free condition. Pupae thus obtained were kept in a sterilized Petri dish and placed in the wooden cage of 60x30 cm. for adult emergence. When the moth started emerging, 25 – 30 days old small cabbage heads were provided for oviposition. Fifth teen per cent honey solution was provided as food for adults in sterilized vial with cotton plug. The moth laid eggs both on ventral and dorsal surface of cabbages leaves. Leaves with eggs were transferred to the cage for mass rearing of larvae. The second and the third instar F1generation larvae were used for bioassay (Figure 3). Of three Fusants B. thuringiensis (F28, F31,and F33

Biological Control of Cabbage Head Caterpillar Crocidolomia binotalis, Zeller by Using Fusants

Bacillus thuringiensis var. kurstaki and Bacillus thuringiensis var. Israelensis Cultured

in Whole Coconut Fruits Siti Sumarmi1* ), Retno Peni Sancayaningsih2), Sebastian Margino3) and RC. Hidayat Soesilohadi4)

1),2),4) Faculty of Biology UGM 3 )Faculty of Agriculture UGM

* )Correspondent author : [email protected]

T



isolates collected from the previous research maintained at Laboratory of Entomology, Faculty of Biology, Uni-versity of Gadjah Mada, were used for bioassay to as-sess their pathogenicity against tested insect. [13]-[14]. To multiply the isolates they were streaked on plain Brain Hearth Infusion Agar (BHIA) plates and incu-bated for 24 h which was later inoculated in whole co-conut [15]-[16]-17]; and was kept for growth under shaking condition at room temperature (28 0C) and in-cubated for 72 h. Then, the culture was examined under phase-contras microscope (magnification of 1200x). haversted in 10 ml of sterile water for taking colony count. A concentration of B. thuringiensis (1.2X10-7cfu/ml) to assess its toxicity against the test insects (Dip Leaf bioassay, described by [18] Tabashnik and Crushing (1987) was adopted with modification. Cab-bage leaves were cut square of 6 cm These leaves were dipped in aqueous solution of the test isolates for 10 minutes. Excess fluid was drained off and the cutting cabbage leaves were dried under shade for 10 min. be-fore transferring to glass jars (5 cm height and 3 cm di-ameter) covered with absorbent gauze. Cutting Leaves were placed slantingly so that larvae can move and feed on either side.

Bioassays were done with three replications per treatment and ten larvae of test insect were released on each the container was covered with absorbent gauze. The cabbage leaves dipped in distilled water alone served as control. Mortality was observed at 24 h, 48 h, and 72 h after treatment and data were subjected to analysis of variance after suitable transformation (arc-sine) and the means were separated by Duncan’s Mul-tiple Range Test (DMRT)[19] and the LC50 and LC 96 were determine by Probit analysis [20]

III. RESULT AND DISCUSSION Among three strains of fusants Bt. F28, F31 and F33

have variations result, the Bt. Fusant F28 was the most pathogenic to the C. binotalis larvae than the others. According to Knowles [21] explored that the variations in efficacy against different lepidopterans may be due to varying number of cry genes and the absence of specific binding sites. The mortality of the second instar larvae of C. binotaliscaused by the Bt. fusants F28 was from 3,33 to 29.66 per cent recorded at 24 h of the treat-ment. The Bt. fusants F31 recorded 4,17 per cent while the Bt. fusants F33 registered 2,50 per cent mor-tality of the second instar larvae (Table 1). Fusant F 28 was the most pathogen to the larvae with the value LC50 and LC90 was 2,74 x1011

and 6,10 x 1013 respectively than the fusant F31 and F33 with LC50 and LC90 valued were 1,03 x 10 17 and 2,14 x 10 23, and 2.33 x 1018 and 1,47x10 2 respectively. But the F33 was not pathogen-ic to the second instar larvae.

TABEL 1.

PATHOGENICITY TEST OF Bt. FUSANTS TO THE SECOND INSTAR LARVAE OF C. binotalis.

Fusant Bt.strains

Mortality of the Second Instar Larvae of Insect at : Total

24 h 48 h 72 h F28

A 16 12 1 29

B 1 1 0 2 C 2 1 0 3 D 1 0 0 1

Per cent 16,67 11,67 0,83 29,17 LC50 3,05x109 3,8x108 3,47x108 LC90 1,75x1011 4,9x109 3,89x109

F31 A 3 7 5 15 B 0 3 8 11 C 1 1 0 2 D 1 0 0 1


F33 A 1 0 0 1 B 1 1 0 2 C 1 0 0 1 D 0 0 0 0


Control 0 0 0 0

Note : Concentration of the treatments

A : 1,75 x 109,; B: 1,75 x 108, C: 1,75 x 107: D: 1,75 x 106 The mortality of the second instar larvae at 48 h after treatments can be seen at Table 1. The Fusant F28 rec-orded 11,67 per cent mortality to the second instar lar-vae while Bt. fusant F 31 was 9.17 per cent and Bt. fusant F33 was 0,83 per cent mortality of the second larvae. After 72 h of the treated time F28, F31, and F33 recorded 29.66 per cent, 24,83 per cent, and 3.33 per cent mortality to the insect larvae respectively . Fusant F 28 was the most pathogenic to the second instar larvae with the value LC50 and LC90 was 2,74 x1011

and 6,10 x 1013 respectively, whereas F31, and F33 were 12,57 x 10 17 and 2,61 x 10 24, however F33 was 2.33 x 1018 and 1,47x10 25.

The mortality of the third insect larvae can be seen at Table 2. The Bt. fusants F28, F31, and F33 recorded 6,67 per cent while isolate F31 registered 3,33, per cent mortality whereas F33 showed 2,50 per cent mortality. The values LC50 and LC90 of F28 . were 2,74 x1011

and 6,10 x 1013 the value LC50 and LC90 F 31 were 1,03 x 10 17 and 2,14 x 10 23, However, F33 was not pathogen-ic to the third instar larvae. At 48 h of the treatments, fusant F28 showed 20, per cent mortality of the third instar larvae whereas F31 was 3,33 per cent mortality , while F33 showed 5,00 per cent mortality.

The values LC50 and LC90 of F28 . were 8,89 x108 and

4,51 x 109 whereas F 33 were 3,12 x 10 29 and 6,4 x 10 48, respectively. However, F31 was not pathogenic to the third instar larvae.

At 72 of the treatments, of Bt. fusant F 28 was the most pathogenic to the third instar showed 29,17 per cent mortality, Isolate F31 registered 13,33, per cent mortality whereas F33 showed 10 per cent mortality. The value LC50 and LC90 of F28 was 7,06 x108

and 4,98 x 109, whereas F31 , and F33 were 3,12 x 10 29 and 6,4 x 10 46, whereas F33 was 8,74 x 1024 and 1,20 x10 42. The F33 was not pathogenic to the third instar larvae



TABEL 2.

PATHOGENICITY TEST OF Bt. FUSANTS TO THE THIRD INSTAR LARVAE OF C. binotalis.

IV. CONCLUSION All fusants Bt strains ; F28, F31, and F33 were

pathogenic to C. Binotalis. The third instar larvae was more susceptible than the second. Fusant Bt F28 and F31 will be assessed their pathogenicity to the instar larvae at small scale field.

ACKNOWLEDGEMENT

The investigators wish to thank to DP2M DIKTI RI for the financial support of the research. 2. LPPM UGM Indonesia for the research coordination and the Dean of Faculty of Biology UGM, Indonesia for give me oppor-tunity to do this research and also Special thanks to I Nyoman Sumerta, SPd. for his sincere assistance in car-rying out the experiments, and Suparmin for administra-tion tasks.

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Fusant Bt.strains Mortality of the third instar

larvae of insect at : Total 24 h. 48 h. 72 h.

F28 A 4 19 2 25 B 3 2 0 5 C 1 1 1 3 D 0 2 0 2


F31 A 1 0 3 4 B 2 1 3 6 C 1 3 1 5 D 0 1 0 1

Per cent 3,33 3,33 5,83 13,33 LC50 1,03x1017 0 3,42x1014 LC90 2,14x1023 0 1,71x1022

F33 A 0 4 0 4 B 1 0 2 3 C 1 0 1 2 D 1 2 0 3

Per cent 2,50 5,00 2,50 10,00 LC50 0 3,12x1029 8,74x1024 LC90 0 6,4x1048 1,20x1042

Control 1 1 0 2 Per cent 6,67

Note : : Concentration of treatments

A : 1,75 x 109,; B: 1,75 x 108, C: 1,75 x 107: D: 1,75 x 106



Abstract— This study was conducted to evaluate the ef-

fect of trehalose levels on post-thaw sperm membrane integrity at different level of trehalose in tris based diluter of Boer Goat semen. Fresh semen was collected from six aged male Boer goats. Immediately after collection using artificial vagina, the semen was evaluated for quality and diluted with tris aminomethane-base extender in 10 folds (1 semen: 9 extender). The effect of different levels of treha-lose (1.5%; 2.5% and 3.5%) in the diluter on the sperm membrane integrity was evaluated in this study. The di-luted semen was freezed with standard method. The result showed that fresh semen collected from Boer bucks in this study indicated a normal quality and therefore, could be used for further treatment. According to the varian analy-sis it was shown that 2.5% trehalose resulted higher sperm quality than those the other levels on post-thaw of Boer Goat semen. It was concluded that the addition 2.5% tre-halose in tris-based medium resulting optimal sperm membrane integrity of Boer goat semen post thawing. It was suggested, that for resulting optimal sperm membrane integrity post thawing of Boer goat semen in tris-based medium should be supplemented with 2.5% trehalose.

Keywords— Boer goat, trehalose, cryopreservation, membrane integrity

I. INTRODUCTION

uring dilution, cooling and freezing, the sperm quality reduce corresponding to appropriate processing technique and cryoprotectant used. The

addition of cryoprotectant in the medium or semen ex-tender can retard the reduce of semen quality in those process. Disaccharides have a stabilizing effect on bio-logical membrane. Trehalose is found in animals capa-ble of enduring cold temperatures, whereas sucrose is found in plants [1].

Cryopreservation are known to damage sperm membranes [2]. This damage includes swelling and dis-ruption of plasma and outer acrosome membranes [3], changes in membrane fluidity [4], disregulation of intra-cellular Ca2+ influx [5] and changes in enzyme activity [6].

Membrane integrity is not only important for sperm metabolism, but also a correct change in the properties of the membrane is required for successful union of the male and female gametes, i.e. for sperm capacitation, the acrosome reaction and the binding of the spermatozoa to the egg surface. The integrity and functional activity of the sperm membrane is of fundamental importance in

the fertilization process and assessment of membrane function may be useful indicator of the fertilizing ability of spermatozoa.

The objective of the present study was con-ducted to evaluate the effect of trehalose levels on post-thaw sperm membrane integrity at different level of tre-halose in tris based diluter of Boer Goat semen.

II. MATERIAL AND METHOD

a. Animal and Sperm Preparation

Ejaculated were obtained from 6 male Boer goats (ori-ginated from Australia Breeding Herd) aged of 2.0 – 2.5 years with about 100 kgs in weight, using artificial vagi-na. The bucks were maintained at Field Laboratory of the Faculty of Animal Husbandry, University of Brawi-jaya Malang. After collection, the semen was evaluated macroscopic and microscopically. Sperm motility was evaluated by placing a drop of well mixed semen on a prewarmed glass slide under a coverslip and examining it at x100 and x400 magnification by phase contrast microscopy. Motility was assessed subjectively on the basis of spermatozoa that were moving either progres-sively or non progressively or those that were nonmotile. Viability and abnormality were evaluated by eosine-negrosine staining. One hundred sperm cells were scored per slide. Sperm concentration was measured with a Thoma hemocytometer. Only semen with indi-vidual motility of sperm of more than 70% and mass motility of 2+ and 3+ was used for research material. Semen collection was regularly conducted twice a week per individu of animal.

The selected semen was diluted with tris-base dilu-ent containing 1.5%; 2.5% or 3.5% trehalose and equili-brated at 5˚C for 2 h. Before loading into 0.25 ml straw, semen was evaluated for the quality and then straw con-taining diluted semen was horizontally placed on liquid nitrogen vapour (-140˚C) for 9 min for pre-freezing. Immediately thereafter, the straw was plunged into liq-uid nitrogen for at least 24 h. Thawing was conducted by transfer the frozen straw into the warm water (37˚C) for 30 sec. For each treatment (1.5%; 2.5% and 3.5% trehalose) was taken for evaluation of membrane integri-ty by hypoosmotic swelling test (HOS test).

2. Hypoosmotic Swelling (HOS) test

The HOS test solution contained 0.49 g Na-sitrate x 2H2 O and 0.9 g fructose in 100 ml aquadest. The os-

The Effect of Trehalose Levels on Post-Thaw Sperm Membrane Integrity of Boer Goat Semen

Nurul Isnaini Faculty of Animal Husbandry, Brawijaya University, Malang, Indonesia


D



motic swelling technique consisted of mixing 0.1 ml semen with 1.0 ml hypo-osmotic solution and allowing the mixture to incubate at 37 C for at least 30 min. The mixture was allowed to stand for at least 1 min before observations were made by phase-contrast microscopy at x400 ; 100 spermatozoa were observed. The percen-tage spermatozoa that showed typical tail abnormalities indicative of swelling was calculated.

III. RESULT

If you are using Word, use either the Microsoft Equa-tion Editor or the MathType add-on (http://www.mathtype.com) for equations in your paper (Insert | Object | Create New | Microsoft Equation or MathType Equation). “Float over text” should not be selected.

a. Characteristics of fresh semen

Table 1 shows that the characteristics of Boer goat fresh semen used in this study was normal.

TABLE 1. CHARACTERISTICS OF FRESH SEMEN

Parameter Mean + SD

Color Creamy

Consistency Less opaque

pH 7.00 + 0.0

Volume (ml) 0.64 + 0.27

Concentration (106 / ml) 3238.00 + 187.78

Mass motility 2+ - 3+

Individual motility (%) 74.50 – 4.38

Life sperm (%) 86.88 + 2.31

Abnormal sperm (%) 8.63 + 1.97

Membrane integrity (%) 72.59 + 6.21

b. Sperm membran integrity post thawing

Sperm membrane integrity post thawing diluted with tris-base diluent containing different levels of trehalose is shown in Table 2.

TABLE 2.

MEAN (+ SD) OF MEMBRANE INTEGRITY SPERM FOLLOWING TREATMENT WITH DIFFERENT TREHALOSE LEVEL POST THAWING

Trehalose levels Membrane integrity

1.5% 38.34 + 5.69b

2.5% 42.12 + 2.66 b

3.5% 29.17 + 6.93a

a.b.c within column significant difference (P<0.05)

Table 2. shows that the membrane integrity of sperm was significantly different (P<0.05) post thawing be-tween trehalose treatment groups. In general, it was shown that 2.5% trehalose in tris-based diluent showed an optimal concentration for maintaining the sperm membrane integrity post thawing compared to the level of 1.5% and 3.5% trehalose. The level of 1.5% trehalose influenced suboptimal effect on the sperm membrane integrity. Migh be due to the low concentration for cryoprotective function in the diluter during freezing process. Vice verse was for the level of 3.5% trehalose.

IV. DISCUSSION

The integrity and functional activity of the sperm membrane is of fundamental importance in the fertiliza-tion process, and assessment of membrane function may be a useful indicator of the fertilizing ability of sperma-tozoa. A property of the cell membrane is its ability to permit the transport of molecules selectively. When ex-posed to hypo-osmotic conditions, water will enter the spermatozoa in an attempt to reach osmotic equilibrium. This inflow of water will increase sperm volume and the plasma membrane will bulge (balloon), giving minimum surface to volume ratio. The sperm tail appears to be particularty susceptible to such hypo-osmotic condi-tions. These induced alteration in sperm morphology are visible with the phase contrast microscope. The ability of the sperm tail to swell in the presence of hypo-osmotic solution is a sign that transport of water across the membrane occurs normally, i.e. is a sign of mem-brane integrity and normal functional activity [7].

The role of trehalose in the protection of sperm dur-ing freezing acted by maintaining the membrane stability of sperm (lipid bilayer) by formation of hydrogen bounds at O2, O3 and O4 of trehalose structure with phosphate-and carbonyl groups of lipid [8]. The former hydrogen bounds either from phosphate- or carbonyl groups of lipid the sperm membrane stability could maintained and the sperm damage originated from dilu-ent and temperature shock could be minimized. In se-men freezing process, trehalose was reported used in combining with gliserol [9] showed that 7.5% trehalose combined with 6% glycerol resulted higher sperm quali-ty after freezing compared to the used of Tris-(hydroxymethyl-aminomethane)-citric-acid-glucose (TGC) [10]. There is evidence that trehalose increased the stability of sperm membrane to the physical and morphological changes during semen dilution and freez-ing.

V. CONCLUSION

Based on the study, it was concluded that the addition 2.5% trehalose in tris-based medium re-sulting optimal sperm membrane integrity of Boer goat semen post thawing. It was suggested, that for resulting optimal sperm membrane integrity post thawing of Boer goat semen in tris-based medium should be supplemented with 2.5% trehalose.

ACKNOWLEDGMENTS

The author thank the Dean of Animal Husbandry Fa-culty Brawijaya University Malang for boer goat facility and the laboratory field staff Sumber Sekar for assis-tance of semen collection and evaluation.

REFERENCES

[1] K.S. Amadeu, R. Faller, and J.J. de Pablo, Molecular Simulation

Study of Phospholipid Bilayers and Insights with Disaccharides. J Biophys, vol. 85 (5), 2003, pp.2830-2844.

[2] R.H. Hammerstedt, J.K. Graham, and J.P. Nolan, Cryopreserva-tion of mammalian sperm: what we ask them to survive. J An-drol, vol. 11, 1990 pp.73-88.



[3] R.C. Jones and D.L. Stewart, The effects of cooling to 5˚C and freezing and thawing on the ultrastructure of bull spermatozoa. J Reprod Fertil, vol 56, 1979 pp. 233-238.\

[4] W.V. Holt and R.D. North. Thermotropic phase transitions in the plasma membrane of ram spermatozoa. J Reprod Fertil, vol 78, 1986 pp. 447-457.

[5] L. Robertson, J.L. Bailey, and M.M. Buhr. 1990. Effects of cold shock and phospholipase A2 on intact boar spermatozoa and sperm head plasma membranes. Mol Reprod Dev, vol 26, 1990 pp. 143-149.

[6] P.F. Watson, The effects of cold shock on sperm cell membranes In: Morris GJ, Clarke A (eds), The effects of low temperatures on biological membranes. London: Academic Press, 1981 pp. 189-218.

[7] R.S. Jeyendran, H.H. Van der Ven, M. Perez-Pelaez , B.G. Crabo, and L.J.D. Zaneveld, Development of an assay to assess the

functional integrity of the human sperm membrane ang its rela-tionship to other semen characteristics. J reprod Fert, vol. 70, 1989 pp. 219-228.

[8] A.K. Sum, R. Faller, and J. de Pablo, Molecular simulation study of phospholipids bilayer and insights of the interactions with disaccharides, Biophysical Journal, vol. 85, 2003 pp. 2830-2844.

[9] B.T. Storey, E.E. Noiles, and K.A. Thompson, 1998. Compari-son of Glycerol, Other Polyols, Trehalose and Raffinose to Pro-vide a Defined Cryoprotectant Medium for Mouse Sperm Cryo-preservation. J.Criobiology, vol. 37, 1998 pp. 46-58.

[10] E.M.E. Aboagla and T. Terada, Trehalose-enhanced fluidity of the goat sperm membrane and its protection during freezing. J. Biol. Reprod, 2003.



Abstract—The research was undertaken to investigate

the antimutagenic effect of Brassicaceae extracts (white headed cabbage, pak choi, and Chinese cabbage) on Swiss-Webster mice induced by lead acetate. The study has been conducting using Completely Randomized Design method consist of five treatments with five repetitions. Mice were pretreated with white headed cabbage, pak choi, and Chi-nese cabbage extracts by cumulative dose 156 mg/kg bw for 7 (1, 3, 5, 7, 9, 11 and 13) consecutive days prior to ex-posure of lead acetate by cumulative dose 150 mg/kg bw (on day 5, 9 and 13). All treatment was given orally. Para-meters that observed were frequency and type of chromo-somal aberration. The data was analyzed statistically using ANOVA and continued with Duncan’s Multiple Range Test (α = 0.5). The highest number of chromosomal aber-ration (86.00%) was showed in lead acetate treatment. The frequency of chromosomal aberration on white headed cabbage, pak choi, and Chinese cabbage extracts treat-ments respectively were 41.80%, 59.80%, and 51.80%. The result indicated that Brassicaceae extracts reduced fre-quency chromosomal aberration on mice causes by lead acetate. Several types of chromosomal aberration detected were chromosome fragment, acentric chromosome, double point, numeric, stickiness and ring chromosome.

Keywords— Antimutagenic, white headed cabbage (Brassica oleracea var. capitata), pak choi (Brassica rapa chinensis), Chinese cabbage (Brassica rapa pekinensis), Lead Acetate

I. INTRODUCTION

RESENTLY, parallel to the rapid growth in indu-strialization environmental pollution is also increas-ing. Heavy metals like Pb, Cd, Cu, Fe, and Hg, con-

tribute significantly to the growing pollution problem. Among the heavy metals, the use of lead in industrial caused widespread environmental contamination [1]. Internal and external factors, including environmental pollutants induce various kind of genetic damage [2]. Lead acetate induced genotoxicity, mutagenicity and carsinogenic Lead is genotoxic itself or enhances the effect of other DNA-damaging agents that can trigger cancer formation [3,4,5,6,7]. Prevention of the cancer and other mutation related disease can be carry on both by avoiding exposure to carcinogen and by favoring the intake of protective factors which fortify physiological defense mechanisms [8].

In recent years, there is increasing awareness that

certain naturally occurring substance in plants provide protection against environmental mutagens or carcino-gen. The Brassicaceae (Cruciferae) family has chemo preventive potential. Antimutagenic and anticarcinogen-ic effect of these vegetables, particularly due to their content in glucosinolate, is hydrolyzed by specific thiog-lucosidases called myrosinases to produces isothiocya-nates, nitriles, and thiocyanates with different biological activity [9,10,11,12,13].This study was conducted to examine the antimutagenic potential of Brassicaceae vegetables white headed cabbage (Brassica oleracea var. capitata), pak choi (Brassica rapa chinensis), and Chinese cabbage (Brassica rapa pekinensis) on mouse chromosomal aberration induced by lead acetat.

II. MATERIALS AND METHODS

Animal experiments were 6-8 week old Swiss Web-ster male mice weighing 20-25 g. Chemicals and sub-stances, lead acetate (Merck), CMC 5% (Merck) , 70% ethanol, colchisine (Merck), Carnoy’s fixative solution, phosphate buffer saline pH 7, KCL 0.56%, Giemsa solu-tion (Merck), extracts from Brassicaceae vegetables. Vegetables extraction based on Harborne method [14].

a. Treatment Procedure

Twenty five 8-10 weeks old mice weighing 20-25 g were houses allowed without food for 3-5 h before treatment. There were five experimental groups and each group consisted of five male mice. Group I positive control was administered with 150 mg/kg bw lead ace-tate (P1) [5] . group II negative control was adminis-tered with CMC 0.5% (P2), Groups III, IV, and V were pretreated with 156 mg/kg bw white headed cabbage extract (P3), 156 mg/kg bw of pak choi extract (P4), and 156 mg/kg bw Chinese cabbage extract (P5) respective-ly for 7 (1, 3, 5, 7, 9, 11, and 13) consecutive days (22.22 mg/kg bw/day) prior to treatment with lead ace-tate dose of 150 mg/kg bw on day 5, 9 and 13 (50 mg/kb bw/day). All treatment was given orally. Chromosomal aberration analysis was done following Bish and Devi method with modification [15]. A hundred well spread intact metaphase were score from each animal under 100X oil immersion.

b. Statistical analysis

Data was presented as mean ± SD. The significance of difference between the data in control and in experi-

Antimutagenic Effect from Brassicaceae Ex-tracts on Swiss-Webster Mice Induced by Lead

Acetate Supartini Syarif 1* ), Melati P. Puteri 1), and Nining Ratningsih1)

1) Jurusan Biologi, FMIPA UNPAD Jl. Raya Bandung-Sumedang KM. 21 Jatinangor 45363


P



mental groups was analyzed with ANOVA and contin-ued with Duncan Multiple Range test.


Pre treatment of different vegetables extracts was given orally prior on to lead treatment in group III, IV and V decreased frequency of chromosomal aberra-tions (table 1.) All types of chromosomal aberrations induced by lead acetate including, numeric abberation, fragment, acentric, ring, stickiness, gap, tri-radial, chro-mosome breaks, double point. The result showed that lead acetate produces mutagenic effect induced chromo-somal aberration. Lead compound do not damage DNA directly, lead ion are known to participate in Fenton reaction produces ROS which can induces oxidative stress and free radicals. In addition, lead ions are report-edly known to inhibit the DNA polymerase β, one of the prime enzymes involved in DNA repair. There are indi-cations for rather indirect mechanisms of genotoxicity, which may be due to an interaction of lead and DNA repair processes [7,16].

TABLE I ANTIMUTAGENIC EFFECT OF BRASSICACEAE EXTRACTS

ON LEAD ACETATE INDUCED CHROMOSOMAL ABERRATIONS IN SWISS WEBSTER MICE.

* One hundred cells were analyzed per animal, for a total of 500 cells per treatment. Values, within columns, with no common super-scripts are statistically different (P<0.05)

In this study revealed the antimutagenic potential of brassicaceae extracs against chromosomal aberration induced by lead acetate. The mechanism for protection of brassicaceae extracts involved chemical property of brassicaceae plants is high content of glucosinolate and their isothiocyanate hydrolysis product as chemoprotec-tor. Isothiocyanate inhibit phase I enzyme (cytochrome P 450). It converts procarcinogens to highly reactive electrophilic that can damage susceptible DNA base. However, isothiocyanate enhanced activities family of phase II enzyme (glutathione, glucuronic acid), these detoxication enzyme were responsible for protective action to xenobiotics in this case lead acetate [8,9,17] . Isothiocyanate has a role in protection enzyme.

Overall, results from this study indicate that brassi-caceae extracts inhibit mutagenic effect on Swiss Web-ster mice induced by lead acetate (chemopreventive).

ACKNOWLEDGMENT

The authors thanks to Rector Padjadjaran University was supported this work by DIPA BLU Foundation

REFERENCES [1] Kumar, G; R. Tripathi. 2008. Lead Induced Cytotoxicity and

Mutagenicity in Grass Pea Turk J Biol (32): 73-78 [2] Alekperov U. and R Guileva 1996. The Inhibition of the Geno-

toxic Effects of environmental Pollutans and Aging processes by Plant mutagen. Tr J.of Biology (23):135-142.

[3] Alghazal,M.A; N. Kovalkoviscova; J. Legath; M. Falis; J. Pistl; R.Sobo; K.Benova;L Sabova and P.Vaczi 2008. Induction of micronuclei in rat bone marrow after chronic exposure to lead acetate trihydrate. Toxicology and Industrial Health 24 (9): 587-593.

[4] Uysal, H,. 1997. Induction of Chromosomal Aberration in Polytene Chromosomal of Drosophila melanogaster by Lead Acetate. Cytologia.62:213-217.

[5] Aly, F.W 2001. Potential Mutagenic Effect of Lead Acetate in mouse Bone Marrow and culture Mouse Spleen Cells. http://www.jst.go.jp/article/char/en

[6] Poma, A. Pittaluqa E; Tucci.A. 2003. Lead Aceate Genotox-iccity on Human Melanoma Cells. Melanoma Res. 13(6): 563-566

[7] Kasuba,V, R. Rosgaj, A Fucic, V.M Varnai and M Piasek. 2004. Lead Acetate genotoxicity in suckling rats. Biologia, Bratislava. 5916:779-785.

[8] Ishaq, G.M.; M.Y. Shah; S.A.Tanki 2003. Cancer Chemopre-vention Through Natural Antimutagenic Agents .JK-Practioner 10(2):101-106

[9] Talalay,P; J.W. Fahey 2001. Phytochemicals from Cruciferous Plants Protect agains Cancer by Modulating Carcinogen Me-tabolism. American Society for Nutritional Sciences.

[10] Miyazawa,M ; T. Nishiguchi and C. Yamafuji 2003. Volatile components of leaves of Brassica rapa L.var.pervidis Bailey. Flavour and Fragrance J. (20):158- 160

[11] Weil, M.J.; Y Zhang; M.G. Nair 2004. Colon Cancer Prolife-rating Desulfosinigrin in Wasabi ( Wasabia japonica). Nutri-tion and Cancer (48):207-213

[12] Jongen,W.M.F. 1996. Glucosinolates in Brassica: Occurrence and Significance as Cancer-modulating agents. Proceeding of the Nutrition Society (55);433-446

[13] Mandelova.L and J.Totusek 2007.Brocoli Juice Treated by high Pressure: Chemoprotective Effects of Sulforaphane and Indole-3 carbinol. High Pressure Research (27):151-156

[14] Harborne, J.B. 1973. Phytochemical Methods, A Guide to Modern Techniques of Plant Analysis. London: Chapmann and Hall.

[15] Bisht,K,S. dan Devi,P,U. 1994.Dose-dependent Increase in The Frequency of Micronuclei and Chromosomal Abberations

by Misonidazole in Mouse Bone Marrow. Mutation Research. 425:57-63.

[16] Acharya,U.R.; Acharya,S.; and Mishra,M. 2003. Lead Acetate Induced Cytotoxicity In Male Germinal Cells of Swiss Mice. Industrial Health. 41:291-294.

[17] Fimognari,C. And P.Hrelia 2007. Sulforaphane as a promising molekule for fighting Cancer. Mutation Research 635 : 90-104

Experimental groups

Metaphase with chromosomal

aberrations/100 cells*

Average Per-centage of

chromosomal aberrations (%

+ S.D)

Lead acetate P1

430 86 + 2.47a

CMC P2

170 34 + 3.51b

White headed cab-bage

P3

209 41.8 + 2.27b

Pak-choi P4

299 59.8 + 6.12bc

Chinese cabbage P5

259 51.8 + 3.57c

Result of one way ANOVA

P 0.000

F 27.28



Abstract— Acid soil properties vary even in the relative-

ly close location. Hence, yield and yield components of a genotype is not similar from location to another location depends on the location condition. The objective of the research was to study the yield and yield components of acid-tolerant soybean promising lines in Ultisols of Banjar-negara at rainy season 2009. The tested materials consisted of 10 promising lines (SC2P2.99.5.4.5-1-6-1, SC2P2.151.3.5.1-10, SC5P2P3.5.4.1-5, SC5P2P3.23.4.1-3-28-3, SC5P2P3.23.4.1-5, SC5P2P3.48.31.1-10, SJ-5/Msr.99.5.4.5-1-6-1, Msr/SJ-5.21.3.7-3-27-1, Msr/SJ-5.23.4.1-3-28-3 and Msr/SJ-5.23.4.1-5) and three check varieties (Tanggamus, Wilis and Grobogan). Experimental design was randomized completely block design with three replications. Plot size was 2,0 m x 4,5 m, plant spacing 40 cm x 15 cm, two plants per hill. The results showed that in strong acid soil Ultisols of Banjarnegara, Tanggamus had the highest grain yield than other promising lines and both check varieties Wilis and Grobogan. Msr/SJ-5.23.4.1-3-28-3 was the most potential promising line having grain yield higher than Wilis and seed size larger than Tanggamus. Wilis was adaptive in slightly acid soil, but Grobogan was not suitable for planting in acid soil. Yield per plant was supported by plant height, filled pods and reproductive nodes. Early maturity with larger seed size varieties, such as Grobogan, was risky due to the relatively high unfilled pods.

Keywords— acid soil, soybean, Ultisols, yield, yield com-ponents.

I. INTRODUCTION

CID Acid soil covers 30-40% of the world’s total land area [1] and approximately 69% of dryland in Indonesia [2]. Acid soil limits crop growth poten-

tial [3] due to the many problems in supplying mineral nutrients to the crops such as deficiencies of calcium, magnesium, molybdenum, and phosphorus, as well as toxicities of aluminum and manganese [4],[5]. However, Al toxicity is the main problem in mineral acid soils [6]. Crop growth decreases as Al saturation in acid soils increases [7]. High aluminum concentration also causes the decrease in chlorophyll content, photosynthesis rate, utilization efficiency of photosynthetically active radia-tion and water utilization efficiency, and increasing transpiration rates [8].

Gene expression is subject to modification by the en-

vironment, consequently genotypic expression of the phenotype is environmentally dependent [9]. Soil fertili-ty, such as soil acidity, is one of the environmental fac-tors which modify the gene expression. Some genotypes evaluations to low pH at seedling level have been re-ported by [10] and at to reproductive level by [11]. The high soybean yield might be the result of the high yield capacity and favorable weather conditions [12]. Hence, ecophysiological parameters should be noticed in select-ing good varieties [13] as well as soil properties [14].

Yield is the main criterion in an evaluation of plants adaptability, because adaptability is the ability of the plant to maintain the production on the diverse environ-mental conditions [15]. However, grain yield is not an independent character [16], but a character which sup-ported by the yield components. As a complex character, grain yield consists of components of quantitative na-ture, where the expression is determined by genetic and environmental factors as well as their interactions [17]. Contribution of yield components to grain yield may a direct effect of a yield component to grain yield or indi-rect effect through other yield components. Direct and indirect effects also varies depending on the environ-mental conditions [18], [19].

II. MATERIALS AND METHOD

Experiment was conducted in rainy season 2009 in Village of Pucung Beduk, Sub-district of Kali Sawah, District of Banjarnegara, Central Java, Indonesia. The design was a randomized completely block design with three replications. The plants materials were 10 acid-tolerant promising lines (i.e. SC2P2.99.5.4.5-1-6-1, SC2P2.151.3.5.1-10, SC5P2P3.5.4.1-5, SC5P2P3.23.4.1-3-28-3,SC5P2P3.23.4.1-5, SC5P2P3.48.31.1 -10, SJ-5/Msr.99.5.4.5-1-6-1, Msr/SJ-5.21.3.7-3-27-1, Msr/SJ-5.23.4.1-3-28-3 and Msr/SJ-5.23.4.1-5), and three check varieties (Tanggamus, Wilis and Grobogan). Each genotype was grown on 2.0 m x 4.5 m, plant spacing of 40 cm x 15 cm, two plants per hill. The crops were fertilized with 75 kg Urea, 100 kg SP36 and 100 kg KCl per ha on the soil before plant-ing. Observations were conducted on plant height, num-bers of branches, numbers of filled and unfilled pods, and 100 seeds weight and grain yield. Soil properties of the experiment location are presented in Table 1.

Yield and Yield Components of Acid-Tolerant Soybean Promising Lines on Ultisols

Heru Kuswantoro 1*), and Purwantoro 1) 1) Indonesian Legume and Tuber Crops Research Institute

Indonesian Agency for Agricultural Research and Development *) Corresponding author: [email protected]

A




The grain yield of acid-tolerant soybean lines and the check varieties was presented in Table 2. Tanggamus as an acid-tolerant check variety yielded the highest grain (1.48 t/ha) followed by Msr/SJ-5.23.4.1-3-28-3 (1.42 t/ha), while Wilis as wide adaptation variety yielded 1.34 t/ha higher than Grobogan (0.69 t/ha) as early ma-turity check variety. Compare to those varieties descrip-tion [20], grain yield of Wilis and Grobogan was lower, while Tanggamus was higher. No acid-tolerant soybean promising lines showed higher grain yield than Tangga-mus. However, all acid-tolerant soybean promising lines showed higher grain yield than Grobogan, except Msr/SJ-5.21.3.7-3-27-1 that showed similar grain yield to Grobogan. Grobogan is a specific location variety, and usually cultivated on favorable environments. Thus Grobogan is not suitable for Ultisols of Banjarnegara. In this research, the water was supplied from rainfall. It lead water deficit on some growth and development stages. Early maturity variety is also susceptible to water deficit when the water deficit occur in vegetative and flowering stage [21].

Wilis showed the highest plant height (63.3 cm) fol-lowed by SC5P2P3.48.31.1-10 (62.0 cm). Other lines with plant height higher than 60 cm were Msr/SJ-5.23.4.1-3-28-3 (60.7 cm) and Msr/SJ-5.23.4.1-5 (61.2 cm) and the check variety of Tanggamus (61.0 cm) (Ta-ble 2). On the other hand, Grobogan showed the lowest plant height (40.9 cm) followed by SC5P2P3.23.4.1-3-28-3 (46.0 cm). Other lines showed plant height higher than 50 cm. [10] also showed the low plant height in acid soil with average of 38.78 cm. Liming can increase plant height [22], [23].

The highest number of filled pods was showed by SC5P2P3.23.4.1-3-28-3 (29.6 pods), while the fewest was SJ-5/Msr.99.5.4.5-1-6-1 (18.3 pods). Among the three check varieties, Tanggamus showed the highest number of filled pods while Grobogan was the lowest (Table 3). It indicated that the genotypes varied in pods number trait. Similar results also reported by [10] where genotypes affect the number of pods per plant. In this experiment, number of pods did not perform optimally, since Tanggamus could produce number of pods from 98.5 to 114.2 in acidic soil Manokwari [24]. Acid soil might suppress number of filled pods by deleterious effect of low pH [11] and low nutrient supply [25]. In addition, water deficit due to the less rainfall might in-duce pod abortion during pod development [26].

Grobogan showed the highest unfilled pods, while the fewest unfilled pods showed by two promising lines (Msr/SJ-5.21.3.7-3-27-1 and Msr/SJ-5.23.4.1-3-28-3) and the two check varieties (Tanggamus and Wilis) (Ta-ble 3). Variability of unfilled pods number was due to the genetic constitution. [27] reported that broad sense heritability of this trait was high (93.1%), suggested that genetic factor had higher effect than environment effect.

Usually in one reproductive node consist of more than one pod. Therefore, reproductive nodes fewer than number of pods. The highest reproductive nodes number was showed by SC5P2P3.23.4.1-3-28-3, Tanggamus and Wilis, while the fewest was showed by Msr/SJ-

5.23.4.1-5, SC5P2P3.48.31.1-10 and SJ-5/Msr.99.5.4.5-1-6-1 (Table 4). However, number of reproductive nodes in this experiment was higher than those obtained in the experiment in Manokwari [24] with the same soy-bean lines under pH 4.89 (personal communication), suggested that lower pH decreased number of reproduc-

tive nodes. Grain size can be measured through 100 grains

weight. The largest seed size was showed by Grobogan (14.13 g/100 seeds) as early maturity check variety with large seed size, while the smallest seed size were showed by Tanggamus and Wilis (7.73 and 8.57 g/100 seeds). Tanggamus and Wilis showed smaller seed size than the description [20] suggested that the two checks

TABLE I GRAIN YIELD AND 100 GRAIN WEIGHT OF ACID-TOLERANT SOYBEAN LINES

ON ULTISOLS IN BANJARNEGARA, RAINY SEASON 2009

Genotypes Yield (t.ha-1) Plant height (cm)

SC2P2.99.5.4.5-1-6-1 1.15ab 55.68abc

SC2P2.151.3.5.1-10 1.08abc 56.22abc

SC5P2P3.23.4.1-3-28-3 1.15ab 46.01cd

SC5P2P3.5.4.1-5 1.18ab 50.19bcd

SC5P2P3.23.4.1-5 1.38ab 51.72abcd

SC5P2P3.48.31.1-10 1.00bc 62.02ab

SJ-5/Msr.99.5.4.5-1-6-1 1.20ab 53.37abc

Msr/SJ-5.21.3.7-3-27-1 0.68c 51.77abcd

Msr/SJ-5.23.4.1-3-28-3 1.42ab 60.67ab

Msr/SJ-5.23.4.1-5 1.26ab 61.22ab

Tanggamus 1.47a 60.98ab

Wilis 1.34ab 63.28a

Grobogan 0.68c 40.87d

Average 1.16 54.92

LSD 5% 0.44 11.95

TABLE II NUMBER OF FILLED PODS AND UNFILLED PODS PER PLANT OF ACID-TOLERANT

SOYBEAN LINES ON ULTISOLS BANJARNEGARA, RAINY SEASON 2009

Genotypes Filled pods.plant-1 Unfilled pods.plant-1

SC2P2.99.5.4.5-1-6-1 24.8abc 0.8d

SC2P2.151.3.5.1-10 20.3bc 0.7d

SC5P2P3.23.4.1-3-28-3 29.6a 1.6b

SC5P2P3.5.4.1-5 26.4ab 1.5bc

SC5P2P3.23.4.1-5 21.3bc 0.9cd

SC5P2P3.48.31.1-10 20.9bc 1.2bcd

SJ-5/Msr.99.5.4.5-1-6-1 18.3c 1.0bcd

Msr/SJ-5.21.3.7-3-27-1 22.1abc 0.7d

Msr/SJ-5.23.4.1-3-28-3 19.0bc 0.7d

Msr/SJ-5.23.4.1-5 20.2bc 1.1bcd

Tanggamus 23.9abc 0.7d

Wilis 22.2abc 0.6d

Grobogan 20.1bc 2.4a

Average 22.2 1.1

LSD 5% 8.05 0.63



varieties showed seed decreasing. All promising lines showed seed sizes between Wilis and Grobogan, where there were three promising lines with seed size more than 10 g/100 seeds (Table 4). The magnitude of seed size depends on seeds filling rate [28].

Relationship among some agronomic characters of ac-id-tolerant soybean lines showed that there were signifi-cant positive correlation between grain yield and plant

height, between filled pods and reproductive nodes, and between unfilled pod and 100 seeds weight (Table 5). It is suggested that the yield per plant was supported by plant height, filled pods and reproductive nodes, while number of filled pods supported by number of reproduc-tive nodes. Similar results also reported by some re-searcher that positive correlation found between number of pods and number of nodes per plant [29] and plant height and grain yield [30]. Grain yield is the total pho-tosynthate that partitioned into seeds, and the magnitude of grain yield is the multiplying of dry matter accumula-tion rate in seeds by seed filling period and by number of seeds [28].

Negative correlation found between grain yield and

100 seeds weight, between plant height and both of 100 seeds weight and unfilled pods (Table 5). Negative cor-relation between plant height and both of 100 seeds weight were also reported by [31]. Usually, positive correlation found between grain yield and 100 seeds weight [32], [33]. However, larger seed size had risk for having higher unfilled pods.

IV. CONCLUSION

In strong acid soil Ultisols of Banjarnegara, Tangga-mus had the highest grain yield than other promising lines and both check varieties Wilis and Grobogan. Msr/SJ-5.23.4.1-3-28-3 was the most potential promis-ing line having grain yield higher than Wilis and seed size larger than Tanggamus. Wilis was adaptive in slightly acid soil, but Grobogan was not suitable for acid soil. Yield per plant was supported by plant height, filled pods and reproductive nodes. Early maturity with larger seed size varieties, such as Grobogan, had risk for having higher unfilled pods.

REFERENCES

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[4] W. J. Horst. 2000. Fitting maize into sustainable cropping sys-tems on acid soil of the tropics. pp. 47-59. In Management and Conservation of Tropical Acid Soils for Sustainable Crop Pro-duction. Proceeding of a consultants meeting. Joint FAO/IAEA Divisions of Nuclear Techniques in Food and Agriculture. Vienna 1-3 March 1999.

[5] R. J. Thomas, M. Ayarza and A. S. Lopes. 2000. pp. 5-28 In Management and Conservation of Tropical Acid Soils for Sus-tainable Crop Production. Proceeding of a consultants meeting. Joint FAO/IAEA Divisions of Nuclear Techniques in Food and Agriculture. Vienna 1-3 March 1999.

[6] D. A. Samac, and M. Tesfaye. 2003. Plant improvement for tolerance to aluminum in acid soils – a review. Plant Cell, Tis-sue and Organ Culture 75: 189–207.

[7] R. J. Haynes, and M. S. Mokolobate. 2001. Amelioration of Al toxicity and P deficiency in acid soils by additions of organic re-sidues: a critical review of the phenomenon and the mechanisms involved. Nutrient Cycling in Agroecosystems 59: 47–63.

[8] X. B. Zhang, P. Liu, Y. S. Yang, and G. Xu. 2007. Effect of Al in soil on photosynthesis and related morphological and physio-logical characteristics of two soybean genotypes. Botanical Stu-dies 48: 435-444.

[9] M. S. Kang. 1998. Using genotype-by-environment interaction for crop cultivar development. Adv. Agron. 35:199–240.

[10] G. O. S. Ojo, and S. A. Ayuba. 2012. Screening of tropically adapted soybeans for aluminium stress tolerance in sand culture. Journal of Applied Biosciences 53: 3812 – 3820.

[11] M. I. Uguru, B. C. Oyiga, and E. A. Jandong. 2012. Responses of some soybean genotypes to different soil pH regimes in two planting seasons. The African Journal of Plant Science and Bio-technology 6:26-37. (Original Paper). (Accessed October 15, 2012).

[12] L. Andric, M. Rastija, T. Teklic, V. Kovacevic. 2012. Response of maize and soybeans to liming. Turk J Agric For 36: 415-420.

[13] G. J. Zhu, G. M. Jiang, N. B. Hao, H. Q. Liu, Z. H. Kong, W. G. Du and W. Q. Man. 2002. Relationship between ecophysiolo-gycal features and grain yields in different soybean varieties. Acta Botanica Sinica 44: 725-730.

TABLE III PLANT HEIGHT AND NUMBER OF REPRODUCTIVE NODES PER PLANT OF

ACID-TOLERANT SOYBEAN LINES ON ULTISOLS IN BANJARNEGARA, RAINY SEASON 2009

Genotypes Reproductive nodes.plant-1

100 grains weight (g)

SC2P2.99.5.4.5-1-6-1 11.38ab 9.87bcde

SC2P2.151.3.5.1-10 10.56abc 9.87bcde SC5P2P3.23.4.1-3-28-3 11.54a 9.70bcde

SC5P2P3.5.4.1-5 11.39ab 8.90ef

SC5P2P3.23.4.1-5 9.20bc 10.13bc

SC5P2P3.48.31.1-10 9.07c 9.10def SJ-5/Msr.99.5.4.5-1-6-1 8.70c 9.23cdef

Msr/SJ-5.21.3.7-3-27-1 9.43abc 10.00bcd

Msr/SJ-5.23.4.1-3-28-3 9.58abc 10.50b

Msr/SJ-5.23.4.1-5 9.08c 9.63bcde

Tanggamus 11.52a 7.73g

Wilis 11.46a 8.57fg

Grobogan 9.98abc 14.13a

Average 10.2 9.8

LSD 5% 2.24 1.02

TABLE V RELATIONSHIP AMONG SOME AGRONOMICAL CHARACTERS OF ACID-

TOLERANT SOYBEAN LINES ON ULTISOLS BANJARNEGARA, RAINY SEASON

2009

Rep. nodes

Filled pods

Unfilled pods

100 seeds

weight

Yield per plant

Plant height -0.059 -0.299 -0.754** -0.666* 0.570*

Rep. nodes 0.762** -0.058 -0.270 0.217

Filled pods 0.148 -0.282 0.060 Unfilled pods 0.665* -0.525 100 seeds weight -0.608*



[14] M. P. de Oliveira, M. H. F. Tavares, M. A. Uribe-Opazo, and L. C. Timm. 2011. Selecting statistical models to study the rela-tionship between soybean yield and soil physical properties. R. Bras. Ci. Solo. 35:97-104.

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[17] A. Sudaric, and M. Vrataric. 2002. Variability and interrelation-ships of grain quantity and quality characteristics in soybean. Die Bodenkultur 53: 137-142.

[18] R. A. Ball, R. W. McNew, E. D. Vories, T. C. Keisling, and L. C. Purcell. 2001. Path analyses of population density effects on short-season soybean yield. Agron. J. 93:187–195.

[19] S. Kobraee and K. Shamsi. 2011. Evaluation of soybean yield under drought stress by path analysis. Australian Journal of Ba-sic and Applied Sci. 5:890-895.

[20] Balitkabi. 2009. Deskripsi Varietas Unggul Kacang-kacangan dan Umbi-umbian. Balai Penelitian Tanaman Kacang-kacangan dan Umbi-umbian. 175 Hlm.

[21] Y. A. Abayomi. 2008. Comparative growth and grain yield responses of early and late soybean maturity groups to induced soil moisture stress at different growth stages. World Journal of Agricultural Sci. 4: 71-78.

[22] M. O. Anetor and E. A. Akinrinde. 2006. Response of oybean [Glycine max (L.) Merrill] to lime and phosphorus fertilizer treatments on an acidic alfisol of Nigeria. Pakistan Journal of Nutrition 5: 286-293.

[23] Purwantoro, H. Kuswantoro, dan D .M. Arsyad. 2007. Identifi-kasi galur-galur harapan kedelai adaptif lahan kering masam. pp. 92-100. In Inovasi Teknologi Kacang-Kacangan Dan Umbi-Umbian Mendukung Kemandirian Pangan dan Kecukupan Energi. Balai Penelitian Tanaman Kacang-kacangan dan Umbi-umbian.

[24] A. W. Rauf. 2010. Uji multilokasi masing-masing 5-6 galur harapan padi dan kedelai dengan produktivitas >20% dari kondisi eksisting di Provinsi Papua Barat. Laporan Hasil Penelitian. Program Insentif Riset Terapan. Balai Pengkajian Teknologi Pertanian Papua Barat.

[25] J. E. Board and A. G. Caldwell. 1991. Response of determi-nate soybean cultivars to low pH soils. Plant and Soil 132: 289-292.

[26] Liu, F. 2004. Physiological Regulation of Pod Set in Soybean (Glycine max L. Merr.) During Drought at Early Reproductive Stages. Department of Agricultural Sciences. The Royal Veteri-nary and Agricultural University. Copenhagen. Dissertation.

[27] Sahay, G. , B.K. Sarma and A.A. Durai. 2005. Genetic variabiu-ty and interrelationship in f2 segregating generation of soybean Glycine max (L) Merril in mid-altitude ofmeghalaya. Agric. Sci. Digest, 25 (2) : 107 – 110.

[28] Harmida. 2010. Respons pertumbuhan galur harapan kedelai (Glycine max (L.) Merril) pada lahan masam. Jurnal Penelitian Sains 13: 13209-13248.

[29] T. Machikowa, and P. Laosuwan. 2011. Path coefficient analy-sis for yield of early maturing soybean. Songklanakarin J. Sci. Technol. 33:365-368.


[31] H. S. Bizeti, C. G. P. de Carvalho, J. R. P. de Souza and D. Destro. 2004. Path analysis under multicollinearity in soybean. Brazilian Archives of Biology and Technology 47: 669-676.

[32] M. Arshad, N. Ali and A. Ghafoor. 2006. Character correlation and path coefficient in soybean Glycine max (L.) Merrill. Pak. J. Bot. 38: 121-130.

[33] M. Showkat and S. D. Tyagi. 2010. Correlation and path coeffi-cient analysis of some quantitative traits in soybean (Glycine max L. Merrill.). Research Journal of Agricultural Sciences 1:102-106.



30 cm

50 cm

BIOLOGICAL CONTROL AND MANAGEMEN OF INSECT PEST ON

STROBERY COMMUNITY: 1. INSECT DIVERSITY IN STRAWBERY COMMUNITY

RCH. Soesilohadi11), S. Sumarmi1), S. Marginio2) dan R. Susandarini3) 1) Lab. of Entomology, Faculty of Biology UGM,

2) Lab. of Microbiology, Faculty of Agriculture UGM, 3) Lab. of Plant Systematic, Faculty of Biology UGM


Abstract— Insect was the most important aspect in de-creasing strawbery fruit in Kopeng, Jawa Tengah. There are need effectively and safely method to control the insect. The objective of the reserch was inventory insect in stro-bery community. Sampling was done every 3 day periode since the strobery panted in the field. The Result showed seven insect ordo visiting strabery community, namely Ordo Neuroptera, Orthoptera, Hymenoptera, Hemiptera, Diptera, Coleoptera, and Lepidoptera. Aphid (Hemiptera: Aphididae), was predominan insect in strowbery communi-ty. Keywords— Spodoptera, Aphid, white fly dan strawbery

I. INTRODUCTION The stroberi, Fragaria chiloensis L planted for com-

mercially fruit production. It will delivery profit around IDR. 54.007.500 ha-1/ year [1]. Strobery growing in high land with low air temperatur. In Indonesia the strowbery have been cultivated in Lembang, and cianjur, West java for a long time ago. In central Java, the strawbery cultivation sited in Kopeng, Central Java.

So many insect visited on strowbery community, and some of them as its pest potency. The acarin and insect kwown as pest in strowbery are 1) mite, Tetranychus sp. dan Tarsonemus sp., 2) Aphid, Chaetosiphon fragaefolii (Hemiptera: Aphididae), 3), Flower borer beetle, An-thonomus rubi, root borer beetle, Otiorhynchus rugoso-striatus and stem borer beetle, O. sulcatus, 4) white fly, Pseudococcus sp., and 5) leaves borer larvae of Lepo-doptera, Spodoptera sp. (Diptera: Lycaenidae) [1]. Recenly, strobery production decreased because the pest has resistant to insecticides [2]. Kopeng, Cental Java (7º39’57”; 110°,41’7”) is an agroecosystem with 1.500 m asl, and air temperature 20 °C at noon.

Strowbery was cultivated in Kopeng with organic me-thod and It has problem with production caused by in-sect pest. Longterm outcomes of the research Will be used Bacillus thuringiensis isolated from soil in the stro-berry community, which it as pathogenic agent for re-duction population of insect pest. Objective of reserch was gathering information and the mapping of insect visiting strowbery community in Kopeng, Cental java, with emphazised on insect pest potencial.

II. MATERIAL AND METHOD

Research was done in strobery farm in Kopeng and

Laboratory of Entomologi UGM, and LIPI for specimen confirmation. Two blocks area, 10 x 10 m2 with (organ-ic block) and without cage screen (non organic block) respectively. One hundred and fivety polybags, (Diame-ter: 30; Height: 50 cm, Figure. 1a) placed in each block. (Figure 1b). A polybag contain four strabery plants (Figure 1a). Observation was done during 5 months from June to November 2013.

Insect collection was done using trap such as Yellow trap, sweeping net and efford unit by choose 6 polybags (6 polybag = 1m2) and decided five unit in the block (Four unit in each side and one unit in the center of the blocks). The spesimen identififying in Laboratory of Entomologi UGM with refferencies, Boror [3], Kalsho-ven [4], Booth et al. [5], Bolton [6], Braby [7], and Lawrence & Bolton [8]. The specimens will be con-firmed in Laboratory of Entomologi LIPI.

♣ ♣ ♣ ♣

(a)

(b)



Figure 1. (a) A polybag with four strobery plants, dan (b) the pattern of polybags squence in the block.

III. RESULT AND DISCUSSION A. Insect visitor on Strowbery Cummunity

There were seven insect order smpled from both two block (organic and non organic) in strabery community. The Ordo were Neuroptera, Orthoptera, Hymenoptera, Hemiptera, Diptera, Coleoptera, and Lepidoptera (Fig-ure 3).

Most of insect visitor on strabery dominated by Ordo Lepidoptera, Coleoptera, Diptera, Homoptera and He-miptera Almost member of Ordo Hemiptera, Homoptera and Lepidoptera sampled from strabery community will be pest potencial. There are two reasons, first the the insects population were higher than those the others, and all of the immature stage eat part of the strobery. Mostly of Ordo Diptera, Coleoptera and Neuroptera were pre-dator. Ordo Hymenoptera, such as honey bees visit strowbery flower, so the insect will be candidate as po-linator. Athough member of Ordo Orthoptera eat stra-bery leaves, they will not be pest, because the popula-tion always low (Figure 2).

Figure 2. Insects visitor on strabery community in organic and non organic block

Aphid (Hemiptera: Apididae) was predominan in both organic and non organic block, but aphid population in non organic block more higher than those in organic block (Figure3). The insect will be potencial as pest. There were significant in increasing aphid population during 30 days observation from 10 at begining to al-most 800 in non organic block (Figure 3). In non organ-ic block, aphid population decreased from 800/block to less than 10/block in 30 days, because insecticides treatment by the farmer. Meanwhile in organic block, aphid population growth in low speed begining from 10/block to 200/block in 70 days and then after 70 days, the population become stationer in around 200/block (Figure 4). No insecticides in organic block.

Figure 3. Population Growth Comparision of aphid on strow-bery in organik and non organic area

Figure 4. Natural enemies and aphid and its competitors (Arachnida, Neuroptera, Homoptera dan Coleoptera) in organ-ic and non organic area

Both in organic and non organic blocks, the fluctua-tion population of natural enemies and aphid competitor following aphid population growth. (Figure 3 and 4). There were competition beetween aphid and its competi-tors, and it cause reduction of aphid population. The competitors was an alternative prey or host for aphid natural enemies, so its will be maintenance the natural enemies. Decreasing natural enemies and aphid competi-tor in line with decreasing aphid population not just only pesticides treatment but the natural enemies and some of competitors will be avoid insecticides treatment because they have highly mobility. There were density depen-dend fluctuation beetween natural enemies and aphid population (Figure 4 and 5).

Caging on stobery cultivation reduced aphid popula-tion (Figure 4), and reducing insecticides treatment. Population of aphid natural enemies in cultivation strowbery with cage was more higher than those in strowbery cultivation without cage (Figure 5).

IV. CONCLUSION 1. There were Neuroptera, Orthoptera, Hymenoptera, Hemiptera, Diptera, Coleoptera, and Lepidoptera visit-ing strobery cultivation 2. Population of Apid (Hemiptera: Aphididae) was hig-est than those other taxa. 3. Aphid population can be minimize in strobery culti-vation by caging.

ACKNOWLEDGEMENT We thanks to the management of PUPT UGM 2013 which financing this research.

REFERENCES [1] Anonim 2008 .Perkebunan stroberi. http://www.ristek.go.id http://www.warintek.ristek.go.id/ perkebunan/stroberi.pdf [2] Bradford MM (1976) A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing theprinciple of protein-dye binding.Anal Biochem 72:248–254 [3] Borror, D.J., L.A. Triplehorn, and N.F. Johnson. 1989. An Intro-duction to Study of Insect. 6th edition.Holt Reinhard and Winston. New York. [4] Kalshoven, L.G.E. 1981. The Pests of Crops in Indonesia.Pt Ichtiar Baru-Van Hoeve, Jakarta. P. 341-344. [5] Booth, R.G. Cox, ML Madge, RB.1991. The Guide to the Insects of Importance to Man; Coleoptera. The University Press Cambridge. [6] Bolton, B. 1994.Identification Guide to the Ants Genera of World. The President and Fellows of Harvard College. USA. [7] Braby. F. Michael 2004. The Complet Field Guide to Butterflies of Australia.CSIRO Publishing Australia. [8] Lawrence, JF. Britton.E. 1994. Australia Beetles. Melbourne University Press.



Abstract - Holothuria leucospilota was used as bioin-dicator of mercury bioaccumulation at ceased tradi-tional gold mining at Lampon estuarine and stone shore. We used the samples with same species as con-trol samples from Stone Shore Beach of Rajegwesi Resort in Meru Betiri National Park. The bioaccu-mulation of mercury in this research was detected by SNI 06-6992.2-2004 (with modification). The result of mercury bioaccumulation shows from 47.83 ppb to 0.03 ppb in the lowest, but it was undetected in sam-ples from Meru Betiri National Park. The mercury accumulation shows in high value from recommend-ed standard value. Protein profile was analyzed using SDS-PAGE method. There are three new bands and one band of protein in bolder from the other bands of the samples. It may show that anomaly form of protein profile caused mercury pollution at this site. Mercury is still remaining to pollute while the mining was ceased. It can be predicted that recidence time of mercury effect caused some anomaly structure to aquatic biota because the protein profile was differ-ent from the normal profile. Key words: protein profile, Holothuria leucospilota, bioaccumulation, mercury

I. INTRODUCTION Lampon shore was one of the areas to wasted of tail-ing traditional gold amalgamation in Banyuwangi Dis-trict. They were used mercury for amalgamation process. Although, the activity of tailing discharging was stopped at least 2 years ago, but the effect of mer-cury polluted still be remaining to aquatic organisms indeed. Mercury bioaccumulation in Gastropods was proofed, they are Nerita argus (3.03 ppm) and Terebra-lia sulcata (3.10 ppm). Hepatopancreas of Nerita argus in severe atropi [1]. The observation result in 2013 shows still be found mercury bioaccumulation in some macrobenthos based on niches and feeding behavior [2], [3]. Pathology effect of mercury pollution is approximate-ly to be monitoring time after time. There is a lot of Ho-lothuroidea in summer season. Holothuroidea as detri-vor, they cleaned of the shore from detritus. They feed-ing behavior can be used to mercury bioindicator in Lampon. Holothuria leucospilota very abundance be-side another genus called Cucumaria. We used Holothu-

ria leucospilota as bioindicator for mercury effect to them protein profile. Mercury is toxic and carsinogenic heavy metal be-cause mercury is non-degradable. Mercury accumula-tion can be caused lethal effect. Mercury in nature as cinnabar (HgS) and we found mercury in sea about 0.15 µg/l. Mercury value standard of water that recommend-ed in Indonesia is 0.001 mg/l (Kep. MenLH No.51, 2004). If mercury concentration in environment higher from standard value, it can be negative impact for organ-isms.

Toxic effect of mercury such as mithocondial damage, nerve disfunction, endocrine disruption, cancer, and memory lost. The risk for chronic toxicity depends on the frequency, intensity, and duration of contact with the contaminant along with the exposure route. Toxicity risk also depends on the inherent toxic potential of the metal itself. All forms of mercury are toxic to humans. Their effects are organ specific and depend on the chem-ical form of the mercury and the exposure level, dura-tion, and route. Different forms of mercury deposit pre-ferentially in different tissue compartments, which ex-plains their different toxic profiles. MeHg (methylmer-cury) is almost 100% absorbed across the intestines and also crosses the blood-brain barrier (BBB). Inorganic mercury is also absorbed through the intestines and crosses the BBB but much less so than MeHg. MeHg is considered the most toxic form of mercury and has a half-life of 70 days in humans ([4]; [5]; [6]; [7]; [8]; [9]; [10]).

Manisseri dan Menon [11] shows, mercury can make reticulum endoplasmic damage. Reticulum endop-lasmic contain lots of Ribosomes. Ribosom is an orga-nella to resposible to make protein cell. The change of protein profile show the change of protein building by ribosoms. Mercury pollution still be remaining in unpre-dictable time at Lampon. It is approximately to tracing mercury effect, included protein profile of Holothuria leucospilota.


Lampon estuaries was administratively located in Banyuwangi, East Java. This research taken placed from October-December 2013. The Method used in this re-search to collect samples was random sampling when low water spring in estuary and rocky shore. The coor-dinate site is 8°37’05.39”S 144°05’11.46”E. Control

Protein profile of Holothuria leucospilota (Echinodermata: Holothuroidea) at Ceased

Traditional Gold Mining, Lampon Banyuwangi District

Susintowati1*) and Nurchayati, N.2) 1,2) Faculty of Teacher Training and Education, 17 Agustus 1945 University, Banyuwangi, Indonesia

* ) Email: [email protected]



samples taken from Stone Shore of Rajegwesi Resort, Meru Betiri National Park.

To identified species of the specimen (Holo-thuria leucospilota) were used the data and sample pic-ture in literatures at laboratory. The specimens were fixation with 4% formaldehyde before preparation to mercury analysis. Mercury concentration were analyse based on SNI method 06-6992.2-2004 (with modifica-tion) and Mercury Analyser at LPPT Gadjah Mada Uni-versity, Yogyakarta. Whereas, protein profile of Holo-thuria leucospilota used SDS PAGE (sodium dodecyl sulfate polyacrylamyde gel electrophoresis) procedure at Falitma Laboratory of Faculty of Biology, Gadjah Mada University [11]. The overall analysis of the data of the results based on protein bands of protein profile and qualitative descriptive analysis.


Bioaccumulation of mercury in Holothuria leu-cospilota still high enough. Although, dischaging of tailing stopped, the accumulation of mercury still re-maining and detected. Bioaccumulation mercury in Ho-lothuria leucospilota from 47.83 ppb to 0.03 ppb in the lowest. But, the samples from Meru Betiri National Park by mercury analyzer tools shows undetected. According to Eisler, the standard concentration of mercury that make lethal sensitivity on aquatic organisms is 0.1 – 2.0 ppb [4]. In Indonesia value standard of mercury based SNI 7387:2009, did not clear yet. But here, if we look in another country e.g. Japan and Canada, 0.3 ppm maxim-al [9].

Based on protein analysis by SDS PAGE all protein bands of specimen from Lampon are same as the specimen from Meru Betiri National Park. But, one of them show different, there is boldest band and three new bands (Figure 1). It is need another works to proof the protein specification. Residence time of mercury in wa-ter and sea sediment is very long time. It could be the reason that bioaccumulation of mercury in the sample of Holothuria leucospilota still detected. There are a lot of symptoms appear caused mercury toxicity in fact DNA damage [10]. The DNA damage can be causing anomaly of protein structure. The effect of profile protein change is pathologycal symptoms of this animal.

Molecule weight of protein of Holothuria leu-copsilota (Tabel. 1) was estimated by using empiric pattern y=-1.049x+2.272 and R2=0.951 based on calcu-lating of Marker molecular weight and it racing factors (rf). There are three main protein bands of Holothuria leucospilota from Lampon dan Meru Betiri National Park based SDS PAGE procedure. But, there is one sample that different from the other samples. It is show, three new bands and one band boldest from the other.

Figure 1. Result of SDS PAGE of Holothuria leucospilota protein profile. Bolder and addition of bands (arrow indeed). Lampon sample (L) and Meru Betiri National Park sample (M) in normal sequence.

Figure 2. The Curve of Molecule Weight (MW) of Marker vs Marker racing factor (rf)

TABLE 1. PROTEIN MOLECULE WEIGHT OF HOLOTHURIA

LEUCOSPILOTA FROM LAMPON Tracking distance

(cm) rf MW (kDa) log MW

0.8 0.133 135.571 2.132

4.3* 0.717 33.127 1.520

4.9 0.817 26.018 1.415

5* 0.833 25.014 1.398

5.2 0.867 23.059 1.363

5.7* 0.950 18.856 1.275 Note: * new band; kDa = kilo Dalton

TABLE 2. PROTEIN MOLECULE WEIGHT OF HOLOTHURIA LEUCOSPILOTA FROM STONE SHORE OF MERU

BETIRI NATIONAL PARK Tracking Distance

(cm) rf BM

(kDa) log BM

0.8 0.133 135.571 2.132

4.9 0.817 26.018 1.415

5.2 0.867 23.059 1.363

B Mark L1 L2 L3 M1 M2 M3 B



The molecule weight of new protein bands are 33.127 kDa, 25.014 kDa and 18.856 kDa, they to be estimate as CBD-BmFKBP13 and Lyzozyme. It need another works to proof that new bands are anomaly proteins caused mercury bioaccumulation in their body or caused by another reasons. In another observation, we found significantly different of spicules density of Holothuria leucospilota from Lampon if compared with samples from Meru Betiri National Park. Density spicules of Lampon sample is less than the sample of Meru Betiri [2]. Spicules Is a skeleton spicules on Holothuroidea. Spicule formation is influenced genetic factors by DNA code, because the spicules form a specific character type recognition. Changes in the protein profile has been implicated in the formation of spicules Holothuroidea included Holothuria leucospilota.

III. CONCLUSION

Bioaccumulation of mercury in Holothuria leucospilota in Lampon still detectable despite amalgamation activity has stopped in long time enough. Bioaccumulation of mercury in the body Holothuria leucospilota suspected to cause a change in the protein profile. However, another works needs to proof it.

ACKNOWLEDGMENT

Many thanks to the Directorate General of Indonesian Higher Education which has provided research grants of beginner lecturer for fiscal year 2013. Thank you also to all the staff of LPPT and Laboratory Falitma, Faculty of Biology Gadjah Mada University Yogyakarta for any help given. Thank you to PPPM 17 Agustus 1945 Ba-nyuwangi University to recommended this research. And thank you to all those involved in sampling and data processing this research.

REFFERENCES

[1] Susintowati and S. Hadisusanto, “Mercury bioaccumulation and Community Structure of Gastropoda at Ceased Tradi-tional Mining, Lampon, Banyuwangi District”, Thesis, Gadjah Mada University, Yogyakarta, 2012.

[2] Susintowati and N. Nurchayati, “Mercury Pathology Effect to Spicules Density of Holothuroidea (Echinodermata) at Ceased Lampon Traditional Gold Mining, Banyuwangi District”, (accepted for publication) in Saintek Journal, Kopertis Wilayah VII Jawa Timur. 2013.

[3] Susintowati and S. Hadisusanto, “Mercury bioaccumulation of macrobenthos based on feeding behavior and niches at Lampon ceased traditional gold mining, Banyuwangi District, East Java”, Paper (presented) in International Conference of Biology Science, at Faculty of Biology, Gadjah Mada University Yogyakarta, September 21-22th, 2013, (accepted for proceeding).

[4] R. Eisler, “Mercury hazards to fish, Wildlife and Invertebrates, A synoptic review” Biologycal Report, 1987, 85(1:10).

[5] B.T. Setiabudi, “Penyebaran merkuri akibat usaha pertambangan emas di daerah Sangon Kabupaten Kulon Progo DI Yo-gyakarta”, Draft Report (unpublished)-DIM, 2005: 1-17.

[6] Darmono, “Lingkungan hidup dan pencemaran, hubungannya dengan toksikologi senyawa logam, UI-Press-Jakarta, 2008.

[7] H. Palar, “Pencemaran dan toksikologi logam berat, Rineka Cipta Publisher, Jakarta, 2008.

[8] W. Widowati, A. Sastiono, and R.R. Jusuf, “Efek toksik logam. Pencegahan dan penanggulangan pencemaran”, Andi Publisher, Yogyakarta, 2008.

[9] Anonimous, “Maximum level of Heavy Metal in Food by SNI 7387:2009. ICS 67.220.20, Indonesia National Standari-zation, Jakarta, 2009.

[10] J. Neustadt, and S. Pieczenik, “Heavy-metal Toxicity With Emphasis on Mercury”, Integrative Medicine, 2011, Vol.10.No.5: 45-50.

[11] M.K. Manisseri and N.R. Menon, “Ultrastructural aberrasion in the hepatopancreas of Metapenaeus dobsoni (Miers) Exposed to Mercury”, J. Mar. Biol. Ass. India, 2006, 48 (1): 89-94.

[12] B.T. Kurien, and R.H. Scofield, “Extraction of protein from gels: a brief review”, Methods Mol. Biol., 2012, 869:403-5.



Abstract— The aim of the present study was to isolate and

characterize the cellulose-degrading bacteria from the man-grove areas of Sembilang National Park South Sumatera. The bacteria capable of growing in the liquid medium con-taining cellulose as the only source of carbon were isolated and their cellulolytic activity on CMC-containing media was confirmed by the congo red clearing zone assay. The isolates were identified based on colony and cell morphological cha-racteristics and biochemical characteristics. The results of the present study show that 12 cellulose-degrading bacteria isolated from the mangrove areas of Sembilang National Park belonged to the species Micrococus sp.(S1SS1), Acinetobacter sp.(S1SS2), Bacillus sp. (S1SS3), Bacillus sp. (S1TS1), Clostridium sp.(S1SS4), Aerococus sp. (S1TS3) dan Pseudomonas sp. (S1TS5), Pseudomonas sp. (S2TS2) Clostridium sp. (S2SS3), Pseudomonas sp. (S3SS1), Clostridium

sp. (S3SS3), and Clostridium sp. (S3TS1).

Keywords : cellulose-degrading bacteria, identification, mangrove areas

I. INTRODUCTION

Indonesian territory consists of 17,508 islands and has a long coastline of about 81,000 km , is the country that has the largest mangrove forest in the world . Extensive man-grove forests in Indonesia reached 4.25 million hectares is the largest mangrove in the world beyond Brazil (1.3 mil-lion ha) , Nigeria (1.1 million ha) and Australia (0.97 ha) [11]. One Indonesian mangrove areas which Sembilang National Park located in South Sumatera . According Su-wignyo et al.[9], various types of mangrove genera that dominat in Sembilang National Park were Rhizophora, Avicennia, Sonneratia and Bruguiera. According to Noor et al.[6], the mangrove ecosystem is an interface between terrestrial and ocean ecosystem. Biodiversity level of mangrove ecosystem was high so that mangrove can grow well. Mangrove forest ecosystems that are capable of pro-ducing a high organic matter, 90 % of organic particles in the water coming from the mangrove vegetation and result in 35-60 % of nutrients that are beneficial to the growth of mangroves. Constituent components of the organic matter is cellulose, hemicellulose and lignin . The importance of microbial diversity in mangrove ecosystems due to the presence of microbes capable of providing nutrients in a mangrove ecosystem that can

grow well without organic fertilizer. Microbial diversity in mangrove ecosystems have each function in degrading organic materials for the growth of mangroves. Thus in conserving the mangrove ecosystem, information and benefit of microbes was required such as cellulose-degrading and lignin-degrading bacteria that play role in litter decomposition in a mangrove ecosystem benefits [8]. The aims of the research was to isolate and characterize cellulose-degrading bacteria in mangrove litter and soil, in Sembilang National Park, South Sumatera.


Sample of mangrove litter and soil bacterial iso-lates were taken from a mangrove forest area Sembilang National Park, South Sumatera. Soil samples as top soil were taken from 20 cm depth at 3 stations with purposive sampling method. In each station, samples were taken at 5 points of soil sampling to represent each study sites. Sam-ples taken in the form of litter collected for 1 day at amount 5 grams of finely ground leaf litter and 5 grams of soil in each erlenmeyer then put in 45 mL distilled water respectively. Serial dilutions to a concentration of 10-6 were performed. One mL sample was taken at the last 3 serial dilutions and grown on CMC (Caboxy Methyl Cel-lulose) medium for growth of cellulose-degrading bacteria in a petri dish with pour plate method, and then incubated at 37oC for 24-48 hours [2]. Selection is done by taking the pure bacterial iso-lates using a needle inserted in the center of the loop that has solid medium in a petri dish, and then incubated at 37oC for 2 days. Bacteria were grown on selective media cellulolytic after spilled congo red and 1M NaCl to form a clear zone is a zone of cellulose-degrading bacteria [2].


Based on the research conducted showed cellulose-degrading bacteria isolates of mangrove litter and soil in Sembilang National Park area as in Table 1.

Thirty pure isolates pure taken from isolation of cellu-lose-degrading bacteria were isolated using CMC medium (Table 1), because CMC medium used is the best sub-strate to induce the synthesis of extracellular cellulolytic enzymes. CMC is a synthetic substrates that serve as model compounds of cellulose. CMC has many amorph-ous regions so soluble in water. CMC concentration used

Screening and Identification of Cellulose-Degrading Bacteria from The Mangrove Areas of Sembilang National Park South Sumatera

Hary Widjajanti 1*), Sarno1), and Novida Rosalia Sinaga 1) 1) Biology Department, Faculty of Mathematic Natural Science, Sriwijaya University,

Inderalaya, South Sumatera, Indonesia




was 1 %, according to research Narasimha et al. (2005), 1 % cellulose concentration is the optimum concentration for the production of cellulase enzymes. The medium used for bacterial growth substrate is a suitable medium for the growth of bacteria [2].

Table 1. Cellulose-degrading bacteria isolates Station Sample Number of cellulose-

degrading bacteria isolates

I 104054’13,2’’E

209’25,5”S

Litter 6

Soil 5

II 104053’41,4”E

205’43,6”S

Litter 4

Soil 4

III 104054’18”E 209’47,4”S

Litter 7

Soil 4

Total 30

Table 2. Screening result of cellulose-degrading bacteria Station Sample Screening result of

cellulose-degrading bacteria

Cellulose-degrading bacteria isolates code

I 104054’13,2’’E

209’25,5”S

Litter 4 S1SS1, S1SS2, S1SS3, S1SS4

Soil 3 S1TS1,S1TS3, S1TS5

II 104053’41,4”E

205’43,6”S

Litter 1 S2SS3

Soil 1 S2TS2

III 104054’18”E 209’47,4”S

Litter 2 S3SS1, S3SS3 Soil 1 S3TS1

Total 12

Screening of cellulolytic bacteria used selective medium are characterized by the formation of clear zone after spilled congo red dye that is used as an indicator of cellulolytic bacteria. Formation of a clear zone is due cel-lulolytic bacteria capable of hydrolyzing cellulose into simple compounds. The formation of clear zone indicates that the cellulose contained in the media is hydrolyzed by cellulase enzymes into simple compounds that cellobiose is then simplified into two molecules of glucose [6]. Clear zone can be formed by washing use 1M NaCl. Clear zone will be clearly after congo red addition. Congo red is the sodium salt of benzidinediazo bis - 1 - naphthylamine - 4-sulfonic acid (C32H22N6Na2O6S2) so that the dye will dissolve and leached by other sodium salts, such as NaCl . Thus , the clear zone is formed will be clearly. Formation of clear zone indicates that the bac-teria are able to degrade cellulose [8]. Based on the characteristics obtained, each bacteri-al isolate can be grouped into several genera were identi-fied [1] [4]. Each cellulolytic bacteria belongs to the ge-nera Micrococus, Ancinetobacter, Bacillus, Clostridium, Pseudomonas, and Aerococus. Isolates of bacteria belonging to the genus Micro-cocus is S1SS1. The bacterial isolates have characteristic shape cocci cells, negative staining and aerobic endos-pores. The characteristics of the genus Micrococcus spherical cell shape , nature of gram-positive, negative indole test, catalase test positive, does not produce acid fermentation of carbohydrates, is motile due to its flagella

as locomotor and there is also nonmotil. This genus can be isolated from soil, water and food products. Isolates of bacteria belonging to the genus Acinetobacter[1]. Genus Acinetobacter discrete forms of cocci, gram-negative, non motile, growth requires oxygen (aerobic), not produce endospores, biochemical test results were positive in cata-lase test and negative on indole and H2S test, can be iso-lated of soil, water and litter decomposition [4]. Other genera that found Bacillus sp. ( S1SS3 ), Ba-cillus sp. (S1TS1 ), Bacillus sp. ( S1SL2 ) , Bacillus sp. ( S1SL4 ) and Bacillus sp. ( S2TL1 ). The fifth Bacillus genera have characteristics as bacillus form, gram-positive, catalase test positive, and form endospores. Dif-ferences in biochemical test on each isolate would classi-fy the genus Bacillus isolates at the species level. Genus Bacillus have characteristics such as bacillus shape, straight, long and short, gram-positive, motile but there are a few nonmotile, form endospores oval, spherical, cylindrical and resistant to a wide range of conditions, aerobic and facultative anaerobic, catalase test positive, are pathogenic in vertebrates and invertebrates, its broad habitat, and can be found in water and soil [1] [4]. Isolates S1SS4 , S2SS3 , S3SS3 , S3TS1 , and S3TL1 belonging to the genus Clostridium. They have bacillus shaped Clostridium characteristics, anaerobic, gram positive, and form endospores. Clostridium discrete cell shape in general, but there are a few bacilli, cocci, gram-positive, motile with flagella peritric partially and sometimes non-motile, anaerobic, form endospores, and negative catalase test, found in water, soil, also in human skin. The difference in the physiological test each isolate in the genus Clostridium, enabling this genus can be grouped in the species level [1] [4]. Isolates S1TS3, S1TL5 and S3SL3 belong to the genus Aerococus. This genus generally have characteris-tics of gram-positive, non motile and catalase test nega-tive. Aerococus belong to the gram- positive, non motile, facultative anaerobic and some are anaerobic, not produce catalase, so based on common characteristics of these three isolates were grouped in a single genus. However, the differences in the nature of the physiological test, Aerococus this genus can be further grouped based on species level [1]. Besides Aerococus also found the genus Pseudomo-nas isolates S1TS5, S2TS2, S3SS1, S1SL5, S2SL1, S2SL2 and S3SL2. These isolates differed in physiologi-cal tests can be grouped by allowing the species level . However , similarity of the characteristic of these isolates, like flagella are motile, gram negative and positive cata-lase test that are grouped into a single genus. This is con-sistent with [1] and [4] Pseudomonas has the characteris-tics bacillus form single or in groups, motile with flagella lies opposite, gram-negative, aerobic and facultative anae-robic, catalase test positive, can be found in soil, water and sea. Genus of bacteria most commonly found is the genus Pseudomonas as mention by Rao [8] that genus of microorganisms that can degrade cellulose and lignin is the genus Pseudomonas.

ACKNOWLEDGMENT

Thank you to Dikti with Hibah Fundamental that funding this research and also to Sembilang National Park Author-



ity thank you very much for your help and togetherness during at Sembilang National Park.

REFERENCES

[1] Buchanan, R.E. & N.E Gibbons. 1974. Bergey’s Manual of Determinative Bacteriology 8th Edition. The Wiilliams & Wilkins Company. USA. 1268 pages.

[2] Cappuccino, J. G & N, Sherman. 2008. Microbiology A Laboratory Manual. Rockland Community College Suffern. New York. xvi + 557 pages.

[3] Hartanti. 2010. Isolasi Seleksi Bakteri Selulolitik Termofilik dari Kawah Air Panas Gunung Pancar, Bogor. Skripsi. Departemen Biokimia Fakultas Matematika dan Ilmu Pengetahuan Alam. IPB. Bogor + 29 hlm.

[4] Holt, J.G,. R.K. Noel, H.A.S. Peter, T.S. James & T.W. Stanlay 1994. Bergey’s Manual of Determinative Bacteriology 9th Edition. The Wiilliams & Wilkins Company. USA. 1268 pages.

[5] Noor, R.Y, M. Khazali & I N.N. Suryadiputra. 1999. Panduan Pengenalan Mangrove di Indonesia. PHKA/WI-IPB. Bogor. Vii + 220 hlm.

[6] Perez, J. J. Munoz-Dorado, T. de la Rubia & J. Martinez. 2002. Biodegradation and biological treatments of cellulose, hemicelluloses and lignin: an overview. Int. Microbiol.

[7] Rao, N. S. Subba. 1994. Mikroorganisme Tanah dan Pertumbuhan Tanaman. Edisi kedua. Universitas Indonesia Press. Jakarta. 353 hlm.

[8] Sahoo, K & N.K. Dhal. 2008. Potential microbial diversity in mangrove ecosystems. A review. Institute of mineral and materials technology Bhubaneswar. India. 249-256.

[9] Suwignyo, R. A. Munandar, Sarno, T. Z. Ulqodry & E.S. Halimi. 2011. Pengalaman Pendampingan dalam Pengelolaan Hutan Mangrove pada Masyarakat. Makalah. Balai Pengelolaan Hutan Mangrove Wilayah II Direktorat Jenderal Bina Pengelolaan Daerah Aliran Sungai dan Perhutanan Sosial, Kementerian Kehutanan Hotel Swarna Dwipa. Palembang. 22 hlm.

[10] Yunasfi, 2006. Dekomposisi Serasah Daun Avicennia marina oleh Bakteri dan Fungi pada Berbagai Tingkat Salinitas. Disertasi. Program Studi Ilmu Pengetahuan Kehutanan, Institut Pertanian Bogor. Bogor. 60 hlm.



Appendix 1 Tabel 3. The results of characterization and identification of cellulose degrading bacteria Isolate Character S1SS1 S1SS2 S1SS3 S1SS4 S1TS1 S1TS3 S1TS5 S2SS3 S2TS2 S3SS1 S3SS3 S3TS1

Macroscopic colony morphology

Plumose Echinulate Filamentous Ramose Convex Papi-late White

Plumose Echinulate Comensal Undulate Umbonate Cream

Effuse Villous Crenate Undulate Convex Papilate Cream

Beaded Villous Crenate Lobate Raised with Concave Brown

Filliform Villous Circular Entire Effuse white

Plumose Beaded Circular Entire Convex Yellow

Filliform Villous Circular Entire Convex Orange

Filliform Villous Irregular & spreading Crenate Convex Ru-gose White

Spreading Echinulate Irregular & spreading Crenate Umbonate Cream

Plumose Beaded Circular, filamentous Crenate Raised with Concave Cream

Filliform Villous Circular Entire Convex Brown

Spreading Villous Irregular, spreading Crenate Convex Papilate Cream

Microscopic cell morphology

Coccus, Gram positive, did not produce spore

Coccus Gram nega-tive, did not produce spore

Bacillus, Gram posi-tive, pro-duce spore

Coccus, Gram posi-tive, pro-duce spore


Bacillus, Gram posi-tive, did not produce spore

Bacillus, Gram nega-tive, did not produce spore

Bacillus, Gram positive, pro-duce spore

Bacillus, Gram negative, did not produce spore

Bacillus, Gram negative, did not produce spore



O2 needed Aerob Aerob Anaerob Fakultatif

Anaerob Anaerob Fakultatif

Anaerob Fakultatif

Anaerob Fakultatif

Anaerob Anaerob Fa-kultatif

Anaerob Anaerob Anaerob

Motility test + - + - + - + - + + - +

Biochemical test :

Simmon’s citrate test + + - - + + - + - + - + Indole production - - - - - - - - - - - - Starch hydrolysis + + + + - + - + + + + + Urea hydrolysis - - - - - - - + - - - - Methyl-red test + + + - - - - + + + - + Voges Proskauer test - - - - - - - - - - - - Catalase production + + + - + - + - + + - - Glucose Fermen-tation

Gas + + + + + - + + + + + + Acid - - - - - + + - - - - +

Lactose Fermen-tation

Gas + + + + + + + + + + + Acid + + + + + + + + + + +

Sucrose Fermen-tation

Gas Acid

H2S and gas production

Gas + + + + + + + + + + + -

Sugar fermenta-tion

+ + - - - - - - + - + +

H2S - - - - + + - - - + - - CONCLUSION

Micrococus sp. (S1SS1)

Ancineto-bacter sp. (S1SS2)

Bacillus sp. (S1SS3)

Clostridium sp. (S1SS4)

Bacillus sp. (S1TS1)

Aerococus

sp. (S1TS3) Pseudomo nas sp. (S1TS5)

Clostridium

sp. (S2SS3) Pseudomonas sp. (S2TS2)

Pseudomonas

sp. (S3SS1) Clostridium

sp. (S3SS3) Clostridium

sp. (S3TS1)

Notes: (+) : Positive (-) : Negative



Abstract— Diabetes mellitus is a chronic metabolic dis-

order which associated with hyperglycemia. It caused by a derangement in secretion or function of the endocrinal portion of the pancreas. The insulin resistance and insulin secretion are well known underlying pathophysiologies of diabetes. There is a close anatomical and functional rela-tionship between its exocrine and endocrine portions. An increased in the serum concentration of pancreatic enzyme is commonly an expression of inflammatory exocrine pan-creatic disease. The aim of the study was to evaluate the correlation between fasting blood glucose, insulin resis-tance and lipase/amylase ratio in type 2 DM. The subjects were categorized into three groups which include: healthy controls (n = 21), prediabetes (n = 12), and diabetes (n=34). That clinical identification was assessed according to ADA criteria in 2013. Plasma glucose, lipid profile testing, total cholesterol, HDL cholesterol and triglycerides were meas-ured using an auto analyzer. Amylase and lipase were measured colorimetric method using Assay Kit. Insulin concentration was measured using Elisa kit. The results showed that there were elevated levels of blood glucose, insulin resistance, and serum pancreatic enzymes (amylase and lipase) activities in type 2 DM. The increased serum pancreatic enzymes activities were positively correlated with increased levels of blood glucose, insulin resistance, and decreased proinsulin level. The ratio of serum li-pase/amylase showed a positive correlation with duration of diabetes, FPG levels, insulin resistance, decreased insu-lin sensitivity, and increased lipase activity. The ratio of serum lipase/amylase will be able to determine the acute phase of alcoholic and non-alcoholic pancreatitis.

Keywords— T2DM, insulin resistance, exocrine inflam-mation

I. INTRODUCTION

DIABETES MELLITUS (DM) is a group of metabolic dis-orders characterized by hyperglycemia resulting from defects in insulin secretion, insulin action, or both. The vast majority of cases of diabetes fall into two categories namely Type 1 diabetes (Type 1 DM) and the more pre-valent Type 2 diabetes (Type 2 DM). Other categories include specific types of diabetes relate to genetic de-fects, the exocrine pancreas disease, endocrinopathies, drug induced and Gestational diabetes [1].

Type 2 DM is caused by a combination of genetic

factors related to impaired insulin secretion and insulin resistance and environmental factors such as obesity, overeating, lack of exercise and stress, as well as aging. The main pathophysiological feature of type 2 DM are impaired insulin secretion and increased insulin resis-tance [2].

Anatomically, the pancreas is a mixed exocrine-endocrine gland. There is also morphological evidence that indicates that the pancreatic exocrine function may be influenced by the pancreatic endocrine hormones. Insulin has a trophic effect on exocrine pancreas. There are multiple defects in the insulin secretion and signal-ing in type 2 DM, which may affect the enzyme synthe-sis and release in the exocrine pancreas [3].

Recent studies have demonstrated an increased inci-dence of pancreatitis in patients with type 2 DM. An elevated serum pancreatic enzyme (amylase and lipase) supports clinical diagnosis of acute pancreatitis.

With this background, the present study was con-ducted in order to elucidate a possible correlation be-tween insulin resistance and the pancreatic exocrine inflammation in Indonesian Type 2 diabetes subject.

II. SUBJECTS AND METHODS

The study was approved by institutional Human ethics committee (Medical Faculty, Brawijaya University) and informed consent was obtained from all participants.

A. Subjects:

This study included 67 subjects having general check-up in Central Laboratory of Dr. Saiful Anwar Hospital for a period of six month. The inclusion crite-rion of them were males and females with type 2 di-abetes mellitus with or without complications or any co-morbid condition like hypertension, coronary artery dis-ease, etc, and also non diabetic healthy individual as the controls. All the subjects, including the controls, were fully informed about the study and their voluntary in-formed consents were taken. The subjects were catego-rized into four groups which include: healthy controls (n = 21), prediabetes (n = 10), diabetes (n=10), and predia-betes or diabetes with exocrine insufficiency (n=26).

Srihardyastutie, A1,2*), Soeatmadji, D.W. 3), Fatchiyah 2), and Aulanni’am2) 1) Biology Doctoral Program, Faculty of Science, Brawijaya University

2) Faculty of Science Brawijaya University, 3) Medical Faculty, Brawijaya University,


Lipase/Amylase Ratio as The Indication of Pancreatic Exocrine Inflammation and The

Correlation with Insulin Resistance in Type 2 Diabetes Mellitus



B. Methods Clinical identification of normal or healthy control,

prediabetes, and diabetes was done according to Ameri-can Diabetes Association criteria. The diagnostic crite-ria of diabetes was assessed according to American Di-abetes Association (ADA) i.e. subjects with a fasting plasma glucose > 126 mg/dL and/or 2 hour plasma glu-cose level > 200 mg/dL and/or HbA1c > 6.5 % were considered to have diabetes; subjects with a fasting plasma glucose 100 to 125 mg/dL (IFG) or 2 hour plas-ma glucose level 140 to 199 mg/dL (IGT) or HbA1c 5.7 to 6.4% were considered to have increased risk for di-abetes (prediabetes); subjects with a fasting plasma glu-cose < 110 mg/dL or 2 hour plasma glucose level < 140 mg/dL or HbA1c < 5.6 % were regarded as a having normal glucose tolerance (NGT) [1]. The exocrine insuf-ficiency was assessed according to increasing of amylase and or lipase level. The considered reference range of normal amylase was 30 - 110 U/L and lipase was 30 - 210 U/L.

Fasting venous blood was collected from all of sub-jects, it was centrifuged (at 1500 g for 15 minutes). The separated plasma was used to assay the HbA1c. HbA1c was measured with ion-exchange high-performance liq-uid chromatography using an automated analyzer (Bio-Rad D10). The separated serum was divided into four aliquot. One was designed for immediate assay of glu-cose and lipid profile which included Triglyceride (TG), total cholesterol (CHOL), high density lipoprotein (HDL), low density lipoprotein (LDL). The other ali-quots were stored at -20oC for subsequent assay for amy-lase, lipase, insulin and proinsulin.

The assay of sample analysis was carried out by us-ing different reagent kits as per procedure which was defined by manufacturer. The immediate assay of sam-ple analysis was measured on a fully automated analyzer. The fasting plasma glucose was measured by the hex-okinase method. The serum triglyceride was measured by the enzymatic method (GPO-POD method, End Point). For determination of total cholesterol, an enzy-matic (CHOD-POD) colorimetric method was used. The direct measurement for HDL and LDL were done by using enzymatic methods.

Insulin concentration was measured by Sandwich enzyme immunoassay method. Insulin concentration was measured using kit from Ucsn, China. Homeostasis model assessment of insulin resistance (HOMA-IR) was used for the direct measurement of insulin resistance and was calculated as follows:

HOMA-IR = [fasting insulin (µU/mL) x fasting glucose (mg/dL)]/405 [4]

The cut-off point to define insulin resistance corres-ponds to HOMA-IR ≥ 3.8 [4][5] The quantitative insulin sensitivity check index (QUICKI) was calculated from fasting plasma glucose (mg/dL) and insulin (µIU/mL) concentrations, as fol-lows:

QUICKI = 1/(log Io + log Go ) [6] Amylase and lipase activity were measured by pho-

tometric enzymatic method. The amylase activity was assayed using BioAssay Systems’ QuantiChromTM α-Amylase Assay Kit (DAMY-100). Lipase activity was

assayed using BioAssay Systems’ QuantiChromTM Li-pase Assay Kit (DLPS-100).

The results were analyzed statistically using SPSS version 16.0 statistical software. The results were ex-pressed as mean ± SD if the variables were continuous, and as percentage, if categorical. Multivariate analysis of variance was used for differences in continuous va-riables. Multiple regression was applied for correlation studies. All statistical tests were two-side and a P<0.05 was considered to be significant.

III. RESULT

The clinical characteristic of the study subjects have been shown in Table 1. The serum amylase and lipase activity in Group IV was found significantly high-er than other groups. Normally, range serum amylase activity was 30 – 110 U/L and lipase was 30 – 210 U/L.

Table I. Clinical characteristics of the study subjects

Characteristics of the subjects Group I (n = 21) Group 2 (n = 10) Group 3 (n = 10) Group 4 (n = 26) p value

Age (years) 45.86 ± 8.04 65.70 ± 11.41 61.60 ± 6.22 55.77 ± 10.52 0.062

Fasting Blood Glucose (mg/dL) 74.81 ± 7.07 109.30 ± 6.07 181.50 ± 53.97 189.81 ± 92.87 < 0.001

HbA1c (%) 4.58 ± 0.41 6.02 ± 0.41 9.18 ± 1.89 9.49 ± 3.16 < 0.001

Cholesterol (mg/dL) 195.24 ± 39.32 197.60 ± 57.05 238.00 ± 56.01 193.15 ± 53.44 0.756

HDL (mg/dL) 54.43 ± 15.99 44.50 ± 11.65 50.90 ± 13.67 44.58 ± 13.61 0.449

LDL (mg/dL) 132.14 ± 38.93 131.00 ± 49.80 169.00 ± 51.04 128.88 ± 45.96 0.889

TG (mg/dL) 120.86 ± 71.65 120.00 ± 47.57 172.80 ± 50.10 186.23 ± 113.81 0.026

Insulin (ng/mL) 409.06 ± 19.04 343.18 ± 17.52 313.53 ± 27.89 322.93 ± 32.46 0.014

Proinsulin (ng/mL) 42.79 ± 11.78 96.21 ± 36.52 114.86 ± 28.46 99.24 ± 39.33 0.001

Amilase (U/L) 46.63 ± 17.10 54.96 ± 18.64 56.91 ± 18.39 601.08 ± 901.68 < 0.001

Lipase (U/L) 120.47 ± 45.59 100.12 ± 58.07 92.95 ± 50.24 1423.10 ± 1075.70 < 0.001

The difference fasting blood glucose, HbA1c le-

vels and Insulin Resistance of the group diabetic with and without exocrine insufficiency were not significant, but it were different significantly with normal and pre-diabetes groups (Fig.1). The increasing levels of blood glucose, HbA1c and insulin resistance were characteris-tic feature of the most patients with type 2 diabetes mel-litus. One of other features that followed of this condi-tion was dyslipidemia (high triglyceride and low HDL cholesterol), as shown in Fig.1 [7], [8].

Insulin resistance is defined as reduced sensitivity of target organs to the biological effects of insulin [9]. The quantification of insulin resistance condition can be performed by evaluating the peripheral insulin sensitivi-ty using mathematical formula, such as Homeostasis model assessment (HOMA), QUICKI (Quantitative In-sulin sensitivity check index), etc [7]. The HOMA in-dex of β-cell function and QUICKI index of insulin sen-sitivity related with decreasing of insulin and increasing of proinsulin production by β-cell, as shown in Fig 2.

The serum amylase and or lipase activity were found to be significantly higher in case group IV as compared to the control (group I), prediabetes (group II) and diabetes subject (group IV). There was significant positive correlation between lipase/amylase ratio and duration of disease (diabetes), FPG, serum lipase activi-ty and HOMA index of β-cell. However, the correlation of lipase/amylase ratio with HbA1c, lipid profile (TG, Cholesterol, HDL cholesterol and LDL cholesterol), insulin, proinsulin concentration, and amylase activity were not found to be significant (Table 2).



Fig. 1. Increasing (A) FPG levels, (B) HbA1c levels, and (C) Insulin

Resistance in control/normal (Group I), prediabetes (Group II), type 2 DM (Group III), prediabetes and diabetes with ex-ocrine insufficiency groups (Group IV).

Table 2.

Correlation of lipase/amylase ratio with duration of disease, blood glucose, lipid profile (Cholesterol, TG, HDL, LDL), insulin, proinsu-

lin, amylase, lipase and insulin resistance of the subjects. Correlation of lipase/amylase

ratio with:Pearson's correlation

co-efficient ( r )p-value

Duration 0.298* 0.014

FPG 0.245* 0.046HbA1c 0.224 NS

Cholesterol -0.12 NS

TG 0.082 NS

HDL -0.105 NS

LDL -0.107 NS

Pro Insulin 0.047 NS

Insulin -0.059 NS

Amilase -0.189 NS

Lipase 0.869** < 0.001

Insulin Resistance Indeks 0.282* 0.021Insulin Sensitivity Index -0.333** 0.006 NS = not significant

Fig. 2. Decreasing insulin concentration correlate with increasing

proinsulin concentration (A), insulin resistance index (B), de-creasing insulin sensitivity index (C); Increasing proinsulin concentration correlate with decreasing insulin sensitivity in-dex (D) and increasing insulin resistance index (E)

IV. DISCUSSION

Diabetes is a global problem and would be the leading cause of morbidity and mortality in the future. Diabetes is invariably associated with derangement in secretion or function of the endocrinal portion of the pancreas.

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The pancreas is dual organ with dual function in our body, as a digestive organ and as an endocrine or-gan. The exocrine gland produces enzymes important to digestion. The endocrine component of the pancreas consists of islet cells that create and release important hormone directly into the bloodstream which act to re-gulate blood sugar. The human exocrine pancreatic se-cretions are affected in diseases which affect the pan-creas. In our study, we found a significantly high amy-lase and or lipase activity in diabetic and prediabetic patients in group IV. The high exocrine pancreatic en-zymes may reflect the impaired exocrine-endocrine inte-ractions of the pancreas.

There is evidence that insulin influence the enzyme synthesis and release in the exocrine pancreas. Insulin is a trophic hormone for the exocrine pancreas, increasing pancreatic enzyme synthesis and cell division on acinar tissue [10]. Our results suggest that the high serum amy-lase and or lipase activity in diabetes and prediabetes with exocrine insufficiency (group IV) are associated with an impaired insulin action due to insulin resistance and inadequate insulin secretion, as was indicated by the increasing level of proinsulin and proinsulin/insulin ra-tio.

Since diabetes is a multifactorial disorder, hyper-cholesterolaemia and hypertriglyceridaemia are mostly observed. Dyslipidaemia, which characterized by raised triglycerides, low HDL and high small dense LDL par-ticles, is very frequently seen in diabetes and the deter-mination of serum lipid profile in the diabetic patients is now considered as a standard of the diabetic care [1], [2], [3].

Pancreatitis is defined as inflammation swelling of the pancreas. Acute pancreatitis is most commonly caused by gallstone or excessive of alcohol use. Chronic pancreatitis occurs over long period of time and result when digestive enzymes destroy the pancreas and near-by tissue. To diagnose the pancreatitis, physicians will often order blood tests to determine if the levels of pan-creatic enzymes (i.e. amylase and lipase) have markedly increased. Amylase is an enzyme made primarily in the pancreas and released into the digestive tract to digest starch and glycogen. Amylase levels rise at the begin-ning of a pancreatic attack and tapper off after 2 days. The normal or reference range for serum amylase varies due to patient factors and the assay used and is typically 20 – 300 units/L. Amylase activity can be 5 – 10 times higher than normal during pancreatitis. Lipase is an en-zyme made in pancreas too and release into digestive tract to digest fats. Like amylase, reference range of lipase is typically < 200 units/L. In acute pancreatitis, lipase level can be 2 – 5 times higher than normal and remain elevated for 4 – 7 days. Amylase and lipase le-vels often rise in parallel and are often ordered together to diagnose acute pancreatitis, as well as monitor chron-ic pancreatitis [11].

In our study, we found that the lipase/amylase ratio correlate with duration of diabetes, fasting glucose con-centration, lipase activity, insulin resistance index and insulin sensitivity index. But, the index had not signifi-cant correlation with HbA1c, lipid profile, insulin, proinsulin and amylase activity. The serum li-pase/amylase ratio had been proposed to distinguish the

etiology of pancreatitis. The serum lipase/amylase ratio could differentiate acute episodes of pancreatitis [12][13]. The serum lipase/amylase ratio would be able to put an insight into possible endocrine-exocrine rela-tionship of the pancreas in the type 2 diabetes patients.

V. CONCLUSION

Our study demonstrated that the high li-pase/amylase ratio correlate with type 2 diabetes, thus suggesting a possible exocrine-endocrine relationship in the disease. However the cut-off value of the marker remains to be elucidate in various other clinical condi-tion of the pancreas, especially in Indonesian subjects.

ACKNOWLEDGMENT

Author thanks to “Directorate General of Higher Edu-cation through Penelitian Unggulan Perguruan Tinggi (Pemula) second batch for 2013 in sponsor and financial support.

REFERENCES

[1] American Diabetes Association, “Standards of Medical Care in Diabetes d 2013,” Diabates Care, vol. 36, pp. S11–S66, 2013.

[2] K. Kaku, “Pathophysiology of Type 2 Diabetes and Its Treatment Policy,” J. Japan Med. Assoc., vol. 53, no. 1, pp. 41–46, 2010.

[3] R. Yadav, J. P. Bhartiya, S. K. Verma, and M. K. Nandkeoliar, “The Evaluation of Serum Amylase in the Patients of Type 2 Diabetes Mellitus , with a Possible Correlation with the Pancreatic Functions,” J. Clin. Diagnostic Res., vol. 7, no. 7, pp. 1–4, 2013.

[4] K. Shirai, “Obesity as the core of the metabolic syndrome and the management of coronary heart disease. Title,” Curr. Med. Res. Opin, vol. 20, no. 3, pp. 295–304, 2004.

[5] M. M. Osman, A. I. Abd. El-mageed, E. El-hadidi, R. S. . Shahin, and N. A. Adel A. Mageed, “Clinical Utility of Serum Chemerin as a Novel Marker of Metabolic Syndrome and Type 2 Diabetes,” Life Sci. J., vol. 9, no. 2, pp. 1098–1108, 2012.

[6] Z. Radikova, “ASSESSMENT OF INSULIN SENSITIVITY / RESISTANCE IN EPIDEMIOLOGICAL STUDIES,” Endocr. Regul., vol. 37, pp. 189–194, 2003.

[7] J. F. Ascaso, S. Pardo, J. T. Real, R. I. Lorente, A. Priego, and R. Carmena, “Diagnosing Insulin Resistance by Simple Quantitative Methods in Subjects With Normal Glucose Metabolism,” Diabetes Care, vol. 26, no. 12, pp. 3320–3325, 2003.

[8] C. De Luca and J. M. Olefsky, “Inflammation and insulin resistance,” FEBS Lett., vol. 582, no. 1, pp. 97–105, 2008.

[9] T. Cederholm, P. Stenvinkel, B. Lindholm, U. Rise, and J. Jesu, “Clinical Correlates of Insulin Sensitivity and Its Association with Mortality among Men with CKD stage 3 and 4,” Clin J Am Soc Nephrol, vol. 9, pp. 1–8, 2014.

[10] A. Andren-Sandberg and P. D. Hardt, “Workshop report: Giessen International Workshop on Interactions of Exocrine and Endocrine Pancreatic Diseases,” J. Pancreas, vol. 6, no. 4, pp. 382–405, 2005.

[11] J. M. Beauregard, J. A. Lyon, and C. Slovis, “Using the literature to evaluate diagnostic tests : amylase or lipase for



diagnosing acute pancreatitis ?,” J. Med Libr Assoc, vol. 95, no. April, pp. 121–126, 2007.

[12] K. Chang, C. Changchien, and C. Kuo, “Clinical Analysis of the Efficacy in Lipase / Amylase Ratio for Acute Pancreatitis,” J Intern Med Taiwan, vol. 16, pp. 113–120, 2005.

[13] A. Devanath, J. Kumari, J. Joe, S. Peter, S. Rajan, L. Sabu, and J. Mary, “USEFULNESS OF LIPASE / AMYLASE RATIO IN ACUTE PANCREATITIS IN SOUTH INDIAN POPULATION,” Indian J. Clin. Biochem., vol. 24, no. 4, pp. 361–365, 2009.



Abstract—Diversity of autotrophic ammonia oxidizers in

agricultural soils, which are responsible for the rate limited step of nitrification in most soils, was investigated. DNA samples were extracted from eight different soils collected from Gintungan agricultural land, Central Java. DNA samples were amplified using three different primer sets targeting 16S rRNA genes of general bacteria, 16S rRNA genes of ammonia-oxidizing bacteria, and amoA genes of ammonia-oxidizing archaea, and were profiled using dena-turant gradient gel electrophoresis (DGGE). Microbial diversity was calculated using Shannon-Weiner index. The DGGE analysis revealed that ammonia-oxidizing archaea and ammonia-oxidizing bacteria communities were diverse and variability in banding patterns was affected by plant species. Our results suggest that in agricultural soil of Gin-tungan, archaea may be the primary ammonia oxidizers.

Keywords—agricultural soils, ammonia-oxidizing

archaea, ammonia-oxidizing bacteria, general bacteria, plant species.

I. INTRODUCTION

OILmicrobial communities play a critical role in eco-system processes, such as nitrogen cycling, nutrient turnover, or the production of trace gases. Soil mi-

crobial activities, populations and communities are go-verned by environmental variables and agricultural sys-tem, as conventional and organic system [1], [2].

In soil environments ammonia oxidizers mediate the first, rate-limiting step of autotrophic nitrification, which is considered to be a key control point in the nitrogen cycle resulting in increased N mobility and loss of oxi-dized N forms through leaching and denitrification [3], [4]. The ammonia oxidizers catalyze the conversion of ammonia (NH3) to nitrite (NO2

–), which is then

converted to nitrate (NO3–) by nitrite-oxidizing bacteria.

Ammonia-oxidizing bacteria (AOB) are affiliated with -Proteobacteria and -Proteobacteria. In terrestrial soil ecosystems, oxidation of ammonia is believed to be mainly carried out by AOB within the -Proteobacteria [5], [6]. Ammonia oxidizers are generally slow growing, difficult to isolate and have therefore have been primari-ly investigated using molecular techniques based on amplification of genes encoding either the 16S ribosom-al RNA or ammonia monooxygenase (amo) [5] – [9].

Recent studies have demonstrated the existence of

ammonia-oxidizing archaea (AOA) within the Crenarchaeota in soil and marine environments using metagenomic and cultivation approaches [10] – [14]. Other studies based on the crenarchaeotal amoA gene have revealed the numerical dominance of AOA over

AOB in natural ecosystems, such as terrestrial soil, freshwater and marine sediments [15] – [24]. However, to date little work has been conducted to investigate the diversity of ammonia oxidizers in Indonesian agricultural soils.The objective of this study was to investigate the diversity of general bacteria and ammonia oxidizers in horticultural soils of Gintungan, Central Java.

In Gintungan, agricultural area is planted with flowers, fruits and vegetables. A previous study conducted on mineral soil samples from this area has shown that nitrification rate was low(<3g (NO3

––N +

NO2––N) g

–1 d

–1) in these soils [25]. Environmental

factors, such as ammonia availability, organic matter content, and/or soil pH may influence the presence of specific types of ammonia oxidizers and nitrification rates in soils [6], [26].

Microbial communities in the environment have tradi-tionally been studied by conventional methods based on cultivation of populations, by measurement of their me-tabolic activities or direct observation using microscopic methods [27]. However, analysis of microbial is a complex task that cannot be achieved by traditional microbial culturing methods. Ward et al. [28] stated that more than 90% of microorganisms existing in nature are not amenable to currently available cultural methods. Here, we examined the diversity of ammonia oxidizers with denaturing gradient gel electrophoresis (DGGE).DGGE has been used extensively for diversity analysis in microbial ecology [29]. Coupled with poly-merase chain reaction (PCR) and primers that target conserved, taxonomically significant genes, it is a tech-nique that allows the comparison and analysis of mole-cular fingerprints of diversity.


A. Sample collection

Soil samples from the eight sites were collected on September 25, 2012. Table 1 describes the types of vegetation present in each sampling plots. The location were randomly selected and covering all the vegetation types. The locations of sample collections were confirmed by the use of Global Positioning System unit (GPS). At each location, soil samples were collected from litter layer. Samples were placed in plastic bags and transported to the laboratory where they stored at –20 ˚C until further processing.

B. DNA Extraction

Extraction of DNA was completed directly after collection, using a commercial kit (PowerSoil DNA extraction kit; MO BIO Laboratories Inc., Carlsbad, CA,

Diversity of Ammonia Oxidizers in Agricultural Soil of Gintungan, Central Java

Rully A. Nugroho, Ph.D.,Gabriel B. Kennardi, S.Si.,andDr. Vincentia I. Meitiniarti Faculty of Biology, Satya Wacana Christian University, Salatiga, Indonesia

S



USA). A 0.7 g subsample from each litter soil sample was processed for DNA extraction according to the manufacturer’s instructions, except that mechanical

disruption of cells was carried out in a mini-beadbeater-1 (BioSpec). Extracted DNA samples were stored at –20 C.

C. DGGE Analysis of 16SrRNA and amoA Genes

The diversity of ammonia oxidizers community was investigated by denaturing gradient gel electrophoresis (PCR-DGGE) fingerprinting of ammonia oxidizers 16SrRNA and amoA genes. DGGE was performed with a Dcode Universal Mutation Detection System (Bio-Rad) as described previously [30]. 16S rRNA genes of AOBwere amplified using primers CTO189f-GC and CTO654r [31], archaeal amoA genes were amplified using primers Arch-amoAf and Arch-amoAr-GC [32], and 16S rRNA genes of general bacteria were amplified using primers 357f-GC and 518r [33]. Amplification was performed in a 25 l reaction volume containing 200 nM of each primer, 0.1 mMdNTPs, 5 g BSA, Taq DNA polymerase (2.5 unit, Promega), the buffer condi-tions recommended by the manufacturer, and 1 l DNA template. 16SrRNA genes of AOB were amplified at 95 ˚C for 3 min; followed by 30 cycles of 95 ˚C for 1 min, 55 ˚C for 1 min, and 72 ˚C for 1 min; followed by 72 ˚C at 10 min [34].Archaeal amoA genes were amplified at 95 ˚C for 5 min; followed by 30 cycles of 94 ˚C for 45 s, 53 ˚C for 1 min, and 72 ˚C for 1 min; followed by 72 ˚C at 15 min [32]. 16SrRNA genes of general bacteria were amplified at 94 ˚C for 4 min; followed by 35 cycles of 94 ˚C for 1 min, 54 ˚C for 1 min, and 72 ˚C for 1 min; followed by 72 ˚C at 5 min [35].

A small portion (5 l) of each PCR product was run on a 1% agarose gel to confirm successful amplification of a DNA fragment of the expected length. The remaining PCR products were subjected to DGGE on a Dcode Universal Mutation Detection System (Bio-Rad) with gels containing a linear formamide/urea gradient of30% to 60%,20% to 45%,or 30% to 55% linear gradient of denaturant for 16S rRNA genes of AOB [36], amoA genes of AOA [23], and 16S rRNA genes of general bacteria[33] assays, respectively. After electrophoresis, the gel was stained with ethidium bromide for 30 min and then visualized using a GelDoc.

D. Data Analysis

For the DGGE analysis, each band was designated as an operational taxonomic unit (OTU) [37]. The band richness of the samples was estimated based on the

number of bands per lane. The band diversity was calculated using Shannon-Weiner index.All gels were aligned using the three markers. The DNA bands that migrated to the same position within each gel were as-cribed a number. Band strengths were estimated visual-ly; weak bands were assigned avalue of 1, intermediate bands a value of 2, and strong bands a value of 3 [38].


Our analysis revealed that soil microorganisms in mineral soil (0-10 cm depth) of agricultural soil of Gin-tunganwere in very low abundance. DNA extraction using PowerSoil DNA extraction kit did not generate enough amounts DNA for further molecular analysis although a nested PCR was carried out. PCR failure was not due to the presence of PCR inhibitors, such as humic substances [39], since PCR products were resulted when known DNA was added to the PCR reaction (result not shown). For that reason, we used DNA extracted from leaf litter for further molecular analysis.

This study found that general bacterial communities from litter samples showed almost similar DGGE band patterns as presented in Fig. 1.This may indicate that the communities are almost similar or related to a degree and share common bacterial members.DGGE also re-vealed high general bacteria diversity (diversity index value was more than 2.0). Each sample produced a complex fingerprint composed of a number of bands (average 12 bands). Muyzer et al. [33] showed that all the bands appearing in a DGGE profile represent differ-

ent species present in the microbial population. However, certain bands (bands A1 – A6) present in the samples indicating that certain bacterial species may adapted to the litter characteristics.

We also observed that the recovered DGGE fingerprints for each population were found to be influenced by the primer set used for the analysis. All agricultural litters did not yield detectable PCR products of AOBamoA genes although a nested PCR was carried out, but PCR products were generated using CTO primers targeting 16S rRNA of AOB. The detection of

TABLE I VEGETATION TYPE IN THE SAMPLING LOCATIONS SELECTED IN THE

STUDY

Plant Coordinate Elevation (m asl a)

Capsicum annuum 7˚12’18”S; 110˚21’27”E 1,286

Capsicum frutescens 7˚12’28”S; 110˚21’26”E 1,248

Brassica oleracea 7˚12’28”S; 110˚21’26”E 1,248

Allium sp. 7˚12’16”S; 110˚21’26”E 1,287 Brassica chinensis 7˚12’28”S; 110˚21’26”E 1,248 Solanum lycopersicum 7˚12’23”S; 110˚21’27”E 1,283 Daucus carota 7˚12’28”S; 110˚21’26”E 1,248 Cucumis sativus 7˚12’28”S; 110˚21’26”E 1,248

Fig. 1.Image of DGGE gel for general bacteria 16S rRNA genes. M = Marker, CF = Capsicum frutescens, BC = Brassica chinensis, DC = Daucus carota, CS = Cucumis sativus, CA = Capsicum annuum, BO = Brassica oleracea, AS = Allium sp., SL = Solanum lycopersicum.



non-AOB-like using CTO primers was not surprising because these primers have a relatively low specificity [5], [34], [40]. The PCR amplification of the AOB 16S rRNA gene generated DGGE patterns as shown in Fig. 2with diversity index value around 2.0 – 3.5.Some bands (bands B1 – B7) were unique to some samples

(Fig. 2). Differences in AOAamoA banding patterns were also

observed (Fig. 3). Differences were found in both the positions of specific bands and the number of bands. While most prominent bands (bands C1 – C4) were found at different positions, some distinct bands were observed in all agricultural litter samples. This indicates that each agricultural litter harbors a specific archaeal community but that a few dominant archaea species may be present in many agricultural litters. The diversity index value of AOA amoAwas less than 3.0.

The DGGE community fingerprint of AOB 16S rRNA genes and AOA amoA genes showed a mixture of

dominant and faint bands that were separated when applied denaturing gradient was used (Fig. 2, Fig. 3). The patterns recovered indicated that in litter samples the AOB 16S rRNA fragments or AOA amoA fragments of only few populations dominated, while many popula-

tions which were less prevalent seemed to be equally abundant.Further studies to sequence 16S rRNA and amoA genes will be essential in order to reveal the pres-ence and identity of ammonia oxidizers responsible for nitrification in agricultural soils.

Interpretation of DGGE patterns needs to be done cautiously as discussed in several reviews [41]. Ampli-fied 16S rRNA fragments of different but phylogeneti-cally related species might have the same electrophoret-ic mobility because they share the identical or similar sequence in the stretch analyzed [42]. Despite several pitfalls of PCR-DGGE based analysis, profiling of am-monia oxidizer communities by denaturing gradient gels proved to be a powerful method allowing a cultivation-independent analysis of large number of soil and litter samples.

IV. CONCLUSION

We can conclude that the diversity of ammonia oxidizers in agricultural soil of Gintungan was high as previously observed in agricultural soils. Although DGGE could reflect the profile of ammonia oxidizer communities in agricultural soils, sequencing is required to reveal the presence and identity of ammonia oxidizers.

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Fig. 3.Image of DGGE gel for ammonia-oxidizing archaea amoAgenes. For details of legends, see Fig. 1.

Fig. 2.Image of DGGE gel for ammonia-oxidizing bacteria 16S rRNA genes. For details of legends, see Fig. 1.



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Abstract -- Sekotong, West Lombok, Nusa Tenggara Ba-

rat is place where people traditionally gold mining. Mer-cury (Hg) is used by traditional gold miners. Hg waste flow towards and can pollute to the ocean through river. Ke-rang bulu (Anadara sp.) is one of the major fisheries com-modity in Lombok. This organism is a filter feeder and sessile animal, they can accumulate metals in their bodies. This research aims to study the distribution and accumula-tion of Hg in Anadara sp. at the Sekotong, West Lombok, Nusa Tenggara Barat and environmental factors that can influence it. Research sites include Medang, Permulae and Gili genting village. The study was conducted by collecting same size sample with free sampling method. Environmen-tal parameters measured include Hg levels in water and sediment, water and air temperature , pH and salinity. The mussel organ analyzed were gills, mantle, visceral mass and shells. Hg level were analyzed using Mercury Analyzer. Data were analyzed using LSD Variance Analysis and Pearson Correlation. The results show that from the high-est level of Hg is gills > mantle > visceral mass > shells. The highest Hg levels found in gills was 0.52 – 0.56 µmol kg-1. From the result showed that the highest Hg level were in Medang which has the Hg content in the water at 10.04 mg L-1 . The content of Hg in the organs was positively corre-lated with Hg levels in the water.

Keywords -- Sekotong. Mercury, Bioaccumulation, Ana-dara sp.

I. INTRODUCTION

ndonesia has about 713 gold mining areas scattered in Sumatra, Java, Kalimantan, Sulawesi and Nusa Teng-gara Barat[1]. Sekotong, located at West Lombok re-

gency, Nusa Tenggara Barat, it is one of a traditional gold mining in indonesia.

Mercury (Hg) is a chemical used by traditional gold miners to coat gold particles formed Hg-Au, after heated by high temperature then Hg will dissolved and leave a lump of pure gold[2]. The Hg waste then passes the ponds and left without further treatment. Water that has been contaminated Hg can flow out of the ponds, and flowing toward the river and pollute the ocean.

In the aquatic environment, Hg form is organic. Hg form in the water is Hg0 and methyl mercury ion (CH3Hg+) both are volatile [3][4]. Hg has the ability to be bioaccumulation and finally undergo biomagnification in the environment when it is in organic form. Hg can be found in the body tissues of marine biota such as clams and others. Mercury can enter to the organism through

the gills or digestive mechanism from plankton as a source of food [5].

Kerang Bulu (Anadara sp.) is kind of mollusk it is class bivalves. Kerang bulu can easily found in coastal intertidal zone. It is lived hiding in the sand and mud. Kerang bulu is one of the major fisheries commodity, and it’s become the most favored food by the people in Sekotong. Kerang bulu (Anadara sp.) is a good bioindi-cator for the environment, because their characteristic as a filter feeder and a sessile animal[6]. They feed itself by screening food from the water, allow them to accumu-late Hg from the environment, whereas a sessile animal which mean they have slow movement allow them as an overview what actually happened in the environment they are lived[7].

Mercury in the water can absorbed by the mussel di-rectly. It is through gill membranes or through food, then transported in the blood and can be accumulated in the mussel organs[8]. Furthermore, Hg binds to a protein called metalothionein[9]. Metalothionein is a protein that is highly sensitive and can cause toxic and can be used as an indicator of pollution. This is based on the pres-ence of metalothionein in the mussels tissue can make a bond with Hg. If the mussel are resistant to high concen-trations of Hg, then Hg can be accumulated in the tis-sues/organs, especially the liver, gills, kidney or mus-cles[10].

Bioaccumulation is also influenced by environmental factors such as temperature, pH, salinity and substrate conditions. Bioaccumulation can be found in large num-bers, from the mussels which had lived long enough in the substrate which containing Hg[11].

Hg accumulation also can occur in humans through the process called biomagnification[9]. Consumed the mussels which is contaminated Hg, bioaccumulation process will occur in the liver and kidneys will then lead to impaired function of organs such as neural degenera-tion, blindness, mental disorders, and chromosome dam-age[12]. This research aims to study the distribution and accumulation of Hg in Kerang bulu (Anadara sp.) at Sekotong, West Lombok, Nusa Tenggara Barat and en-vironmental factors that can influence it.

II. RESEARCH METHODS

A. Research Site

Sekotong, West Lombok is located about 28.7 km

Distribution and Accumulation of Mercury in Kerang Bulu (Anadara sp.) at Sekotong Re-gion, West Lombok, Nusa Tenggara Barat. Haikal Prima Fadholi 1*), Rindra Aryandari 1), Rahadyan Aulia 1) Andhika Puspito Nugroho 1)

and Mulyati Sarto 1) 1) Faculty of Biology, Gadjah Mada University, Yogyakarta, Indonesia

*) Corresponding author : [email protected]

I



southwest of the Mataram city, the capital of the prov-ince Nusa Tenggara Barat. About 2.000 households spreaded in 8 villages. Gold mining and processing area spread over 3 villages in the sekotong it was Buwun Mas, Kerato, and Pelangan. Kerang bulu was obtained at 3 different points in Medang, Permulae, and Gili Gent-ing. Almost all households have gelondong unit to gath-er gold from stone or mud (Fig. 1) it is operated throughout the day. And the wastes was sent into the rivers and sewers that close to the sea. Temperature, pH of sea water, salinity, and the content of Hg in water and sediment is measured as environmental factors.

Fig. 1. Gelondong unit.

B. Materials

This study need Mercury Analyzer (MA) as a main equipment to measured the Hg levels. The chemicals used in this study is HNO3 solution, HCl solution, HClO4 solution for sample destruction process. Hydrox-ylaminehydrochloride solution, KMnO4 solution, and SnCl2.2H2O solution is used in MA.

C. Sample Preparation

The sample target is Kerang bulu which has size 3.5 - 5.5 cm (Fig. 2), 3 individuals from each location by free sampling method. Kerang bulu was separated into shells, gills, mantle and visceral mass in the laboratory. The organs was stored in oven at 60°C to obtain a constant dry weight. Destruction sample organ by acid destruc-tion, using hot plate. Organ and water samples was de-structed by using HNO3:HCl (4:1).

D. Sample Analysis

Destructed sample then inserted into 10 mL volume-tric flask and aquades was added. Then reacted with 0.1 ml KMnO4, 0.1 ml Hydroxylaminehydrochloride and 0.5 ml SnCl2.2H2O. Hg content was analyzed by using MA.

III. RESULT

The Hg accumulation in organs of sea mussels Ana-dara sp. at 3 location was shown in Table I. The data of Hg accumulation in waters and sediment was shown in Table II, and environmental parameters was shown in Table III.

The concentration of Hg that accumulated in organs of Anadara sp. in 3 locations showed varying results (Table I). ANOVA showed that the levels of Hg in the same organ in different places showed significant differ-

ences. And also showed that the Hg concentration in different organs in the same place were significantly different. Mercury accumulation in water and sediment at 3 locations showed varying results. The result also showed the correlation of Hg content in organs with Hg content in waters, as shown in Figure. 2

Table I. Hg Accumulation in Organs of Kerang Bulu (µmol kg-1)

Location Organs

Shells Gills Mantles Visceral

Mass

Medang 0,13-0,37 0,52-0,56 0,39-0,57

0,36-0,46

Permulae 0,02-0,37 0,14-0,24 0,22-0,33

0,17-0,29

Gili Gent-ing

0,0002-0,03

0,10-0,24 0,11-0,14

0,12-0,23

Table II. Hg Accumulation in Waters and Sediments

Location Waters (µg L-1) Sediments (µmol kg-1)

Medang 10,04 – 10.05 0,18 – 0,26 Permulae 0.95 – 0,96 0,03 – 0,68

Gili Genting 0.75 – 0,76 0,002 – 0,03

Tabel III. Environmental Parameters

Location

Parameters

Air Tempera-tures (oC)

Water Tem-peratures (oC)

Salinity (‰)

pH

Medang 31 33.5 40 6

Permulae 25 29.5 39 5.15

Gili Gent-ing

29.75 31 40 6.09

Fig. 2. The correlation of Hg levels in organs with Hg levels in wa-

ters.

Correlation analysis showed that the accumulation of Hg in water had strong positive correlation with Hg levels in the shells (r = 0.96, p < 0.05) and mantle (r = 0.97, p < 0.05), but also had weak positive correlation to Hg levels in the gills (r = 0.99, p > 0.05) and visceral mass (r = 0.99, p > 0.05).

IV. DISCUSSION

In this study, the accumulation of Hg in each organ has a varies value, it was shown that environmental parameters in 3 locations especially the Hg concentrations in the water and sediment were different



(Table II). The results show that from the highest level of Hg is gills > mantle > visceral mass > shells.

Gills accumulated Hg from 0.10 to 0.56 µmol kg-1 . The concentration of Hg in gills was the highest. Generally, the absorpsion of Hg in water is methyl mercury ion form (CH3Hg+) and Hg0 and absorbed by shellfish directly through the water that passes the gill membranes[8]. Gills can accumulate more Hg because Hg enter to the muscle is through the gills first. In the gill membranes, absorbed Hg will bind to a protein called metalothionein, then it was distributed to various organs like mantle and visceral mass via blood vessels in gills[9]. Mantle accumulating Hg from 0.11 to 0.57 µmol kg-1 it was the second highest. Water that enters through the gills will be distributed into mantle cavity first, which has located between the body and the shells, also because of metalothionein distribution also widespread on mantle[13]. The visceral mass accumulated Hg from 0.12 to 0.46 µmol kg-1. Its lower than mantle. Detoxification mechanisms and secretion of Hg in visceral mass is very active. Hg which is distributed to the visceral mass also binds to ,metalothionein and then widespreadly distributed in the digestive gland. Hg which absorbed in the gastro-intestinal tract will be actively or passively diffuses and transported, then excreted through the kidneys and intestines. Excretion through the kidneys can can occur only when pH and the amount of amino acids that can bind Hg are in balance [13]. The intestine can excrete Hg from mucus membranes actively[13]. Whereas, shell was the lowest of Hg accumulation in the range of 0.0002 to 0.37 µmol kg-

1. Shells can’t select the metal which entered its hard tissue. Although there is no metalotionein, shells still can accumulate Hg in a few number[14].

Mercury levels in organs had positive correlation with the Hg levels in water, but it had varied correlatation with the Hg levels in sediment. As filter feeder, the mussels filtered anything that present in water. Mercury dissolve in methyl mercury (CH3Hg+) and Hg0 in the water, the increase of Hg levels in water also increasing Hg levels that accumulated in the body of marine biota[15]. This is due to the accumulation of Hg transported by metalothionein and distributed throughout the body. Hg levels in water is different with sediments. The sea water is so dynamic. The Hg levels in water can fluctuate everytime[16]. Increasing content of organic matter in a body of water and sediment also cause heavy metal content in the sediment increased[15]. Based on Table II., ANOVA results showed that the levels of Hg in water at three locations were significantly different. The highest Hg levels was found in Medang. Which has Hg concentration in the water reached at 10.04 µg L-1 or 0.1004 mg L-1. about 0.002 to 0.68 µmol kg-1 on sediment. Based on the Decree of the Minister of Health No. 907/MENKES/SK/VII/2002 consumption water limit is set at 0.001 mg L-1 and the maximum Hg levels for water is 0.005 mg L-1. Hg levels in water on Medang is above the limit of Hg levels in water. It cause the accumulation of Hg in these area is very high. Medang located nearest to the river that directly connect to tailing sources that is vat to sludge processing of gold mining. Permulae and Gili Genting, Hg concentration still below

on limit. Although on Permulae, the Hg levels in water was very close with the limit of water consumption with Hg levels at 0.00096 ppm. It because the location where close to the conventional gold processing industry, so that waste disposal activities less active than Medang. Gili Genting had the lowest Hg levels because the gold processing activities around the site is low, only a small river which connects the source of waste to the sea.

The measurements of environmental parameters was shown in Table III. Some of the parameters that may affect the levels of Hg in sea water, besides due to in-creasing gold mining activity around the area, can be caused pH and salinity become low. The results showed pH of water was about 5.15 to 6. Methyl mercury (CH3Hg+) can be easily formed at pH 6 or smaller, and salinity may affect the bioavailability of Hg, methyl mercury at high salinity will undergo demethylation procces[17]. The temperature of the water and the air were high. It was about 25 – 30oC. Increasing the water temperature tends to increase the accumulation and tox-icity of heavy metals, due to the increasing metabolic rate of aquatic organisms[18].

V. CONCLUSION

The results show that from the highest level of Hg is gills > mantle > visceral mass > shells. Gills accumulate Hg in the highest level with 0.52-0.56 µmol kg-1 at Me-dang. Medang has the highest Hg levels with 10.04 µg L-1 in water. Mercury levels in each organ is positively correlated with Hg levels in water. Factors affecting bioaccumulation and distribution of Hg are the human activities near the sampling location, Hg levels in water, Hg levels in sediment, pH, salinity, and temperature of water and air.

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Abstract—Microalgae use photosynthesis to convert so-

lar energy into chemical energy, such as lipid and they can be a replacement for oil-based fuels. They are among the fastest growing plants in the world, and about 50% of their weight is oil. This lipid oil can be used to make biodiesel. Unfortunately, there are only some of potential strains isolated from Indonesia and most of the biodiesel produc-tions are usually using a single strain. Then, although they are rich of oils, their biomass productivity is still low. Sa-linity treatment can be used to increase their biomass as well as their lipid content. Therefore, the research aim was to study the effect of salinity on the growth, dry weight and lipid content of mixed microalgae isolated from Glagah, Yogyakarta. The mixed microalgae were cultured in 3NBBM medium with different salinities or types of water (sea water, brackish water, and fresh water). The cultures were incubated at light intensity 3000 lux under dark:light exposure of 12:12 hours for 7 days. The number of cells was counted every 24 hours with a Haemocytometer, and the biomass was calculated based on the dry weight. The lipid content was measured on days 0, 3, and 7 using Nile Red (NR) staining, and then the amount of lipid was ana-lyzed using a fluorescence microscope and measured with CellProfiler 2.0 software. The highest dry weight and lipid content were found in seawater medium, they accounted for 3.42 mg/ml and 13,58% at day 7, respectively. Whe-reas, the highest number of cells was found in freshwater medium, this was 9.8 x 106 cells/ml.

Keywords—salinity, growth, dry weight, lipid content

I. INTRODUCTION

HE worldwide increase of human population and transportation has generated greater energy con-sumption of petroleum fossil fuels that lead energy

crisis because of depleting fossil fuel reserves [1]. Therefore, for solving the issue above, a renewable energy asan alternative resource should be developed. Microalgae-based biodiesel production could be a po-tential source for the future renewable energy [2]. Mi-croalgae as a potential candidate for biodiesel produc-tion has generated significant interest [3][4], because the organismis the most efficient biological producer of oil

and biomass source due to use photosynthesis to convert solar energy and combine water with fixing CO2 into chemical energy, such as lipid and it can be a replace-ment for oil-based fuel [5][6].

Currently, biodiesel productionwas dominated by commercial single strains microalgae [3]. Most of the commercial single cell cultivations of microalgae have low biomass production and lipid content due to various problems such as culture condition and susceptibility to contaminant [7]. Those problems can be overcome through the search and selection of the local microalgae strains for biodiesel production which have highest growth rate, biomass productivity, and lipid content [8][9].Furthermore, the use of mixed culture of the se-lected local strainsshow that cells grow faster and high biomass yield because the cells are capable for utilizing organic carbon sourcesoptimally [10].

Even so, optimization technology is commonly done by regulating environmental conditions and culture me-dium [11]. By adjusting the salinity was reported able to increase biomass production, such asin Scenedesmus almeriensis culture [12] and Scenedesmus sp. Culture [13]. [14] also reported that Botryococcus braunii adapted to grow in low salinity was able to increase the biomass production, hydrocarbon, fat, carbohydrate, and carotenoids. Microalgae have a response against in-crease of salinity and osmotic stress on the environment by accumulating small molecules components for osmo-regulation [6]. [11] and [15] describe that the increasing of salinity leads to slight increase in the total lipid con-tent of algae, but excessive salinity gives an negative effect on growthdue to salt stress causes microalgae tend colonies-form in the growth phase, inhibit the photosyn-thesis and decrease the growth rate

Therefore, it is important to study the effect of differ-ent salinities in culture medium of the mixed microalgae culture isolated from Glagah cell on growth, dry weight, and lipid content for biodiesel substrate.

The Effect of Salinity on Growth, Dry Weight and Lipid Content of the Mixed Microalgae

Culture Isolated from Glagah as Biodiesel Substrate

Eko Agus Suyono1*), Winarto Haryadi2), Muhammad Zusron1), Matin Nuhamunada1), Sri Rahayu1), and Andhika Puspito Nugroho1)

1) Faculty of Biology, University of Gadjah Mada, Indonesia 2) Department of Chemistry, Faculty of Mathematics and Natural Sciences,

University of Gadjah Mada, Indonesia

* )Corresponding author:[email protected]

T




A. Algal cultures

The mixed microalgae culture isolate Glagah were obtained fromGlagah beach in the coastal area of South Yogyakarta-Indonesia. Microalgae samples were iso-lated in Laboratory of Biotechnology, Faculty of Biolo-gy, UGM by using the microcapillary pipette method and serial dilution method [16]. The isolated microalgae was not identified yet, however, according to morpho-logical characters, they consisted of three genus, there were Chlorella, Scenedesmus [17], and Nannochlorop-sis [18].

The strains were cultivated in modified sea water me-dium of f/2 for selection. Then, survived strains were cultivated in modified 3NBBM medium + vitamins medium [19] They were grown in 500 mL glass bottles and incubated at light intensity of 3000 lux under dark:light exposure of 12:12 hours for 7 days at 18-25oC.

B. Cultivation condition with different salinity

For all experiments, the microalgae were grown in modified 3NBBM medium + vitamin in 500 mL glass bottle under condition described above. The effect of salinity was investigated at 3NBBM + vitamins diluted in fresh water, sea water, and brackish water (mixture of fresh water:sea water (1:1)) with three replications. The cultures were inoculated into glass bottles of different salinity medium with ratio 50 mL stock culture and 200 mL the medium.

C. Determination of cells growth

To compare cell growth in different salinity, cells were counted using a light microscope and Haemocyto-meter Neubauer 1 mm every 24 hour. Sample was sha-ken to homogenize, then 900µL of sample put into mi-crotube 2 mL mixed with 100 µL of alcohol 70% for fixing. Number of cells was calculated as follows:

D. Determination of algal biomass

The biomass production was measured based on dry weight of the culture for seven days. Sample culture (2 mL) was transferred into microtube 2 mL. Sample was centrifuged at 3300 rpm for 10 minutes. Supernatant was discarded, then washed using distillate water. Cell suspensions on bottom microtube were dried in the in-cubator oven at 34oC to constant weight.

E. Determination of lipid content by Nile Red stain-ing

Lipid content was measured by using Nile Red (9-(Diethylamino)-5H–benzophenoxazin-5-one) staining (Qin, 2005). Nile red staining was conducted to detect intracellular lipid droplets [20]. The cultured cells (1 mL) were collected into microtube and 0.01 mg/mL then was added with Nile Redfor staining. Stained microal-gaecells were observed by Fluorescent Microscope to get the lipid fluorescence. The lipid fluorescence from neutral lipid will be smeared yellow-orange [21]. Then, lipid fluorescence was quantified using image analysis software CellProfiler 2.0 as a intracellular lipid droplet [22].


The comparison of, cell growth, dry weight, cell quota andlipidcontent in mixed cultures of microalgae isolated from Glagah, Yogyakarta using various salinities of medium in bacth cultures were investigated. Those data are presented in Fig. 1A, Fig. 1B, Fig. 1C and Fig. 1D.

As can be seen from Fig 1A., the microalgae were growth better in fresh water medium as similar to their habitat. The highest number of cells was found in freshwater medium reached 9.8 x 106 cells/mLat day 7, followed by brackish water and sea water treatments. However, the highest total dry weight was found in sea water treatment accounted for 3.42 mg/mL, followed by brackish water and fresh water. Both sea water and brackish water treatments reached a pick at day 5, but fresh water treatment was at a pick at day 4. (Fig. 1B).It can be assumed that microalgae growth in fresh water medium were stimulated to proliferate their cells rather than construct their cell walls. The fresh water nutrientwas absorbed by microalgae cells did not used much to construct of the cell wall, but to stimulate cell division. Then, their cells became smaller and their cell walls were relatively thinner and lighter. On the other hand, sea water nutrient provided more and suitable compounds for producing carbohydrate stored in the cell walls as sellulose and hemiselluse, so that the total dry weight was higher.

Figure 1. Growth (A), dry eight (B), cell quota (C) and lipid content (D) of the mixed microalgae cultures isolated from Glagah were cultured in fresh water ( ), brackish water ( ), and sea water ( ).

The trend of cellquota (dry weight per cells) of each treatment (Fig. 1C) was different from total dry weight (Fig. 3). The highest cell quota wasin the brackish water treatment at day 5 with reached 3.79 ng/cell, followed by sea water treatment and fresh water treatment, they accounted for 1.52 ng/cell and 0.17 ng/cell, respectively (Fig. 1C). Therefore, the total dry weight in sea water was the highest, but the dry weight per cells in brackish water was the highest. This was happen because the cell division in brackish water was growth very slow (Fig. 1A).

A

B C C

B

A B

C D



Lipid contents of the microalgae in all treatments tend to increase during their growth. The highest lipid content of the microalgae was found in seawater treatment at day 7 accounted for 13.58%, followed by brackish water treatment (5.86%), and the lowest was found in fresh water treatment (0.88%) (Fig. 1D). Increasing of lipid content was caused by the accumulation of lipid under stress conditions. These results were similar to the research done by Takagi et al. (2006) in Dunaliella cells. This was also confirmed by [23] that the increase of salinity could accelerate the total lipid content in microalgae cells.Under high salinity condition, the microalgae were stimulated to enhance their lipid production as osmoprotectant which necessary to protect them from salt stress [24].High sa-linity water might effect on intracellular osmolarity due to hypertonic medium condition over the microalgae cells. Then, it caused release of water inside of the mi-croalgae cells into the environment. The mechanisms for encountering salinity stress were regulated by producing lipid in large quantities to prevent the escape of water from microalgae cells. So, the high amounts of lipid lead to enhance the microalgae dry weight. Therefore, the high salinity treatment could be used to as method to increase the biomass productivity and stimulate lipid production of microalgae for biodiesel substrates.

IV. CONCLUSION

Different salinity have potential for increase the num-ber of cells, dry weight and lipid content on the mixed microalgae cultures isolates from Glagah. The highest dry weight and lipid content were found in seawater medium, they accounted for 3.42 mg/mL and 13,58% at day 7, respectively. Whereas, the highest number of cells was found in freshwater medium, this was 9.8 x 106 cells/mL.

ACKNOWLEDGMENT

All authors would like to sincerely acknowledge to Directorate General of Higher Education, Ministry of Education and Culture, Indonesia for funding this re-search.

REFERENCES

[1] Amin, S. 2009. Review on biofuel oil and gas production processes from microalgae. Energy Conversion and Manage-ment. 50:1834-1840.

[2] Chisti, Y. 2008. Biodiesel from microalgae beats bioethanol. Trends in Biotechnology. 26:126-13.

[3] Chisti, Y. 2007. Biodiesel from microalgae. Biotechnology Advances. 25: 296.

[4] Gouveia, L. and A. C. Oliveira. 2009. Microalgae as raw materi-al for biofuels production. Journal of Industrial Microbiology Biotechnology. 36:269-274.

[5] Demirbas, A. 2010. Use of algae as biofuel sources. Energy Conversion and Management. 51:2738-2749.

[6] Richmond, A. 2004. Handbook of microalgal culture: biotech-nology and applied phycology. Blackwell Science. India.

[7] Ogbonna, J.C., H. Yoshizawa, and H. Tanaka. 2000. Treatment of high strength organic wastewater by a mixed culture of pho-tosynthetic microorganism. Journal of Applied Phycology. 12: 277-284.

[8] Mutanda, T., D. Ramesh, S. Karthikeyan, S. Kumari, A. Anan-draj, & F. Bux. 2011. Bioprospecting for hyper-lipid producing microalgal strains for sustainable biofuel production. Biore-source Technology. 102:57-70.

[9] Griffiths, M.J. & S.T.L. Harrison. 2009. Lipid productivity as key characteristic for choosing algal species for biodiesel production. Journal of Applied Phycology.. 276:23-25.

[10] Friday, E.T. 2010. Mixed cultivation of Euglena gracilis and Chlorella sorokiniana: a production method of algae biomass on a large scale. Journal of Applied Biosciences. 35:2225-2234.

[11] Hu, Q., M. Sommerfeld, E. Jarvis, M. Ghirardi, M. Posewitz, M. Seibert, & A. Darzins. 2008. Microalgal triacylglycerols as feedstocks for biofuel production: perspectives and advances. ThePlant Journal. 54:621-639.

[12] Kaewkannetra, P., P. Enmak, & T.Y. Chiu. 2012. The effect of CO2 and salinity on the cultivation of Scenedesmus obliquus for biodiesel production. Biotechnology and Bioprocess Engineer-ing. 17:591-597.

[13] Peng, J., K. Yin, J.P. Yuan, G.X. Cao, M. Xue, S.F. Wu, & J.H. Wang. 2012. Characterization of a newly isolated green micro-algae Scenedesmus sp. as a potential source of biodiesel. African Journal of Biotechnology. 11(9):16083-16094.

[14] Moradi, M., & A.M. Ismail. 2007. Responses of photosynthesis, chlorophyll fluorescence and ROS-Scavenging systems to salt stress. During seedling and reproductive stages of rice. An-nals of Botany. 99: 1161-1173.

[15] Rao, A. R., C. Dayananda, R. Sarada, T.R. Shamala, & G.A. Ravishankar. 2007. Effect of salinity on growth of green alga Botryococcus braunii and its constituents. Bioresource Tech-nology. 98:560-564.

[16] Andersen, R.A. 2005. Algal culturing techniques. Elsevier Aca-demic Press. London.

[17] Vuuren, S.J.V., J. Taylor, C.V. Ginkel, & A. Gerber. 2006. Easy identification of the most common freshwater algae. School of Environmental Sciences and Development: Botany North-West University. Pretoria.

[18] Elumalai, S., R. Sakthivel, B.I. Santhose, & P.A. Murugan. 2011. Isolation, identification, morphological studies and lipid granules staining (Nile red) of different micro-algae for biodiesel production from fresh water and saline water. Journal of Expe-rimental Sciences. 2(7):26-29.

[19] Aghajanian, J.G. 1979. A starch grain-mitochondrion dictyo-some association in Batrachospermum (Rhodophyta). Journal of Phycology. 15:230–232.

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[24] Singh, S.P., & B.L. Montgomery. 2013. Salinity impacts photo-synthetic pigmentation and cellular morphology changes by dis-tinct mechanisms in Fremyella diplosiphon. Biochemical and Biophysical Research Communications. 433:84-89.



Abstract—It is well established knowledge that soil ani-mals and their interactions with microorganisms play a primary role in the mineralization of nutrients, and hence nutrient acquisition and the growth of plants. Soil micro-arthropods have a strong influence on vital ecosystem processes, such as the decomposition of organic matter and nutrient mineralization. The mesofauna (microarthropods and enchytraeids) also affect soil structural processes through their production of faecal pellets and biopores. This has strong effect on structural stability and water-holding capacity of the soil. Human activities, such as agri-culture activities, influenced abundance and diversity of soil mesofauna due to changes on structure and composi-tion of vegetations. This study was carried out at cocoa plantation with different planted periods, 34-year-old and 24-year-olds. Method used for sampling was pitfall trap with 12 sampling time during 3 months sampling periods. In total, 22 species collected during sampling, which were Lasius fuliginosus, Cardiocondyla sp, Netelia sp, Forficula auricularia, Telostylinus sp, Megaselia scalaris, Drosophila sp, Monoclona sp, Diptera larvae, Onthophagus sp, Lebia moesta, Haptoncus luteolus, Quedius sp, Phylus coryli, My-calesis sp, Melanoplus sp, Orocharis sp, Euryopis sp, Liny-phiidae sp, Labidostomma sp, Ceratophysella pratorum, Lumbricus terestris. This study showed higher number of individuals collected at older plantation, 858 individuals, than younger one, 441 individuals. On the other hand, di-versity of soil fauna at older plantation were slightly higher than younger one, 1.49 and 1.44 respectively. Based on this result, it can be concluded that time of land use changes caused more effect of abundance than diversity for soil fauna community.

Keywords— soil fauna, diversity, cocoa plantation, ab-undance, land use change.

I. INTRODUCTION

NDONESIAN cocoa plantations had rapid develop-ment since the early 1980s. In 2002 the area of Indo-nesian cocoa plantation recorded of 914,051 hectares,

of which most (87.4%) run by the people and the rest 6.0% of the country's farming and 6.7% of large private farming. Indonesia is currently listed as the third largest cocoa producer in the world with a total area of 1,563,423 hectares and a production of 795,581 tones [1]. Cocoa plantations in PTP Nusantara VIII Cikum-pay, Raamandala, West Bandung has large land area 594.70 hectares, with land-clearing system that is not concurrent. Opening the earliest land is around in 1977 and most recently in 1991. Based on opening land, it could be said that the age of the cocoa crop is not uni-form.

The macrofauna are the most conspicuous fauna of soil, and also the most documented in term of their biol-ogy and impact on soil fertility. Inside soil, those fauna create various highly organized, both structural and function, microcommunities. These communities highly affected by the changes in soil environment both by nat-ural [2] and human activities [2], [3]. This condition made soil fauna as a good environmental indicator [4], [5]. Many soil arthropods, including Collembola, Oriba-tida, live a sedentary life in soil in close relationship with the external conditions of their ecological niches. As a consequence, the structure of the microarthropod community closely reflects the environmental factors affecting the soil, including the impact of human activi-ties, and could be considered a bioindicator of soil health [6]. The macrofauna have the ability to create their own spaces, through their burrowing activities, and can have large influences on gross soil structure [7].

Within a heterogeneous land-use system, like cocoa plantation in Cikumpay, Rajamandala, West Java, the different plant species and soil management may lead to different living condition to litter and soil fauna. Agro-forestry systems are generally considered to have a posi-tive effect on the conservation of biodiversity by mini-mum tillage, quantity, and quality of litter, diversifica-tion, and especially incorporation of tress (of several species), shade, deep and perennial root systems that create a more suitable environment for soil faunal com-munities [8], [9]. Soil management practice can have dramatic effects upon soil invertebrate communities [10], [11] and may lead to indicator change in soil func-tioning.

This study determined the abundance and diversity of soil meso and macrofauna in two different planted pe-riods cacao agroecosystem in PTP Nusantara VIII Ci-kumpay, Rajamandala, West Bandung.


The study was conducted on the PTP Nusantara VIII Cikumpay, Rajamandala, located West Bandung, Indo-nesia (594,70 ha, 220-375 m altitude). The average val-ues of annual temperature was 27.5⁰C with range from 23 to 32⁰C and annual rainfall 2453.05 mm. The topo-graphy of this area has partially flat, sloping, and hilly with steep slopes. The soil had varied types, namely Latosol, Regosol, Grumosol, Podsolic and Alluvial with acidity ranged from 3.9-7. Samples collected during December 2011, January 2012, and February 2012.

Soil fauna samples were taken from two cocoa planta-

Ida Kinasih 1*), Tri Cahyanto 1), Ina Andriana 1) and Ramadhani Eka Putra 2) 1) Department of Biology, Faculty of Science and Technology, UIN Sunan Gunung Djati Bandung,

Indonesia 2) School of Life Sciences and Technology ITB, Indonesia


Soil Mesofauna Diversity in Two Different Ages of Cocoa (Theobroma cacao L.) Plantation

I



tions with different planted periods, older plantation (34-years-old) and younger plantation (24-year-olds). There were 12 species of weeds in younger plantation, meanwhile only 9 species of weeds in older plantation (Table 1). Litter in older plantation was more numerous than younger plantation. Furthermore, older plantation was more shaded than younger plantation.

To collect ground invertebrates, each site was divided into two plots. Six pitfall traps (plastic containers of 8 cm in diameter and 11 cm in height) were set in 5 m distance between the traps in each plot. The traps were

partially filled with 10% detergent and left for 48 hours. The lids of the containers were set above the traps to prevent the traps from flooding.

Before the sampling, the environmental variables (soil temperature, soil moisture and soil acidity) were record-ed at each point in each sampling. The average of the values were used.

Fauna was collected in 70% alcohol and counted un-der a binocular microscope. The invertebrates were the classified into higher taxonomic levels and counted for

their density and richness. Furthermore, those taxa were identified into species level. Invertebrates was divided into different functional groups: herbivores, microbial grazers, predators, saprophagous, social, and other in-sect (that represent more than one feeding habit) [12].

The diversity was measured with the Shannon index. Redundancy analysis (RDA) using CANOCO version 4.5 [13] was carried out to analyze the variation in the faunal communities data with respect to environmental variables.

III. RESULTS

21 species, an unidentified taxon and 1299 inverte-brates were collected across two different planted pe-riods in this study. Insecta (Hymenoptera, Diptera, Co-leoptera, Lepidoptera, Hemiptera, Orthoptera) were dominated in all sites, followed by Collembola (Podu-romorpha), Dermaptera, Arachnida, Actinenida and

Lumbricina (Table 2). The total number of species was slightly higher in the site with older plantation than younger one. Two times more invertebrate specimens were collected in the older plantation (858) compared with younger plantation (441). On other hand, diversity of invertebrates at older plantation were slightly higher than younger one, 1.49 and 1.44 respectively.

Lasius fuliginosus was dominated the invertebrate sample both in young and old plantation (Table 2), fol-lowed by Ceratophysella pratorum, and Cardiocondyla sp. Some species responded differently to different planted periods sites i.e Megaselia scalaris, Forficula auricularia, and Labidostomma sp, showed high prefe-rence to site with older plantation.

The fauna collected allocated to six functional groups. Formicidae dominated the social insect group both in younger plantation and older plantation and represented 48.3% and 52.6% of animal recovered in this study. Poduromorpha (Collembola) was the only taxa which occured as microbial grazers and represented 22.4% of total fauna both in younger plantation and older planta-tion (Tables 1, Fig. 1.). The rest were listed as follows in

TABLE 2 COMPARISON OF INVERTEBRATE FAUNAL ABUNDANCE AND RICHNESS

BETWEEN TWO DIFFERENT PLANTED PERIODS

TABLE I COMPARISON OF INVERTEBRATE FAUNAL ABUNDANCE AND RICHNESS

BETWEEN TWO DIFFERENT PLANTED PERIODS

+: present -: absent

Fig. 1. Comparison of functional group of soil fauna between two different planted periods



the order of abundance: herbivore (14.7% and 12%), predator (7.9% and 4.4%), other insect (5.9% and 4.8%), and saprophagous (0.7% and 3.8%).

Generally, RDA ordination revealed a clear separa-tion of faunal communities, except at one site in older plantation (O9) (Fig. 2.) The first and second axes ex-plained 17.7% and 26.6 of variation, respectively. The species-environment correlation coefficients for the first and second RDA axes were 0.68 and 0.83, respectively, suggesting a strong relation between species and envi-ronment factors for the second RDA axis. The first axis seems to represent the soil acidity (pH), and the second axis is closely related to the soil humidity (RH) and soil temperature (T).

IV. DISCUSSION

As a whole, soil fauna living in above ground were af-fected by the management practices, planted period and characteristic of that area. Planted period can affected density and richness of total fauna, social insects, preda-tors, saprophagous, herbivores, and other insects. There is not an apparent the differences for diversity of total fauna between younger plantation and older plantation. This probably because those sites have similar structures and maintain steady conditions. However, RDA ordina-tion showed clearly separated between younger planta-tion and older plantation.

Furthermore, the thickness of litter in older plantation could be explain of the highest density of saprophagous in older plantation. The number of weeds species in agroforestry provide a diversity of microhabitas, contri-bute to a larger density and soil biological diversity [8], [14] and the spatial heterogeneity of the litter layer in mixed tree plantation lead to small scale differences in the composition of faunal communities [15]. This litter on the soil surface serves as a source of energy and nu-

trients (large amount of labile C and N) that improves faunal habitat and protects them from predators [16].

Cacao agroforestry systems provided a useful refuge for microbial grazers (Collembola) and social insect (majority Formicidae) [17]. Formicidae was the most abundance individu both in younger and older planta-tion. Shaded cacao systems protect a large number of ant species from several different components of the ant community by forest-like structure of this agroecosys-tems [18].

Poduromorpha (Collembola) are among the second

abundant arthropod groups both in young and older plantation, furthermore older plantation had more higher abundant of Poduromorpha. This finding suggest that litter thickness could affect the population of this Col-lembola. Collembola playing an important role in micro-fragmentation of litter and fungi colonie and the major component of soil ecosystems. It is known that most of them live in the litter and feed on fungi or decaying ma-terials [7].

V. CONCLUSION

Soil fauna in cacao agroforestry were affected by the management practices, plantation age and characteristic of that area. It shown by the diversity of soil fauna at older plantation were slightly higher than younger plan-tation, 1.49 and 1.44, respectively. Social insects (For-micidae) and microbial grazers had the highest abun-dance both in younger and older plantation.

REFERENCES

[1] Saragih, R. (2013, May). Indonesia's Cacao: Optimistic number one in the world [Online]. Available: http://ditjenbun.deptan.go.id/bbpptpmedan/berita-179-kakao-indonesia--optimis-nomor-satu-didunia.html. In Indonesian.

[2] Dindal, Daniel L. Soil Biology Guide. John Wiley & Sons, Inc. 1990.

[3] Eggleton, P., A.J. Vanbergen. D. T. Jones, M.C. Lambert, C. Rockett, P.M. Hammond, J. Beccaloni, D. Marriot, E. Ross, and A. Giusti. Assemblages of soil macrofauna across a Scottish land-use intensification gradient: influences of habitat quality, heterogeneity and area. Journal of Applied Ecology. 2005. 42:1153-1164.

[4] Paoletti, M.G. and Bressan, M. Soil invertebrates as bioindica-tors of human disturbance. Crit.Rev.Plant Sci., 1996. 15, 21-62.

[5] Frouz, J. Use of soil dwelling Diptera (Insecta, Diptera) as bio-indicators : a review of ecological requirements and response to disturbance. Agric. Ecosys. Environ. 1999. 74, 167-186.

[6] Knoeppa, J.D., Coleman, D.C., Crossley Jr., D.A., and Clark, J.S. Biological indices of soil quality: an ecosystem case study of their use. For. Ecol. Manage. 2000. 138. 357–368.

[7] Coleman, David C., Crossley Jr., D.A., Hendrix, Paul F. Fun-damentals of soil ecology. Elsevier Academic Press, USA. 2004.

[8] Brown GG, Ro¨mbke J, Ho¨fer H, Verhaagh M, Sautter KD, Santana DLQ. Biodiversity and function of soil animals in Brazilian agroforestry systems. In: Sistemas Agroflorestais: Bases Cientı´ficas para o desenvolvimento sustenta´vel. UENF, Campos dos Goytacazes, Gama-Rodrigues AC, Barros NF, Gama-Rodrigues EF, Freitas MSM, Viana AP, Jasmin JM, Marciano CR, Carneiro JGA, Eds., 2006. pp 217–242.

[9] Lo´pez-Hernańdez D, Arau´jo Y, Lo´pez A, Hernańdez-Valencial I, Hernańdez C. Changes in soil properties and earthworm populations induced by long-term organic fertilization of the sandy soil in the Venezuelan Amazonian. Soil Sci. 2004. 169:188–194.

[10] Decae¨ns T, Jimenez JJ, Barros E, Chauvel A, Blanchart E, Fragoso C, Lavelle P. Soil macrofaunal communities in permanent pastures derived from tropical forest or savanna. Agric Ecosyst Environ. 2004. 103:301–312.

[11] Rossi, J. P and Blanchart, E. Seasonal and land-use induced variations of soil macrofauna composition in the Western Ghats, shouthern India. Soil Biology & Biochemistry. 2005. 37. 1093-1104.

[12] Moco MKS, Gama-Rodrigues EF, Gama-Rodrigues AC, Correia MEF. Caracterizaçaõ da fauna eda´fica em diferentes coberturas vegetais na regiaõ norte fluminense. Rev Bras Cienc Solo .2005. 29:555–564.

[13] ter Braak, C.J.F. & Smilauer, P. CANOCO Reference Manual and CanoDraw for Windows User’s Guide. Software for Canonical Community Ordination (version 4.5). Micro Computer Power, Ithaca, New York. 2002.

Fig. 2. RDA ordination, showing the distribution of invertebrates in younger plantation site (Y, open triangle) and older plantation site (O, closed triangle). RH: soil humidity; T: soil temperature; pH: soil acidity



[14] Laossi KR, Barot S, Carvalho D, Desjardins T, Lavelle P, Martins Metal. Effects of plant diversity on plant biomass production and soil macrofauna in Amazonian pastures. Pedobiologia (Jena). 2008. 51:397–407.

[15] Lavelle P, Senapati B, Barros E. Soil Macrofauna. In: Trees, crops and soil fertility. Schroth G, Sinclair FL Eds.CABI Publishing, Wallingford, 2003. pp 303–324.

[16] Barros E, Neves A, Blanchart E, Fernandes ECM, Wandelli E, Lavelle P. Development of the soil macrofauna community under silvopastoral and agrisilvicultural systems in Amazonia. Pedobiologia (Jena). 2003. 47:273–280.

[17] Moco, MKS., Gama-Rodrigues, E.F., Gama-Rodrigues, AC., Nachado, RCR. Soil and litter fauna of cacao agroforestry sys-tems in Bahia, Brazil. Agroforest Syst. 2009. 76:127-138.

[18] Delabie JHC, Jahyny B, Nascimento IC, Mariano SF, Lacau S, Campiolo S et al. Contribution of cocoa plantations to the conservation of native ants (Insecta: Hymenoptera: Formicidae) with a special emphasis on the Atlantic forest fauna of southern Bahia, Brazil. Biodivers Conserv. 2007. 16:2359–2384.



Abstract— Recent report showed increasing production

of rice and cassava due to demand for staple food and raw material of bioethanol. This condition increases post harv-est waste in which 100 and 20.8 million tons originated from rice production and cassava production, respectively. Commonly, farmer burn this waste or use it as compost and ruminary fed which provide small amount of profit. Recently, another approach for post harvest waste man-agement by bioconversion which believed could provide better profit to farmer has been proposed. Bioconversion defined as conversion of biomass of macromolecule into smaller molecule by biological processes. In this study, larvae of black soldier flies (Hermetia illucens) were used as bioconversion agent due to their simple biological system and short life cycle. Previous studies also confirmed their efficiency as bioconversion agents of various organic wastes but agricultural wastes. Conversion rate of waste for dried rice stalk and cassava tuber skin was 25% and 45% after 15 days (black soldier flies life cycle), respectively. Residue produced from bio-conversion process maintained, on average, more than 50% of chemical composition of wastes with elevated water con-tent. It can be concluded that H. illucens showed high po-tency as bioconversion agent of organic wastes with high lignin and cellulose content.

Keywords— agriculture wastes, bioconversion, black soldier flies, cassava, dried rice stalk

I. INTRODUCTION

GRICULTURE is one of human activities that pro-duced significant amount of waste and increasing significantly due to increasing demand on agricul-

tural products. In Indonesia, two agricultural products showed increasing pattern on production are rice and cassava. Production of dry rice stalk, as post harvest waste of rice, estimated 100 million ton [1] while cassa-va tuber skin, as waste of cassava, estimated 20.8 mil-lion ton [2], annually. All of these wastes have great potency as raw material of organic based product due to high content of cellulose, hemicellulose, lignin [3]-[6], small amount of protein [7][8], and significant amount of carbon [9]-[10]. Commonly treatment of dry rice stalk is pilling and burning while application as livestock feed not recom-mended due to low energy and protein content also low digestibility [11](Sitorus, 2002). On the other hand, cas-

sava tuber skin usually applied as livestock feed [10]. Another common practice for organic wastes is com-posting which applicable for large amount of waste. Generally common practice of composting is application of bacteria and earthworm as composting agent [12] in which final product of this process is limited to fertiliz-er. Recent developments showed development of compost-ing into bioconversion. Bioconversion is a process of conversion of large molecules into smaller molecules by living organism. One of bioconversion agent is insect larvae such as Black Soldier Fly (BSF) (Hermetia illu-cens). Study on bioconversion by BSF showed the abili-ty of BSF to convert organic wastes into protein and fatty acid in form of body mass [13]-[16] that could be used as replacement fishmeal which value of USD 1883 per metric tonne in February 2013 [17]. Even though BSF known for their ability as bioconverter, their appli-cation for converting agriculture with high cellulose content hardly known. Based on this information, pur-pose of this study is study the ability of BSF to convert-ing dry rice stalk and cassava tuber skin.

II. METHODS

A. Hermetia illucens egg collecting

Eggs of H. illucens were collected by ovitraps filled with bait made of combination of shrewd coconut and manure (1:1). Ovitraps were kept in various places in Bandung. Collected eggs were reared at Laboratory of Toxicology, School of Life Sciences and Technology, Institut Tekno-logi Bandung at room temperature (24-28oC), humidity 70-80%, and photoperiod 12:12.

B. Dry rice stalk and cassava tuber skin

Dried rice stalk abd cassava tuber skin originated from traditional farming of Soreang, Bandung Timur. Prior application, all material was crushed into powder by commercial food processor.

C. Research methods

Ten third instar larvae were kept inside plastic con-tainer filled with 30 g dry rice stalk and 10 g cassava tuber skin. As control group, dry rice stalk and cassava tuber skin was kept in similar container at same room without any larvae. Each treatment was replicated 20

Study on Bioconvertion of Cassava Tuber Skin and Dry Rice Stalk By Black Soldier Flies

(Hermetia illucens)

Ateng Supriatna, Ramadhani Eka Putra, Robert Manurung and Rizkita R. Esyanthi School of Life Sciences and Technology, Institut Teknologi Bandung, Bandung Indonesia


A



times and carried out for 15 days. Mortality of larvae and conversion rate of each material by BSF were meas-ured every 3 days. Conversion rate was defined based on formulae

Conversion rate =

D. Data Analysis

Mean difference of conversion rate between treatment and control group was analyzed by Student’s T-test. All statistical analysis was carried out with Statsoft Statisti-ca ver. 8.

III. RESULTS

A. Conversion rate of dry rice stalk by BSF

Conversion dry rice stalk by BSF showed in Figure 1. This result showed that in average biomass of dry rice stalk was reducing higher when treated with BSF. By the end of experiment, biomass of dry rice stalk was re-duced by 25% when treated with BSF while only 21% reduction when BST was omitted.

Fig. 1. Conversion rate of dry rice stalk for 15 days by BSF (closed bar) and without BSF (open bar). (*) indi-cated significant mean differences.

B. Conversion rate of dry rice stalk by BSF

Conversion cassava tuber skin by BSF showed in Figure 2. This result showed that in average biomass of cassava tuber reduced less when treated with BSF. By the end of experiment, biomass of dry rice stalk was reduced by 45% when treated with BSF while it reached 61% reduction when BST was omitted.

Fig. 2. Conversion rate of cassava tuber skin for 15 days by BSF (closed bar) and without BSF (open bar). (*) indicated significant mean differences.

C. Composition of organic waste material after con-version by BSF

Analysis on research material showed 1.92% reduc-tion of fiber of dry rice stalk and 9.97% of fiber of cas-

sava tuber skin by BSF. Along with reduction of fiber, we also recorded reduction on other material (Table 1).

TABLE 1

COMPOSITION OF RESEARCH MATERIAL AFTER CONVERSION

Composition Dry Rice Stalk Cassava Tuber Skin

Before After Before After

Fiber (%) 19,76 17.84 17.95 7.98

Protein (%) 5,35 2.96 6.87 4.05

Water con-tent (%)

9,61 39.80 10.63 23.84

Ash (%) 26,69 20.69 4.68 7.72

Carbon (%) 37,89 22.82 47.40 39.77

Nitrogen (%)

0,86 0.67 1.05 0.72

C/N ratio 44,18 34.05 45 55.23

Phosphor (%)

0,18 0.19 0.18 0.22

Potassium (%)

2.50 2.11 0.95 2.90

D. Composition of BSF at the end of study

Analysis on content of protein and lipid of BSF at the end of study showed that on BSF reared on cassava tu-ber skin produced higher content of protein and lipid (Table 2).

TABLE 2

PROTEIN AND LIPID CONTENT OF BSF AFTER TREATMENT

Composition Percentage

Dry rice stalk

Cassava tuber skin

Protein (%) 11.66 13.14

Lipid (%) 2.03 7.25

IV. DISCUSSION

A. Conversion rate of dry rice stalk by BSF

Reduction of dry rice stalk biomass by BSF probably due to activity of some digestive enzyme inside salivary gland and digestive tract such as leucine arylamidase, α-galactosidase, β-galactosidase, α-mannosidase, and α-fucosidase. α-galactosidase [18]. However, conversion rate is relatively low which is probably caused by high content of lignin inside stalk structure. Further study is conducting to improve the conversion rate by applica-tion of addition decomposing agents.

B. Conversion rate of dry rice stalk by BSF

Result showed that BSF relatively less efficient in re-ducing biomass of cassava tuber skin. We believed this condition caused by fermentation of cassava tuber skin. Unlike rice starch, this material contain less lignin and much easier to undergo fermentation.



C. Composition of organic waste material after con-version by BSF

Reduction of fiber and other material showed the abil-ity of BSF to digest both dry rice stalk and cassava tuber skin. This ability may possible due to availability of some microbe on their digestive system. Previous study had found Bacillus substilis inside digestive system of BSF [19]. This bacteria produced protease [20], lipase [21][22], and celulase [23]-[26].

D. Composition of BSF at the end of study

Compare with study by Rachmawati et al. (2010), protein content of BSF fed with dry rice stalk and cassa-va tuber skin lower than BSF fed with fermented starch and house flies artificial feed (14.6% and 15.3%, re-spectively). However lipid content of BSF fed on cassa-va tuber skin much higher than fermented starch and house flies artificial feed (0.03% and 3.8%, respective-ly). We suspect this related with original content of pro-tein and lipid of feed and fermentation process.

V. CONCLUSION

This study is preliminary study on bioconversion of agricultural wastes by BSF. The result showed the abili-ty of BSF to digest to material contains high cellulose and lignin also the potency as alternative protein and lipid source. Further study is conducting to improving the process of bioconversion.

ACKNOWLEDGMENT

We thanks Rizal Jamjam and Suyitno for his valuable help in collecting samples and maintain insect collec-tion.

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Abstract—Coffee (Coffeaarabica and Coffeacanephora) are considered as important cash crop major plant for ex-port in Indonesia. The price of product basically depends on quality of fruit produced. One of key process that highly influenced quality of fruit is pollination. Research showed that high yield usually was obtained from plantation close by forest due to benefit of pollinating insects inhabited the forest. However, due to land use change for housing, many plantation areas in West Java changed from classical plan-tation into urban plantation. We examined the possible insects that provide pollination service to coffee planted at urban area. This study focused on the diversity of flower visiting insects, behavior (flower handling time and forag-ing rate) and pollination efficiency (determined by fruit set) of each species. In total, 11 insects species visiting cof-fee flowers dominated by Hymenoptera in term of numbers and diversity. Local honey bee, Apiscerana, exhibited long-est flower handling time (4.38seconds/flower) while Ame-gillasp. foraged more flowers each visit that other species (26.57 flowers/visit). Foraging rate and time of insects highly correlated with average temperature, humidity, and light intensity. Insect visitation significantly increased fruit set (ANOVA P<0.05) and among all visiting insects, Ame-gilla sp. has highest pollination efficiency, with 80.83% . During this study we also found the importance of wild and garden flowers to maintain insect when coffee flowers were not available.

Keywords—coffee, fruit set, pollination, urban planta-

tion

I. INTRODUCTION

GRICULTURE is human activities designed to fulfill energy demand of human. In classical farming sys-tem, agriculture is a complex landscape mosaics

consisted of noncrop and crop habitat [1]-[3]. Increasing human population led to development of more intensive agricultural system that increasing isolation from natural habitats. This condition negatively affect community structure [4][5]and important ecological services pro-vided by it [6][7]. Ecological service is defined as as a wide range of conditions and processes within natural ecosystems, and the species that are partof them that help to sustain and fulfil human life [8] for example pol-lination [8]-[10].

Indonesia also experienced similar problem. Increas-ing population made most of agriculture area became urban agriculture as most of surrounding area trans-formed into human dwelling. During this study, we ex-amined the effect of this changing to pollination service provided by nature. As subject of study we used coffee as this plant considered as important cash crop and de-pend predominantly on wind for pollination [11] [12],

and insect pollination has been assumedto make only a small contribution to total pollen transfer [13].

II. METHODS

A. Research Area

Research was conducted at coffee plantation of Seko-lahPertanian Pembangunan (SPP-SPMA) BT andUni-versitasWinayaMukti (UNWIM). Both plantation lo-cated between 800-900 m above sea level and sur-rounded by housing of DesaGunungmanik, Kecamatan-Tanjungsari, SumedangJawa Barat. The plantation itself is mixed plantation whereas highland coffee (C. arabi-ca) and lowland coffee (C. canephora) planted among cocoa, durian, guava, and longan trees.

B. Pollination experiments

We carried out three pollination experiments on open and bagged mature flower buds to examine the benefit of local insects population for fruit set of coffee. We selected three different branches on each five different coffee shrubs and replicated this in three plantations resulting in altogether 45 branches. During this study we did not separate between c. arabicaand C. canephora. The three pollination experiments were as follows: (1) open pollination, in which flowers had access to insect and wind pollination; (2) wind pollination, in which insect pollination excluded by coarse mesh gauze; (3) self-pollination (within plant pollination), hand pollina-tion using pollen of same flowers of same plant. In hand-pollination experiments, pollen was transferred to stigmas with fine brush then pollinated flowers bagged with bag made of coarse mesh gauze. Seven weeks after the end of the flowering period, bags were removed and total numbers of green fruit per branch were counted.

C. Flower-visiting insects

Abundance and species richness of flower visitor were observed during flowering period. Observations were carried out on sunny days between 06.30 to 17.00. for 30 min. After each 30-min observation period, in-sects were caught by sweep netting for 5 min.

During this observation, flower handling time, forag-ing rate, and pollination efficiency also measured for each insect species along with environmental factors such as average temperature, humidity, and light intensi-ty. Pollination efficiency was measured based on method developed by Olsen (1997).

D. Data Analysis

Differences among fruit set produced by different pol-

Pollination Agents of Coffee at Small Urban Plantation in Sumedang, West Java, Indonesia

Ramadhani Eka Putra1), Agus Dana Permana1), NdenRissa Hadikusumah1), and Ida Kinasih2) 1) School of Life Sciences and Technology, InstitutTeknologi Bandung, Indonesia

2)Department of Biology, Universitas Islam Negeri Bandung

A



lination procedure were analyzed using ANOVA with Tukey as post hoc test. Foraging rates, flower handling time, and pollination efficiency analyzed using Student T-test.

Pearson correlation test was used to find correlation between environmental factors and abundance of flower visiting insects also between flower handling time and pollination efficiency. All statistical analysis was carried out with IBM SPSS ver. 19.

III. RESULTS

A. Fruit set of different pollination systems

Fruit set of coffee was 79.02% in open pollination and 66.53% in wind pollination. Fruit set of open polli-nation was significantly higher than self pollination (Ta-ble 1).

TABLE 1

FRUIT SET AFTER DIFFERENT POLLINATION TREATMENT

No. Experiment % Fruit set

1 Open pollination 79.02 a

2 Wind pollination 66.53ab

3 Self pollination 56.49b Different letters show significant differences

between experiments

B. Flower visiting insects

We found 11 species (4 orders) and 138 individual of insects visiting coffee flowers within 30 hours (1800 minutes) observation. Flower mostly visited higher by Hymenoptera than Diptera, Lepidoptera, and Coleoptera (Fig. 1).

Fig. 1. Proportion of insect visiting coffee flowers at study site

C. Foraging activity

Daily foraging activity of flower visiting insects de-termined by total number of individuals. Observation showed three peak periods of activities in early morning, midday, and afternoon for abundance. On the other hand, there was no clear peak for number of species visited coffee flowers.

There was negative correlation between temperature and light intensity to foraging activity while humidity showed positive correlation (Table 2). The result indi-cated the preference of most insects to forage during low temperature and high humidity period while they pre-ferred to shade area than open area which explained two peak period foraging activities at Fig.2. On the other hand, some species (mostly order) Lepidoptera showed

high preference to forage during midday which ex-plained peak activity during midday (Putra, unpublished data).

Fig. 1.Daily changes of total number of individual and species visiting

coffee flowers at study site

TABLE 2 CORRELATION BETWEEN FORAGING

ACTIVITY AND SELECTED ENVIRONMENTAL FACTORS

Temperature Humidity Light Intensity

(°C) (%rH) (lux)

- 0.842 0.843 - 0.622

On average, Hymenoptera frequently visited coffee

flowers than other orders. Among all identified insects, Amegillasp. (Hymenoptera) had highestforaging rate, followed by Euremasp. (Lepidoptera), Xylocopasp. (Hymenoptera), and Halictidae sp. (Hymenoptera) (Ta-ble 3).

TABLE 3 FORAGING RATE OF FLOWER VISITING INSECTS

COLLECTED AT STUDY AREA

Spesies Order N Foraging Rate (Flowers.min-1)

Xylocopasp Hymenoptera 15 20.45 Apiscerana Hymenoptera 47 13.69 Amegillasp Hymenoptera 31 26.57 Halictidaesp Hymenoptera 3 20.00 Rygchiumsp Hymenoptera 12 16.36 Mycalesissp Lepidoptera 8 3.12 Eurema sp. Lepidoptera 4 21.82 Hesperidaesp Lepidoptera 1 6.00 Graphiumdoson Lepidoptera 1 10.00 Scatophagidaesp Diptera 15 4.79 Cetonidaesp Coleoptera 1 12.00

D. Flower handling time and pollination efficiency

Amegilla sp. was the most efficient insect in term of coffee pollination at our study area even though had short flower handling time (Table 4). Further analysis did not show significant correlation between pollination efficiencytoflower handling time(R2 = -0.1220, p > 0.05) and foraging rate (R2 = 0.2570, p > 0.05).



IV. DISCUSSION

A. Fruit set of different pollination systems

This study also showed that coffee could maintain their fruit set even without pollination service from in-sects. However, cross pollination accommodated by insect improve fruit set about 12% and 22%, of wind and self pollination respectively(Tabel 1). Result agrees with previous studies on improvement of fruit set due to insect pollination [10], [15]-[17]. Fruit set recorded dur-ing this study similar to study conducted on large agro-forestry coffe fields by Klein [15] which concluded that smaller plantation may produced high fruit set as long healthy pollination service is available.

TABLE 4

FLOWER HANDLING TIME AND POLLINATION EFFICIENCY OF FLOWER VISITING INSECTS COLLECTED AT STUDY

AREA

Spesies N Flower Han-dling Time (s)

Pollination Effi-ciency (%)

Xylocopasp 15 2.93 77.10 Apiscerana 47 4.38 80.30 Amegillasp 31 2.26 80.83 Halictidaesp 3 3.00 0 Rygchiumsp 12 3.67 60.79 Mycalesissp 8 19.25 27.78 Eurema sp. 4 2.75 0 Hesperidaesp 1 10.00 0 Graphiumdoson 1 6.00 0 Scatophagidaesp 15 12.53 62.50 Cetonidaesp 1 5.00 0

B. Flower visiting insects

We only found 11 species (4 orders) within 30 hours (1800 minutes) observation. This numbers much lower than similar study of Klein [15] which is could be ex-plained by (1) study area is located in the middle of hu-man dwelling and closed natural habitat located 3 km apart. Under this condition, plantation is isolated and local remaining population is hardly receive additional individual from natural habitat[7][18][19] and (2) cha-racter of agriculture in term of level of disturbance as it directly influence the availability of nesting area and habitat condition[20][21], size of agriculture [22], and whether the agriculture area could provide continuous food supply during non-flowering period which is lack-ing at intensive agriculture[23][24]. Foraging activity

Activity pattern of flower visiting insects are differ temporally within days and seasons [25][26]. Under this condition, diverse of flower visiting insects provide in-surance for coffee pollination especially solitary bees as social bees mostly found only in peak flowering sea-son[20][27].

C. Flower handling time and pollination efficiency

During this study we found that neither of flowers handlingtime and foraging rate correlated with pollina-tion efficiency. This condition could be caused by (1) developing fruit aborted because competition of neigh-bouring fruits [28][29] (2) Flowers probably require a minimum number of pollen grains to produce fruit [30][31].

In this study we also found that wild bees were more efficient pollinator than tropical honey bees (Apiscera-na) which considered as best pollinator by many far-mers. This could be explained by (1) honeybees spent more time in branches with dense flowers which in-crease the possibility of visiting more flowers in same plant [32]. In advance this condition could increase the possibility of self pollination in which reduce fruit set [15], (2) honeybees prefer to collect nectar and contact stigma less frequent [33] compare with solitary wild bees that more efficient in depositing pollen on stigmas [34].

V. CONCLUSION

Wild flower visiting insects could improve productio-nof urban coffee plantation through improvement of fruit set as result of perfect pollination.These wild in-sects had difference in activity and pollination efficien-cy, in which diversity of these pollination agents may crucial to maintain production rate of urban coffee plan-tation.On the other hand, isolation of urban plantation from forest threatened continuous supply of pollinating insects to plantation. Thus it is necessary to maintain local population of wild bees or application of domesti-cated pollinationagents to ensure adequate pollination of coffee flowers.

ACKNOWLEDGMENT

We thanks Suyitno for his valuable help in preparing field equipment and maintain insect collection.

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Abstract— Rawa jombor is a lenthic water ecosystem

that have similar characteristic with a reservoir or swamp, located at Krakitan village, Klaten, Central Java. The wa-ter used for food culinary by the villagers, and also there are a lot aquaculture can be found. Zooplankton is one an importat biotic component in aquatic ecosystem as a sec-ondary chain which is connect to the next trophic level. Zooplankton can be used as controller of phytoplankton abundance and as bio-indicators of water quality and a productivity of fisheries in Rawa Jombor. The study aimed to determine the diversity and abundance of zooplankton in Rawa Jombor swamp. Data was taken at two zone: aqua-culture zone and free zone. On each zone there are 3 sam-pling sites. Twenty litres water sample has taken used van Dorn water sampler 5 litres on each sampling site. The sample was filtered by Wisconsin Plankton Net and filtrate was fixed by 4% formalin. Environmental parameters that was measured include water temperature, air temperature, water depth, light transparency, DO, CO2, alkalinity, pH, nitrate and phosphate content. Zooplankton samples were observed with a total strip counting method using SRCC (Sedgwick Rafter Counting Cell). Results show there were 5 functional group in both locations, which consists of 30 species in the Free Zone and 21 species in Aquaculture Zone. Functional groups found include Copepods, Clado-cera, Rotifers, Rhizopods and Ostracods. Species that have highest important value are Nauplius sp. and Cyclops ster-nus, belong to Class Copepoda.

Keywords— Zooplankton abundance, Rawa Jombor,

Copepods

I. INTRODUCTION

Generally, zooplanktons are microscopic aquatic animals that movement is influenced by winds, currents, and tides. Zooplankton is an important biotic component in the chain. In the swamp ecosystem, zooplankton became a secondary chain linking the primary chain (phytoplankton) with a chain on the next level (shrimp and fish), so that the diversity and abundance of zooplankton organisms can control phytoplankton's abundance and become bio-indicators of water quality and fisheries productivity in the swamp [2]-[4],[6]. Zooplankton community are very sensitive to environmental variations, factors that influence its existence in swamp ecosystem is the level of dissolved organic and inorganic compounds, the availability of food (phytoplankton), competition, and predation. Zooplankton diversity and abundance of information can provide an important indication of the waters, or disruption in the aquatic environment [12],[13] .

Zooplankton is an important component of the food web in aquatic environments, especially fresh

water. Loss of Cladocera species members in large quantities can reduce the predation on lower trophic levels (phytoplankton), so that phytoplankton abundance and disrupt ecosystem functioning, and lower trophic levels above survival, such as planktoniovore fish [5]. Based on the function of providing food in the fishery, zooplankton can be distinguished into two groups. There are the zooplankton groups that eaten by fish and shrimp, for example Copepoda and Cladocera, and the adverse zooplankton groups (noxious) is a protozoan. Adverse zooplankton may overflow if there is a decrease in physico-chemical quality of the water [10].

Research on the diversity and abundance of zooplankton was done at the turn of the rainy season to the dry season, in the Rawa Jombor swamp. This ecosystem is divided into four zones, namely floating restaurant zone, aquaculture zone, water hyacinth zone, and free zone. Water circulation in the ecosystem Rawa Jombor can be said consequently less water will stagnate so long on the ecosystem accumulation of organic matter such as water hyacinth, floating stalls leftovers, and giving pellets in ponds and other activities will affect the diversity and abundance of zooplankton in the next Rawa Jombor swamp ecosystem will determine the quality and productivity of these waters.

On the diversity and abundance of zooplankton research, the use of two zones, they are the free zone and aquaculture zone. Both of zone (a) Aquaculture zone is an barriers area and used for fish farming, on this waters there is also additional of pellets provision, (b) Free Zone is an area of open water that could be used by peoples for fishing, animal livestock drinking, and other activities.

This research aims to study the diversity and abundance of zooplankton in Rawa Jombor swamp. In focus to study about the species composer of the community of zooplankton in the Rawa Jombor swamp. Moreover also study about oxygen conditions, CO2, alkalinity, pH, water temperature, air temperature, light penetration, nutrient levels such as phosphate and nitrate in the Rawa Jombor swamp.

II. METHODS

A. Study site

Diversity and abundance of zooplankton re-search was conducted in the Rawa Jombor swamp, Kra-kitan Village, District Bayat, Klaten, Central Java, on the afternoon of March 16, 2013.

Diversity and Abundance of Zooplankton in Rawa Jombor, Klaten, Central Java

Khairunnisa Arumsari 1*), Suwarno Hadisusanto 1) Rindra Aryandari 1), and Haikal P. Fadholi 1)

1) Faculty of Biology, Gadjah Mada University, Yogyakarta, Indonesia *) Corresponding author : [email protected]



Fig. 1. Rawa Jombor swamp. Location and sampling site (taken from Google earth June 6, 2013)

B. Methods

Plankton sample in this study were taken from two locations, they are free zone and aquaculture zone, every zone taken from 3 sampling site (Fig. 1.), every sampling site taken 2 bottle sample as replicates and 1 bottle extra samples. Every sampling sites, samples of plankton sampled by water sampler modification of Van Dorn water sampler 5 liters. Water samples were col-lected in a 10 liter bucket. In this study, water samples were taken are composite sample, was sampled 4 times, so that the total volume taken is 20 L. Then sample was filtered with a Wisconsin plankton net. Then the filtrate was collected in a flacon 10cc bottle properly labeled, then fixed with 4% formalin. Then the flacons wrapped in plastic and put in a box samples [11].In addition to several physical and chemical variables, such as the light transparency, DO, CO2, alkalinity, pH, water tempera-ture, air temperature and dissolved nutrient levels in-clude phosphate (PO4

3-), and nitrate (NO3) were meas-ured. Then sample was identified at laboratory with total strip counting method [13], use sedgewick rafter count-ing cell (SRCC), and light microscope and also plank-ton identify books [3,11].


Based on the results, 30 species of zooplankton were distributed randomly in the free zone and aquacul-ture zone (Figure 2.). Most number of species were found in the free zone consist of 30 species of zooplank-ton, while in aquaculture zone observed 21 species of zooplankton (Figure 2.). From the results indicate that the diversity of zooplankton in the free zone was more diverse than aquaculture zone.

Fig. 2. Number of species in the Free Zone and Agriculture Zone.

In this study, zooplankton species were grouped into five functional groups, they were: (1) copepods, (2) Cladocera, (3) Rotifers, (4) Ostracods, and (5) Rhizopods. Functional group of Copepods and Cladocera were the most abundant group in the study locations (Figure 2), while the group that had the highest diversity is Rotifer, reaching 10 species in free zone (Figure 2.). The following table also presented diversity of zooplankton in the free zone and aquaculture zone at Rawa Jombor swamp (Table 1.).

TABLE 1. ZOOPLANKTON DIVERSITY IN THE FREE ZONE AND

AQUACULTURE ZONE

No Species Name Free Zone Agriculture Zone

COPEPODA

1 Nauplius √ √ 2 Ergacillus versicolor √ √ 3 Cyclops sternus √ √ 4 Calanus minor √ √ 5 Spesies A √ 6 Spesies B √ √ 7 Eodiaptomus japonicas √ √ 8 Diaptomus glaciaris √ √

CLADOCERA 9 Cheriodaphnia lacustris √ √

10 Cheriodaphnia reticulate √ √

11 Diaphanosoma branchyu-rum √ √

12 Daphnia pulex √ √ 13 Moina macrocopa √ √

ROTIFERA 14 Brachionus pala √ √ 15 Lepadella acuminata √ 16 Brachionus forficula √ √ 17 Brachionus falcatus √ 18 Epiphanes clavilata √ 19 Euclanis dilatsis √ 20 Keratella valga √ √ 21 Keratella serrulata √ 22 Filinia terminalis √ √ 23 Tricocera sp. √

OSTRACODA 24 Cypria apthalmica √ √ 25 Notodromus monocha √ √ 26 Candona hyalina √ 27 Spesies C √ √

RHIZOPODA 28 Centropyxis aculeta √ √ 29 Centropyxia ecornis √ √ 30 Euglypha cristata √

Based on the results, it was known that the

most abundance zooplankton in the both of zone were copepods, the second group were Cladocera and the third were Rotifer. It is same with Trykov results of the study in 2011 in Reservoir Kardzhaly also found that the most abundant zooplankton were copepods, Cladocera and the third level was Rotifera. They were three zoop-lankton groups that can be eaten by the fish. The three groups of zooplankton abundance was an indicator that the water quality of Rawa Jombor swamp was good, it mean lush and sufficient to provide nutrients for the predators (fish). Besides that, in this study noxius zoop-lankton group such as Euglypha cristata also rarely found (Figure 3.).



Fig. 3. : Number of Individuals per liter in the Free Zone (blue histogram with black value) and Agriculture Zones (red histogram with red value).

Although in both zones, the total of functional groups

were same, but the abundance values was different, can be seen in (Figure 3.), overall, zooplankton in the free zone was more abundance than in aquaculture zone. Copepod such as Cyclop sternus and Nauplius were the most abundance species in both places, and the abundance value of Cyclop sternus in free zone reached 232,750 individuals per liter, while in aquaculture zone 111,000 individuals per liter and the abaundance value of Nauplius in free zone reached 229,833 individuals per liter, while in aquaculture zone was 138, 417 individuals per liter.

It was related to the physico-chemical quality of water that supports the zooplankton growth. Although the free zone and aquaculture zone in one swamp but there were some different physico-chemical quality between these zones, such as DO, light transparency, water temperature, and phosphate (Table 1.).

The water temperature in the aquaculture zone was higher than the water temperature in the free zone, which was 29.6°C in aquaculture zone and 28.5°C in the free zone (Fig. 4.). According research [4] the high temperatures can be zooplankton lethal, it’s affect lower abundance of zooplankton.

Light transparency in the free zone was higher than the aquaculture zone, which respectively were 0.42 m and 0.36 m (Fig. 4.). Light transparency affects the distribution and abundance of phytoplankton, which affects zooplankton abundance as primary consumers.

.

Figure 4. physico-chemical quality (1) free zone and (2) aquaculture zone (2) include Dept, Transpiration, DO, CO2, Alkalinity , pH, Wa-ter temperature, Air Temperature.

High phosphate content stimulus the abundance of

zooplankton population, according [1] phosphate was a primary component of ATP was used in DNA replica-tion, so the higher phosphate in free zone than aquacul-ture zone was the ones of determining factor in the ab-undance of zooplankton. Besides that, there were fish culture in aquaculture zone. So it was possible that zooplankton in the aquaculture zone had been eaten by fish culture.

IV. CONCLUSION

From this study it can be concluded that the 5 functional groups of zooplankton found in both places, which consists of 30 species in the free zone and 21 species in aquaculture zone. Species that has the highest abundance was Nauplius then Cyclops sternus, derived from Class Copepoda. Physicochemical parameters that regulate the diversity and abundance of zooplankton in the Rawa Jombor swamp are DO, water temperature, transparency, light and phosphate levels.

ACKNOWLEDGMENT

I would like to express my very great appreciation to my friend, Riana Nindita, for her valuable support on this project. NungkeDiah for her supporting.I would also like to my thanks to Abid Zulfikar, Mr. Bambang, Miss Shinta for their contribution in collecting my data. La-boratory assistant is also gave useful contribution on providing of equipment.



REFERENCES

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[2] Cox, D. R. 1959. The analysis of exponentially distributed life-times with two types of failures. Journal of the Royal Statistical Society, 21, 411-421.

[3] Davis, C.C. 1995. The Marine and Fresh Water plankton. Michigan state Univercity press. Chicago

[4] Goldman, C.R and A.J Horne. 1983. Limnology. Internasional Student Edition. Mac. Grow Hill. Int. Book.Co. Tokyo..

[5] Maline, K. M., K.D,Koupal, B.C, Peterson, and W.W, Hoback. 2011. Distribution of Zooplankton in Harlan County Reservoir, Nebraska. Transactions of the Nebraska Academy of Sciences and Affiliated Societies. 7:5

[6] Marce1,R., M, Comerma, J.C, García, J, Gomà and J,Armengol. 2005. The zooplankton community in a small, hypertrophic Mediterranean reservoir (Foix reservoir, NE Spain). Limnetica, 24(3-4): 275-294

[7] Mohammad, SS., and N.B.S, Ali 2012. Investigation on Identi-fication, Density and Distribution of Zooplankton in Lar Reser-voir. World Journal of Fish and Marine Sciences 4 (2): 211-217

[8] Nybakken, J.W and M. D, Bertness. 2005. Marine biology. 6 ed. Benjamin Cummings. San Fransisco. P:48-56

[9] Pinel –Alloul, B., E, magnin., Codin-Blumer, G, and Ross, P. 1982. Zooplankton population dynamics in a small Reserviour (james Bay, Quebec). Canada Water Resource Journal 7:1

[10] Rocha, O., T,Matsumura-Tundisi, E.L.G.,Espíndola, K.F., Roche, and A.C., Rietzler, 1999. Ecological theory applied to reservoir zooplankton, In: Theoretical reservoir ecology and its applications. J.G. Tundisi & M. Straškraba (Eds.), pp. 457-476, Brazilian Academy of Sciences, International Institute of Ecolo-gy, Backhuys Publishers, ISBN 90-5782-034-X, São Carlos.

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[13] Wetzel, R.G. 1983. Limnologi. 2nd edition. Saunders college publishing. Philadlpia



Abstract—The objective of study was to isolate, identify

and characterize the cellulolytic yeast isolated from sec-ondary forest in South East Sulawesi, Indonesia. We iso-lated 142 strains and obtain 43 strains (32.28%) were cel-lulolytic yeasts consist of 26 species residing within 10 ge-nera. Candida was the most diverse genus consisting of 15 species. It is important to note that several strains residing within this genus could be candidate for new taxa, among others Candida aff. Cylindracea PL2W1, Candida aff. In-sectorum PL3W6, Candida afffriedrichiiMKL7W3 , Candi-da afflessepsii, Candida aff. tenuis. Five new candidates for novel species of cellulolytic yeast close to Yamadazymamex-icana: were Yamadazymaaff. mexicana(5 strains). Pichia, Pseudozyma, Sporodiobolus, and Sporobolomyceswere oth-er cellulolytic yeasts found in South East Sulawesi. It is obvious, that leaf litter was a good source of cellulolytic yeasts. This cellulase producing yeasts dominate this bi-ome, and production of extracellular cellulase is critical strategy for such yeast to survive in cellulose rich ecosys-tem such as leaf-litter. This finding would suggest that yeasts play key role on hydrolyzes of cellulose and impor-tant resources for sustainable energy research.

Keywords— Cellulolytic yeasts, secondary forest, South East Sulawesi, leaf-litter.

I. INTRODUCTION

p to date ecological studies of yeast from natural habitats have been conducted extensively mostly in temperate regions [1],[2]. Taxonomy and ecology

data indicate a need for additional studies in tropical ecosystems, particularly in Asia [3],[4]. Indonesia is a tropical nation comprised of over 17,000 islands is rich

in biodiversity, having unique flora and fauna [5],and presumably microbes as well. Rifai[6] estimated Indone-sia has more than 200,000 species of fungi. However, little information on species diversity of Indonesia indi-genous yeast and yeast-like fungi has been generated. Studies of Indonesian yeasts primarily related to their role in fermented foods [7],[8]. Early studies of yeast from natural environment in Indonesia include Deinema in 1961, who found Candida bogoriensis from the sur-face of leaves of the flowering shrubs Randiamelleifera (Rubiaceae) in Bogor. In recent years, more studies have been performed to explore yeast diversity in Indonesia [9]-[10]. While microorganism are very important sources for bioindustry however few study conducted to assess the important of forest as genetic resources for many interest. Cellulase are primarily obtained from cellulolytic fungi: Trichodermareseei, T. viride, T. lig-norum[11], T. koningi, Chrysosporiumlignorum, C. pruinosum, Fusariumsolani, Neusrosporacrassa, As-pergillus[12], Penicelllium, Acremoniumstric-tum[13],[14], including that from several group of cellu-lolytic bacteria: Cellulomonas, Cellulosimicrobiumcel-lulans [15] Clostridium clariflavum, Bacillus group, Flavobacterium, and Paenibacillus[16], and Fermicutes and Actinomycetes group ([17];[18]). On the hand the role of yeast on biodegration of cellulose is few ex-plored, particularly using yeast from tropical forest ([19];[20]).

Sulawesi has high biodiversity, and reported to have high biodiversity of flora,[21] and fauna [22];[23]. At the microbial diversity level, further study is needed to verify the species richness of this area, particularly cel-lulolytic yeast. We evaluated two areas in South East

Novel Cellulolytic Yeast Isolated From South East Sulawesi, Indonesia

Atit Kanti1,2*), Nampiah Sukarno3), Latifah K Darusman4), Endang Sukara5), I Made Sudiana2), Helbert6), and Kyria Boundy-Mills7)

1)Microbiology Study Program. Graduate School of Bogor Agricultural University, Indonesia

2)Research Centre for Biology, Indonesian Institute of Sciences (LIPI), Cibinong Science Centre, 3)Department of Biology, Faculty of Mathematics and Natural Sciences, Bogor Agricultural Universi-

ty, Darmaga Campus, Bogor 16680 4)Department of Chemistry, Faculty of Mathematics and Natural Sciences, Bogor Agricultural Uni-

versity, Darmaga Campus, Bogor 16680 5)Research Centre for Biotechnology, Indonesian Institute of Sciences (LIPI), Cibinong Science Cen-

tre 6)R&D Units For Biomaterials, Indonesian Institute of Sciences (LIPI), Cibinong Science Centre, In-

donesia 7)Phaff Yeast Culture Collection, Department of Food Science and Technology, College of Agricul-tural and Environmental Science, University of California, One Shield Avenue, Davis, CA 95616,

USA


U



Sulawesi that have different covering vegetation. Leaf litter, soil and leaf surfaces are common habitats for yeast [24]-[26]. This paper is concerned with the isola-tion of cellulolytic yeast from natural habitats in South-east Sulawesi, Indonesia and their phylogenetic affilia-tion based on partial 26S ribosomal RNA and ITS se-quences.


A. Sampling sites and Samples collection

Leaf and leaf litter samples were aseptically collected from two sites that differ in elevation, forest type, and land use type. PapaliaProtected Forest is a tropical low-land monsoon wet forest dominated by evergreen tree, with a high humidity and high rain fall. Papalia, located in South Konawe, GPS S 04 13’ 526”, E 122 44 301”, having altitude <1000 m asl. Whereas MekonggaPro-tected Forest covers lowland and upland rain forest sites included hill forest, montane forest with elevation gra-dient ranges from 100 m to 2500 m asl. Land use type is dominated by cocoa plantations. Mekongga rain forest is located near Tinukari, North Kolaka, GPS S 03 38’ 085”, E 121 04 311”. Leaf and leaf litter sampling were conducted in 2009 and 2010. Six leaves and 6 leaf litter samples were collected in Papalia, and six leaves and 10 leaf litter were collected from Mekongga Protected For-est.

B. Isolation of Yeasts

Yeasts were isolated from leaf, leaf litter, and soil using published methods [27]. One g of soil or leaf litter was added to 25 mL of saline/Tween (0.85% NaCl, 0.01% Tween 80, v/v) buffer in a 7 oz Whirl-Pak filter bag (Cat.# B01385WA, Nasco, Salida, CA, USA) and shaken to suspend the microbes. The bag had two sepa-rated compartments which allowed separation of sus-pension from debris. Two hundreds µL of suspension were plated on Rose Bengal Chlorampenicol Agar (RBCA) plates supplemented how much chlorampheni-col. Leaves were plated using two methods, washing and direct plating. For washing, leaves were added to 10 mL of saline/Tween buffer in a 7 oz. Whirl-Pak filter bag and processed as detailed previously. Aliquots of 200 µL and 50 µL of these samples were plated on RBCA, which prevents growth of bacteria and slows down the spread of molds. For direct plating, the leaf and leaf litter were weighed and cut into small pieces of about 2cm2. The leaf and leaf litter were washed with 30 ml of sterile distilled water and vortexed for 5 min. Washed materials were placed directly onto RBCA plates.

Ballistospore-producing yeasts were isolated from leaves using the ballistospore-fall method. Briefly, asep-tically collected segments of leaves were attached to the underside of a Petri dish lid using Vaseline, and the plate was incubated lid-side up. Ballistospores ejected onto the surface of the agar germinated, and yeasts were cultivated. Incubation of the plates was done for 5 days at room temperature. Strain purification was done at least twice by selecting the different type of yeast colony and plated on potato dextrose agar (PDA, Cat.# CM0139, OXOID,city, country) at room temperature.

C. rDNA sequence determination

Yeast DNA template was prepared from freshly-grown cells on the PDA plate and used for colony PCR. Five uL of lysed yeast cell suspension was used for PCR amplification of the partial 26S rDNAsubunitwith pri-mers NL1 and NL4 [28], using GoTaq master mix (Promega, M7122). PCR products were visualized on 2% agarose and sequenced with both primers using Big Dye terminator v3.1. Cycle Sequencing Ready Reaction Kit (Applied Biosystems) following the manufacturer’s instructions. The partial 26S sequences determined in this study were compared to those in the EMBL/GenBank/DDBJ databases using the nucleotide Basic Local Alignment Search Tool (BLASTn) [40]. The ITS1/5.8S/ITS2 region of selected strains was also amplified with primers ITS1 and ITS4 [29] when spe-cies identifications were ambiguous.

D. Phylogenetic Analysis

Sequence were aligned using CLUSTALX [30]. The distance matrix for the aligned sequence was calculated using the two-parameter methods of [31]. The neighbor-joining (NJ) method [32] was used to construct all phy-logenetic trees.

E. Screening of CMC activities

Cellulolytic yeast is defined as yeast grow on CMC as the sole carbon sources, and produce CMC-ase. To ob-tain the cellulolytic each yeast was grown on CMC agar containing 3 g L-1 yeast extract, 5 g L-1 peptone ,10 g L-1 CMC, K2HPO4 5 g L-1,(NH4)2SO40,5 g L-

1,MgSO4.H2O0,2 L-1, FeCl3.6H2O 0,01 g L-1, MnSO4.H2O 0.001 gL-1. ,20 g L-1 agar (pH 6.2 ± 0.2) [33]and incubated for 5 days at room temperature. Cel-lulolytic yeast was determined by pouring 2 mL congo red 1 M solution into grown colonies, and kept for 10 minutes. Observing clear zone was done by pouring the congo red solution and replaced with NaCl 0.1N. Cellu-lolytic yeast was indicated by formation of clear zone surrounding colonies and the ratio between clear zone divided by colony’s size indicating cellulolytic capacity.

F. Production of CMCase

Cellulolytic yeast was grown in CMC broth medium (3 g L-1 yeast extract, 5 g L-1 peptone ,10 g L-1 CMC, K2HPO4 5 g L-1,(NH4)2SO40,5 g L-1,MgSO4.H2O0,2 L-1, FeCl3.6H2O 0,01 g L-1, MnSO4.H2O 0.001 gL-1. (pH 6.2 ± 0.2)pH 6.2 ± 0.2) at 25°C for 48 h as a pre-culture. The culture medium was inoculated with 2% of the pre-culture and incubated at 25°C with shaking at 120 rpm for 72 h. To assess the ability of culture to hydrolyze cellulose from agriculture waste, 2 % grinded printed paper, straw and bamboo leaf were used separately as carbon sources to medium containing 3 g L-1 Yeast ex-tract, 5 g L-1 peptone, pH 6.2 ± 0.2 and grown at rotary shaker (125 rpm) at 25 °C.

G. Preservation of Yeast Cultures

Yeast isolates were preserved by two methods, in 20 % glycerol solution at -80°C [46], and by lyophiliza-tion[34]. Yeasts were deposited in the Indonesian Cul-ture Collection (InaCC, www.biologi.lipi.go.id) at the Indonesian Institute of Sciences, Research Center for



Biology; the Forest Microbes Collection (FORDACC, http://forda-mof.org/) at the Forestry Research and De-velopment Agency, Indonesian Ministry of Forestry; and the Phaff Yeast Culture Collection, Department of Food Science and Technology, University of California Davis (UCDFST, phaffcollection.ucdavis.edu).


A. Diversity of cellulolytic yeast

Numerous of yeasts were isolated from two sites (Mekongga and Papalia) in South East Sulawesi. The cellulolytic yeast was defined as yeast grow on CMC used as the sole carbon sources and produce CMC-ase (Endoglucanases EG1, EC. 3.2.1.4 ) hydrolyze soluble, substituted celluloses such as Carboxymethyl Cellulose (CMC) by attacking the carbohydrate chain (1-4,β glu-cosidic bond) internally and randomly. This can be visu-alized by culturing yeast both on liquid and solid media contain CMC. Formation of clearing zone on CMC-media poured with 1 % congored and production of CMC-ase were used to screen cellulolytic yeast. We obtained numerous species of yeasts (Fig. 1). Of 142 strains tested 43 strains were cellulolytic yeasts consist of 10 genera and 26 species, of whose Candida was the most diverse genus consisting of 15 species (Figure 2), and more divers was the non-cellulolytic yeast (Fig.1)-.

Novel taxa of cellulolytic yeasts Comparison of the D1/D2 region of LSU rDNA data

showed 14 strains belonging to 11 different species had homology values less than 99%, indicating that they may be novel species. The results of ITS sequence analysis of these isolates confirmed that they are likely novel species. Our molecular analysis revealed that these yeast isolates are phylogenetically diverse and distributed within the phyla Ascomycota (genera Candida and Ya-madazyma).

The novel species candidates were mostly residing within genus Candida (13 species: Candida aff. cylin-draceaPL2W1, Candida affinsectorumPL3W6, Candi-da afffriedrichiiMKL7W3, Candida afflessep-siiPLE3W1, MKL7W4, Candida aff. tenuisPL3DP3, MKL6W4. We isolated 5 strains of cellulolytic yeast close to Yamadazyma Mexicana: Yamadazymaaff. mex-icanaMKL6DP1, MKL6DP2, MKL8W2, MKL6W2, MKL6W1), and our study reveal detection of many un-described yeast from Indonesia. Further study is needed to describe the novel taxa found in this study.

B. Phylogeography of cellulolytic

Sample sources were collected from secondary forest in Mekongga and Papalia, South East Sulawesi. Both places harbor numerous species of Cellulolytic (Fig. 4 and 5).There were 15 species including 6 candidates for novel species (3 strains of Yamadazymaaff. scolyti, Y. aff. Mexicana ,Candida aff. tenuis, C. aff. lessepsii, C. aff. friedrichii, and C. aff. endomychidarum ) Fig. 4).Eleven species including 3 candidates for novel spe-cies of cellulolytic yeast (Candida aff. tenuis, C. afff. insectorium, C. aff. cylindracea) were found in Papalia (Fig. 5). Yamadazyma were only isolated from Me-kongga, andAsterotremella and Sporodiobolus were only cultivated from Papalia.

Fig. 1. Diversity of non-cellulolytic yeast isolated from South East Sulawesi

Number of strains



Fig. 2. Diversity of cellulolytic yeast from South East Sulawesi t

Fig. 3. Candidate for novel species of cellulolytic yeast

Fig. 4. Cellulolytic yeast isolated from Mekongga.

Fig. 5. Cellulolytic yeast isolated from Papalia

C. Litter cellulolytic yeast

Litter are god sources for cellulolytic yeast, as indi-cated by numerous cellulolytic yeasts obtained from liter collected both from Mekongga (Fig. 5) and Papalia ( Fig. 6).

D. Cellulolytic yeast on leaf

Seven species of cellulolytic yeasts were isolated from Papalia leaf, and none was from Mekongga. Spo-rodiobolus were common genera, and Sporodiobolu-sruineneniaeand Candidaintermediawere dominant in leaf (Fig 8).



Fig. 6. Mekongga cellulolytic yeast from litter

Fig.7. Papalia cellulolytic yeast from litter

Fig. 8. Cellulolytic yeasts obtained from Papalia leaf

Little information was previously available about

yeasts on the island of Sulawesi, Indonesia, one of the five largest islands that makes up this richly biodiversity and biogeographically significant region [9],[35],[36]. We found a broad taxonomic diversity of cellulolytic yeast species from this exploratory survey. Because plant surfaces and leaf litter have been sampled exten-sively [1]; [37]; [38], novel taxa were not expected. However, numerous potentially novel species of cellulo-lytic yeast were obtained. Novel strains of known spe-cies were obtained, expanding the known geographic

and habitat range of these known species. A variety of ascomycetous and basidiomycetous

yeasts were cultivated. Basidiomycetous yeasts are more likely to utilize a broader range of carbon sources than ascomycetes, and have been cultivated more frequently from low-nutrient habitats such as leaf surfaces [39].The most frequently cellulolytic yeast isolated from South East Sulawesi were Yamadazyma, Pseudozyma and Candida. Candida was isolated from both locations and ubiquitous on litter. Candida is a polyphyletic genus, with species placed in 14 families within the class Sac-charomycotina. In fact, over 400 of the 1600 known species of yeasts have been placed in the genus Candida [40]. Due to its taxonomic diversity, it is not surprising that Candida is ecologically diverse also, occupying niches including human infections, soil [54], insect frass, fruit[55]. Important applications using Candida species include agent for bioremediation, Candida cate-nulata[56], and biofertilizer, Candida tropicalis HY [54].

Yamadazyma, Pseudozymaand Candida, isolated re-peatedly in this study, is a well known species having wide distribution and having high xylose transport ca-pacity [57].

This study supports other studies that concluded leaf litter and plant leaf surfaces are good sources of a diver-sity of yeast cultures for research [1],[30],[58]. We found leaf litter are good sources for cellulolytic yeast. The strains isolated in this study were deposited in three public culture collections (InaCC, FORDACC and UCDFST).

Isolation of numerous species of yeasts include novel taxa reaffirm that South East Sulawesi are rich in biodi-versity of flora, fauna and microorganism, and potential genetic resources for sustainable development.

ACKNOWLEDGMENT

Yeasts used in this study were isolated and identified as part of a collaborative project between the University of California Davis and the Government of the Republic of Indonesia, funded by Grant Number U01TW008160 from the US National Institutes of Health Fogarty Inter-national Center, the NIH Office of Dietary Supplements, the National Science Foundation and the Department of Energy. This project was supported by the USDA Agri-cultural Food Research Initiative of the National Food and Agriculture, USDA, Grant #35621-04750. The con-tent is solely the responsibility of the authors and does not necessarily represent the official views of the Fogar-ty International Center or the National Institutes of Health, the Office of Dietary Supplements, the National Science Foundation, the Department of Energy, or the Department of Agriculture.

This research was a part of the Ph.D. thesis disserta-tion of AtitKanti, submitted to Bogor Agricultural Uni-versity as fulfillment to obtain a doctorate degree in mi-crobiology. We would like to thank AnisMutirani, and YeniYuliani of LIPI; Sarah A. Faulina, Aryanto and SiraSilaban of FORDA for their assistance.

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CHEMISTRY



Abstract— Types of carbohydrate affect the growth and manganese peroxide production of Phanerochaete chrysos-porium. Agricultural waste is a carbohydrate composed of cellulose, hemicelluloses and lignin. The purpose of this study was to determine the effect of agricultural wastes on growth of P. Chrysosporium and itsmanganese peroxides enzyme activity (MnP). The N-limited media were used as the growth media. Agricultural wastes that tested are ba-gasse, rice husks, corn cob, straw and sawdust. Glucose was used as positive control while medium without carbon source as negative control. The results showed that the formation and an increase in the number pellet cell of P. chrysosporiumwas clearly visible in media containing rice husk and bagasse, while corncob, sawdust and straw was not seem to clear the formation of the cell pellet. Media contaning of straw, corn cob, bagasse and rice husk pro-duces MnP with the highest activity as much as 0,090, 0,033, 0,014, 0,011 U/mL respectively while the sawdust was not able to produce MnP.

Keywords—agriculture, waste, carbon source, Phanerochaete and mangan peroxide

I. INTRODUCTION

enomics and proteomics studies showed that Pha-nerochaete chrysosporium can be a source of en-zymes that play a role in the metabolism of lignin.

Genomic studies indicate that P. chrysosporium has hundreds predictable sequence encodes an enzyme lig-nin peroxidase (LiP), manganese peroxidase (MnP), copper radical oxides, cytochrome, flavin and multicop-per oxides [1]. Proteomics studies show that there are 117 enzymes that are composed of cellulose-degrading enzymes i.e. endoglucanase, beta-glucosidase and exog-luconase,hemicelluloses decomposition enzymes i.e. xylanase, acetilxylanase, esterase, mannosidase and mannanase, pectin decomposer enzymes i.e. poligalaktu-ronase, rhamnogalakturonase and arabinose, as well as lignin-degrading enzyme consists of a group oxidore-ductases[2].

Manganese peroxidase (MnP) is one of the extracellular heme proteins involved in the metabolism

of lignin degradation [3]. This enzyme is known to be applied to the bioremediation process various xenobiotic compounds such as phenols, polycyclic aromatic hydrocarbons, trinitrotoluen and textile dyes [4,5,6]

Growth of P. chrysosporium and production the MnP enzyme is strongly influenced by the carbon source. Previous research claimed that the MnP produced by P. chrysosporium in response to the limited amounts of nitrogen, carbon and sulfur in the growth medium [7], but the other research showed that the highest LiP and MnP activity produced on the amount of excess glucose in the medium [8]. The difference of extracellular proteins profil was produced by P. chrysosporium grown on different carbon sources [2]. Uncertainly, how the influence of agricultural waste to the growth and activity of manganese peroxidase produced by P. chrysosporium is still unknown.


A. Microorganism

Phanerochaete chrysosporium cultures were obtained from the collection of the Laboratory of Microbiolo-gyITB, were sub cultured in PDA agar slant at 30oC. N-limited mediacontaining carbon source were used as growth medium. The compositionof N-limited media per liter consists of: 0.1 g NaCl, 1.2 g of K3PO4 (Na2HPO4), 1.0 g of NH4Cl, 0.2 g KCl, 1.2 g MgSO4.7H2O, 0.1 g of CaCl2 and 10 g of carbon source.The glucosewas used as a positive control and N-limited media without a car-bon source as a negative control. Agricultural wasteswere used as carbon sources are bagasse, straw, rice husks, sawdust and corn cob

B. Preparation of agricultural waste as carbon source

Bagasse and sawdust can be directly washed several times with aquades to remove impurities, dried at 100 °C until constant weight, and then sieved with a sieve flour (± 30 mess). The part that is not filtered mashed in a blender and sieve again.

EFFECT OF AGRICULTURAL WASTE ON THEGROWTHAND MANGANESE PEROXIDASE PRODUCTION OF

Phanerochaete chrysosporium

Evi Susanti1), Aulani’am2), Suhardjono3)and Tri Ardiyati3) 1 Doctoral Program of Biology Department, Brawijaya University, Malang, Indonesia

and Department of Chemistry, The State University of Malang, Indonesia 2 Department of Chemistry, Brawijaya University, Malang, Indonesia

3 Department of Biology, Brawijaya University, Malang, Indonesia *) Corresponding author:[email protected]

G



(a) (b)

Straw, rice husks and corn cob washed, cut into small pieces, and then dried under the sun until completely dry before it wasblended and sieved with flour sieve.

C. Growth and isolation manganese peroxidase ofP. chrysosporium

Growth and isolation of the manganese peroxidase done by adding suspension sporesof P. chrysospori-uminto 250 mlErlenmeyer flask containing 100 ml of growth medium. The number of spores around 1,1.106 spores/ml for each Erlenmeyer. The inoculum were in-cubated at 30 °C and 100 rpm.

The suspension spores obtained by adding four milli-liters of 0.02 % Tween 80 into subculturedof P. chry-sosporium that had been grown for 14 days. The spores were extracted with the help of a sterile needle ose, left it to stand for 5 minutes, and poured it in a sterile con-tainer. The suspension spores were vortexing for 10 minutes and left to stand for 30 minutes before were used.

Observations texture residue, residue dry weight and enzyme activities carried out since the 4th to 9th. The experiments were performed two times repetition. The residue obtained by the growth medium was poured past the filter paper. Filter paper containing the residue was dried at 105 °C for 2 hours .

D. Determine of Manganese peroxidase activity

Determination of MnP activity refers Paszczynski based on the ability of MnP oxidizes Mn (II) to Mn (III). Mn (II) do not absorb at 238 nm, whereas Mn (III) absorbs strongly with ekstinsik molar coefficient of 6500 M-1 cm-1 [9].

III. RESULT AND DISCUSSTION

A. Effect of agricultural waste to the growth of the P. chrysosporium

The residual dry weight data were obtained (Fig. 1) can not directly reflect the growth of P. chrysosporium except in the control treatment. The negative control does not contain a carbon source and a positive control containing glucose. Glucose was soluble of carbon source, while agricultural wastes werw highly insoluble. It makes that the increase ofresidual dry weight each day in the control just derived from the growth of P. chry-sosporium, whereas the residual dry weight on growth medium containing agricultural wastes suspected influ-enced of two events i.e. the degradation of agricultural waste and growth of P. chrysosporium.

Assuming weight of cells at zero day are none, and it was happen in all treatment, then the increase in the number of cells at negative control was very low com-pared to the positive control. Residual dry weight of the positive control in the fourth and fifth day almost ten times higher than the negative control. This reinforces the notion that P. chrysosporium requires a carbon source for growth.

Fig. 1. Residue dry weight of each growth medium containing various carbonsources for the growth time of 4 to 9 days

In general, the decreased of residues dry weight from

growth medium containing agricultural waste was done between fourthuntil sixth days incubating, while the in-creased between sixth until eighth days. The decreased presumably due to the rate of degradation of agricultural wastes by P. chrysosporiumwasgreater than the growth rate. Furthermore, the increase in residue dry weight presumably due to a faster growth rate of P. chrysosporium.

At the fourth day seemed to be two groups of agri-cultural waste that is difficult to degrade group of agri-cultural wastes and easily degraded. Agricultural waste that is difficult to degrade were corn cob, sawdust and straw. The one that are easily degraded were bagasse and corncob . Theoretically agricultural waste easily degradable carbon source will supply much faster than a difficult to degraded agricultural waste. The faster the carbon source available then the cell growth was also faster. This opinion was in line with observations on the shape of the resulting residue (Fig. 2). Formation and an increase in the number of cell pellet P. chrysosporium in the group that is difficult to degrade agricultural waste couldnot be observed. The shape of cell pellet was unclear form because almost all of which seemed to be an agricultural waste that has not been degraded. In con-trast, the agricultural waste that easily degraded result cell pelet that had observed as at the positive control. Based on these results can be concluded that bagasse and rice husk carbon source can be a potential alterna-tive for the growth of P. chrysosporium than corn cob, straw and rice husk.



(c) (d)

(e) (f) (g)

Fig. 2. Residuesof P. chrysosporium in growth medium with different carbon sources.(a) negative control,(b) positive control, (c) rice husk, (d) bagasse, (e)corncob, (f). straw and (g) sawdust.

B. Effect of agricultural waste in the activity of man-ganese peroxidase (MnP) produced by P. chrysospo-rium

Effect of agricultural waste to the activity which produced manganese peroxide showed no clear pattern (Fig. 3). Agricultural wastes are easily degraded produced low MnP activity, while the agricultural waste that is difficult to degrade produce higher MnP activity whereas sawdust did not produce MnP.The highest MnP was produced at different day. Growth medium contain-ing straw on 5thday, corncobs on 8th day, baggase on 9th day and rice husk on 6th daywith MnP activity val-ues respectively 0.090, 0.033, 0.014 and 0, 11 U / mL, Based on this, the straw and corn stalks can be used as an alternative carbon source to produce MnP from P. chrysosporium.

Fig. 3. Activity of manganese peroxidase (MnP) of P. chrysosporium in various carbon sources during the growth period of 4 to 9 days

IV. CONCLUTION

Bagasse and rice husk influence the formation of pel-let cells of P. chrysosporium whereas corncobs, sawdust and straw are not known yet. Straw and corn cob pro-duce manganese peroxidase by high activity that is equal to 0.090 and 0.033 U/ mL, bagasse and rice husk pro-duce low activity manganese peroxidase that is equal to 0.014 and 0.11 U/mL whereas sawdust completely una-ble to produce manganese peroxidase.

REFERENCES

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[7] Li, D., M. Alic., J.A. Brown & M.H. Gold. 1995. Regulation of manganese peroxidase gene transcription by hydrogen peroxide, chemical stress and molecular oxygen, Applied and Environ-metal Microbiology,Vol 61, No. 1, pp.341-345

[8] Wang, P., X. Hu, S. Cook, M.Begonia, Lee, S.Ken & H.Hwang. 2008. Effect of culture condition on the production of lignino-lytic enzymes by white rot fungi Phanerochaete chrysosporium (ATCC 20696) and separation of its lignin peroxidase. World Journal Microbiology Biotechnology. No. 24, pp. 2205-2212

[9] Zahmatkesh, M., Tabandeh F., & Ebrahimi, S. 2010. Biodegra-dation of Reactive Orange 16 by Phnerochaete chrysosporium Fungus: Application in a Fluidized Bed Reactor. Iran Journal Environmental Health Science Engineering, Vol .7, No. 5, pp. 385-390



THE STUDY OF ADSORPTION ON CR(VI) IN NATURAL CLAY SURFACE MODIFIED

WITH SURFACTANT CTAB (CETYLTRIMETHYLAMMONIUM

BROMIDE)

Maksum1*), Susi Nurul Khalifah, 1), and Anton Prasetyo1) 1)Departement of Chemistry, Science and Technology Faculty,

State Islamic University of Maulana Malik Ibrahim Malang *)Correspondence author: [email protected]

Abstract—Clay is a mineral particles composed of silica-alumina base frame, has a layered structure and a hol-lowed space causing surface becomes very widespread and effec-tive as an adsorbent. Adsorbent of clay is very low to ad-sorb anion, such as Cr(VI) formed in HCrO4- at pH 2. This research has been conducted in the clay of activation pro-cess chemically with the variation of H2SO4 0.5, 1.0, 1.5 and 2.0 M and physically with the variation in the tem-perature of 200, 300, 400¬ °C as well as modifying the sur-face with the variation of CTAB surfactant 25, 50, 75, and 100 mM to enhance the adsorption of Cr(VI). The results of study showed that the treatment on the activation and modifica-tion of natural clay can increase the adsorption capacity of Cr(VI) is greater. Adsorption capacity (Qe) before activat-ing the natural clay is 0.0971 mg/g, while the Na-clay in-creased adsorption of Cr(VI) at 7.85 % as indi-cated by Qe = 0.1756 mg/g of natural clay. The use of 0.5 M H2SO4 acti-vation of adsorption increased by 21.44 % to the value of Qe = 0.3115 mg/g. The treatment of physi-cally activation after activating the best chemical showed at a temperature of 200 °C with an increase of 29.82 % ad-sorption of value Qe = 0.3953 mg/g. While modification to the clay treatment results in the best physical activation get CTAB 25 mM concentration with increased adsorption of 94.54 % with a value of Qe = 1.0425 mg/g.

Keywords—natural clay, activation, modification and ad-

sorption of Cr(VI)

I. INTRODUCTION Indonesia has abundant clay as one of its natural ma-

terials, one of them is placed in Gandusari Area Treng-galek Regency that have been used by people around as tile-making material. Besides being used as the tile-making material, clay also can be used as an economical adsorbent which has large adsorption capacity. This happens because clay has high surface area, chemically stable, varying surface structure, high capacity of ion exchange and the presence of Bronsted and Lewis acids [3].

The application of clay as an adsorbent material is widely used as an alternative material to overcome waste problems, especially the waste of heavy metal like chromium. Chromium usually comes from industrial metal plating, metal corrosion inhibitors, tanneries, paint, textile and wood preservatives [9]. Chromium which stable in water can be found as Cr (III) in the

form of cationic compound (Cr3+) and Cr (VI) in the form of anionic compounds such as HCrO4-,CrO42- and Cr2O72- [4]. Cr(VI) Metal is more toxic, has a higher solubility, more corrosive and more carcinogenic than Cr(III) [9].

The ability of clay to adsorb anion like Cr(VI) is very low because clay has negatively charged surface. Sever-al methods have known to increase the adsorption ca-pacity of clay by chemical and physical activation and modifying the clay surface.

In this research, chemical activation has done by us-ing a variation of H2SO4 and physical activation by heating at high temperature and modification on the sur-face also has carried out with variation of CTAB to en-hance the adsorption ability of clay to Cr(VI).

The addition of CTAB concentration has done above CMC (critical micelle concentration) in order to change the clay surface in which negatively charged becomes positively charged, thereby the ability of clay to adsorb Cr(VI) in anionic form will be greater. Furthermore, a comparison of the adsorption ability of each natural clay is done, Na-clay, Na-clay which is chemically and phys-ically activated and the surface of Na-clay modified by CTAB.

II. RESEARCH METHODS This study consists of several stages, namely benefi-

ciation, sample preparation, Na-clay preparation, Na-clay activation, modification of clay surface activation result with CTAB surfactant and determination of Cr(VI) by using UV-Vis Spectroscopy method and cha-racterization with FTIR. A. Beneficiation

Natural clay dried at room temperature, then crushed and dissolved by aquades. The mixture is stirred until the clay is soluble and then left a few days until the clay forms into three parts. The top part is an organic com-pound and salt which dissolved in water. The middle part is clay, while the bottom is gravel, sand and other impurities which have a high specific gravity. The result of clay which has obtained is repeated ones more to ob-tain perfect separation. After that, the result of beneficia-tion dried under the sun [1].



B. Sample Preparation Clay fraction which has been dried is pulverized to a

powder, and then sieved by a sieve of 200 meshes. Fur-thermore, natural clay is characterized with FTIR. C. Preparation of Na-Clay

A total of 100 g of clay incorporated into 1000 mL of NaCl 1 M and stirred for 24 hours at a temperature of 70-80 0C. Residues incorporated into 1000 mL of NaCL 6 M while stirring for 24 hours. The residue washed with aquades to remove residue of chloride ions. The filtrate is tested with a solution of AgNO3 1 M until unformed white precipitate of AgCl. Then, Clay that has been free of chloride ions is dried in an oven at 100 0C for 24 hours (Wijaya, et al., 2005 in [9]). D. Na-Clay Activation

Na-clay is chemically activated by means each of them is weighed for 5 g and put into 4 Erlenmeyer 250 mL. Then added 100 mL of H2SO4 with various con-cen-tration of 0.5, 1.0, 1.5, and 2.0 M [12], while stir-ring for 5 hours [2]. The mixture is filtered and washed with hot water (60-70 0C) until free of sulfate ions which can be characterized by the formation of white precipitate of BaSO4 (Negative test to BaCl2) [12]. Af-ter that, do the adsorption to Cr(VI).

The result of chemically clay activation continued to physical activation by drying sample in a furnace at temperature of 200; 300; and 400 0C for 6 hours [2], and then stored in a desiccators. After that, do the ad-sorption to Cr(VI). E. Modification of clay surface activation result with CTAB surfactant

A total of 2 g of sample result of the best physical ac-tivation added 100 mL of CTAB with concentration of 25, 50, 75 and 100 mM and stirred in a shaker at room temperature for 4 hours at speed of 200 rpm. The sus-pension which obtained is filtered and washed with 100 mL of aquades in 2 times. The result of clay which has modified is dried at room temperature [10]. After that, do the adsorption to Cr(VI). F. Determination of Cr(VI) with Spectrophotometry Me-thod

Cr(VI) Solution of 20 ppm 50 mL conditioned at pH 2 by using H2SO4 0.1 M/NaOH 0.1 M, and then added 3 mL of buffer solution pH 2. Then as much as 0.5 g of natural clay put into Erlenmeyer 250 mL, then added Cr(VI) solution of 25 mL. The mixture is shaked for 30 minutes at speed of 200 rpm at room temperature of 25 0C.

The mixture which is obtained filtered with a filter paper. The filtrate which is obtained pipetted in 5 mL and placed in a glass baker which is conditioned at pH 2 by using H2SO4 0.1 M/NaOH 0.1 M. Then added 3 mL of buffer solution pH 2 and 2 mL of diphenylcarbazide (0.25 %). After that, put in a volumetric flask 25 mL and diluted with aquades up to mark boundaries. The solu-tion is allowed to stand 5-10 minutes. Then absorbance measured by UV-Vis Spectroscopy at a wavelength of 540 nm.

Procedure above is repeated for the adsorption of Cr(VI) on Na-clay, Na-clay which chemically activated with concentration of 0.5, 1.0, 1.5, and 2.0 M, Na-clay which physically activated with concentration of 200; 300; and 400 0C, and the modification of clay surface activation proceed with CTAB concentration of 25; 50; 75 and 100 mM.

Metal ion concentration calculated by using a stan-dard curve. The difference in initial and final concentra-tion of the Cr(VI) solution is the amount of Cr(VI) which can be adsorbed by clay. Furthermore, the adsorp-tion capacity can be calculated by equation [2]:

In which Qe is the adsorption capacity per weight of

clay (mg/g), V is the solution volume (L), C0 is the ini-tial concentration of solution (ppm), Ce is the final con-cen-tration of solution (ppm), m is the clay mass (g). G. Characterization of FTIR (Fourier Transform In-frared)

FTIR is used to identify the presence of key function-al groups in the compound structure like clay. In this study, natural clay, Na-clay, Na-clay which activated by the best chemistry, Na-clay which activated by the best physics, Na-clay which modified CTAB and CTAB are characterized by using infrared light. Infrared spectra are recorded using a Fourier Transform Infrared Spec-trophotometer (FTIR) with KBr pellet method in the region of 4000-400 cm-1 [5].

III. RESULT AND DISCUSSION Natural clay which used in this study come from

Gandusari area Trenggalek regency which has analyzed XRD by [7] and the result shows that natural clay is composed of many components such as chloritoid, feld-spar, kyanite, potassium magnesium, iron, alumunium, titanium, phylloalumosilicate, cronstadite, and fayalite. The XRD data also shows that the clay which used has low crystallinity, it is characterized by diffractogram peaks which are not sharp as in figure 1.

Figure 1. The result of XRD clay in Gandusari area [7]. A. Natural Clay Preparation with Beneficiation Method

Natural clay beneficiation process uses aquades as the solvent for few days. Natural clay which diluted with aquades will separate into 3 parts. Organic compounds

(Co – Ce)

V

m

Qe =



and dissolving salt are in the upper part, middle part of clay, sand and other impurities with a higher specific gravity than the clay which is at the bottom.

Clay beneficiation result sieved by using a sieve of 200 meshes to get smaller particles size and equal so it will be obtained a larger clay surface area. The result of the sieve which passes 200 meshes is used as a sample test and is characterized with FTIR.

Adsorption method of Cr(VI) carried out by Batch method in which Cr(VI) solution is contacted with natu-ral clay, Na-clay, Na-clay which chemically activated, Na-clay which physically activated, and Na-clay which modified CTAB surfactant. Determination Cr(VI) uses UV-Vis Spectroscopy method which reacted Cr(VI) so-lution with 1,5-difenilkarbazida until produces complex compound that has purple color. B. Adsorption of Cr(VI) in Natural Clays The calculation result of adsorption capacity shows that natural clay has Qe of 0.0971 mg/g. The adsorption re-sult in natural clay is relatively small because there are a lot of impurities so that its adsorption capability is low. C. Na-clay Preparation and adsorption of Cr(VI) in Na-clay

Natural clay which obtained still has a lot of other cat-ions such as K+, Mg2+, and Ca2+ and also has unequal cation size so that it should be done a uniformity in the cation size which existing in interlayer clay area. This uniformity is intended to produce the same layer dis-tance and improve its adsorption ability.

The first stage uses a solution of NaCl 1 M which done at 70 0C for 24 hours, it has an aim to initiate the reaction of cations exchange besides Na+ so that it can open and activate the space inter layers of clay. The cations exchange expects that mostly cations other than Na+ can be replaced by Na+. Heating process which done has an aim to increase the likelihood of collisions between Na+ ions with negative charge clay [15].

The second stage uses a solution of NaCl 6 M which done at room temperature for 24 hours, it has an aim to replace cations which have greater valence than Na+ can be replaced by Na+.

The adsorption process of Na-clay with Cr(VI) ob-tains adsorption capacity of (Qe) 0,1756 mg/g with an increase of adsorption capacity of 7,85 % from natural clay. The result of Na-clay adsorption is higher than the natural clay. This happens because Na-clay surface which has negatively charge to cation Na+ can improve the adsorption of Cr(VI). D. Adsorption of Cr(VI) on Na-clay which chemically activated

Chemically Clay activation has an aim to increase the clay activation as an adsorbent. According to [5], they stated that the using of H2SO4 can eliminate other im-pu-rities from lattice structure so that the clay physically becomes active.

Chemical activation process of Na-clay done by soak-ing the variation of H2SO4 concentration of 0.5; 1.0; 1.5 and 2.0 M for 5 hours. The aim of treatment with various concentrations to determine the effect of the active property of resulting clay to the H2SO4 concen-tration in adsorbing Cr(VI).

The result of Cr(VI) adsorption on Na-clay which chemically activated obtains a relation between Na-clay which activated by various concentration of H2SO4 and the amount of Cr(VI) which adsorbed per unit weight of adsorbent (Qe) as Table 1 and figure 2.

Based on table 1 and figure 2, it can be observed that with the addition of H2CO4 on the surface of Na-clay can provide the result of adsorption of Cr(VI) which is greater than the natural clay and Na-clay. This happens because the activation of acid can increase the acidity quite highly and can eliminate other impurities from the lattice structure so that clay surface area becomes high in which it can adsorb Cr(VI) greater [2].

Variations of H2CO4 concentration which used in chemical activation affect the adsorption capacity of Cr(VI). H2CO4 concentration which highly can adsorb Cr(VI) anions is 0.5 M that indicated by Qe value of 0.3115 mg/g with an increase of adsorption capacity 21.44 %. This provides that acid activation process can neutralize negative charge on the clay surface so that on the side of active clay can be positive charge.

TABLE 1 THE RELATION Of ADSORPTION CAPACITY (Qe) Cr(VI) ON NATURAL CLAY, Na-CLAY AND Na-CLAY WHICH CHEMICALLY

ACTIVATED WITH VARIOUS CONCENTRATION OF H2SO4

Sample Qe (U1) (mg/g)

Qe (U2) (mg/g)

Qe (U3) (mg/g)

Natural Clay 0.0963 0.0851 0.1098

Na-Clay 0.1847 0.1664 0.1756

Na-Clay+H2SO4 0.5 M 0.3159 0.3099 0.3087

Na-Clay +H2SO4 1.0 M 0.2832 0.2748 0.2735

Na- Clay +H2SO4 1.5 M 0.2403 0.2429 0.2429

Na- Clay +H2SO4 2.0 M 0.2158 0.2089 0.2130

The higher of H2SO4 concentration of 1.0, 1.5, and

2.0 M decreased adsorption of Cr(VI) than with H2SO4 0.5 M. This is due to the possibility of damage and unst-able clay structure which caused by the highest acid concen-tration.

E. Adsorption of Cr(VI) on Na-clay which physically activated Clay structure with three-dimension cause clay has cavi-ties which filled by water molecules. This research has warmed clay which has chemically activated by using an electric furnace at temperature of 200; 300 and 400 0C



for 6 hours. The heating process on clay has an aim to evaporate the water molecules that cover clay surface so that its specific surface area can increase [7].

Figure 2 The relation of adsorption capacity (Qe) Cr(VI) on natural clay, Na-clay and Na-clay which chemically activated with various concentration of H2SO4

The result of clay physical activation temperature var-

iation followed by the adsorption process on Cr(VI) which obtains a relation between clay physical activa-

tion temperature variation and the amount of Cr(VI) which adsorbed by per unit weight of adsorbent (Qe) as shown in Table 2 and figure 3. Determination of the adsorption best temperature on Cr(VI) has an aim to know the temperature of physical activation in which Cr(VI) solution can be maximally adsorbed by adsor-bent.

Based on the Table 2 and Figure 3 it can be seen that when it heated, Na-clay which activated by the best chemistry can provide greater Cr(VI) adsorption result. This is due to the physical activation can vaporize water molecules that cover clay surface so that the specific surface area increases.

In this research, the temperature of physical activation which is capable to adsorb Cr(VI) greater is temperature of 200 0C that indicated by Qe value = 0.3953 mg/g with an increase of adsorption capacity of 29.82 %. It can be stated that at this temperature occurs a water re-lease which placed in interlayer clay and H+ ions cannot be separated [14].

TABLE 2

THE RELATION OF ADSORPTION CAPACITY (Qe) Cr(VI) ON NATURAL CLAY, Na-CLAY, Na-CLAY WHICH ACTIVATED BY THE BEST CHEMISTRY, AND Na-CLAY ACTIVATED BY PHYSICS WITH VARIOUS TEMPERATURE

Sample Qe (U1) (mg/g)

Qe (U2) (mg/g)

Qe (U3) (mg/g)

Natural Clay 0.0963 0.0851 0.1098

Na-Clay 0.1847 0.1664 0.1756

Na-Clay+H2SO4 0,5 M 0.3159 0.3099 0.3087

Na-Clay+H2SO4 0,5 M+ 200 C 0.3965 0.3958 0.3937

Na-Clay+H2SO4 0,5 M+ 300 C 0.3053 0.2970 0.3008

Na-Clay+H2SO4 0,5 M+ 400 C 02757 0.2711 0.2731

Figure 3 The relation of adsorption capacity (Qe) Cr(VI) on natural clay, Na-clay, Na-clay which activated by the best chemistry , and Na-clay activated by physics with various temperature

At a temperature of 300-400 0C the adsorption capac-

ity of clay to adsorb Cr(VI) decreased as indicated by the value of Qe 0.3010 mg/g and 0.2733 mg/g. This is might be due to the higher heating causes the higher density of crystal structure and more regularly, so that the clay is less reactive as adsorbent. According to [15], heating at 300 0C to Na-bentonite was able to cause damage to the 001 field. Clay will experience structural and inter-layer damage at temperature above 250 0C [12]. The surface area of the alunite increased with in-

creasing calcinations temperature (100-200 0C) so that it can absorb dyes and decreased surface area on calci-nations temperature (300-400 0C) [9]. F. Adsorption of Cr(VI) on Na-Clay Modified CTAB

Increasing adsorption of Cr(VI) can be done by modi-fy-ing clay surface with CTAB surfactant as has re-ported by [2] that the result of modified clay with HDTMA surfactant is the most effective process to in-crease the adsorption ca-pacity of Cr(VI). Adsorption of Cr(VI) by natural clay modified CTAB is based on its CMC concentration (critical micelle concentration) of 1 mM. This study has used the CTAB concentration ex-ceed its CMC, they are 25, 50, 75 and 100 mM. It is intended to form a bilayer or multilayer coating in which positive charge groups are placed on the clay surface so that by the form of bilayer or multilayer coating, it makes the surface property of the clay will change to be positive charge, so the ability of clay to adsorb Cr(VI) in the form of anions will be more maximal [11].

The result of clay modification which activated by the best physics with CTAB followed by adsorption of Cr(VI) process. The result of adsorption can be known that there is a relation between Na-clay modified with various concen-tration of CTAB surfactant and the amount of Cr(VI) which adsorbed per unit weight of adsorbent (Qe) as in the Table 3 and Figure 4.



Based on the Table 3 and Figure 4, it can be seen that with the CTAB modified on the clay surface which acti-vat-ed by the best chemistry and physics can provide the best adsorption result of Cr(VI) which is greater than natural clay without modified with CTAB. This is due to the pres-ence of CTAB surfactant which places on the clay surface adsorb Cr(VI) greater.

The best concentration of CTAB modified on the clay surface is highly able to adsorb Cr(VI), that is concen-tration of CTAB 25 mM of Qe 1.0425 mg/g with an increase of adsorption capacity 94.54%. It can be stated that at this concentration the positive side of CTAB is on the clay sur-face, so that the clay surface is filled by positive sides which neutralized by B- ion. In which this Br- ion potentially as anion exchange with HCrO4- anion so that the clay modified by CTAB is able to ad-sorb Cr(VI) maximally. At the concentration of CTAB 50; 75 and 100 mM decreased the adsorption of Cr(VI) than with 25 mM CTAB which does not have any signif-icant difference. It is assumed that the external surface of the clay is not all filled by the posi-tive side of CTAB.

TABLE 3

THE RELATION OF ADSORPTION CAPACITY (Qe) Cr(VI) ON NATURAL CLAY, Na-CLAY, Na-CLAY ACTIVATED BY THE

BEST CHEMISTRY, Na-CLAY ACTIVATED BY PHYSICS AND Na-CLAY MODIFIED WITH CTAB VARIATION

Sampel Qe (U1) (mg/g)

Qe (U2) (mg/g)

Qe (U3) (mg/g)

Natural Clay 0.0963 0.0851 0.1098 Na-Clay 0.1847 0.1664 0.1756 Na-Clay+H2SO4

0,5 M 0.3159 0.3099 0.3087

Na-Clay+H2SO4

0,5 M+200C 0.3965 0.3958 0.3937

Na-Clay+H2-

SO4 0,5 M+200C+25 mM

1.0400 1.0454 1.0421

Na-Clay+H2SO4

0,5 M+200C+50 mM

1.0333 1.0318 1.0336

Na-Clay+H2SO4

0,5 M+200C+75 mM

1.0278 1.0279 1.0281

Na-Clay+H2SO4

0,5 M+200C+100 mM

1.0271 1.0256 1.0259

Figure 4 The relation of adsorption capacity (Qe) Cr(VI) on Natural clay, Na-clay, Na-clay activated by the best chemistry, Na-clay acti-vated by physics and Na-clay modified with CTAB variation

Figure 5 Illustration of adsorption Cr(VI) in the form of HCrO4- on the clay modified CTAB over its CMC.

The mechanism of adsorption Cr(VI) on natural clay-modified by CTAB over its CMC as follows [14]:

Clay-(CTA)2+-Br- + HCrO4

- ↔ Clay-(CTAB)2HCrO4 + Br-

The process of adsorption Cr(VI) on the clay which

modi-fied CTAB surfactant exceeds its CMC involves anion ex-change between bromide ion of CTAB with ionic species Cr(VI), so that there are more Cr(VI) which adsorbed [14].

G. FTIR characterization FTIR characterization has an aim to know the main functional groups of clay. Spectra clay for infrared area divided into two frequency groups, namely area between 4000 and 3000 cm-1 is the stretching vibration of the adsorbed water or -OH octahedral groups and area in 1400 to 800 cm-1 which caused by a vibration of Al-OH or Si-O.

Figure 6. IR spectra of natural clays, clay-Na, Na-activated clays best chemistry, Na- The best physics of activated clay and Na-clay modified CTAB best

Based on Figure 6, it can be stated that during the treat-ment of chemical activation, physics and its surface modification with CTAB surfactant do not change/damage the clay structure. On the clay surface modification treat-ments are new spectra at wavelengths of 2851 and 2920 cm-1 indicating the presence of CH stretching [11].



IV. CONCLUSION The treatment of activation and modification on natural clay can highly improve the adsorption capacity of Cr(VI) as indicated by the value of Qe (adsorption ca-pacity). Ad-sorption capacity of natural clay has Qe = 0.0971 mg/g and Na-clay with Qe = 0.1756 mg/g with an increase 7.85 % of natural clay. Clay which activated by H2SO4 0.5 M has an adsorption increase of 21.44 % with Qe = 0.3115 mg/g. Natural clay physically acti-vated after activated by the best chemistry has obtained at temperature of 200 0C with Qe = 0.3953 mg/g which indicates an adsorption increase of 29.82 %. While the best concentration of CTAB surfactant on modified nat-ural clay has obtained at CTAB concentra-tion 25 mM with the largest value of Qe 1.0425 mg/g which indi-cated by an adsorption increase of 94.54 %.

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Abstract—Liquid phase selective hydrogenation of fur-fural to furfuryl alcohol on Ni/ γγγγ-Al 2O3catalyst was studied. The influence of different amount of impregnated nickel salt in aluminum oxide and reaction time on catalytic activ-ity and selectivity were examined. Among 5%, 10%, and 15% Ni/γγγγ-Al 2O3, the last catalysts provide the best result.It has been found that15% Ni/γγγγ-Al 2O3gave the conversion of furfural up to 15% and selectivity for furfuryl alc ohol as high as 100%. This result is obtained under 150oCafter 150 minutes of reaction time.

Keywords—hydrogenation, furfural, furfuryl alcohol, nickel

I. INTRODUCTION

The chemistry of furfural is well developed and reported as promising chemicals since its derivatives gives varie-ty applications in the chemical industry. Furfural is mainly used as intermediate chemical in the manufactur-ing of solvent, such astetrahydrofuran (THF), methyl tetrahydrofuran (MeTHF), furfuryl alcohol, plastics, and agrochemicals [1]. One important chemical coming from furfural is furfuryl alcohol. This compound is mainly used in the manufacture of resins, as a starting material for the synthesis of tetrahydrofurfuryl alcohol (THFalc), important intermediate for the manufacture of fragrance, vitamin C, and lysine [2].

Furfuryl alcohol (FFalc) is prepared industrially by catalytic hydrogenation of furfural.The two ways to pro-duceFFalc is liquid and vapor hydrogenation. In liquid phase, high-pressure and temperature is required. In a vapor phase, it is depend on the type catalyst used in the reaction [3]. For over five decades, copper cromite has been the most powerful catalyst for furfural hydrogena-tion in liquid and vapor phase with selectivity up to 98% [4]. The severe drawback from Cu-Cr based catalyst is their toxicity, which causes experimental problem. Many attempts have been made to develop new catalysts that are environmentally friendly.

Furfural is suitable compound to test the selectivity of catalysts because the presence of both C=O and unsatu-rated C=C bonds in the structure. It is well known that group 9 and 10 metal, such as Rh, Ir, Ni, Pd, and Pt, generally an active catalysts to hydrogenate C=C rather than C=O in α,β-unsaturated system [5]. Then, the ne-cessary modification is required to improve selective hydrogenation of C=O group. Platinum has been re-

ported as a furfural hydrogenation catalyst [6], but the utilized of such precious metal in this system is econom-ically high cost. Thus, different catalysts, such as copper supported on activated carbon, Raney Ni, and amorph-ous Ni alloys dispersed on different support have been used in the hydrogenation of furfural, not only in liquid phase but also in vapor phasereaction [7].

In the present work, the hydrogenation of furfural on a series of 5%, 10%, and 15% Ni supported on γ-Al 2O3 have been studied. The effect reaction time on the activi-ty and conversion of furfural (FFald) to furfuryl alcohol (FFalc) is investigated.

II. EXPERIMENTAL

A. Chemicals

Furfural, with a purity of >98% was obtained from-Merck Germany used without distillation.High-purity hydrogen gas (>99.99%) from local vendor was used without further purification. Nickel nitrate hexahydrate (Ni(NO3)2.6H2O)and aluminum oxide (γ-Al2O3) were supplied by Merck Germany.

B. Preparation of catalysts

The catalysts were prepared according to the method given by Miloneet al. [8]. Aluminum oxide (γ-Al2O3) having surface area 150-400 m2/g, pore size 0.5-1 cm3/g with radius 3-12 nm was slowly added to methanol solu-tion contain5%, 10%, and 15% ofNi salt and stirred for 24 h at ambient temperature. The solvent were slowly removed by rotary evaporator at 35 oC for 1 hour. All the catalysts were dried at 120 oC for 2 hours and cal-cined at 450 oC for 3 hours. Before hydrogenation reac-tion, all the catalysts were reduced at 450 oC for 5 hours.

C. Catalysts characterization

The characteristic of Ni/γ-Al 2O3 catalyst were investi-gated by X-ray diffraction (XRD, Philips X’pert with Ni-filtered Cu Kα radiation) operated at 40 kV and 30 mA. The surface morphology and the particle size was determinedby Scanning Electronic Microscopy (SEM) TM3000. The BET surface area was evaluated from nitrogen adsorption isotherms at 77 K performed in Quantochrome NovaWin2.

SitiMariyahUlfa*and ElvinaDhiaulIftitah Faculty of Science, Brawijaya University, Malang, Indonesia

*Corresponding author:[email protected]

Selective Hydrogenation of FurfuralUsing Ni/γ-Al 2O3 Catalyst



D. Liquid phase hydrogenation of furfural

Catalytic hydrogenation of furfural was performed in modified two-neckedglass reactor fitted with sampling and hydrogen valve. Before used, the Ni/γ-Al 2O3catalyst (0.5 g) wasreduced in glass reactor with hydrogen flowed for 30 minute at 100 oC(95 mmHg). The activa-tion of catalysts was repeated for three times to com-pletely replace air in the reactor. Then 5 ml of furfural were added to the reactor and stirred. After the neces-sary connection between reactor and the hydrogen gas cylinder was duly made, H2 passed into the reactor and reaction temperature gradually increased up to 150 oC. The reaction time was counted after the setting tempera-ture obtained. The progress of the hydrogenation reac-tion maintain by sampling a sufficient number of micro-samplein 30, 60, 90, 120, 150, and 180 minutes. After the reaction complete, all the products analyzed by means of a Shimadzu QCMS-QP2010S gas chromato-graph spectrometer massa with RastekstabilwakR-DA column and FID detector.


A. Catalysts characterization

Figure 1 compile the profile of surface morphology (SEM) of 5% Ni/γ-Al 2O3(5Ni/Al), 10% Ni/γ-Al 2O3 (10Ni/Al), and 15% Ni/γ-Al 2O3 (15Ni/Al). Due to the different concentration of nickel deposited on Al2O3, the distinct differences of the morphology and particle size were observed. The particle size of catalyst (A) 5Ni/Al is in the range 50-100 µm. The sample (B) 10Ni/Al and-sample (C) 15Ni/Al is in the similar particle size range 30-60 µm. The smaller particle size implies a higher surface energy of the particle [9].

Figure 1. SEM of (A) 5Ni/Al; (B) 10Ni/Al; (C) 15Ni/Al calcined

at 450oC

The XRD patterns of the 5Ni/Al and 10Ni/Al cata-lysts compared to γ-Al 2O3 after reduced at 450oC under hydrogen were depicted in Figure 2. The sharp diffrac-tion at 2θ = 44 and 66o correspond to the γ-Al2O3(440).

Figure 2. XRD spectra of the γ-Al2O3, 5Ni/Al, and 10Ni/Al re-

duced at 450oC

The new peaks appear at the 2θ = 44.5, 52, and 76o in agreement with Ni(111), Ni(200), and Ni(222) species, respectively [5]. The specific diffraction of NiO at 2θ = 43 and 63 didn’t observed which indicate that Ni2+ transformed to Ni0 after H2 treatment at 450oC. Another peak detected were at 2θ = 37 and 66o correspond with NiAl 2O4 from the reaction of Ni2+ with γ-Al 2O3.

TABLE 1.

BET SURFACE AREA AND POROSITY OF THE CATALYSTS

Catalyst BET surface area (m2/g)

Pore volume (cm2/g)

Average pore diameter (Å)

5Ni/Al 10Ni/Al 15Ni/Al

123.919 108.270 101.905

0.252

0.221

0.206

40.8268

41.0688 40.7094

The BET surface area and the pore volume of the syn-

thesized Ni/γ-Al 2O3 catalysts are shown in Table 1. The BET surface and the pore volume of 5Ni/Al is the larg-est compared with 10Ni/Al, and 15Ni/Al. No significant change in the pore size distribution from 5Ni/Al, 10Ni/Al, and 15Ni/Al was observed. The increasing of the salt loading decreases the surface area as well as the pore volume of γ-Al2O3. The decreasing of surface area



was attributed to nickel which fills up the pores of the support [8].

B. Influence of reaction time on activity and selec-tivity

The hydrogenation of furfural (FFald) was studied at 150 oC (95 mmHg) in the modified glass reactor. Scheme 1 showed the hydrogenation of furfural on 5~15% Ni/γ-Al2O3. The result showed a continuous de-crease in the furfural (FFald) concentration and the for-mation of furfuryl alcohol (FFalc) as a majorreaction product. Unreacted 5-methylfurfural (MF) was also ob-served in the final reaction product. It is suggested that the catalyst is highly selective to reduce C=O bond from furfural but unreactive for any starting materi-al.According to the detection of MF, lies in fact that all raw materials used for the manufacture of furfural con-tain some methyl pentosanwhich might hydrolyzed to MF [1]. The direct catalytic reaction of alkylmethylfur-furalwithin the starting materialalso reported to give MF [10].

Scheme 1. Hydrogenation product of furfural on 5~15% Ni/γ-Al2O3

The conversion of FFald and the selectivity of FFal-con 5Ni/Al, 10Ni/Al, and 15Ni/Al are shown in Figure 3. Product conversion by the activity of15Ni/Al gave the highest result compared to 5Ni/Aland 10Ni/Al. This catalyst converted FFald to FFalc up to 15% within 150 minutes of reaction time. The 5Ni/Al catalyst is consi-derably as good catalyst by 2.1% conversion of starting material after 120 minutes. Theactivity of 10Ni/Al showed 2.0% conversion ofFFald after 180 minutes. It is noteworthy that the higher the salt loading gives the in-creasing activity of the catalysts. However, conversion of FFald to FFalc should be optimizedto increase the product formation. Mäki-Arvela reported that tempera-ture of the reductionand time were influence the activity of the catalysts. Catalytic activity and selectivity of Au/TiO2 catalyst in the hydrogenation of crotonaldehyde exhibit a maximum after increasing catalysts-reduction temperature [11].

The selectivity formation of FFalcbyhydrogenation reaction of FFaldon Ni/γ-Al 2O3 is depicted in Figure 3. It observed that within 60 minute of reaction time, the selectivity of 5Ni/Al increased sharply from 27.9% to 85.7% then prolonged the reaction to 120 minutes com-pletely reduce FFald to FFalc up to 100% conversion. Similarly, 10Ni/Al and 15Ni/Al also exhibit a similar selectivity, raised up from 63.4% and 77.5% to 100%, after 60 minute of reaction time. However, prolonged the reaction time until 180 minutes reduce the selectivity of 5Ni/Al catalyst to 46.9%, but 10Ni/Al and 15Ni/Al remain unchanged until 180 minutes of reaction time.

Figure 3. Conversion of furfural and selectivity for furfuryl alco-

hol as a function of reaction time. Reaction conditions: T = 150 oC

(90 mmHg); furfural = 3 ml; catalyst = 0.5 g; and for

5Ni/Al; and for 10Ni/Al; and for

15Ni/Al.

It is likely that within the range of the reaction time, all the catalysts showed the higher selectivity to the forma-tion of FFald. However, the decreasing selectivity of 5Ni/Al after 180 minutes is considered bythe formation of side product.Structure determination of side product is under consideration.Baijunet al.reported the forma-tion of furfuryl alcohol (FFald) and tetrahydrofurfuryl alcohol (THFald) are parallel reaction, whereas THFald is the final product from further hydrogenation of FFald [12].

IV. CONCLUSION

A series of 5~15% Ni/γ-Al2O3 catalysts were prepared by wet impregnation method. The characterization of catalysts conducted by SEM, XRD and BET surface area. The activity of these catalysts utilized for hydroge-nation reaction of furfural to furfuryl alcohol at 150 oC for 30 to 180 minutes of reaction time. On the basis of the result presented herein, 15% Ni/γ-Al 2O3showed the higher activities among others by 15% conversion of furfural to furfuryl alcohol with the selectivity up to 100% after 150 minutes reaction.

ACKNOWLEDGMENT

This work was supported by DPP/SPP research grant through DIPA Faculty of Science, University of Brawi-jaya No. 16/UN10.9/PG/2013and Student Creativity Program (PKM-P) research grant from The Directorate General of Higher Education, Indonesian Ministry of Education.

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[3] A. Corma, S. Iborra, and A. Velty, “Chemical routes for the transformation of biomass into chemicals,” Chem. Rev., vol. 107, pp. 2411–2502, 2007.

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[6] A. Kaufman and J. C. Adams, “The use of platinum oxide as catalyst in the reduction of organic compounds. IV. Reduction of furfural and its derivatives,” J. Am. Chem. Soc., vol. 41, pp. 769–771, 2012.

[7] J. Kijenski, P. Winiarek, T. Paryjczak, A. Lewicki, and A. Mi-kolajska, “Platinum deposited on monolayer supports in selec-tive hydrogenation of furfural to furfuryl alcohol,”Appl. Catal. A: Gen, vol. 233, pp. 171–182,2002.

[8] C. Milone, C. Gangemi, G. Neri, A. Pistine, and S. Galvagno, “Selective one step synthesis of (–)menthol from (+)citronellal on Ru supported on modified SiO2,” Appl. Catal. A: Gen., vol. 199, pp. 239–244, 1999.

[9] S-P Lee and Y-W Chen, “ Selective hydrogenation of furfural on Ni–P, Ni–B, and Ni–P–B ultrafine materials”, Ind. Eng. Chem. Res., vol. 38, pp. 2548–2556, 1999.

[10] W. Yang and A. Sen, “Direct catalytic synthesis of 5-methylfurfural from biomass-derived carbohydrates,” Chem. Sus. Chem., vol. 4, pp. 349–352, 2011.

[11] P. Mäki-Arvela, J. Hájek, T. Salmi, and D. Yu Murzin, “Che-moselective hydrogenation of carbonyl compounds over hetero-genous catalysts,” Appl. Catal. A: Gen., vol. 292, pp. 1–49, 2005.

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Abstract—The aim of this research is to prepare potentiometric sensor prototype as a coated wire oxalate ion selective electrodes (CWE) for urinary oxalate determination by potentiometric method. The sensor is composed of a platinum (Pt) wire that is coated directly on the membrane surface.The membrane’s sensor consist of a mixture an active material of Chitosan was protonation using acetic acid 3%v/v in order to have anion exchange properties and Aliquat-336-oxalate as additive material, polyvinylchloride (PVC) as supporting material, dibuthylphtalate (DBP) as plasticizer = 4:1:33.5:61.5 (% w/w) dissolved in tetrahydrofuran (THF) solvent (1:3 w/v).The characterization of the basic properties of sensor included : sensitivity and linearity of response (detection limit), response time, influence of pH and temperature, soaking time, selectivity against foreign ions and also life time. The sensor shows a good Nernstian slope of 29.9 ± 0.1mV/ decade in wide linear range concentration from 1.0 × 10 -5 to 1.0× 10 -1 M .The detection limit of 2,56x10-6M (0.22 ppm),respond time fast (20 seconds) and was found usable in pH range of 3.0 – 7.0 and temperature of 20-50oC. Selectivity was obtained over HPO4

2-,SO42-, PO4

3-, Cl-

,H2PO4-,I -,SCN- ,creatinine and also urea thats contained

in the urine. The electrode is reproducible and stable for nearly 2 months. This kind of CWE was successfully ap-plied in determination of oxalate anion in urine samples at concentrations corresponding levels of oxalate kidney stones light and medium provide average accuracy of 98.72% and an average precision of 99.81%.

Keywords—Coated Wire Ion Selective Electrode (CWE), potentiometric sensor ,Chitosan, oxalate,membrane.

I. INTRODUCTION

ROLITHIASIS is a symptom that is mostly caused by a multifactorial metabolic disorder. Originators in part because a diet rich in fat and protein,

lacking fiber intake combined with inactivity, resem-bling the so-called modern industrialised life style and genetic predisposition enhance developing urolithiasis [4] Typical symptoms of an acute stone colic are, inter alia, agony, sickness and hematuria. Urinary calculus formation is caused by disturbed urinary compositions with altered urinary pH, increased concentrations of lithogenic components as, e.g. calcium, oxalate, phos-phate and a lack in inhibitoric substances as, e.g. citrate and magnesium. Calcium oxalate represents the most frequent mineral phase found in uroliths with a frequen-cy of approximately 70–75% [1]

Oxalate is one of the important nutrients in the human diet found principally in spinach, beet leaves, etc. Oxa-late is primary chelator of calcium ion, so it forms che-lates with calcium. Oxalate ion also inhibits the calcium adsorption in the body and if it is not sufficiently de-graded, it may accumulate in the body. It plays a crucial role in the formation of most renal stones. In the body oxalates can be found as two forms in vivo: oxalate ions and calcium oxalate monohydrate (COM) crystals that readily form in the presence of calcium[ 2]. Owing to the high recurrence rate of calcium oxalate stone forma-tion in case of inadequate treatment, evaluation of the individual causes for calcium oxalate urolithiasis is of utmost clinical importance [1]. Urinary stone formation has been evolved to a widespread disease during the last years. The reasons for the formation of urinary stones are little crystals, mostly composed of calcium oxalate, which are formed in human kidneys. The early diagnosis of the risk for urinary stone formation of patients can be determined by the “Bonn-Risk-Index” method based on the potentiometric detection of the Ca2+-ion or oxalate ion concentration and an optical determination of the triggered crystallisation of calcium oxalate in unpro-cessed urine [1].

Most of the analytical methods like ion chromatogra-phy, neutron activation analysis, atomic absorption spec-trophotometry and mass spectrometry etc. have been reported for the determination of oxalate ions at a very low concentration levels, but these methods require ex-pensive instrumentation, delicate and expert handling of the sample and instrument. Hence potentiometric deter-mination based on ion selective electrodes offer several advantages such as ease of preparation, low cost, simple procedure and easy instrumentation. It gives relatively fast response, wide linear range, good selectivity, high detection limit and online applications as compared to other analytical methods[1,3].

During the last decade, a number of studies have fo-cused on properties of functionalized chitosan, a rela-tively new class of an adsorbent, chelating agent and ion exchanger. Chitosan, a poly-[1-4]β-D-glucosamine, is a derivative of chitin, a naturally occurring polysaccharide found in insects, arthropods and crustaceans. Chitosan is hydrophobic, bio-degradable, bio-compatible and low toxicity so widely used for various applications includ-ing pharmaceutical, biotechnology and wastewater treatment. Chitosan is well known for complexing tran-sition metal ions, through chelation at its amino group[1]. It was also shown that chitosan can be used as

A New Oxalate Ion Sensors Based on Chitosan Membrane

Atikah1*), R. Retnowati2), H. Sulistyarti3), B.Siswojo4), Z. Rismiati5)

1,2,3,5) Department of Chemistry, Faculty of Mathematics and Natural Sciences, University of Brawijaya

4) Department of Electrical Engineering, Faculty of Engineering, University of Brawijaya *)Corresponding author: [email protected]

U



potent ionophores for the preparation of ion selective-sensors[4,5]. The potentiometric selective coefficients of ISEs sensitive to organic anion included oxalate ion have been reviewed in detail. The application of func-tion so far, several experimental studies have demon-strated that the generation of a membrane potential of those type of ISEs could be attributed to permselective ion transport across the liquid membrane/solution inter-face, i.e., charge separation through a preferential uptake of a primary ion by a sensing element in the liquid membrane, leaving its hydrophilic counter ion in an aqueous sample solution and usually exhibit the Hof-meister pattern with the largest selectivity to lipophilic cations [6,7].

These can also be used in complex and coloured me-dia. Therefore, there has been progressive growth in the development and application of potentiometric sensors based on polymeric membrane ion selective electrodes incorporating ionophores for the detection of different cations and anions and other biologically important compounds[3]. Recent studies in different laboratories showed incorporation of different novel materials as ionophores for the ion selective electrodes[4]. A strong interaction of the anions and the ionophore as well as the steric effect associated with the structure of the ligand gives rise to selectivity sequence. Thus the research on sensing materials for anion as well as developments in-cluding new synthetic ionophores, miniaturization of the detecting device like coated wire selective ion electrode (CWE) etc. makes it an ever-expanding culture for re-search in chemical sensors [2,8].

In this paper, we wish to introduce a highly oxalate ion selective potentiometric sensor based on a heteroge-neous membrane of chitosan as ion carrier membranes and Aliquat-336-oxalae as additive material supported by polymeric polyvinyl chloride (PVC) of high molecu-lar weight and plasticizer dibutyl phthalate (DBP) then its application for the determination of urinary oxalae ion as early diagnosis of the risk for urinary stone forma-tion of patients can be determined by the “Bonn-Risk-Index” method based on the potentiometric detection of the oxalae ion concentration combine with Ca2+ determi-nation by atomic absorption spectrophotometric (AAS) and their result compared by an optical determination of the triggered crystallisation of calcium oxalate in unpro-cessed urine.

II. METHODOLOGY

A. Apparatus and emf measurements All potential measurements were performed using the

following assembly: Hg, Hg2Cl2 (Sat’d)//sample solu-tion/PVC membrane/Pt-wire electrode. A pH-meter (Fisher E 520) was used for potential measurements at 26°C ± 0.5oC. The activities of ioxalae ion (C2O4

2-) ions in the urine were calculated according to the Debye–Hückel approximation. B.Reagent and solution

Chitosan powder isolation results from the shell of jerbung shrimp (Penaeus merguinensis) with a degree of deacetylation 68% (w/w) is use as ionophore was protonated using Acetic Acid (3%), Aliquat-336 oxalae

as additive material,polyvinyl chloride (PVC) of high molecular weight , dibutylphtalate (DBP) as a plsticizer were purchased from sigma, tetrahydrofuran is products from E.Merck. Platinum wire (99,9% ; ∅ 0.5 mm) is products from Aldrich and RG-58 Coaxial cable as connector ISE to mV potentiometer. All other reagent used were of analytical reagent grade, and doubly dis-tilled water was used throughout. Ca oxalate, Acetic Acid (3%), Aliquat 336-S,NaOH, Na3PO4, CaCl2, creatinine, uric acid.

C. Construction and calibration of the electrodes

The membranes electrode was prepared by mixing thoroughly by dissolving protonated chitosan, Aliquat-336-Oxalat, PVC, DBP plasticizer in THF solvent (1:2 v/w). This solution was deposited directly onto a plati-num wire approximately 0.5 mm in diameter and 10 cm in length whose tip had been melted in flame to form a spherical button was soldered to a length of RG-58 coaxial cable, and the solvent was evaporated for ap-proximately 30 minutes and then allowed to stand over-night in the oven at 50oC. A membrane was formed on the platinum surface and the electrode was allowed to stabilize overnight. Prior to use the electrode was initial-ly conditioned by soaking it overnight in a 0.1M solu-tion of Naoxalate (Na2C2O4) to be measured. When not use, the electrode was store in air between use and re-conditioning immediately before using by soaking for at least 1 hour in a 0.1M solution of Na2C2O4. The utility, composition of polymer membrane, respond characteris-tic, and selectivity coated wire oxalae ion selective elec-trode (CWE) were investigated. The electrode potential measurement was made under constant conditions by taking 25 mL of solution for each measurement in a cell thermostated at 26 ± 0.5 oC , immersing the electrode to a constant depth in the solution, and stirring at a con-stant rate by means of a magnetic stirring bar. In all ex-periments the electrode potential measurement was car-ried out from low concentration to high concentration. The electrode tip was rinsed with deionized water and then immersed in one of the standard solution.

D. determination of oxalate ion in urine

Urine samples were taken from the Central Laboratory of Clinical Pathology Hospital laboratory which have completed used for clinical pathology examination (samples are no longer used or discarded) of patients with kidney stones (calcium oxalate crystals containing microscopic examination based on positive optical crystal with no indication of the patients showed symptomatic urolithiasis) taken from 50 patients with the risk of kidney stones and high light each 25 samples and 10 urine samples taken from normal patients as a whole amounted to 92 samples. Each urine sampel put in a polyethylene tubes. The samples were immediately cen-trifuged and stored at 4oC. A 1.0 mL aliquot of the sam-ple was transferred into a 10-mL measuring flash and diluted with distilled water. For each analysis, the oxalae sensor and double –junction Ag/AgCl reference elec-trode were immersed in the same solution, and the po-tential reading were recorded. A typical potentiometric calibration plot was made by plotting the potential



change against the logarithm [C2O42-] concentration. The

obtain calibration curve was used for subsequent deter-mination of C2O4

2- in unknown samples. The results of determination of C2O4

2- on both the optical microscopy method and the potentiometric method using oxalate ion sensors tested for their accuracy, precision and also for early diagnosis of the risk for urinary stone formation of patients can be determined by the “Bonn-Risk-Index” (BRI) method based on the ratio of potentiometric detec-tion of the urinary oxalae ion and Ca2+-ion concentration by AAS and their result compare to an optical microsco-py determination of the triggered crystallisation of cal-cium oxalate in unprocessed urine.


A. Influence of membrane composition The different aspect of membrane preparation based

on protonated chitosan as ionophore and Aliquat 336-oxalate as additive material containing different PVC/plasticizer ratios were mix in THF solvent (1:2 ratio v/w) were studied and the results revealed that the amount of ionophore, the nature of solvent mediator, the plasticizer/PVC ratio significantly influence the sensitiv-ity of ion selective electrodes. Membrane with a composition ratio of wt% PVC: DBP: the active ingredient Chitosan-oxalate; additives Aliquat 336-oxalate = 4: 1: 33: 62 in THF 1: 3 volume gives Nernstian properties with prices Nernst factor of 29.9 mV / decade concentration, means that the optimum composition meets the theoretical Nernst factor for monovalent anion, because the active ingredient membrane forming a homogeneous phase with membranes visible supporter of the smallest ∆ dm price for the active ingredient chitosan [4,5]. Non Nernstian response of the oxalate ion CWE, most probably due to saturation or non-uniformity of the membrane. Use of the DBP plasticizer as a solvent mediator for preparing a coated wire oxalate ion-selective electrode(oxalae ion CWE) need to fulfill four principal criteria: high lipophi-licity, solubility in the polymeric membrane (no crystal-lization) as well as no exudation (one phase system) and good selectivity behavior of the resulting membrane. It should be noted that the nature of plasticizer influences both the dielectric constant of the membrane and the mobility of ionophore and its complexed associatiated with oxalae ion [9,10]. Thus, based on the result ob-tained on the optimazation of the membrane composi-tion, the membrane 4 with the optimized composition of percent ratio (w/w) of the active ingredient protonated chitosan -oxalate; Aliquat 336-oxalate additives:: PVC:DBP = 4: 1: 4: 1 in THF 1: 3 volume was selected for preparation the polymeric membrane electrode for I- ion. Nernstian responses obtained on the composition ratio of PVC / plasticizer 1.2 as obtained by other researchers [3].

The specifications of oxalate ion CWE base on chitosan carrier are as follows: Sensitivity (Nernst fac-tor) of 29,9± 0,252 mV / concentration decade of oxa-late concentration over the range 1.10-5-1.10-1 M, with the detection limit of 42.56x10-6 (0.22 ppm), They have relatively fast response (20 seconds), satisfactory reproducibility, and life times more than two months

and was found to be very selective toward oxalate ions with the selectivity sequence against foreign ions in order: oksalat2-> HPO4

2->SO42-> PO4

3->Cl-≈H2PO4-≈I-

≈SCN-> kreatinin> urea. The observed selectivity pat-tern for proposed sensor significantly same from the Hofmeister selectivity sequence (i.e. selectivity based on lipophilicity and charge density of anions), usable in wide pH range of 3-7and temperature of 20-50oC, need soaking time of 75 minutes in 0.1M in oxalate solution. This result states that oxalate ion CWE has a optimal character for the potentiometric measurement of oxalate analysis. However, the lipophilicity of the anion still plays an important role, and only the simultaneous con-sideration of both the lipophilicity and interaction of the anion with zeolite allows one to explain the selectivity patterns. Therefore, ion exchange selectivity is mainly determined by two factors:i.e the charge of an ion and its solvation, since the interaction between anions and ion exchange groups on chitosan is electrostatic [8].

B. Application

The new coated wire oxalae ion selective electrode was satisfactorily applied to the determination of oxalae ion cover from 9 urine samples of kidney stone patients were examined in the Clinical Pathology Laboratory and measurements performed 20 times at room temperature 27 ± 1oC.The analysis were performed by direct potenti-ometry using the standard curve technique. Good reco-veries in all matrices were obtained. From this results we can conclude that the proposed sensor was success-fully applied to determining the oxalae content in bio-logical samples .The results obtained that coated wire oxalate ion selective electrode can be used as sensors for the determination of oxalate in urine to detect renal stone disease before clinical symptoms arise with giving rat average accuracy of 98.72% and an average precision of 99, 81% which shows measurement accuracy and good precision.

Distribution potentiometric measurements oxalate concentration in urine and measurement of calcium ions in the urine by AAS method and also early detection of urolithiasis risk categories based on price of BRI and optical microscopy base on the statistical test Chi Square. Percentage error determination urolithiasis risk category according the “Bonn-Risk-Index” (BRI) method (The BRI method is based on the potentiometric detection of the free Ca2+-ion concentration(activity) by means of an ion-selective electrode (ISE) together with an optical determination of the induced crystallization of calcium oxalate in native urine. The BRI is determined as ratio: BRI = [Ca2+]/(Ox2-) [1] and optical microscopy said that Count X2 = 72.35 while the table on the confidence limits P X2 = 95% and degrees of freedom (DB) = (3-1) (3-1) = 4, then X2 (0.95) (4) = 9.49. Arithmetic mean X2> X2 table, which means there is a very real relationship between the % difference BRI results using oxalate sensor with each category on the risk of urolitiasis. It means the measurement results urinary oxalate causing urolithiasis in all population categories are independent. The validation results of potentiometric method according to the calculation



results indicated by the percentage of correspondence between the two methods, i.e., 69.6% or 30.5% gives an error, that is 12% sensor method smaller than the microscope method and 18.5% error sensor method greater than microscope metod.To test whether % differences in BRI outcomes toward the optical microscope with a population of BRI category is independent or not chi squared test needs to be done and the result test state that BRI method are sensitive and specific for high-risk category of patients suffering from urolithiasis because of the formation of calcium oxalate crystals. For patients with a high risk of urolithiasis giving a sensitivity of 75% and specificity of 75%. Moderate risk for urolithiasis patients, giving a sensitivity of 95% and specificity was also 60%, whereas the risk for urolithiasis patients with mild risk gave a sensitivity of 86.6% and a specificity of 72.1%. Thus potentiometric method can be used as an alternative method besides optical microscopy method

IV. CONCLUSION

The membrane composition influence the Nernstian character of oxalae sensor. The membrane with the composition of ratio of wt% the active ingredient Chitosan-oxalate; additives Aliquat 336-oxalate: PVC: DBP= 4: 1: 33: 62 in THF 1: 3 dissolved in THF solvent (1:2 w/v) was selected for preparation the polymeric membrane electrode for oxalae ion and can be use as chemical sensor for oxalae ion in the construction of coated wire oxalae ion selective electrode which has optimum characteristics for oxalate ion analysis. Method validation results showed that oxalate ion CWE produced have optimum characteristics for sensor oxalate ions suitable for urinary oxalate analysis pro-vides of an accuracy of 98.72% and precision of 99.81%beside to the soptical microscopy.

This kind of CWE was successfully applied to detect high-risk patients with urolithiasis (sensitivity 75% and specificity 75%), moderate risk of urolithiasis (sensitivity 95% and specificity 60%) as well as mild risk of urolithiasis (sensitivity 86.8% and specificity of 72.1% ), have compatibility with the optical microscope method of error of 69.6% or 30.5% error, that is 12%

smaller than of optical microscope and 18.5% larger than of optical microscopy method

ACKNOWLEDGMENT

The study was funded by Competitive Research Grant, the Directorate General of Higher Education, Indonesia Ministry of National Education with the contract num-ber: 366/SK/2012 To the Ministry of National Education and University of Brawijaya are gratefully Acknowl-edged

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[3] Ardakani,M. M., F. Iranpoor., M. A. Karimi, and M. Salavati-Niasari (2008) A New Selective Membrane Electrode for Oxalate Based on N,N'-Bis(salicylidene)-2,2-dimethylpropane-1,3-diamine Ni(II), Bull. Korean Chem. Soc., Vol. 29, No. 2 pp 398- 403

[4] Cruz, J.,M. Kawasaki.,and W.Gorski.(2000,February).Electrode Coatings Based on Chitosan Scaffolds, Anal. Chem,72: (4):680-686

[5] Isa,I.M.,S.Ab Ghani.(2007).Development of Prototype heterogeneous Chitosan Membrane using Different Plasticizer for Glutamate Sensing, Talanta,71:452-455 from http://www.elsevier.com/locate/talanta

[6] Pretsch,E. (2007). The New Wave of Potentiometric Ion Sen-sors,Trends in Analytical Chemistry., 26(1): 46-51 from http://www.elsevier.com/locate/trac

[7] Okada,M.H, and T.Ohki, (2009), Hydration of Ions in Confines Spaces and IonRecognition Selectivity, Analytical Science., vol. 25,pp 167-175,

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[10] Stefan R. I. Draghici, G. E. Baiulescu (2000), Sensors and Actuators B, 65, 250–252



PHYSICS



Abstract—Indonesia is one of the most country in the

world which has the high tectonic activity. It caused by ring of fire passed Indonesia Region. Ring of fire zone is the subduction zone or convergent plate motion. It cause earthquake often occured on Indonesia region. Mentawai Island region is one of the common area of earthquake in Indonesia region. This area located at west of Sumatera which subduction zone between Eurasia plate tectonic (continental crust) and Indo-Australia plate tectonic (oceanic crust). Relocation of hypocentrum earthquake on this area is important because it as early warning determination. The relocation hypocentrum earthquake on this area using Modified Joint Hypocentrum Determination (MJHD) method.The earthquake data on this area is taken from 2009-2012. Moreover our research create seismicity mapping in the Mentawai Island region based on result of relocation hypocentrum earthquake using MJHD method. The results used to early warning determination on Mentawai island as to minimize seismic hazard

Keywords—Earthquake, MJHD, Subduction zone, Plate

tectonic

I. INTRODUCTION

NDONESIA is one of the most country in the world which has the high tectonic activity. It caused by indonesia has three plate tectonic motion, they are

Eurasia, Indo-Australia, and Pasifik. This subduction zone is called ring of fire zone. It starts from west of Indonesia region continuous to east of Indonesia region. That region to common area of earthquake on Indonesia.

One of our focus is Mentawai island, it located on the west of Sumatera. This zone belonging subduction zone (Eurasia plate tectonic and Indo-Australia plate tectonic), and the common area of earthquake on the west of Sumatera Island. Moreover, the geological setting at this area is dominated sedimentary and mixing from continental and oceanic. Relocation of hypocentrum earthquake on this area is important because this relocation of hypocentrum to identification tectonic condition on this area including the knowing the seismic gap which may be a big source of earthquake in the future. The results is used to early warning determination as to minimisize seismic hazard especially for occupant on this area and the surrounding.

II. RESEARCH METHOD

The method on this research is using Modified Joint Hypocentrum Determination (MJHD). This method is created by [1], Japan seismologist (1990-1992), This method is used to determination relocation of earthquake. The following inversion steps of this method is :

The earthquake parameters is lattitude and longitude

of earthquake, time of common earthquake, the change of that parameters influence the change significant the hyprocentrum of earthquake depth. The excellent of this method calculate many hypocentrum of earthquake simultaneously and correction about lateral heterogenity in subsurface. Lateral heterogenity in subsurface attenuate the p-wave propagation, the effect is mislocation the hypocentrum of earthquake. The input data is taken from 2009-2012 years and recorded by

Relocation of Hypocentrum Earthquake in Mentawai Island Region as Data Support

Determination of Earthquake Early Warning

Ahmad Marzuki S 1*), Munawarah1), and Iven Ganesja 1) 1) Department of Physics, University of Indonesia, Depok, Indonesia


I

Start Reformat data to mjhd format

Station selection process : station.f

Input data for mjhd07.f : mjhd07.inp

mjhd07.f

The desired residual

No

Yes mjhd.out mjhd.print

mjhdoutselect.f

mjhd.outp STOP

STOP



BMKG (Badan Meteorologi dan Geofisika). The data has magnitude > 5 SR, including time of common earthquake, the recorded station, arrival time p-wave, longitude and lattitude coordinate, and the depth of hypocentrum.

The start of inversion using MJHD step of determine the value of Minimum Number of Earthquake at Each Station (MNEQ), and Minimum Number of station at Each Earthquake (MNST) This value as input data of station program, the function to determine the number of station that match the requirments of the value MNEQ and MNST. The others parameters is lattitude and longitude the earthquake as initial data, the depth of margin earthquake in km, maximum residu of travel time (RESS), the number of maximum iteration (ITRT) is 5, standar deviation, the number of station and the number of earthquake which not participate in calculation, reading accurate (RANKAB), minimum magnitude is > 5 SR, the value of slope, the hypocentrum corrected using MJHD is plotting in General Mapping Tool (GMT), the function of GMT showing the hypocentrum corrrected distribution from the surface and their cross section.

III. RESULTS, ANALYSIS, AND DISCUSSION

At this part we show the inversion results using MJHD method, compare the results before relocation correction (picture 1), and after relocation correction (picture2),then interpretation the results. At the picture 1 (before relocation correction), the distribution of hypocentrum on Mentawai island dominated at shallow depth (0-20 km), and but at picture 2 (after relocation correction), the distribution of hypocentrum depth on

Mentawai island dominated at 25-50 km, and the distribution on seismicity mapping show dominated at shallow-intermediet depth earthquake near the Mentawai island, and deeper far from Mentawai island. In geological setting the enough great angle of subduction zone between Eurasia plate motion and Indo-Australia plate motion at west of Sumatera island. It caused by as oceanic crust ages and cools, so it thickens. Thick crust (far the spreading zone) tends to subduct at a greater angle than thin crust (near the spreading zone). The more depth of hypocentrum, indicating that the great angle of convergent plate motion. The plate has point of rupture, where if the force between the plate (ocean crust and continental crust) at subduction zone more great than rupture point of the plate then the plate will be rupture. This rupture create a propogate wave on the subsurface medium. Its traveling on the subsurface medium get attenuate the energy when this wave propagate on the weathering zone. So, the observer at the surface calculated the time traveling of wave from source to surface. In reality, time of calculation and time of observe get shiftting because weathering zone at subsurface. So, time of calculated and time of of observed must be correction. It’s match with seismicity mapping.

Based on seismicity mapping after relocation correction, the Mentawai island dominated by shallow to intermediet depth earthquake, this earthquake dangerous for Mentawai occupant and its surrounding.

Picture 2. Hypocentrum of Earthquake in Mentawai Island before relocation correction 2009-2012 data with all magnitude > 5 SR

Picture 3. Hypocentrum of Earthquake in Mentawai Island after relocation correction 2009-2012 data with all magnitude > 5 SR

Picture 3. The Seismicity Mapping After Relocation Correction using MJHD

On this seismicity mapping we can look that the red circle indicate that the earthquake at the shallow to intermediet depth, and the yellow circle indicate that the earthquake at the intermediet to deeper. The red circle dominated at the subduction zone with dominated



intensity is 3-7 SR (Richter Scale), and the yellow circle dominated at the far of subduction zone with dominated intensity is 3-5 SR ( Richter Scale ). It indicated that the collison between Eurasia plate and Indo-Australia plate at subduction on the flattening angle (0-80 degree). Based on seismicity mapping the Mentawai island dominated with variate earthquake intensity with the focal depth near the Mentawai island. Based on geological setting, lithology of Mentawai island dominated from continental and oceanic rock ( Melange formation ) with active fault such as reversed fault or thrust fault. This zone called fore arc zone. Focal depth shallow to intermediet because Mentawai island near the subduction zone, it very dangerous for local citizens because source of earthquake near the surface. The effect of this earthquake very destruct. We can not to predict when the earthquake create, but we can predict that the earthquake at the Mentawai island variate intensity, because the lithology at the Mentawai island is dominated clastic sediment. In general the earthquake at the Mentawai island created by collision between Eurasia plate and Indo-Australia plate at the subduction zone with focal depth shallow to intermediet.

IV. CONCLUSION

The depth of hypocentrum on the Mentawai island is shallow to intermediet (25-50 km), and their distribution on the west of Mentawai island indicating that subduction zone between Eurasia plate motion and Indo-Australia plate motion. The convergent plate motion between Eurasia and Indo-australia has the enough great angle.

ACKNOWLEDGEMENT

The Author Thank to BMKG for their cooperation in providing the earthquake data on the Mentawai island from 2009-2012 year used in this study and Mr. DR. ENG. Supriyanto as geophysics lecture on Department of Physics University of Indonesia for his constructive suggestion and transfer his knowledge to interpretation the result.

REFERENCES

[1] N. Hurukawa and Imoto. 1992. Modified Joint Hypocentrum Determination. Japan.

[2] Thompson &Turk. 2000. Introduction to Physical Geologi.

USA.



Abstract— A pilot project for implementing the UB bio-

mass stoves and measuring their impacts over 100 households in Palangka Raya rural area has been performed. The reci-pients were located in two villages, one village (Petuk Bukit) whose inhabitants was predominated by Dayak Native and the other (Habaring Hurung) by transmigrants from Java. The pilot project was supported by Energy and Environment Partnership (EEP) Indonesia for one year. It was found that after intensive coaching and monitoring, beneficiaries increase the usage of biomass stoves and adapt new habits in cooking preparation. By the end of project, 100 % of households in Habaring Hurung used the UB biomass stoves for their daily cooking, while in Petuk Bukit the num-ber was 78%. It was also found that 60% households in Ha-baring Hurung relied on UB biomass stove only, while the rest 40% were in combination with traditional woodstove and kerosene stoves. In practical use, the firewood reduction ac-counted for 59% less than that of traditional wood stove and the stoves produce much less smoke than the traditional woodstoves.

Keywords—UB stove, biomass, deforestation.

I. INTRODUCTION

VER 40% of the world's population still burns vari-ous forms of biomass, such as wood, dung, charcoal,

or crop residues or coal as a cooking fuel, see e.g [1,2]. They cook over open fires or on rudimentary cookstoves. This way of cooking is not only inefficient, but also emits a harmful smoke that causes range of deadly chronic and acute health effects such as child pneumonia, lung cancer, chronic obstructive pulmonary disease, heart disease, and low birth-weight [3].

Though currently the Indonesia’s government has launched kerosene to LPG national conversion program, the use of biomass as daily cooking fuels is still predomi-nate in most rural area. Moreover, inadequate supply of hydrocarbon fuel at an affordable price and unsafe lique-fied petroleum gas stoves for low income communities have led to the growing use of firewood as fuel for daily activities, including by cutting down trees in nearby fo-rests. The combination of deforestation, inefficient fuel combustion and high consumption has significantly created vast environmental degradation and global warming. Indo-

nesia’s consumption on wood-fuel was among the highest in the world i.e. 70.72 million m3 in 2006 and 65.03 mil-lion m3 in 2008 [4].

Central Kalimantan is one of province in Indonesia which has been experiencing highest rate of deforestation. Deforestation is mostly attributed to logging for the con-version of forests to plantations for palm oil and to supply the pulp and paper industry. In addition, it also has major energy crisis whereas supply of fossil fuel is limited. Due to poor availability of kerosene and gas fuel, households in Central Kalimantan commonly use firewood for cooking harvesting it from forests surrounding their villages.

As an agricultural-based nation, Indonesia possesses an abundant source of biomass from residues, e.g. rice, su-garcane, palm oil, logging, sawn timber, coconut, and oth-er agricultural wastes. These residues and wastes are ac-tually sources of energy that can provide fuel for rural households. It was with this justification that INOTEK Foundation had been partnering with Muhammad Nurhuda in developing and disseminating an innovative UB Bio-mass Stove and a range of biomass fuel. Nurhuda has received mentoring facilitation in RAMP Indonesia pro-gram that is implemented by INOTEK. Technologically, the UB biomass Stove is designed with innovative pre-heating, gasification, counter flow and turbulence mechan-ism [5,6]. The external laboratory tests has shown that the stoves can reach thermal efficiency as high as 49%, which is comparable to the fossil-based cooking stove [7]. With such high efficiency, it is expected that the use of UB bio-mass stove can reduce the amount of firewood needed for daily cooking as well as reduce the green house gas (GHG) produced from combustion process

II. METHOD

Through the project, 100 energy efficient UB biomass stoves were taken into use in the villages of Habaring Hu-rung and Petuk Bukit, which are rural areas in Palangka Raya, Central Kalimantan. Petuk Bukit village is predomi-nated by Dayak Native whereas Habaring Hurung is by transmigrants from Java. The choice of two villages socio-logically were based on different characters of inhabitants. Each village received 50 stoves, and the stoves were then distributed among the households readily for usage in their

Reducing Deforestation and GHG Emission with UB Biomass Stove and Fuel as Alternative

Energy for Community Muhammad Nurhuda 1*), Setyowati Rahayu 2), and Dhoni Saputra 2)

1) Faculty of Science, Brawijaya University, Malang, Indonesia 2) Yayasan Inovasi Indonesia (INOTEK), Jl. Jenggala 2 No 9, Kebayoran Baru, Jakarta


O



daily cooking activities. The project was funded by the Energy and Environment Partnership (EEP) Indonesia (www.eepindonesia.org) for one year.

The data collection were made by interviewing the be-neficiaries of the stoves in every three months. Socializa-tion, coaching and mentoring were carried out by involv-ing Yayasan Mitra Insani as a local partner of the project.


The project started after the signing of the contract be-tween INOTEK and EEP-Indonesia in May 24, 2012 and were accomplished in the period of June 2012 up to June 2013. The project was designed from the beginning to in-volve active participation of local people with the goal to raise awareness among the people on the significance of health and nature conservation.

In the beginning of the project, there were some con-straints, both technically and non-technical. The non tech-nical problem was due to refusal from the local leaders, such as installment scheme, the selection criteria of benefi-ciaries, and also collecting data base and conducting inter-view. All barriers could be solved with intensive approach and explanation on the purpose of the project, and by iden-tifying formal and informal leaders and opinion leaders to get support from them. The technical problem was due to fuel requirement, which, if not carefully prepared, could lead into improper usage of the stove such that the com-bustion emits a lot of smokes in their kitchen. However, after extensive coaching and monitoring, the beneficiaries started to accept the new habits in cooking using the bio-mass stove.

To introduce the UB biomass stoves, knowledge transfer was achieved by giving training, and one to one coaching. The training did not relate the technical aspects only, such as operating biomass stove, preparing woods or biomass fuel, maintenance of the stoves, but also income generating activities and simple accounting. To involve active partici-pation among beneficiaries and raising awareness and ownership, the selected beneficiaries are requested to pay IDR 200,000,- . The payment was made using an install-ment scheme. The installment payment was collected by representative of beneficiaries and the money will be used as revolving fund. Dissemination of project were achieved through media and by distributing promotion materials.

In the beginning of the project, very rare beneficiaries used the stoves for daily cooking. It was found that most beneficiaries put their stoves in rack after non-successful first usage. The reason was the beneficiaries considering the combustion in the stoves as it in their traditional clay stove, though each stove was accompanied with manual use procedure. However, after 6 month the project was running, the monitoring track showed some good trends and encouraging results. The beneficiaries were getting used to biomass stoves. It was found that 99% of benefi-ciaries continue to use biomass stoves every day or in combination with other stoves. It was only one beneficiary in Petuk Bukit which returned the stove to the project im-plementers, and claimed to be reluctant to cutting woods into pieces.

Fig. 1. Usage of biomass stove in kitchen.

TABEL 1 PERCENTAGE OF USAGE OF UB BIOMASS STOVE IN

COMBINATION WITH OTHER COOKSTOVES:

Type of stoves Habaring Hurung

Pethok Bukit

UB stove only 60% 16% UB + kerosene 10% 76% UB + clay 16% 0% Clay+UB+ Kerosene

14% 4%

Clay+UB+LPG+ Kerosene

0% 2%

NA 0% 2% Total 100% 100%

Referring to table 1, it is shown that among 100 house-holds, 38% have used the UB stove exclusively. Further data analysis has shown that the stoves were used to boil water (78%), side dish (81%), and cooking rice (20%). The lower percentage in using the stove for cooking rice were due the fact that the size of the stoves were too small for cooking rice for large family members, since it is nor-mal in the rural area of Palangka Raya that one house is occupied with family member that is larger than 10. The controllable flame intensity of UB biomass stove may be the reason why the stoves are mostly used for preparing the side dish.

Finally, in Tab. 2 we show the saving potential of both firewood and kerosene. The data was collected at the time the project was approaching to end. On average, the use of UB biomass stove could reduce the real consumption of woods up to 59% compared to using traditional clay stoves, and reduce the use of kerosene up to 50%. The comparisons were made based on the monthly needs, be-fore and after implementing the UB biomass stove. The saving in using kerosene was found to be depend on the kerosene supply and whether the biomass stoves were used exclusively or in combination with other stoves.

From Tab. 1 and Tab 2 we can immediately see that the households in Habaring Hurung, which are mostly trans-migrant from Java, could be better accepting the cooking new habits compared to households in Petuk Bukit, which are predominated by Dayak native. The reason are proba-



bly the Java transmigrant could better adapt the new ha-bits, that by the condition, they are far away separated from their families in Java and thus must hardly struggle for surviving in the new land.

TABLE. 2: THE COMPARISON OF MONTHLY NEEDS OF FUELS, BEFORE

AND AFTER IMPLEMENTING THE UB STOVE.

Project Loca-tion

Type of fuel

Monthly amount of fuel Saving (%)

Before After

Habaring Hu-rung

Woods 102 kg 41.3kg 59% Kerosene 11.8 L 5.8 L 51%

Petuk Bukit Wood 86.9 kg 40.8 kg 53% Kerosene 15.1 L 7.7 L 49%

Despite the difference level of results in habaring Hu-rung and Petuk Bukit, it is clear for Tab. 2 that the benefi-ciaries required less wood for their daily cooking. Less fuel means also less smoke in the kitchen, since the emis-sion is always proportional the amount of combusted fuel. Thus, in regards to the deforestation, the use of UB bio-mass stove could help preventing the people from cutting the woods in forest and thus reducing the potential of de-forestation.

IV. CONCLUSION

As conclusion, a UB biomass stove pilot project to measure the real reduction of biomass usage for cooking has been performed in rural area of Palangka Raya district, Central Kalimantan for one year. The sample areas were chosen to be the Habaring Hurung and Petuk Bukit village,

which represent two different cultures. It is found that the potential reduction for fire woods accounts for 60% com-pared to the traditional clay stove. Furthermore, adaptation of new cooking habits can only be realized after intensive coaching and monitoring.

ACKNOWLEDGMENT

The authors and involved project members greatly ap-preciate the Energy and Environment Partnership with Indonesia (EEP Indonesia) for their financial supports. The project is of coded under project ID 205-5060401741509211.

REFERENCES

[13] Global Alliances for Clean Cookstoves: http://www.cleancookstoves.org/

[14] Hedon Household Energy Network: http://www.hedon.info/tiki-index.php

[15] Carlos Torres-Duque, Darío Maldonado, Rogelio Pérez-Padilla, Majid Ezzati, and Giovanni Viegi "Biomass Fuels and Respiratory Diseases", Proceedings of the American Thoracic Society, Vol. 5,

No. 5 (2008), pp. 577-590. [16] http://www.fao.org/docrep/013/i2000e/i2000e00.htm. [17] M. Nurhuda, Kompor Biomass Dengan Gasifikasi Terpanaskan

Dan Pembakaran Turbulen, paten register number P00201000217, 2010.

[18] M, Nurhuda, Kompor Briket Dengan Pre-Heating dan Pembakaran Secara Counter Flow, patent register number P00201100059, 2011.

[19] David Beritault and Veronique Lim, Stove Performance Report, UB03, Envirofit G3300 and M5000, Ezystove, Geres, Phnom Penh, Cambodia, 2013.

[20] Anonymous, INOTEK END-PROJECT COMPLETION REPORT FOR EEP

INDONESIA, 2013.



Abstract — Research has been conducted to examine the

bioefficacy of one push aerosol that is currently on the mar-ket. This study aims to determine bioefficacy (KT) of one push aerosols on insects and determine whether there is a residue generated by these insecticides within 1 hour of ob-servation by using a glass chamber size of 70x70x70 cm and particle counters Ptrak 8525 models. The average percentage mortality of Aedes aegypti mosquito is entirely equal 100 %. The fastest KT 50 and KT 90 on a product A (transfluthrin 21.3 %) is 573 s and 1462 s , followed by product B ( metof-luthrin 3.5 % ) which has a value of 792 s and 1879 s . Prod-uct C (transfluthrin 25 %) has KT 50 and KT in 1277 s and 2867 s . Within 60 minutes of observation was found residue on each product , product A have particle concentration 897 pt / cc , product B 1047 pt/cc , and the product C 493 pt/cc .Product A is the the most effective to kill mosquitoes because it has a greater concentration of particles is 17703 pt / cc ra-ther than product C and also for product) as the active ingre-dient contained in a product that is transfluthrin 21.3 % even though the product B has an average concentration greater than the average concentration of product A is 18350 pt /cc .

Keywords— bioefficacy, one push aerosol, insecticide

I. INTRODUCTION

HE use of insecticides is one of the effective way to control mosquitoes, cockroaches, ants or other insects that are common in the home. Insecticides are sold

widely in various forms both fuels and aerosols. For aero-sol itself there are various brands available in the market such as Baygon, vape, Mortein and others.

Bioefficacy emerging insecticides that used today is one push aerosols. Aerosol is a term used for the preparation of thin mist spray with high-pressure system. Aerosol types can also be distinguished by size. Aerosol particle size is usually expressed in particle radius assuming a sphere-shaped particles. According to the version of the Aitken particle size divided into three categories, namely: • Aitken particles (nucleation mode) with a size range be-

tween 0.001-0.1 µ m; • large particles (accumulation mode) measuring between

0.1-1 µ m, and • giant particles (particle coarsa mode) which size > 1 µ m

radius. [2]. The use of one push aerosol somewhat more efisient be-

cause just a single tap is enough to free us from mosqui-

toes for a 10 hours . At the time inhaled, aerosol particles can get rid of the respiratory system's natural defenses and lodge deep in the lungs. Aerosol very dangerous for people with diseases such as asthma, bronchitis, and emphysema (swelling in the lungs because blood vessels intruding air), as dangerous for people with liver disease. High levels of these objects in the air can trigger asthma attacks, lung damage, and supports carcinogenesis, and premature death.

In contrast to the usual aerosol spray which should in some places into the room to obtain the same results. Giv-en these differences, there are needs to determine the bio-efficacy (knock down time) of one push aerosols on insects which is Aedes aegypti and determine whether there is a residue generated by these insecticides.

II. RESEARCH METHODS

Stages of the study are as follows : 1 . Testing Using Glass Chamber

Glass Chamber is a box made of glass measuring 70x70x70cm with one wall open the door. Glass Chamber must be ensured in a contaminated state. Insecticides are used in this case is one push Vape ( Transflutrin 21.3 % ), Force Magic Microns ( 3.5 % Metoflurin ) and Hit (Trans-flutrin 25 %). Gauging levels of mosquito repellent spray is done in the following way : one push- aerosol mosquito to be tested , weighed , and then sprayed for one second outdoors . Then after a severe insect repellent sprayed weighed again and the difference in weight is recorded (in grams). Gauging levels of spray made with three replica-tions.

A total of 20 Aedes aegypti (age 7-8 days) is released into the Glass Chamber and wait one minute. One push- aerosol insect repellent sprayed 1 time press into the Glass Chamber. Observations were made for 60 minutes. The number of mosquitoes fainting calculated at any specified time interval , which are : 0:50 ; 1:25 ; 2:00 ; 2:50 ; 3:00 ; 3:50 ; 5:00 ; 15:00 ; 30.00 and 60.00 minutes . Then all the mosquitoes moved into the plastic tube , given the wet cotton sugar solution and stored ( holding ) for 24 hours at room temperature 27 °C. To determine the time fall / lame ( Knock-down Time ) 50 % ( KT-50 ) used probit analysis. KT-50 is the time required for the drop / knock out 50 % of the population of mosquitoes in certain doses. As for knowing the difference bioefficacy between the treatment

Measurement of Bioefficacy and Its Effects on One Push Aerosol Insecticide by Using Glass

Chamber Firdy Yuana1), Chomsin S. Widodo2), and Sukainah Quraisyiyah3)

1) Faculty of Science, Brawijaya University, Malang, Indonesia 2) Faculty of Science, Brawijaya University, Malang, Indonesia


T



and control groups performed the t-test as a condition of ANOVA test. Bioefficacy insecticides have a sense of the effectiveness of the insecticide itself. Percentage collapsed and died in the experiment was 100 % after bioefficacy calculated using the formula :

( P + Q ) : R X 100 % (1) Description : P : Number of mosquitoes fainting

Q : The number of dead mosquitoes R : Number of mosquitoes tested

2 . Preparation of test animals Test animals used is dengue fever mosquito (Aedes ae-

gypti) were sterile dengue virus androgynous females , aged 7-8 days as many as 400 individuals . 3 . Particle measurements using P-trak 8525 models

The next stage is to measure the concentration of par-ticles contained in a spray product using the particle coun-ter is the P-Trak 8525 Model. The first step is to measure the temperature in the chamber by using a thermometer and the pressure using a barometer. Then, the P-trak hose connected to a glass chamber as well as the hose connects to the glass chamber with air pump. After that, one push aerosol insect repellent manually sprayed into the glass chamber for a second . Then spray the glass allowed to stand in the chamber during a predetermined time is: 0:50 ; 1:25 ; 2:00 ; 2:50 ; 3:00 ; 3:50 ; 5:00 ; 15:00 ; 30.00 and 60.00 minutes . After that, the concentration of particles is measured using a P-Trak 8525 Model and be repeated three times for each time. Automatically particle measure-ment data will be stored in the P-Trak then the data will be processed by using the 0rigin 8.1 software. The concentra-tion of particles obtained from the difference between the minimum and maximum values contained in the P-Trak program. Shape measurement circuit is shown in Figure 1.

Figure 1. Shape measurement circuit is shown

To calculate the total concentration of particles of insec-ticide used the following equation

(2) Information:

III. RESULT

From the measurement, the total concentration of aero-sol particles in one push can be seen in Figure 1. The con-centration of particles in the early minutes to 0.50 minutes product A the average particle concentration reached 17703 pt/cc , product B 18350 pt / cc, and for products C 10037 pt /cc. And then decreased at minute 1:25, 2:00

minutes and 2:50 minutes. This can be happen because most of the particles of the active materials undergo depo-sition (particles to the surface of the glass forming cham-ber, causing droplets with larger size).

In the next minute is minute 3:00, the particle concentra-tion increased on average significantly due to the 3:00 minute droplets stick to the glass chamber will change the shape of solids into gases that can be caught by the P-Trak. And at minute of 3:50, the particle concentration decreased again from the previous minutes. The decrease is largely due to the concentration of the particles that have accumu-lated particles into the air to experience deposition on the surface of the glass chamber. In the next minute, the con-centration of particles continued to decline slowly, until the last minute which are 60.00 , a product of particle con-centration 897 pt/cc , product B 1047 pt/cc , and the prod-uct C 493 pt/cc. This indicates that after 60 minutes the particle is not evaporated entirely or the particles accumu-late in the air is not all evaporated.

Fig. 1. Total concentration of particles.

By using ANOVA analysis shows that the three prod-

ucts had average concentrations were significantly differ-ent. From the ANOVA analysis was obtained that Product B has an average particle concentrations are higher than most other products, whis are 7427.7 pt/cc . The average particle concentration of the second highest is a product that is 4478.9 pt / cc and the lowest concentration is the product C is 3340.9 pt / cc.

Can be seen in Figure 1 that at minute 3:00, the particle concentration decreased , suggesting that at minute 1:25 ; 2:00 and 2:50 of the particles undergo deposition around the surface of the glass and experience the deposition chamber where the material will undergo a process of change in the form of a gas or liquid that is small be solid also called desublimasi so that the particles can not be ex-ploited by the P - trak .

At minute 3:00, the active ingredient volatile at tempera-tures above the room temperature, causing the particles are shaped colorless crystals undergo evaporation. When it is evaporated, the solid particles are transformed into a gas that can be sucked back by the P-Trak. This is why at the minute 3:00, particle concentration values increase. The workings of the active ingredients contained in insect re-pellent one push aerosol is is pyrethroid which attack the nervous system of the mosquito Aedes aegypti that can



cause paralysis. The active ingredients are micro-sized so it also attacks the circulatory system, the hormonal system, and respiratory system and can settle in the lungs and led to the death of the Aedes aegypti mosquitoes.

Henceforth , the testing of bioefficacy KT 50 and KT 90 by using three types of drugs push one of the aerosol mos-quito Aedes aegypti mosquitoes were placed in a glass chamber. Test results bioefficacy KT 50 and KT 90 can be seen in table 1.

TABLE 1. The relationship between the concentration of particles produced per

insect repellent spray for each product with the duration of paralysis

Test bioefficacy one push repellent aerosol showed that

mosquito mortality on all products are 100 % . KT 50 and KT 90 of the most effective is a product containing 21.3 % with variation transfluthrin paralysis time mosquitoes. Af-ter that followed by B product with the active ingredient metofluthrin 3.5 %. KT 50 and KT 90 a C product with the active ingredient transfluthrin 25 % has the lowest amount. Differences KT 50 and KT 90 at any one push repellent products due to differences in aerosol particle number concentrations in each product has a different amount.

It is seen that the product A , which contains 21.3 % transfluthrin , faster crippling mosquito Aedes aegypti is 11 seconds ( KT 50 minutes to 0.5 ) than the product C is 33 seconds ( KT 50 minutes to 0.5 ) which contains trans-fluthrin 25 % , even though the active ingredient product C higher than product A. This is because the value of the average concentration of product A is higher than product C. Types of active ingredients listed on the packaging of the product also affect the timing of mosquitoes death. It is shown in the tables above indicate that the product kills mosquito A faster than product B even though the products have an average concentration value higher than a product , for example in 0.5 minutes , the product A has a value of 17703 particle concentration pt / cc whereas product B has a particle concentration of 1830 pt / cc

IV. CONCLUSION

From the results of the research can be concluded that Product B has the largest concentration followed by prod-uct A and product C. Bioefficacy test bioefikasi of three different insect repellent products have a value of KT 50 and KT 90 distinct and not depending on the magnitude of the concentration of the active substance and the number of particles generated by one push-aerosol insect repellent. Product A has the most rapid efficacy followed by product B and C. There is still a residue of insecticide after 60 mi-

nutes of observation, this is indicated by the presence of a concentration of particles in the observation area

REFERENCES

[1] Ardley, Neil. 1998. Percobaan Ilmu Pengetahuan. Sema-rang. PT Mandiri Jaya.

[2] Biswas, P. 2009. Measurement and Capture of Fine and Ultrafine Particles from a Pilot-Scale Pulverized Coal Combuster with an Electrostatic Precipitator. J. Air&Waste Manage. Assoc.

[3] Departemen Pendidikan Nasional. 2004. Ensoklopedi Sains dan Kehidupan. Jakarta. CV Tarity Samudra Berlian.

[4] Djojosumarto, Panut. 2012. Available : http://www.google.co.id/Panduan%20Lengkap%20Pestisida%20&Aplikasinya.html.

[5] Ganiswara, S.G., Setiabudi, R., Suyatna, F.D., Purwantyas-tuti, Nafrialdi. 1995. Farmakologi dan Terapi (Edisi 4). Ba-gian Farmakologi FK UI: Jakarta

[6] Gillett, J. D. 1972. The Mosquito: It’s life, Activities and Impact on Human Affairs. Doubleday, Garden City. New York

[7] Hartanto, L.N. 2004. Biologi Dasar. Yogyakarta: Penebar Surabaya.

[8] Heller, J.L. 2010. Insectisida Poisoning. Medline plus. English.

[9] Kurnianti, Novik. 2013. Available: www.google.co.id/Petunjuk Aplikasi Pestisida.html

[10] Lucas, JR; Shono, Y; Iwasaki, T; Ishiwatari, T; Spero, N; Benzon, G. 2007. Journal of the American Mosquito Con-trol Association. Laboratory and field trials of metofluthrin (SumiOne) emanators for reducing mosqito biting outdoors. US. 23(1): 47-54

[11] M, Sugono. 2005. The biological activity of a novel pyreth-roid :Metofluthrin. Sumitomo Chemical Co., Ltd., Hyogo. Japan

[12] Sigit SH, Koesharto FX, Hadi UK, Gunandini DJ, Soviana S, Wirawan IA, Chalidaputra M, Rivai M, Priyambodo, Yu-suf S dan Utomo S. 2006. Hama Pemukiman Indonesia. Pengenalan, Biologi, dan Pengendalian. Bogor. Penerbit Unit Kajian Pengendalian Hama Pemukiman. Fakultas Ke-dokteran Hewan IPB.

[13] Staf pengajar Farmokologi. 1995. Absorbsi dan Ekskresi. Bagian Farmakologi FK UNLAM: Banjarbaru.

[14] T, Nazimek. et al. Content of Transfluthin. Departement of Toxicology, Institute of Rural health. Lublin. Poland

[15] Widiarti, dkk. 1997. Uji Bioefikasi Beberapa Insektisida Rumah Tangga terhadap Nyamuk Vektor Demam Berda-rah. Stasiun Penelitian Vektor Penyakit, Pusat Penelitian Ekologi Kesehatan Badan Penelitian dan Pengembangan Kesehatan. Salatiga.

[16] Womack, M. 1993. The yellow fever mosqito, Aedes aegypti. Wing Beats. Florida

[17] Zoolner, G; Orshan, L. 2011. Journal of the Society for vector Ecology. Evaluation of metofluthrin fan vaporizer device against phlebotomine sand flies (Diptera: Psychodi-dae) in a cutaneous leishmaniasis focus in the judean Desert. Israel. 36 Suppl: S157-65



Abstract—This paper investigates numerically the probe pulse effect on the dynamic alignment, generated by pump pulse, in a double pulses experiment. To this end, we compare the dynamic alignment by pump pulse only, with that ob-tained by using both pump and probe pulses. The numerical calculation demonstrates that the probe pulse slightly raises the degree of alignment without changing its general charac-teristics, both in time and frequency domain

Keywords—double pulses experiment, probe, alignment, time domain, frequency domain

I. INTRODUCTION

he double pulses experiment is one of the interesting research topics in atomic and molecular physics, no less because of its potential applications as a source of

coherent ultraviolet light and/or generation of ultrashort attosecond laser pulses [1], for molecular imaging [2], for controlling molecule-laser interaction [3], etc.

In double pulses experiments, an ensemble of gas is first subject to a femtosecond pump pulse to controls their wave-function to set them in rotational motions and wait for the molecules to be aligned at a later time (a few pico-seconds) when they could undergo a ‘rotational revival’ [3]. The second more intense femtosecond probe pulse, was delayed with respect to the first by successively in-creasing the time intervals td, is given to generate the ob-served signals [4], either in ionization [5], dissociation, optical Kerr effect (OKE) [6], photoelectron angular dis-tribution (PAD) [7], above threshold ionization (ATI) [8], or high harmonic generation (HHG) schema [4]. By plot-ting the observed signal as a function of delay time be-tween two pulses, one obtains the dynamic signal describ-ing the molecular behavior due to the interaction.

The typical pump-probe experiment set up by using HHG as a probe, is depicted in Fig. 1. A laser beam is split into two parts by a beam splitter (BS) to separate the pump and probe pulses, with a specific ratio of intensity between them. The probe pulse is then delayed with a controllable delay line system D and, if needed, can be also rotated with a polarizer P. The two pulses is then mixed again with a beam mixer (BM) where the probe pulse is delayed by

from the pump pulse and its polarization is rotated with respect to that of the pump pulse by an angle , if desired. The pump-probe pulse sequence is focused by a system of lenses (F) and is then subjected to a molecular gas jet: the pump pulse dynamically ‘aligns’ the molecules whereas the probe pulse generates HHG signal from the aligned molecules. The HHG signal is then recorded by a detector system. Depending on the experimental purpose,

the double pulses experiment can be arranged in several different ways.

Fig. 1. Schema of pump-probe experiment. The explanation is given in text

So far it is assumed that the alignment is due to the pump pulse only, while the second one only gives the ob-served signal, without rotating it during the short duration of the probe pulse. However, it is worthwhile to observe, whether the probe pulse affects on the dynamic signal, or not. The question has been raised in Ref. [9]. Here, the discussion is extended, including the eighth revival and its Fourier spectrum.

II. COMPUTATIONAL DETAILS

To overcome the question, I directly compare here the alignment by the pump pulse only, with that obtained by using both the pump and the probe pulses.

For the second schema, we allow the possibility that the probe pulse also may rotate the molecule before generating the observed signal. The total field is assumed to consist of the sum of the two fields:

In the above indices 1 and 2 stand for pump and probe pulse, respectively. td is the delay time between the two pulses. In above, ε10 and ε10 are peak field whereas

and are time profile of the field. The

effective pulse then reads

For two pulses, dynamic alignment is characterized by

pump and probe peak intensity, pump and probe time pro-files, delay time between them, and interaction time (i.e. delay time between entering probe pulse and observing

The Effect of Probe Pulse in a Double Pulses Experiment

Abdurrouf Faculty of Mathematics and Natural Sciences, Brawijaya University, Malang, Indonesia

Corresponding author: [email protected]

T



signal). The schema for alignment by the pump and the probe

pulses is the following. The probe pulse is given after the pump pulse. We let the probe pulse to interact with the molecule at a given time and then take the some expecta-tion values and plot as function of the delay time . is the quantum measurement of dy-namical alignment of a rotating molecule, given by

, which is the expecta-tion value of the with θ is the angle between the mo-lecular axis and the pump/probe polarization direction; the double angular brackets stand for the expectation value with respect to the wave packet states (inner brackets) and the statistical average with respect to the Boltzmann distri-bution (outer brackets) of the initially occupied rotational states.

In this paper, I calculate two expectation values of a di-atomic linear molecule , those are the first alignment degree , which is the first term in ionization signal [5], and the second alignment degree

, which is the first term in high har-monic generation signal [4]. The properties of are listed in Tab. I.

TABLE I PROPERTIES OF O2

Symbol Quantity Value

Rotational constant 1.4297 cm-1

parallel polarizability 2.35 (A3)

perpendicular polarizability 1.21 (A3)

number of even-j wave function 0

number of odd-j wave function 1


In experiment, I use here the pump and probe pulses with peak intensity of and

. Both pulses have Gaussian profile with FWHM (full width at half maximum) 40 fs. The gas of is kept at room temperature 300 K. I choose the pulse duration, 40 fs, as the delay time between enter-ing probe pulse and observing signal

The results are shown in Fig. 2, for (up-per panel) and (lower panel). From the upper figure, it is shown that there is no difference be-tween the signal due to the double pulses (black, solid lines) compare to that of single pulse (blue, dashed lines). Both signal revive with a period of , which is given by

For O2, . The signals also show a half and

quarter revival, which is a character of .

Fig. 2. Dynamics alignment of (upper pan-el) and (lower panel) of at 300 K subject to laser pulses of and with FWHM 40 fs. We keep the second pulse to interact for 40 fs. Solid lines for double pulse and dashed lines for one pulse.

In general, the probe pulse does not change the dynam-

ics of , except in three aspects. First, the probe pulse intensity adds up the intensity of the pump pulse and enhances the alignment process prepared and generated by the pump pulse, so that the expectation value of with two pulses is higher than the one with pump pulse only. Second, the alignment enhancement by the probe pulse depends on the delay time between the two pulses and reaches a maximum when the slope of alignment degree is positive. This means that the degree of alignment is enhanced before its maximum and as a conse-quence reaches the maximum earlier than the alignment with one pulse only. Third, there are a small revival struc-ture at in signal due to two pulses (as shown in Fig.

3), where they are absent in the case of single pulse signal. The similar situation occurs in (Fig

2. lower panel). Both signal has a period of 11.6 ps and show a half, quarter, and eighth revivals, as a characteris-tics of . In general, there is no signifi-cant difference between the signal due to single and double pulses.



Fig. 3. The revival structure at (panel a), (panel

b), (panel c), and (panel d) of of

for double pulses. The pulses parameters and tempera-ture are similar to those in Fig. 2.

To see more clearly, we Fourier transform the dynamic

signal and get the spectrum in frequency domain, as shown in Fig. 4 for and Fig. 5 for

. From Fig. 4, one can see that there is no difference be-

tween spectrum of single and double pulses. Both spec-trum consist of peak series located at (10,18,26,34,….)Bc or . As we know, the gene-rates a transition with The transition with

0∆ =J creates a peak at frequency zero, whereas the tran-sition with is associated with phase differences with

(4) with c in cm/s. The absence of series (4Jeven+6)Bc indicat-ing that O2 has Jodd only. The series is peaked at 50 Bc corresponding to . I note here, that the revival structure at does not contribute in Fourier spectrum

because of its small depth modulation, which is about one by hundred compare to that of half and fourth revival.

Fig. 4. The Fourier spectrum of of for single pulse (upper panel) and double pulses (lower panel). The pulses parame-ters and temperature are similar to those in Fig. 2.

Fig. 5 shows the Fourier spectrum of . The dynamic of

is associated with phase differences . While the transition with is related to series of (10,18,26,34,….)Bc, the transition with is asso-ciated with phase differences with , (5) and related to series of (28, 44, 60, 76, 92, 108, 124, 140,….) Bc. According to Eqs. (4) and (5), the Fourier spectrum of of O2 consists of series of

and . It was shown in Fig. 5, that both Fourier spectrums show series of (10, 18, 26, 34,….) Bc as representation of and series of (28, 44, 60, 76, 92….)Bc due to tran-sition. The series is peaked at 50 Bc cor-responding to , similar to those of

. On the other hand, the series is peaked at 140 Bc corresponding to

. The detail of Fourier spectrum and its role in dynamic alignment can be found in Ref. [10]. I also note that the Fourier spectrum of of double pulses has similar intensity compare to that of sin-gle pulse. It is related to the depth modulation of two dy-namic signals. On the other hand, the intensity of the Fourier spectrum of is smaller than that of single pulse. It can be understood, because the depth



modulation of for double pulse is not similar to that of single pulse.

Fig. 5. The Fourier spectrum of of for single pulse (upper panel) and double pulses (lower panel). The pulses parameters and temperature are similar to those in Fig. 2.

IV. CONCLUSION

In summary, it has been demonstrated that the probe pulse does not change the signal, in both time and frequen-cy domain. Then, we can neglect the effect of probe pulse

in the observed signal in a double pulses experiment.

REFERENCES

1) A.H. Zewail, “Femtochemistry: Atomic-scale dynamics of the chemical bond”, J. Phys. Chem. A, vol. 104, no. 24, pp. 5660 – 5694, June 2000.

2) J. Itatani, J. Levesque, D. Zeidler, H. Niikura, H. Pepin, J.C. Kieffer, P.B. Corkum, and D.M. Villeneuve, “Tomographic imaging of molecular orbitals”, Nature, Vol 432, no 7019, pp. 867 – 871, Dec. 2004.

3) J. Itatani, D. Zeidler, J. Levesque, M. Spanner, D.M. Villaneuve, and P.B. Corkum, “Controlling high harmonics generation with molecular wave packets”, Phys. Rev. Lett., vol. 94, no. 12, pp. 123902, March 2005.

4) K. Miyazaki, M. Kaku, G. Miyaji, A. Abdurrouf, and F.H.M. Faisal, “Field-Free Alignment of Molecules Observed with High-Order Harmonic Generation”, Phys. Rev. Lett., vol. 95, no. 24, pp. 243903 (1-4), Dec. 2005.

5) P. W. Dooley, I. V. Litvinyuk, K. F. Lee, D. M. Rayner, M. Spanner, D. M. Villeneuve, and P. B. Corkum, “ Direct imaging of rotational wave-packet dynamics of diatomic molecules”,. Phys. Rev. A, vol. 68, no. 2, pp 023406, August 2003.

6) B.J. Sussman, J.G. Underwood, R. Lausten, M.Y. Ivanov, and A. Stolllow, “\Quantum control via the dynamic Strak effect: Application to switch rotational wave packets and molecular axis alignment”, Phys. Rev. A, vol. 73, no. 5, pp. 053403, May 2006.

7) M. Tsubouchi and T. Suzuki, “Photoionization of homonuclear diatomic molecules aligned by an intense femtosecond laser pulse”, Phys. Rev. A, vol. 72, no. 2, pp. 022512, 2005.

8) T. K. Kjeldsen and L. B. Madse, “Alignment-dependent above-threshold ionization of molecules”, J. Phys. B: At. Mol. Opt. Phys., vol. 40, no. 1, pp. 237 – 245, Jan. 2007

9) A. Abdurrouf and F. H. M. Faisal, “Theory of intense-field dynamic alignment and high-order harmonic generation from coherently rotating molecules and interpretation of intense-field ultrafast pump-probe experiments”, Phys Rev. A, vol. 79, no 2, pp. 023405, Feb. 2009

10) F. H. M. Faisal, A. Abdurrouf, K. Miyazaki, and G. Miyaji, “Origin of Anomalous Spectra of Dynamic Alignments Observed in N2 and O2”, Phys. Rev. Lett., vol 98, no, 14 pp. 143001, April 2007.



Abstract— The Teluk Mandar is located around Makassar strait, and is situated within a complex tectonic region at the edge of Eurasian plate. Gravity data analysis have been performed to identify the subsurface structure Complete Bouguer gravity anomalies derived from satellites altimeter measurements were used in this analysis. We separate the Bouguer gravity into two parts, the regional and the residual gravity, by using Power Spectrum based filtering. The residual gravity data were then analyzed using integrated gradient interpretation techniques, such as the Horizontal Gradient (HG), and Second Vertical Derivative (SVD). These techniques detected many faults and several sedimentary basin that characterized by negative Bouguer gravity anomalies. The results of present study will lead to an improved understanding of the geological structure in Pare-pare region, especialy inside Sengkang sedimentary basin area.

Keywords—Gravity, Teluk Mandar, Horizaontal Gradient, Second Vertical Derivative

I. INTRODUCTION

HE Teluk Mandar is located around Makassar strait between 3.556 oS and 4.9853 oS latitudes; and 119.4083 oE -- 120.3083 oE longitudes, is situated

within a complex tectonic region at the edge of Eurasian plate, covers an area of about 12.869 km2(Figure 1). It is well-known that Sengkang sedimentary basin is located in the onshore of South Sulawesi province.

Gravity data from Geosat and ERS-1 satelites are analyzed using advanced gravity data processing to determine structures that affected the basin and to estimate the sedimentary thickness. It is known that gravity interpretation suffers from non-uniqueness. Different subsurface features produce same gravity field. Care was taken to constrain the analysis results using integrated gradient method [4].

1)Department of Physics, Faculty of Mathematics and Natural Science, University of Indonesia, Kampus UI Depok, Indonesia, 16424 2)Aso Volcanological Laboratory, Graduate School of Science, Kyoto University, Minami Aso, Aso, Kumamoto 869-1404, Japan *) Corresponding author: [email protected]

II. GRAVITY METHOD

In principal, the goal of gravity surveying is to locate and describe subsurface structures from the gravity effects caused by their anomalous densities. Gravity studies in regioanal basin area, in particular, can provide unique in-sights into shallow sub-surface density variations associ-ated with the structural and magmatic activity of basinal system [2].

Fig 1. Teluk Mandar region, South Sulawesi Province

Nowadays, gravity data could be acquared from satellites. Satellite altimetry has provided the most comprehensive images of gravity field at ocean basins with accuracies and resolution approaching typical shipboard gravity data.

The analysis of gravity data uses three approaches to reduce the error in satellite-derived gravity anomalies to 2–3 mGal from 5 to 7 mGal. First, the raw waveforms were retracked from 11 months of ERS-1 satellite data [4] and 18 months of Geosat/GM satellite data resulting in improvements in range precision of 40% and 27%, respectively. Second, the recently published EGM2008 global gravity model at 5 min resolution [1] were used in the remove/restore method to provide 5-min resolution gravity over the land and 1-min resolution (8 km 1/2 wavelength) over the ocean with a seamless land to ocean transition. Third, a biharmonic spline interpolation method including tension [6] were used to construct residual

Gravity Data Analysis of Teluk Mandar, Makassar Strait

Supriyanto 1*), T. Noor 1), Y. Mark 1), and Y. Sofyan 2) 1)

Department of Physics, Faculty of Mathematics and Natural Science, University of Indonesia, Kampus UI Depok, Indonesia, 16424

2)Aso Volcanological Laboratory, Graduate School of Science, Kyoto University, Minami Aso, Aso, Kumamoto 869-1404, Japan


T



vertical deflection grids from seven types of inconsistent along-track slope measurements.

Comparisons between shipboard gravity and the global gravity grid show errors ranging from 2.0 mGal in the Gulf of Mexico to 4.0 mGal in areas with rugged seafloor topography. The largest errors of up to 20 mGal occur on the crests of large seamounts.

III. GRAVITY MEASUREMENT AND DATA PROCESSING

Gravity data retracked from Geosat and ERS-1 altimetry satelites have been provided by The Satellite Geodesy research group at Scripps Institution of Oceanography, University of California San Diego. A total of 3904 point of gravity measurement were distributed at Teluk Mandar region at the total area of about 12.869 km2 ; with point interval of about 1.8 km (1 minute). The gravity data set consists of longitude and latitude coordinates; and complete bouguer anomaly (CBA).

All data processing steps are shown in Figure 2. In this study, we assume that the final output of several steps of processing of satellite-derived gravity anomaly explained above is a Complete Bouguer Anomaly (CBA) as shown in Figure 3. The CBA data ranges between -21 and 114 mGal. Negative anomalies were observed mainly in the offshore of Teluk Mandar region. Meanwhile, highest positive gravity values were detected on the onshore of Teluk Mandar region and, it can be interpreted by high density of volcanic rock instrusion.

Fig 2. Flow processing of gravity data

There is a general assumption that the negative value of

the CBA could be interpreted as a sedimentary basin. However, the CBA value come from all bodies anomaly at various depth. Moreover, in contrast, the Sengkang sedimentary basin is covered by relatively high of CBA, not negative value. Therefore, to be more confident for determining the location of sedimentary basin, we need to extract residual gravity anomaly from the CBA.

We have separated the CBA into two parts, the regional and the residual gravity anomalies, by using power spectrum based filtering. The regional gravity reflects source of gravity anomaly from deeper part which is more than 10 km. We have decided to analize residual gravity, rather than regional gravity. In term of gravity data, we have expected that sedimentary basin indicated by low residual gravity anomaly inside Sengkang sedimentary basin area (Figure 4).

Fig. 3. Complete Bouguer Anomaly (CBA) map of Teluk Mandar region, South of Sulawesi. Coast line and Sengkang Basin boundary are shown in blue line and black line respectiveley.

Fig 4. Residual gravity anomaly of Teluk mandar region

The residual gravity resulted from filtering were then

analyzed using two integrated gradient methods, such as the Horizontal Gradient (HG) and Second Vertical Derivative (SVD) methods. These techniques detected many faults as the boundary of the sedimentary basin. Moreover, the depth structure of formation inside the Sengkang sedimentary basin have been delineated.

IV. RESULT AND DISCUSSION

A. Horizontal Gradient

The horizontal gradient method was used extensively to locate the boundaries of regions of contrasting density from gravity data [3]. The greatest advantage of the



horizontal gradient method is that it is least susceptible to noise in the data because it requires the calculation of only two first-order, horizontal derivatives of the field [4].

22

( , )H H

H x yx y

∂ ∂ = + ∂ ∂

(1) The map of horizontal gradient from Teluk Mandar is

shown in Figure 5. It shows that the boundaries/faults are located at the maxima of the horizontal gradient. Some new faults were detected as well. These faults are located at, or near the volcanic area. Moreover, some geological faults are not corroborated by the horizontal gradient technique. This discrepancy may be the fact that the horizontal gradient detects only faults that displaced formations vertically causing density contrasts.

Fig 5. Result of Horizontal Gradient analysis

B. Second Vertical Derivative

In order to identify certain type of fault, the Second Vertical Derivative (SVD) analysis has been aplied to the CBA free noise. The SVD shows and isolates structural features that are identical and complimentary to those already identified with the horizontal gradient maxima. The Euler solution superimposed on the SVD clearly outlines the various contacts and structures of the faults and basin boundaries.

To determine fault type, we first make a line that perpendicular to the certain fault line interpreted by Euler calculation. Second, evaluate the maxima and minima of the SVD value along the line. If the absolute of maxima number is greater than absolute minima number, the fault type is identified as a normal fault. Otherwise, the fault type is reverse fault.

Fig 6. Second Vertical Derivative map of Teluk Mandar region

Fig 7. SVD value variation along AB line. The fault type is normal fault because absolute maxima number is greater than absolute minima number.

V. CONCLUSION

Integrated gradient interpretation techniques, Horizontal Gradient (HG) and Second Vertical Derivative (SVD). Have ssuccessfully detected many faults and several sedimentary basin that are characterized by negative Bouguer gravity anomalies. The structural high interpreted from SVD analysis high has covered the area around of 250 km2.

ACKNOWLEDGMENT

We would like to acknowledge the support of the Directorate of Research and Public Service, University of Indonesia, who gave us financial support under the Hibah Penelitian Unggulan Perguruan Tinggi (PUPT) – BOPTN 2013 scheme with the contract number: 1350/H2.PPK/HKP.05.00/2013.



REFERENCES [1] Mikhailov, V., Galdeano, A., Diament, M., Gvishiani, A., Agayan,

S., Bogoutdinov, S., Graeva, E., Sailhac, P., (2003). Application of artifcial intelligence for Euler solutions clustering. Geophysics 68, 168e180. Mikhailov et al., 2003

[2] Pavlis, , N. K., S. A. Holmes, S. C. Kenyon, and J. K. Factor (2008), An Earth Gravitational model to degree 2160, paper presented at General Assembly, Eur. Geosci. Union, Vienna.

[3] Reid, A.B., Allsop, J.M., Granser, H., Millett, A.J., Somerton, I.W., (1990). Magnetic interpretation in three dimensions using Euler deconvolution. Geophysics 55, 80e91

[4] Sandwell, D. T., and W. H. F. Smith (2005), Retracking ERS-1 altimeter waveforms for optimal gravity field recovery, Geophys. J. Int., 163, 79–89, doi:10.1111/j.1365-246X.2005.02724.x.

[5] Thompson, D.T., (1982). EULDPHda new technique for making computer-assisted depth estimates from magnetic data. Geophysics 47, 31e37.

[6] Wessel, P., and D. Bercovici (1998), Interpolation with splines in tension: A Green’s function approach, Math. Geol., 30(1), 77 – 93, doi:10.1023/A:1021713421882.



Abstract—Archaeological fragments of bone that are ex-

posed to a wetland environment take up fluorine from the surrounding soil. The fluorine ion exchanged the hydroxyl group in the hydroxyapatite (Ca10(PO4)6(OH)2)of the bone, forming chemically more stable fluorapa-tite(Ca10(PO4)6(F)2). Based on our data 14C radiocarbon, the age of two Tamban’s subfossil are 485-± 5 and 5684 ± 16year ago, respectively. The IR spectrum is sharp band 3500-4000 cm-1 in the hydroxyapatite. Tamban’s subfossil and Tahura’s bone that spectrum is assigned to the OH stret-ching mode and considering the fossilization have been a con-servations in wetland environment. In the region 800-900 cm-1, the subfossil and bone implies that carbonate and silicon substitution don’t induce vacancies at the OH- site. In here, we report that modeling Ca2+replaces Cu2+, Cd2+ and Zn2+ions, which can be described by a diffusion model, con-tain information on the exposure duration of the Tamban’s subfossil object, several attempts to use metal profiling as a relative dating method.

Keywords—14C, FTIR and relative dating method.

I. INTRODUCTION

HE chemical composition of the mineral and the organic part of bones has been used palaeodiet and palaeoclimate reconstruction. However, the burial

period, bones have been in contact with sediments, soil and water. Partial of complete dissolution, erosion, and precipitation, recrystallization, ion uptake by sorption and diffusion, hydrolysis, and polymerization may lead to changes in the chemical composition and structures. The state of preservation is very variable and depends mainly on direct environmental conditionsuch as groundwater and sediment temperature, soil hydrology, and pH, reduction-oxidation (redox) potential and temperature, mechanical pressure, biological factors and particle transport. They are of great importance to understand the alteration process in soils and the impact of environments conditions on bone/fossil conservation[1]. Very few studies have ad-dressed multi-element ionic exchanges between soil solu-tion and bones. Ionic interactions with soil solution would

involve competition between a wide range of ions for the various lattice position in bone mineral (apatites) [2]. In this context, the preservation of the geochemical signal in biological apatites that are relevant for studies related to between 14C dating, rate diffusion of ion metal (Cu2+, Cd2+ and Zn2+), and shift of FTIR spectra.

In the Borneo, elephants have a very limited distribu-tion, being restricted to approximately 5% in northeast. Fossil evidence for the prehistoric presence of elephants on Borneo is limited to a single specimen of tooth from a cave in Brunei. Two history popular, considering the geo-graphic proximity to Borneo, the elephant trade that flou-rished in Sumatra, Java and Paninsular Malaya during 16th-18th centuries may have been the source. Alternatively, Borneo’s elephant presented to the Sultan of Sulu in 1750 to Borneo northeast by East India Trading Company. These animals presumably originated in India [3].

Conversely, if elephant occurred naturally on Borneo, they would have colonized the island during Pleistocene glaciations, when much of the Sunda shelf was exposed and the western Indo-Malayan archipelago formed a single landmass designated Sundaland. Thus, the isolation of Borneo’s elephant from other conspecific population would minimally date from the last glacial maximum 18,000 year ago, when land bridges last linked the Sunda islands and the mainland [4]. If Borneo’s elephants are indigenous origin, this would push the natural range of Asian elephant 1300 Km to east, and as a unique popula-tion at an extreme of the species’ range, Borneo elephants’ in situ conservation would be apriority and ex situ cross-breeding with other population would be contraindicated [3].

Initially, Borneo elephant were classified as a unique subspecies (Elephas maximus borneoensis) based on mor-phological differences other population [5]. Subsequently, they were subsumed under the Indian Elephas maximus indicus[6] or the Sumatran Elephas maximus sumatren-sis[7] subspecies, based on assumption of their introduc-tion to the region or on the reasoning that morphological divergence was insufficient to warrant separated status. According to Fernando (2003), HVS mtDNA analysis showed that Borneo’s elephant are genetically distinct with

Modeling of Relative Dating Using Spectroscopy Analysis of Tamban’s Subfossil T.B. Susilo*#, U. B. L. Utami*, R. Nurmasari*, R. Mujianti*, M. Habibi*** , S. Mawadah*** , A. Prada-

na*** , S. Hayati*** , Y. Seftiawan*** , O. Soesanto** , B. I. Prayogo**** and Satrio *****

*Prodi Kimia FMIPA UNLAM, Jl. A. Yani Km 35,5 Banjarbaru, Kal Sel; *** Mahasiswa Kimia FMIPA UNLAM, Jl. A. Yani Km 35,5 Banjarbaru, Kal Sel; ** Prodi Matematika FMIPA UNLAM, Jl. A. Yani

Km 35,5 Banjarbaru, Kal Sel; **** Museum Lambung Mangkurat, Jl. A Yani Km 35, Banjarbaru, ***** Pusat Aplikasi Teknologi Isotop dan Radioaktif-Badan Teknologi Atom Nasional (PATIR-

BATAN), Jl. Lebak Bulus Raya No. 49, Pasar Jumat, Jakarta,

*#Corresponding author: [email protected]

T



molecular divergence indicative of a Pleistocene coloniza-tion of Borneo and subsequent isolation.


The Fourier Transform Infrared (FTIR) spectra were recorded on Bruker Optic IFS66s/S interferometer equipped with an attenuated total reflectance (ATR) unit. The range frequencies was 650-4000 cm-1 and the typical experimental condition utilized a resolution of 4cm-1, a velocity of 6-10 KHz, a gain of 16x, an apodization Black Harris 3-term, a Mertz phase correction and zero filling 2, on a double sided, forward-backward acquisition mode. A KBr beam splitter was used for the M-IR source. Subse-quently, aliquots of approximately 2 mg subfossil Tam-ban’s elephant were ground and pressed into a KBr pellet and the infrared spectra were measured on a Perkin Elmer Spectrum One instrument [8].

For isotope 14C dating, carbonate in calcined subfossil Tamban’s elephant obtained is the most reliable source of inorganic carbon. The subfossil was demineralized in a 1% hydrochloric acid (HCl) solution several days. The ex-tracted gelatin-like collagen was thoroughly washed with distilled water. In order to remove the humic acid, the col-lagen was treated with an 0.1 N sodium hydroxide (NaOH) for several days. The remaining collagen was again washed with distilled water, dried and carbonized by heating at 800oC in an oxygen-free environment. The phosphorous compounds were removed by treating the collagen with “aqua regia”, a mixture of nitric acid (HNO3) and hy-drochloric acid (HCl). The cleaned collagen was then washed with distilled water, dried, and used for carbon dioxide gas (CO2) preparation [9].For metal Cu, Cd and Zn carried out by atomic absorption spectroscopy (AAS) [10]. Modeling dating relative referenced to Lagrange me-thod of interpolation [11], [12] and [13].


A. Examination of sample preservation by FTIR

The major peculiarities for a diagenetically altered bone are an increase in crystal size and a decrease in protein content. Thus information on the state of degradation can be obtained from FTIR spectroscopy (Fourier Transform Infrared Spectroscopy) by observing the characteristic splitting of the double peaks at 563-604 cm-1 correspond-ing to phosphate vibration ν(PO4)3- indicating mineral phase modification, e. g changes in crystallinity and ion exchange. A low value for the splitting factor (SF) indi-cates a high amount of amorphous material in the mineral phase and obtained as described in Reiche et al. (2003). The intensity of the organic CO-signal at ≈ 1650 cm-1(compared to the signal of inorganic CO32- at ≈ 1450 cm-1 provides an indication of the degree of collagen de-gradation and expressed as C=O and C-C bonding, where a high value represents a high degradation of collagen in the sample [8].

The IR spectra of all specimens (Tahura’s bone , Tam-ban’s subfossil and Antonakos’s data) show broad bands in the high energy region that are propably water related band (table 1). They show peak positions without leading to any

conclusions. The sharp band 3500-4000 cm-1 in the HAp, Tamban’s subfossil and Tahura’s bone spectrum is as-signed to the OH stretching mode; presence this mode from the spectra of Tahura’s bone and Tamban’s subfossil implies the carbonate content and silicon substitutions did not induce vacancies at the OH sites. The increase in in-tensity with decrease in carbonate content and the presence of structurally bound OH in the little carbonated Tahura’s bone and Tamban’s subfossil has been reported in a very recent work.

Table 1, shows the 650-900cm-1 region of the IR spec-tra of all the samples. In the region, all substituted samples display very weak bands that can be assigned to the vibra-tion4CO32- (ν4 CO32-)and ν2 CO32- modes at energies similar to the previously reported exceptfor the Tahura’s bone and Tamban’s subfossil [8]. The absence ν2CO32-, the subfossil and bone implies that carbonate and silicon substitution don’t induce vacancies at the OH- site, proba-bly considering the fossilization have been a conservation in wetland environment. The weak intensity of the absorp-tion band near 1640cm-1 corresponding to νCNH of the amide group [1]. According to Abeyratne et al (1997) [14], FTIR trace for bone and fossil, phosphate is indicated by double troughs around 600cm-1 and width trough at 1036 cm-1. Carbonate is shown bay the narrow dip at about 875 cm-1 and the wider one at 1425cm-1. The Tamban’s sub-fossil and Tahura’s bone was measured with FTIR by ex-amining the splitting factor of PO4 anti-asymmetric bend-ing mode peak at wave number 563cm-1.

The FTIR spectra of both bone and subfossil are charac-terized by intense band between 1300-2000cm-1 and 2300-3000 cm-1 indicating an abundant contribution of alkyl chains. Strong aliphatic absorptions centered at around 2860-2930cm-1 are assigned to asymmetric stret-ching vibrations from CH2 and symmetric stretching vibra-tions from CH2 methylene group, respectively. The ab-sorption of symmetric bending of CH3 with possible con-tribution from (CH)n bending. The presence a long poly-methelynic chains (n ≥ 4) is indicated by the absorption at around 720cm-1. The absorption at 1710cm-1 shows the presence of carboxyl group. The weak absorption signal attributed to aromatics C=C ring stretching vibration peaks at round 1620cm-1.

B. Radiocarbon Dating and Originality of Bor-neo’sElephant

In the Indian subcontinent, the dispersal elephants car-ried out by human intervention through trade and warfare. Human activity has changed the natural distribution pattern of elephants. Special originality Borneo’s elephants still discussion. There are two popular hypotheses, first, refer to the documents that the sultan of Sulu has received a gift elephant from the Indian (Elephas maximus indicus) in 1750 and domesticated in North Borneo[6]. According to Medway (1977)[7], elephant Borneo derived from Suma-tran elephant or Elephas maximus sumatrensis. The next hypothesis is indigenous or not introducer. Fernando (2003) [3] stated that based on the analysis of D-loop mtDNA fragment, population showed evidence of indi-genous and not introducer. Mac Kinnon (1996) [4] also



stated that the Borneo elephants have colonized the Pleis-tocene about 18,000 years ago when the bridge was formed Sundaland shift. Our 14C dating data also show that the findings of the elephant subfosil from Tamban village, district Batola shows the age range 485±5 and 5684±16 years ago. This information supported the second hypothe-sis.

C. Modeling Relative Datingand Cation Exchange Cu2+, Cd2+ and Zn2+

The use of entropy of hydration (∆Hh) in addition to crystallographic ionic radii improve predictions concern-ing the abilities of various cationic to substitute in to Ca2+ lattice position. The heat of hydration provides a measure of the strength of ion water molecule bond. Strong bonds are indicated by highly negative ∆Hh values. In the context fossilization, the Cu2+(-2100 kalJ/mol), Zn2+(-2044kalJ/mol), Cd2+(-1806 kalJ/mol) cationic substitutein to Ca2+(-1592 kalJ/mol) lattice hydroxyapatite. Larger ca-tionic tend to associate less strongly with water due to their increased radii (Å) and reduced surfaced area[2].For radii Cu2+, Zn2+, Cd2+and Ca2+cations are 0.73Å; 0.74Å, 0.95Å, and 1.00Å, respectively.The degree of hydration increases with increasing ionic charge. In the case of this experimen-tal, the third cationicchargeare the same so that is 2+. Ac-cording Scimiklas (2003) [15] and Smiliklas (2007) [16] and Chen (1997) [17], the exchange Cu2+, Zn2+, and Cd2+cationicin to Ca2+ lattice hydroxyapatite can be ex-pressed as follows. [Ca10(PO4)6(OH)2] + n X2+→ [CA10-nXn(PO4)6(OH)2] + n

Ca2+ X2+ : Cu2+, Zn2+, or Cd2+cationic n : number of moles

Ionic exchange between soil solution and bone should be a dominant process in apatite mineral diagenesis. Expe-rimental evidence indicates that Ba2+, Mg2+, Pb2+, Sr2+, Na+, CO3

2- an F- enter the crystal surface from the bound hydration layer of mineral hydroxyapatite (Ca10(PO4)6(OH)2). Ion exchange involves the isomorphic

substitution of solution ions for normal hydroxyapatite lattice position [2]. In our data, Cu2+, Cd2+ and Zn2+cationic replace lattice Ca2+, after elephant subfossil from Tamban (Batola-Kalsel) and elephant bone (Tahura park) have been buried for 1, 2, 4, and 6 month, respec-tively. Carbon dating showed 485-± 5 and 5684 ± 16 BP (Before Present) (table 2). Modeling dating relative have been construct based on radiocarbon dating versus spec-troscopy data (table 2) using multi-interpolation method (table 3 and figure 1) [12].e. g. relative dating Y(Zn) = -1.4333e-007t2 +0.00063794t + 0.14854 and rate diffusion Y(Zn)/dt = -2.8666e-007t + 0.00063794 (table 3 and figure 1).

TABLE 2. PERCENT OF MATERIAL SUBFOSSIL VERSUS FOR BURIED

(YEAR)

FG: Elephant subfossil buried 485 and 4148 year (BP) from Tamban village; RG: Elephant bone buried 7 year from Tahura park

TABLE 3.

MODELING DATING RELATIVE BASED ON MATERIAL VS TIME (YEAR).

Materi-al

t2 (year) t (year) C (con-stant)

Ash 1.2178e-005 -0.049814 64.138 Organic -1.163e-005 0.045528 33.06 Cu -3.2659e-008 0.00013609 0.004815 Cd -3.1.3912e-

005 1.3912e-005

0.0053648

Zn -1.4333e-007 0.00063794 0.14854

Figure 1. Equation shown that type related between ash and organic material (%) vs t (time-year) (A), equation Cu, Cd and Zn content(%) versus t (time-year) (B).

IV. CONCLUSION

Figure FTIR analysisand14Cdatingsuggest thatthere is Elephas maximusborneensisandindigenous in the Borneo. Combinationanalysis ofCu, CdandZncontent of elephant subfossiland14Cradiocarbondatingshownnon-linear equa-tions.

Year Sam-ple

Ash% Organ-

ic% Cu% Cd%

Zn%

485.083

FG

76.667 21.260 0.011 0.008 0.488

485.167

61.290 38.710 0.003 0.005 0.004

485.25 60.000 40.000 0.009 0.007 0.253 485.5 61.290 34.630 0.005 0.007 0.002

7.083

RG

70.968 18.832 0.008 0.008 0.4 7.167 73.333 14.907 0.009 0.009 0.499 7.250 67.857 18.143 0.013 0.007 0.406 7.500 63.333 24.907 0.006 0.008 0.32

485 23 70 0.118 0.017 0.692

4148 59.70 32.29 0.0008

3 0.0003

1 0.0180

HAp CHAp 15M

CHAp 25M

SiHAp CFAp 530 CFAp 840 Tamban’s Subfossil

Tahura’s Bone

Assignment

574s 577s 584s 592s 590s 602s 563s 563s ν4 CO3and ν1 PO4

856 wbr

674 vm noisy [759vw 805vw

668 m [713vm 760vw 813vw

680vw noisy spectrum

668 vw 690w 720w

668w 727w 754vm

871vw 760 vm

871vw ν4 CO3

844m] 872s 880m 893w

847sh] 873s 880m

873w 882w 865s 877s 864m 876m ν2 CO3

961s

954sh 961s 933sh 961s

936sh 962s 964s 956sh 963s 992sh

ν1 PO4

1015s ν3 PO4 and ν1 CO3

1029vs 1060sh 1029vs

1029vs 1045vs 1060sh 1091s 1174m 1223m 1409m 1427m 1444m 1468sh

1029vs 1060sh 1094s 1410s 1450s 1470s 1498sh 1568sh

1029vs1061sh1093vs 1030vs1060sh 1093vs 1160m 1427s 1456s 1468s [1482sh 1506vm 1518vm 1538vm 1558vm]

1025vs1045sh 1093s 1146m 1162w 1424m 1452m [1470sh 1506vm 1518vm 1538vm 1558vm]

1033vs 1334vm 1411m 1550vm 1658m 1982vm 2075vm 2252vm 2337m 2939m 2970m

1041vs 1411m 1627m 2291vm 2368m

ν3 CO3 and SiO4

3400br 3567m

3700br 4073br

3553br 3460br 3400br 3750br 3425br 3927m

3410br 3749m

OH- ion or moisture

TABLE 1. ROOM TEMPERATURE OBSERVED IR MODES AND THEIR

ASSIGNMENT

Code: [HAp: (Ca10(PO4)6(OH)2)]; [CHAp 15M: (Ca10(PO4)6(OH)2) 15M]; [CHAp25M: (Ca10(PO4)6(OH)2) 25M]; [SiHAp : (Ca10(PO4)6-x(SiO4)x(OH)2)]; [CFAp 530: (Ca10(PO4)6(F)2) heat treat at 530 oC] [CFAp 840: (Ca10(PO4)6(F)2) heat treat at 840 oC, 4,8 % loss CO2]

A

B A



ACKNOWLEDGEMENT

Higest thank to head of Lambung MangkuratMuseu-mandheadof community forestpark(Tahura-Banjarbaru) for gave elephant bone and subfossil sample.

REFERENCES [1] Reiche, I., Favre-Quattropani, L., Vignaud, C., Bocherens, H.,

Charlet, L., Menu, M., 2003. Amulti-analytical study off bone di-agenesis: the Neolithic site of Bercy (Paris,France), Measurement Science and Technology 14, 1608–1619.

[2] Pate, F. D., Hutton, J. T., Norrish, K, (1989), Ionic exchange be-tween soil solution and bone: toward a predive model, Applied Geochemistry, vol.4, pp. 303-316.

[3] Fernando, P., Vidya, T. N. C., Payne, J., Stuewe, M., Davison, G., Alfred, R. J., Andau, Patrick, Edwin, B., Kilbaourn, K., Melnick, D. J., (2003), DNA Analysis Indicates That Asians Elephant Are Native to Borneo and are Therefore a High Priority for Conserva-tion, Plos Biology, 1: 110-115.

[4] Mac Kinnon, K., Hatta, G., Halim, H., Manggalik, A., (1996), The Ecology of Kalimantan, Hong Kong, Periplus Edition, Ltd. 802 p.

[5] Deraniyagala,P.E.P., (1955), Some extinct elephants, theirs rela-tives, and the two living species, Colombo, Ceylon, government Press, 161.p.

[6] Shoshani, J., dan Eisenberg, J. F., (1982), Elephas Maximus, Mamm sp., 182: 1-8.

[7] Medway L., (1977), Mammals of Borneo, Monogr Malay Br, R Asian Soc, 7: 1-72.

[8] Antonakos, A., Liarokapis, E’, and Leventaouri, T., (2007), Micro-Raman and FTIR studies of synthetic and natural apa-tites,Biomaterilas, 28: 3043-3054.

[9] Keates, S. G., (2010), The Chronology of Pleistocene Modern Humans In China, Korea, and Japan, Radiocarbon, 52: 428-465

[10] Walsh, A., (1955), The application of atomic absorption spectra to chemical analysis, Spectrochim. Acta 7: 108–117.

[11] Hussein, K. A., (2011), The Lagrange Interpolation Polynomial for Neural Network Learning, International Journal of Computer Science and Network Security, vol. 11, no.3.

[12] Bozogmanesh, A. R., Otadi, M., Kordi, A. A. S., Zahibi, F., and Ahmadi, M. B., (2009), Lagrange two-dimentional interpolation method for modeling nanoparticle formation during RESS process, Int. J. Industrial Mathematics vol. 1, No. 2, 175-181.

[13] Gasca,M., and Sauer, T., (2001), Polynomial interpolation in va-riables, Advances in Computational Mathematics,

[14] Abeyratne , M., Spooner, N.A., Grun, R., and Head, J., (1997), Multidating studies of Batadomba Cave, Sri Langka, Quaternary Science Reviews, vol.16, pp. 243-255.

[15] Scimiklas I., D., (2003), Cadmium immobilization by Hydroxyapa-tite, Chem, Ind, 57(3), 101-106.

[16] Scimiklas, I., Onjia, A., Raicevic, S., Janakovic, D., and Mitric, M., (2007), Factor influencing the removal of divalent cations by hydroxyapatite, Journal Hazardous Material, 152, 876-884.

[17] Chen, X., Wright, J. V., Conca, James, J. L., and Peurrung, L. M., (1997), Effect of pH on Heavy Metal Sorption on Mineral Apatite, Environmental Science & Technology, vol 31, no. 3



Abstract—The particle motion analysis of seismic waves

around Cangar hydrothermal area has been investigated to estimate the epicenter and hypocenter. The determination of epicenter and hypocenter are based on the direction of the particle motion, by using single-h methods. On each sta-tion CGR01 and CGR02 three events are chosen. From the particle motion analysis, five epicenters obtained, based on the intersection of particle motion direction on both sta-tions. They located at (112°32’2,04” E ; 7°44’32,208” S), (112°32’2,04” E; 7°44’32,1” S), (112°32’0,96” E ; 7°44’31,49” S), (112°32’3,57” E ; 7°44’32,58” S), and (112°32’3,44” E; 7°,67” S). Whereas the hypocenter of earth-quake ranging from 30–60 meters depth. The epicenter and hypocenter related to hydrothermal activities in subsurface.

Keywords—sparticle motion analysis, seismic, hydrothermal, Cangar.

I. INTRODUCTION

EOTHERMAL is one of the natural energy sources originated from the rocks interaction and

heat flow in the earth. Indonesia has 40% of the world geothermal sources, from Sumatera, Java, Nusa Tenggara to Sulawesi. One of them is Cangar hotspring in East Java.

Some researchs have been conducted to find geothermal potential using some geophysics methods such as: geoelectric, geomagnetic and gravity. Research results used geoelectric method showed the existence of the geothermal potential south to the hotspring in the depth of 24,7 meter [1]. Research used geomagnetic method showed the existence of the geothermal sources in the north and west direction from the hotsprings[2]. Meanwhile, researchs used gravity method predicting that there is geothermal potential as much as ±2.064.640 m3 in the coordinates of 7.7406° S and 112.5339°E [3]. Nevertheless, research using seismic method based on the microseimic analysis to find area having geothermal potential has never been conducted yet. Geothermal energy can be defined as energy that naturally produced by the earth. Earthquake in the geothermal area connected to sesar movement along the geothermal fluids flow [4]. Earthquake with magnitude less than 3, known as microearthquake. According to Hol-land (2002) [5], by studying microeartquake on the geothermal location, inrerrelationship of the crack sytems

that control fluids migration on the geothermal area can be determined.

According to Utama et al (2013) [6], microseismic or microtremor is one of the passive seismic methods for recording the vibration of the earth caused by vulcanic activity, waves, meteorology regional condition, human activities, etc. Microseismic method usually used for exploration, mining, as well as geothermal.

One of the methods to investigate the crack existence in the geothermal field is the microseimic particle motion. Horizontal and vertical components of the particle motion used for determining epicenter and hypocenter of the mi-croseismic.

II. RESEARCH PROCEDURE

This research used microseismic data in Cangar area, East Java, with two recording stations CGR01 and CGR02. Research flow as seen in figure 1. Data recorded by TDS have 3 components which are North-South (NS), East-West (EW) and Up-Down (UD). These three components will be used for analysing microseismic particle motion. Data in the time domain will be transformed to the frequency domain using FFT (Fast Fourier Transform) so that frequency spectrum for each component obtained.

Spectogram analysis is needed to know variation of the harmonic signal frequency to time. This process aims to determine frequency limit that will be used in filtering process. Filtering used Butterworth band-pass filter, because filter of this type has an advantage in band-pass filtering.

Particle motion plotting in horizontal and vertical components was used for determining epicenter and hypo-center of a microseismic. Microseismic epicenter predicted by examining the particle motion direction, and then calculated roughly. The same process was applied for determining hypocenter distance of a microseismic in a grothermal field. Interpretation on the existence of the geothermal potential, need to be correlated to the results of the prior researchs.

Particle Motion of Seismic Waves Recorded from Hydrothermal Area

at Cangar, East Java, Indonesia Wasis1*), Sukir Maryanto1, Dahlia Kurniawati1

1Geophysics Lab., Dept. of Physics, Brawijaya Univ., Jl. Veteran, Malang 65145, Indonesia

*)email : [email protected]

G



.

Figure 1. Research Flow Chart

Frequency spectrum analyzed using FFT, where data recorded in time domain will be transformed to frequency domain. According to Ihsan (2011) [7], frequency limit determination based on dominant frequency, showed that there was a signal in that frequency. Microseismic data in Cangar area has a dominant frequency more than 15 Hz (Figure 2). This high dominant frequency could becaused by hydrothermal activities influence. The same result yielded by the spectogram that applied the STFT (Short Time Fourier Transform) principles that is frequency variation to time.

0 5 10 15 20 25 30 35 40 45 500

50

100

150

200

250

300

350

Frekuensi

Am

plitu

do

Figure 2. Frequency Spectrum of Microseismic

Figure 3. Microseismic Spectrogram

Frequency limit determined by the frequency spectrum analysis and spectogram will be applied in filtering process. Filtered signal will be sampled every 1 second in order to know particle motion direction. Based on the particle motion plotting for horizontal and vertical components, position of the epicenter point can be determined.

Figure 4. Particle Motion CGR01: a) horizontal component (b) vertical component

Based on the particle motion analysis, seismic activities in Cangar area, East Java, there are 5 epicenter points with subsurface hydrothermal activities. This is supported by geothermal manifestation distribution around that area. The dominant rocks in Cangar, according to geoelectric, geomagnetic, and gravity surveys are basalt and tuff. Tuff rocks contain many cracks from where fluids (water) flow (Rakhmanto, 2011). Cracks happened because of volcanic activities or tectonic in Mount Arjuno-Welirang area. Fluids under the surface will be heated by hot rocks, so that the hot fluids activities increased and earthquake took place. Figure 5 shows three epicenter points of the microseismic that could be represent unrevealed hydrothermal sources

- 2 5 0 - 2 0 0 - 1 5 0 - 1 0 0 - 5 0 0 5 0 1 0 0 1 5 0 2 0 0 2 5 0

- 7 0

- 6 0

- 5 0

- 4 0

- 3 0

- 2 0

- 1 0

0

1 0

2 0

3 0

4 0

5 0

6 0

7 0

S

N

W E-250 -200 -150 -100 -50 0 50 100 150 200 250

-50

-40

-30

-20

-10

0

10

20

30

40

50

S

N

W E

Particle Motion

Interpretation

Filtering

Finish

Epicenter; Hypo-center

Frequency Spectrum

FFT Spectrogram

Data Conversion

Start

Data Selection

Secondary Data



. Figure 5. Geothermal Distribution Map in Cangar Area, East Java

III. CONCLUSION Particle motion analysis on the horizontal and

vertical components can be used for predicting the location of the epicenter and hypocenter of the microeartquake. Based on the research, the center of the earthquake is in depth of 16 to 60 meter. Epicenter distribution are on the 5 points which are: (112°32’2,04” E; 7°44’32,208” S), (112°32’2,04” E; 7°44’32,1” S), (112°32’0,96” E; 7°44’31,49” S), (112°32’3,57” E;7°44’32,58” S), and (112°32’3,44” E; 7°44’32,67” S).

1. Epicenter and hypocenter determination related to the hydrothermal activities in the subsurface. A high dominant frequency spectrum showed that there are fluid activities heated by the hot rocks around them.

REFERENCES

[1] Rakhmanto, F., 2011. Tomografi Geolistrik Daerah Panasbumi Weli-rang-Arjuno (Studi Sumber Air Panas Cangar Batu). Tesis S2. Un-iversitas Brawijaya Malang.

[2] Afandi, Akhmad. 2011. Studi Potensi Panas Bumi Di Daerah Cangar Kota Batu Jawa Timur

[3] Zaman, Muhammad Badaruz. 2011. Studi Potensi Panas Bumi Di Pemandian Air Panas Cangar, Kota Batu, Jawa Timur Dengan Menggunakan Metode Gayaberat. Skripsi S1. Universitas Brawijaya Malang.

[4] Holland, Austin Adams. 2002. Microearthquake Study Of The Salton Sea Geothermal Field, California: Evidence Of Stress Triggering. The University Of Texas. El Paso.

[5] Utama, W., Tri Martha Kp, Dwa Desa W., And Makky S. Jaya. 2013. Application Of Ensemble Empirical Mode Decomposition (Eemd) For Identification Of Hydrothermal Dynamics In The Subsurface, Case Study Mt. Lamongan, East Java. Proceeding Itb Geothermal Workshop. Bandung.

[6] Ihsan, Agung Budi. 2011. Karakterisasi Mikrotremor Di Daerah Sekitar Sungai Porong Desa Kebonagung Sidoarjo. Skripsi S1. Un-iversitas Brawijaya Malang.

ÊÚ

ÊÚ

#S#

#

##

7°44

'34"

LS

7°44'34" LS

7°44

'33"

LS

7°44'33" LS

7°44

'32"

LS

7°44'32" LS

7°44

'31"

LS

7°44'31" LS

7°44

'30"

LS

7°44'30" LS

112°31'58" BT

112°31'58" BT

112°31'59" BT

112°31'59" BT

112°32'00" BT

112°32'00" BT

112°32'01" BT

112°32'01" BT

112°32'02" BT

112°32'02" BT

112°32'03" BT

112°32'03" BT

112°32'04" BT

112°32'04" BT

112°32'05" BT

112°32'05" BT

112°32'06" BT

112°32'06" BT

112°32'07" BT

112°32'07" BT

112°32'08" BT

112°32'08" BT

N

EW

S

Malang

30 0 30 Meters

ÊÚ Stasiun CGR01ÊÚ Stasiun CGR02

Episenter gempa#S 1# 2# 3# 4# 5

Keterangan:



MATHEMATIC AND

STATISTIC



Abstract—Microarray is an analysis for monitoring gene ex-pression activity simultaneously. Microarray data is data gen-erated from microarray experiments having characteristics of very few numbers of samples where the shape of distribution is very complex and the number of measured variables is very large. Due to this specific characteristic, it requires special me-thod to overcome this. Bayesian Model Averaging (BMA) is a Bayesian solution method that is capable to handle microarray data with a best single model constructed by combining all poss-ible models in which the posterior distribution of all the best models will be averaged. There are several method that can be used to select the model components in Bayesian Model Averag-ing (BMA). One of the methods that can be used is the Occam's Window method. The purpose of this study is to measure the performance of Occam's Window method in the selection of the best model components in the Bayesian Model Averaging (BMA). The data used in this study are some of the gene expres-sion data as a result microarray experiments used in the study of Sebastiani, Xie and Ramoni in 2006. The results showed that the Occam's Window method can reduce a number of models that may be formed as much as 65.7% so that the formation of a single model with BMA only involves the model of 34.3%.

Keywords—Bayesian Model Averaging, Microarray Data, Model Components Selection, Occam’s Window Method.

I. INTRODUCTION Microarray data is the data obtained from a microarray experiment that is an experiment with a particular analysis technique to monitor the activity of thousands genes expres-sion simultaneously [1]. Microarray data have several cha-racteristics i.e. -limited availability of the number of samples because of limited budget, resources and time. Though the availability of the number of samples is limited, the measur-able characteristic variables can be hundreds or even thou-sands of gene expression. By these special characteristics, it

is possible that the nature of the distribution of gene expres-sion data will be very complex in which the distribution of the data is probably not a normal distribution [2]. Due to these specific characteristics, it requires special method to overcome this. Bayesian is a statistical analysis method that does not consider the number of samples (especially for very small sample size) and to any form of distribution. Moreover, Bayesian method is based on information from the original data (driven data) to obtain the posterior probability distribu-tion which is a product of the prior distribution and the like-lihood function [3]. Model Parameter in Bayesian method is viewed as a random variable in the space of model parameter and allows for the formal combination of different from the prior distribution and facilitates the iterative updating of new information which thus overcome the problem of uncertainty and complexity of the model in the data [4] . Bayesian Model Averaging (BMA) is a Bayesian solu-tion to model uncertainty in which the completion of the model by averaging the posterior distribution of all the best models. The basic principles of the BMA is form the best single model by considering all possible models that could be formed so that the purpose of the BMA is models incor-porate uncertainty and obtain the best model [5]. There are several method that can be used for the model components selection in the BMA of which Occam 's Window method of [5]. This method is quite simple and widely used in research related the BMA in which obtained quite good results in the model components selection in the modeling of the BMA [5] and [6]. Various studies have been done related to the Bayesian Model Averaging (BMA), among others [6], [7], [8], [9], [10] and [11]. In this study will be used Occam's Window method of [5] to select the component model in the modeling of the BMA for microarray data.

Model Components Selection in Bayesian Model Averaging Using Occam's Window for

Microarray Data Ani Budi Astuti1*), Nur Iriawan2), Irhamah3) and Heri Kuswanto4)

1) PhD. Student at Statistics Department of Mathematics and Natural Sciences, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia

1) Mathematics Department of Mathematics and Natural Sciences,

Brawijaya University, Malang, Indonesia

2), 3), 4) Statistics Department of Mathematics and Natural Sciences, Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia

*) e-mail: [email protected]



II. MICROARRAY, BAYESIAN MODEL AVERAGING AND OCCAM’S WINDOW METHOD

A. Microarray Techniques and Microarray Data. According to [1], microarray technique is a technique of data collection by using the platform (reference) which is a duplicate of the original object identifier. The measurement data of a microarray technique called Microarray Data [12]. There are a variety of different technologies have been de-veloped for microarray techniques, among which is a Syn-thetic Oligonucleotide Microarray Technology [13]. Gene expression data is the measurement data from Microarray techniques so that the gene expression data has the characte-ristics of microarray data. According to [2], the data obtained from experiments with microarray technique has the follow-ing characteristics: 1. The number of samples that can be observed very limited

(slightly) because of limited budget, resources and time. Though the availability of the number of samples is limited, the measurable characteristic variables can be hundreds or even thousands of gene expression.

2. The nature of the distribution of data will be very complex in which the distribution of the data is probably not a normal distribution.

By looking at the characteristics possessed by the mi-croarray data then to analyze of the microarray data requires special handling because it is generally the basis of parame-tric statistical method, especially for the comparative analy-sis requires a large number of samples. If the basis of this statistical method is not fulfilled then the conclusion of the analysis can not be accounted for [9]. Bayesian Method.

Bayesian is a statistical method based on the combina-tion of two information that is the past of data information as the prior information and the observations data as a constitu-ent likelihood function to update the prior information in the form of posterior probability distribution model. Bayesian method is based on information from the original data (dri-ven data) to obtain the posterior probability distribution and it is does not consider the number of samples (especially for very small sample size) and to any form of distribution. Bayesian method allow for the formal combination of differ-ent from the prior distribution and facilitates the iterative updating of new information which thus overcome the prob-lem of uncertainty and complexity of the model in the data. The Rational of Bayesian method derived from Bayes Theo-rem thinking concept invented by Thomas Bayes in 1702-1761[3], [4], [14] and [15].

In Bayesian method, the parameters of the model θ is

seen as a random variable in the parameter space θ . Sup-pose there are x observational data with likelihood func-tion )|( θxf then the known information about the parame-

ters θ before the observations were made is referred to as

prior θ namely )(θp . Posterior probability distribution of

θ , namely )|( xp θ determined by the rules of probability

in Bayes theorem [3] as follows:

)1.2()(

)()|()|(

xf

pxfxp

θθθ =

where

== )]|([)( θxfExf ∫∈Rx

dfxf θθθ )()|( if θ

continous and

)]|([)( θxfExf = =∑∈Bx

pxf )()|( θθ if θ discrete.

)(xf is a constant called the normalized constant [4]. So

that the equation (2.1) can be written as: )()|()|( θθθ pxfxp ∝ (2.2)

Posterior ∝ Likelihood Function x Prior Equation (2.2) shows that the posterior probability is propor-tional to the product of the likelihood function and the prior probability of the model parameters. This means that the update's information prior to use information of samples in the data likelihood to obtain the posterior information that will be used for decision making [16]. B.1.1. Markov Chain Monte Carlo (MCMC) Algorithms with

Gibbs Sampler Approach. According to [17], [18] and [19], MCMC algorithms with Gibbs sampler approach can be described as:

Step 1. Set initial values for)(kθ at 0=k so that

( ))0()0(1

)0( ,..., rθθθ =

Step 2. Sampling process to obtain the value of jθ from the

conditional distribution by the sampling for )1( +kθ in

r steps as follows:

2.1. Sampling )1(

1+kθ from ( ))()(

21 ,...,,| kr

kxp θθθ

2.2. Sampling )1(

2+kθ from

( ))()(3

)(12 ,...,,,| k

rkkxp θθθθ

.

.

2.r. Sampling )1( +k

rθ from

( ))(1

)(2

)(1 ,...,,,| k

rkk

r xp −θθθθ Step 3. Doing iteration in step 2 as M times with ∞→M C. Bayesian Model Averaging (BMA). C.1. Basic Concepts of Bayesian Model Averaging (BMA) The basic concept of Bayesian Model Averaging (BMA) is the best single model formed by considering all



possible models that could be formed. BMA is a Bayesian solution for model uncertainty in which the completion of the model uncertainty by averaging the posterior distribution of all the best models. The purpose of the BMA is to combine models of uncertainty in order to obtain the best model [5] and [6]. According to [20], the prediction parameters using the BMA approach uses data derived from a combination of

hierarchical models. If known ,...,, 21 qMMM is the set

of models which may be formed from M and ∆ is the value to be predicted, then the BMA prediction begins with deter-mining the prior probability distribution of all the parameters

of the model and the model kM . Posterior distribution of

∆ if x is known to the data as follows:

)3.2()|(),|()|(1∑

=

∆=∆q

kkk xMPxMPxP

where q is the sum of all the models that may have formed. Posterior distribution of ∆ if known the data x is the aver-age of the posterior distribution if known models weighted by posterior probability models. While the posterior proba-

bility of the model kM is:

)4.2(

)()|(

)()|()|(

1∑

=

=q

lll

kkk

MPMYP

MPMYPxMP

where

)5.2()|(),|()|( ∫= kkkkkk dMPMxPMxP θθθ

Equation (2.5) is the marginal likelihood of the model kM .

Prior probability of kθ if known model kM is

)|( kk Mp θ and )|( kk Mp θ is the likelihood and

)( kMp is the prior probability of kM if model kM is true.

Implicitly, all probabilities depend on the model M so that the expected value of the coefficient of ∆ obtained by aver-aging the model of M , that is:

)6.2(),|()|()|(1∑

=

∆=∆q

kkk xMExMPxE

The value of )|( xE ∆ in the equation (2.6) shows the

weighted expected value of ∆ in every model possible com-bination (weights determined by the prior and the model). While the variance of )|( x∆ is:

)7.2())|()|()],|([),|(var()|(1

22∑=

∆−∆+∆=∆q

kkkk xExMPxMEMxxVar

C.2. Model Components Selection in Bayesian Model Aver-aging (BMA).

Based on the basic concept of Bayesian Model Averag-ing (BMA), the components of the model will be selected to be included in the equation (2.3) of q number of models

that may be formed. There are several method for selecting the components model that will be included in the equation (2.3) based on its posterior probabilities, which are Occam's Window method [5]. Occam's Window method is quite sim-ple and widely used in research related to the BMA and give good results in the selection of components model in the BMA [5] and [6]. According to [5], the rationale of Occam's Window method in selecting the component model in the BMA modeling based on the posterior probability of the model. The model that will be accepted by this method (the model can fit in modeling BMA) must satisfy the following equation:

A’= )8.2()|(

))|((max: c

xMP

xMPM

k

llk ≤

where A’ is the posterior odds to the model-k and c values

of 20 is equivalent to %5=α if using the test criteria with p-value [21]. If a model has a value of A’ is greater than 20, then the model is not included in the modeling of the BMA and must be removed from the equation (2.3) and otherwise if a model has a value of A’ is less than or equal to 20, then the model will be included in the modeling of the BMA and should be included in the calculation of equation (2.3). In the

equation (2.8), ))|((max xMP ll is the model with the

highest posterior probability score and )|( xMP k is the

value of the posterior probability of the model-k. In the vari-ous applications of Occam's Window method is generally able to reduce the large number of components model so that it becomes less than 100 models of even less than 10 models. Reduction of component model that only one or two models are very rare but may occur [5].

III. MATERIAL AND METHODS

The data used in this study are some of the data used in the study [22]. Selection of component models in the BMA modeling begins with determining the most appropriate form of distribution to the data and parameter estimator and then based on the distribution model raised several distribution models by MCMC method with the Gibbs sampler approach to obtain some models that might be formed. Selection of component in the BMA modeling using Occam's window method [5] with the following formula:

A’= )|(

))|((max: c

xMP

xMPM

k

llk ≤ . The BMA Modeling in

the equation (2.3) is based on the result of model components selection from Occam's Window method.



IV . RESULTS AND DISCUSSION

A. Description of Gene Expression Data on Diseased and Health Conditions with Poly Detector and mRNA Method. Results of Descriptive statistics for gene expression da-

ta on the deseased and healthy condition can be seen in the following figure:

Figure 4.1. Mean Value of Gene Expression with Poly Detector Method

Figure 4.2. Mean Value of Gene Expression with mRNA Method

Based on Figure 4.1 and Figure 4.2 for the 10 ID genes were observed known that there are differences in gene expression for diseased and healthy conditions in which there are sever-al ID genes showed that in healthy condition is more expres-sive than the deseased condition that is H55933, R39465-2, U14973, R02593, T51496, H80240 and T55131 for Poly Detector method and U14973 for mRNA method and other-wise there are several ID genes showed that in deseased con-dition is more expressive than the healthy condition that is R39465-1, R85482 and T65938 for Poly Detector method and H55933, R39465-1, R39465-2, R85482, R02593, T51496, H80240, T65938 and T55131 for the mRNA me-thod. B. Identification of Distribution and Parameter Estimator for

the Data The results of the identification to distribution and pa-rameter estimator for gene expression data can be seen in Table 4.1 and Table 4.2 below:

TABLE 4.1

DISTRIBUTION SHAPE DAN ESTIMATOR PARAMETER FOR GENE EXPRESSION DATA WITH POLY DETECTOR METHOD

TABLE 4.2 DISTRIBUTION SHAPE DAN ESTIMATOR PARAMETER FOR GENE EXPRESSION DATA WITH MRNA METHOD

Based on Table 4.1 and Table 4.2, it can be seen that there are some differences in the distribution of ID genes in dis-eased and healthy conditions that is 6 ID genes with Poly Detector method and 5 on the mRNA method and some oth-er ID genes that have the same distribution that is 4 ID genes in Poly Detector method and 5 on the mRNA method. In addition, most of the data have non normal distributions that is lognormal distribution and there are some others have 2-parameter exponential distibution. C. Model Components Selection in BMA with Occam's

Window method. The results of the identification to model components selection in BMA with Occam’s Window method can be seen in Table 4.3 and Table 4.4 below:

TABLE 4.3



PERCENTAGE OF COMPONENT MODELS INCLUDED IN THE BMA MODELING WITH OCCAM'S WINDOW

FOR POLY DETECTOR METHOD.

TABLE 4.4 PERCENTAGE OF COMPONENT MODELS INCLUDED IN THE BMA

MODELING WITH OCCAM'S WINDOW FOR MRNA METHOD.

Based on Table 4.3 and Table 4.4, it can be seen that the total of overall mean to percentage of the component models included in the BMA modeling at 34.3% that is derived from this calculations (32.54+63.64+4.17+36.88)/4). This means that in a study with Occam's Window method can reduce the component models in the BMA modeling was 65.7% so that in the formation of the BMA modeling involves only 34.3% of the overall model may be formed.

V. CONCLUSION

Based on the results of research conducted, it can be concluded that most of the gene expression data as a result of microarray experiments have nonnormal distributions both in healthy and diseased conditions. In addition, there are differ-ent type of data distribution in healthy and diseased condi-tions and there is also the same type of data distribution in healthy and diseased conditions . There are several genes ID that have the value of the expression in diseased condition is stronger than healthy condition and otherwise there are sev-

eral genes ID that have the value of the expression in healthy condition is stronger than diseased condition. The average percentage of the component model that can be included in the BMA modeling with Occam's Window method as much as 34.3%. This means that the Occam 's Window method can reduce the component model may be formed as much as 65.7% so that in the form of the BMA modeling involve only 34.3% where it would further simplify the model without reducing the validity of the model is formed.

ACKNOWLEDGMENT

This research is part of the doctoral research at the Sta-tistics Department of Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia. We would like to thank the group of researchers Sebastiani, P., Xie H. and Ramoni, M.F. to the use of data, Head of the Mathematics Depart-ment and Dean of FMIPA UB Malang.

REFERENCES

[1] Knudsen, S. (2004). A Guide to Analysis of DNA Microarray Data. Second Edition. John Wiley & Sons, Inc., New Jersey, Canada.

[2] Muller, P., Parmigiani, G., Robert, C., and Rouseau, J. (2002), “Optim-al Sample Size for Multiple Testing: the Case of Gene Expression Microarrays,” Tech. rep., University of Texas, M.D. Anderson Can-cer Center.

[3] Gosh, J. K., Delampady, M. and Samanta, T. (2006). An Introduction to Bayesian Analysis Theory and Method. Springer, New York.

[4] Gelman, A., Carlin, J. B., Stern, H. S. and Rubin, D. B. (1995). Baye-sian Data Analysis. Chapman & Hall, London.

[5] Madigan, D. and Raftery, A. E. (1994). Model Selection and Account-ing for Model Uncertainty in Graphical Models Using Occam’s Window. Journal of the American Statistical Association.Vol.89. 428: 1535-1546.

[6] Hustianda, V. F. (2012). Comparison of Bayesian Model Averaging and Multiple Linear Regression in Predicting Factors Affecting Number of Infant Death in East Java. Thesis. Statistics Department. FMIPA-ITS, Surabaya. (in Indonesia).

[7] Liang, F. M, Troung, Y, and Wong, W. H. (2001). Automatic Bayesian Model Averaging for Linear Regression and Applications in Baye-sian Curve Fitting. Statistical Science, 11(4): 1005-1029.

[8] Brown, P.J., Vannucci, M. and Fearn, T. (2002). Bayesian Model Aver-aging with Selection of Regressors. J. R. Statist. Soc. B Part 3. 519–536.

[9] Sebastiani, P., Xie H. and Ramoni, M.F. (2006). Bayesian Analysis of Comparative Microarray Experiments By Model Averaging. Interna-tional Society For Bayesian Analysis.1, number 4, pp. 707-732.

[10] Purnamasari, R. (2011). The use of Bayesian Model Averaging (BMA) method with Markov Chain Monte Carlo (MCMC) approach for Wind Speed Daily Averages Forecasting in Juanda Meteorological Station. Thesis. Statistics Department. FMIPA-ITS, Surabaya. (in In-donesia).

[11] Kuswanto, H. and Sari, M. R. (2013). Bayesian Model Averaging with Markov Chain Monte Carlo for Calibrating Temperature Forecast from Combination of Time Series Models. (on Review).

[12] Shoemaker, J. S. and Lin, S. M. (2005). Method of Microarray Analy-sis IV. Springer, New York.

[13] Duggan, J. D., Bittner, M., Chen, Y., Meltzer, P. and Trent, J. M. (1999). Expression Profiling Using CDNA Microarrays. Nature Ge-netics. 21: 10-14.

[14] Box, G. E. P. and Tiao. (1973). Bayesian Inference in Statistical Analysis. MA: Addison-Wesley, Massachusetts.



[15] Zellner, A. (1971). An Introduction to Bayesian Inference in Econo-metrics. John Wiley, New York.

[16] Iriawan, N. (2003). Simulation Technique. Teaching Modules. ITS, Surabaya. (in Indonesia).

[17] Gamerman, D. (1997). Markov Chain Monte Carlo. Chapman & Hall, London.



Abstract—Monitoring spectral characteristic of rice plant is

important to obtain information about the age of rice during it growth. This study examines multi temporal spectral characteristics of three varieties of high yielding rice plant in Malang using Landsat 8 image. The varieties consist of IR64, Ciherang and Memberamo. Normalized Difference Vegetation Index (NDVI) is used to detect the condition and the age of rice plants. The comparison of their vegetation indices shows that all these three varieties have different growth patterns, where the most distinct pattern found in IR64.

Keywords—High Yielding Rice Plant, Landsat-8, Monitoring, Spectral Characteristic

I. INTRODUCTION

ICE is a staple food source in almost all regions of Indonesia. Therefore, the increasing of population resulted the increasing of the demand for rice. However,

the extent of agricultural area more and more reduced and turned into residential area and other uses. Thus, continuos monitoring and identification of the rice plant are needed to determine the availability of rice.

Satellite image is one of the method that can be used to monitor rice plant during it’s growth. This process can be done by using the data of spectral characteristics of the plant during its growth phase. Some research on monitoring crop growth have been carried out. Most of these research utilizes a medium resolution imagery such as NOAA-AVHRR and MODIS [1]-[5] and RADARSAT [6]–[7]. In Indonesia, the prediction of the greenery rate of agricultural crops, especially rice has been conducted continuously by Space Agency (LAPAN) using NOAA and MODIS satellite imagery. But, for a small scale of agricultural land parcell, this image could not be used because in one pixel contains a variety information of land uses. This will reduce the accuracy of the identification process [8]. Occording to this limitation of small resolution imagery, some research were conducted by using medium resolution imagery such as Landsat ETM + [9]–[10]. This image with a resolution of 30 m could be used for small scale agricultural land parcel. ETM+ imagery also has a revisit time every 16 days, so that appropriate if it is used for monitoring the growth of rice that has a growth cicle between 110 to 125 days .

In the study was conducted by Nuarsa et al, the spectral

identification process was carried out on Ciherang varieties. As in Indonesia, the type of rice planted is vary widely. In East Java, especially in Malang district, a type of rice that are usually planted is high yielding varieties of rice. Each type of high yielding varieties has difference characteristics and difference growth cycle until harvest time. In addition, each type also has different yields product. According to the Center for Rice Research Bereau [11] , rice IR64 ages ranged from 110 to 120 days with average yield is 5 tons/ ha and can reach a maximum of 6. For Ciherang variety has a life cycle of 116 to 125 days with an average yield about 6 tons/ha and can reach a maximum of around 8 tons. While Membramo variety has a lifespan of 115 to 120 days with the average yield is 6.5 tons/ha.

As already known, that the Landsat 7 satellite was damaged since May 2003 and resulted the captured images that contain stripping data. This image causes the identification process does not produce optimum accuracy. To replace this satellite, in February 2013, NASA launched Landsat 8 with characteristics similar to the Landsat ETM+ in contextual of resolution, correction methods, and the characteristics of the sensor (http://landsat.usgs.gov). However, Landsat 8 has added characteristic as perfecting of Landsat ETM+ such as the number of bands, the lower range of the electromagnetic waves spectrum that can be captured by the sensor and 16 bit value range of each pixel. The increasing of quantification for each pixel will improve the ability to distinguish each interpreted object.

To determine the sufficiency of rice in more detail, needs a research to monitor the growth of each rice plant varieties. Therefore, this study is aimed to monitor several varieties of rice plant during their growth using Landsat 8 image data.


A. Study Area

Location of study is Malang regency. Administratively, the area is part of East Java province, Indonesia. Geographi-cally, the study area is located between 7o50 ' – 8o09 ' South Latitude and 112o33 ' – 112o44 ' East Longitude.

Rice plant phase consists of three phases namely vegeta-tive (early growth until panicle formation/primordial), repro-ductive (primordial until flowering) and maturation (flower-ing to mature grain). In general, most tropical rice varieties

Monitoring of High-Yielding Varieties of Rice Plants Using Multi Temporal Landsat-8 Data

Candra Dewi 1)

1) Program of Information Technology and Computer Science, Brawijaya University

R



have reproductive phase of approximately 35 days and a maturation phase of approximately 30 days. According to the Center for Agricultural Research and Development of Agri-culture Ministry (BPPT), the rice planting follows certain patterns season called dasarian. The dasarian is calendar system for rice cultivating established by the Ministry of Agriculture. However, based on the data obtained from the field survey, the majority of agricultural areas have cultivat-ing time that does not comply with this calendar system (TABLE 1). The field survey found that the average rice cul-tivating season is twice a year with each planting cycle was about 4 months (120 days).

TABLE I RICE CULTIVATING SEASON AT SOME SUB DISTRIC IN MALANG

(SOURCE: [12], [13] AND FIELD SURVEY)

No Sub District Dasarian (BPPT)

Field Survey *MT I/ MH

*MT III / MK II

1 Blimbing Oct II - III Jun II – III Jul, Sep

2 Kedungkandang Nov I - II Jul I – II Jul, Aug, Sep, Oct

3 Lowokwaru Oct II - III Jun II - III Aug, Sep, Oct

4 Sukun Nov I - II Jul I – II Aug, Sep., Oct

5 Karangploso Oct II - III Jun II - III Sep, Oct

6 Kepanjen Nov I - II Jul I – II Aug, Sep, Oct, Nov

7 Lawang Oct II - III Jun II - III Sep, Oct

8 Pakis Nov I - II Jul I – II Sep, Oct, Nov

9 Pakisaji Oct II - III Jun II - III Aug, Sep, Oct

10 Singosari Oct II - III Jun II - III Aug, Sep, Oct

*MT (Cultivating Season), MT I (Wet Season/MH), MT III (Dry Sea-son/MK).

B. Landsat-8 Data

Landsat 8 satellite provide data in the form of digi-tal values with a spatial resolution (pixel) 30m to the visible region, near infrared and middle infrared. The characteristics of Landsat 8 are recognized using Operational Land Manag-er sensors (OLI). Landsat 8 has shorter bands interval than Landsat ETM+ intervals and with the addition of two bands. Landsat-8 allegedly had better geodetic and geometric accu-racy.

Data collected to LDCM Mission by the Operation-al Land Imager (OLI) instrument will improve the measure-ment capability in the future. With the "Ultra-Blue" band (Band 1) which is used for coastal and aerosols study, as well as Band 9 is useful for detecting cirrus clouds and two thermal bands provide more accurate surface temperature (TIRS 1 and TIRS 2). The spectral characteristics of Land-sat-8 are shown in TABLE 2.

TABLE 2 SPECTRAL CHARACTERISTICS OF LANDSAT-8

Band Spectral Range

(µµµµm) Band Divi-

sion Spatial Resolu-

tion (m)

1 0,43 – 0,45 Ultra Blue 30 2 0,45 – 0,51 Blue 30 3 0,53 – 0,59 Green 30 4 0,64 – 0,67 Red 30 5 0,85 – 0,88 NIR 30 6 1,57 – 1,65 SWIR1 30 7 2,11 – 2,29 SWIR2 30 8 0,50 – 0,68 PAN 15 9 1,36 – 1,38 Cirrus 30 10 10,6 – 11,19 TIRS 1 100 11 11,5 – 12,51 TIRS2 100

Overall Malang is located in Path 118 Row 066 on Land-

sat 8 image. Based on the field survey, the monitoring is ex-amined for three varieties namely Ciherang, IR64 and Mem-bramo. For this study, the sample data of rice field is planted on early August. To monitor the characteristics for one rice growth cycle is used as much as 6 time series images with different acquisition date (August 13, 2013; August 29, 2013; September 14, 2013; September 30, 2013; October 16, 2013 and November 1, 2013). Some of the images that are acquired on November and December can not be used due to the existing of the clouds with a fairly high percentage.

C. Methods

The step of monitoring spectral characteristic for this study consists of two parts. The first is calculating the vege-tation index value to determine differences in the pattern of the three varieties in one rice growth cycle. The second is determines the relationship between the vegetation index and rice age.

This study uses 135 pixels that representing the pixels for IR64, Ciherang and Membramo for the analysis. These pix-els are taken from 5 different agricultural areas in each ac-quisition date of Landsat image. Then, the average value was used to represent the value of each variety. The vegetation indices analysis in this study used normalized difference vegetation index (NDVI). The calculation of NDVI uses formula as in (1).

RNIR

RNIRNDVI

+−= (1)

Where NIR and R are Near Infra Red and Red bands


Spectral reflectance patterns of some plants will be differ-ent depending on the color of the leaves (chlorophyll), leaf structure and water content in the leaves. This spectral will of course influence the vegetation indices pattern of three rice varieties (Figure 1).

The picture shows that the differences of vegetation indic-es patterns for the three varieties are clearly seen by using NDVI. Of the three varieties, IR64 has the most different pattern than the other two varieties. The peak reflectance of



IR64 occurs at the age of 33 days and 68 days. For Ciherang, the peaks reflectance can be found on days 17 and 68. While peak reflectance of Membramo found at days 68. Of the three varieties, it can be identified that in average the peak reflectance is at the age of 68 days. At this age, the three varieties are in the reproductive phase in which the leaves of paddy is lush and entered a flowering period.

NDVI

0

0,1

0,2

0,3

0,4

0,5

17 33 49 65 81

Age (Day)

ND

VI

IR64 Ciherang Memberamo

Fig. 1. NDVI graph of IR64, Ciherang and Memberamo.

Due to the study was conducted by Nuarsa et all, using

multiple bands give better relationship than using single band. Therefore, this study uses NDVI to utilize relation-ship between rice age and spectral value of Landsat-8 da-ta. Furthermore, the relationship between rice age and NDVI was evaluated using determination coefficient (R2).

Base on the experiment, the best equation that can be used to show the relationship between rice age and NDVI was polynomial for Ciherang and Memberamo, while for IR64 was power. Figure 2 to Figure 4 show this relation-ship for three varieties of rice that are evaluated in this study. The higher of R2 value shows the stronger relation-ship between rice age and NDVI. The figure shows that the strong relationship of these varieties can be seen on Memberamo variety with the value of R2 is 0.9175. While the other shows the weak relationship with the values of R2 are about 0.2928 and 0.1034 respectively for IR64 and Ciherang.

IR64

y = 228,64x1,5556

R2 = 0,2928

0

20

40

60

80

100

0 0,1 0,2 0,3 0,4 0,5

NDVI

Ric

e A

ge

Fig. 2. Relationship between NDVI and rice age of IR64

Ciherang

y = -2106,1x2 + 1483,1x - 203,37R2 = 0,1034

0

20

40

60

80

100

0 0,05 0,1 0,15 0,2 0,25 0,3 0,35

NDVI

Ric

e A

ge

Fig. 3. Relationship between NDVI and rice age of Ciherang

Memberamo

y = -4290,7x2 + 2739,4x - 361,5R2 = 0,9175

0

20

40

60

80

100

0 0,1 0,2 0,3 0,4

NDVI

Ric

e A

ge

Fig.3.Relationship between NDVI and rice age of Memberamo

IV. CONCLUSION

This study shows that all three varieties have different pat-tern of growth since it is evaluated using NDVI. Based on the calculation of vegetation indices can be seen that the three varieties have a different growth pattern, where IR64 variety has a growth pattern that is most easily recognized than Membramo and Ciherang varieties. Otherwise, the stronger relationship between rice age and NDVI can be found in Memberamo variety.

REFERENCES

[1] S. Panigrahy, J. S. Parihar, and N. K. Patel, “Rice Estimation in Orissa Using NOAA-AVHRR Data”, Journal of the Indian Society, of Remote Sensing, 20, 35-42, 1992.

[2] X. Xiao, S. Boles, J. Liu, D. Zhuang, S. Frolking, C. Li, W. Salas, and B. Moore, “Mapping Paddy Rice Agriculture in South and Southeast Asia Using Multi-Temporal MODIS Image”, Remote Sensing of Environment, 100, 95-113, 2005.

[3] T. Wataru, O. Taikan, and Y. Yoshifumi, “Investigating an Integrated Approach on Rice Paddy Monitoring Over Asia with MODIS and AMSR-E”, Proceedings of The Conference of The Remote Sensing Society in Japan, 40, 173-174, 2006.

[4] H. Sun, J. Huang, A. R. Huete, D. Peng, F. Zhang, “Mapping Paddy Rice with Multi-Date Moderate-Resolution Imaging Spectroradiome-



ter (MODIS) Data in China”, Journal of Zhejiang University SCIENCE A, Vol. 10, Issue 10, 1509-1522, 2009.

[5] H. O. Kim, and J. M. Yeom, “Multi-Temporal Spectral Analysis of Rice Fields in South Korea Using MODIS and RapidEye Satellite Im-agery”, Journal of Astronomy and Space Sciences, 29(4), 407-411, 2012, http://dx.doi.org/10.5140/JASS.2012.29.4.407.

[6] F. Ribbes, T. le Toan, « Rice Field Mapping and Monitoring with RADARSAT Data”, International Journal Remote Sensing, 20(4): 745-765, 1999.

[7] Y. Shao, X. Fan, H. Liu, J. Xiao, S. Ross, B. Brisco, R. Brown, and G. Staples, “Rice Monitoring and Production Estimation Using Multitemporal RADARSAT”, Journal of Remote Sensing for Envi-ronment, 76, 310-325, 2001.

[8] A. H. Strahler, L. Boschetti, G. M. Foody, M. A Friedl, M. C. Hansen, M. Herold, P. Mayaux, J. T. Morisette, S. V. Stehman, and C. E. Woodcock, “Global Land Cover Validation : Recommendation for Evaluation and Accuracy Assessment of Global Land Cover Maps”, Office for Official Publication of European Communities, Available:http://wgcv.ceos.org/docs/wgcv26/GloballandCoverValidation_JefMorisette.pdf, 2006.

[9] S. Uchida, “Monitoring of Planting Paddy Rice With Complex Cropping Pattern int The Tropical Humid Climate Region Using Landsat and MODIS Data – A Case of West Java, Indonesia, International Archieves of The Photogrammetry, Remote Sensing and Spatial Information Science, Volume XXXVIII, Paet 8, Kyoto, Japan, 477- 481, 2010.

[10] I. W. Nuarsa, F. Nishio, and C. Hongo, “Spectral Characteristics and Mapping of Rice Plants Using Multi-Temporal Landsat Data”, Jour-nal of Agricultural Science Vol. 3, No. 1: 54-67, 2011.

[11] BPPT, “Description the Variety of Paddy”, Centre of Research of Paddy Plant (BPPT), Agriculture Department, 2009.

[12] BPPT, “Integrated Planting Calendar on Cultivating Season (MT) I 2013/2014 Malang City, East Java Province”, The Center for Agricultural Research and Development (BPPT), Ministry of Agriculture, 2013.

[13] BPPT, “Integrated Planting Calendar on Cultivating Season (MT) I 2013/2014 Malang Regency, East Java Province”, The Center for Agricultural Research and Development (BPPT), Ministry of Agriculture, 2013.



Abstract— The shallow water (wave) equations govern shallow

water flows. We solve the shallow water equations using a finite volume method. A necessary condition for a consistent finite vo-lume method to be stable (hence, convergent) is that the method satisfies the Courant–Friedrichs–Lewy (CFL) condition. Num-bers representing this condition are called CFL numbers. In this paper, the effects of CFL numbers to the convergence rate of the finite volume method are investigated. Setting a CFL number to the method gives varying time steps in the numerical evolution. We compare results between those produced by imposing a CFL number and imposing a fixed time step to the numerical method. We shall show which strategy is more efficient and produces more accurate solutions in solving the shallow water equations.

Keywords—convergence rate, Courant–Friedrichs–Lewy, finite volume method, shallow water equations.

I. INTRODUCTION

HE system of shallow water equations is a well-known mathematical model that describes shallow water waves and flows. We are interested in solving these equations as

the solutions are useful in the simulations of real world prob-lems such as dam break floods and tsunamis. In this paper we implement a finite volume method to solve the shallow water equations. The method is chosen due to its robustness in deal-ing with smooth and non-smooth solutions [9, 10].

In finite volume methods, a necessary condition for conver-gence is that the Courant–Friedrichs–Lewy (CFL) condition be satisfied [3, 9, 10]. This condition is related to the time step-ping in the integration of the shallow water equations with respect to time after the equations are discretized with respect to space. This means that we can use either a fixed time step as long as the CFL condition is satisfied at every time step or a varying time step based on a fixed CFL number. Here a CFL number represents a positive number such that the CFL condi-tion is satisfied.

This paper investigates the influence of CFL number to the accuracy of numerical solutions produced by the finite volume method. The accuracy of the finite volume method, of course,

depends on the accuracy of the integration technique imple-mented to the space and time. To focus on our investigation, we use a single integration technique for the space variable, that is, we use a second order method for the space integration. Then we compare the performance of a second order method for the time integration by presenting the errors between im-plementing a fixed time step and a fixed CFL number.

This paper is organized as follows. In Section II we recall the shallow water equations in one dimension. The finite vo-lume method is presented in Section III. Numerical results are given in Section IV. Finally some concluding remarks are drawn in Section V.

II. GOVERNING EQUATIONS

The shallow water equations are ,0)( =+ xt huh (1)

( ) .)( 2221

xxt ghBhughhu −=++ (2)

where t denotes the time variable, x denotes the space varia-ble, ),( txh is water height or depth, ),(txu is velocity, )(xB

represents the bottom elevation or topography, and g is the

acceleration due to gravity. The absolute water level (stage) is defined as

.)(),(:),( xBtxhtxw += (3)

A number of authors have proposed numerical techniques to solve these shallow water equations (1) and (2). Some of them are [1, 2, 5-8, 11, 12, 15, 16].

III. NUMERICAL METHOD

As we mentioned, we use a finite volume numerical method to solve the shallow water equations. In a semi-discrete form, the finite volume method is

( ) njjjj tt

xt

dt

dSFFQ +−

∆−= −+ )()(

1)(

21

21 (4)

where Q is an approximation of the conserved quantity, F is an approximation of the analytical flux and S is a discretiza-tion of the analytical source term. See the References [1, 6, 15]

The Courant–Friedrichs–Lewy Number Influences the Accuracy of Finite

Volume Methods Sudi Mungkasi1,*) and Noor Hidayat2,3)

1) Department of Mathematics, Faculty of Science and Technology, Sanata Dharma University, Mri-can, Tromol Pos 29, Yogyakarta 55002, Indonesia

2) Doctoral Program, Faculty of Science and Technology, Airlangga University, Surabaya, Indonesia 3) Department of Mathematics, Brawijaya University, Malang, Indonesia


T



for more details of this type of scheme. This scheme is called semi-discrete because we have discretize the shallow water equations with respect to space, but the time variable is still continuous [3, 9, 10].

To get a second order method in space, we use a linear re-construction for quantities stage, height, bed, velocity and momentum. Then in order to suppress artificial oscillation due to the space reconstruction, we implement the minmod limiter. This limiter gives a limitation to the values of the gradients in the linear reconstruction of the aforementioned quantities. Af-ter that, numerical fluxes are computed based on these recon-structions. We use the Lax-Friedrichs numerical flux function. We refer to [9, 10] for the formulation of this flux function.

The next step is to integrate the semi-discrete form (4) with respect to time. We actually can use any standard method of Ordinary Differential Equations (ODEs) solver. However, be-cause we have used a second order method in space, it is better to use either a first or second order method in time. This is because we will never get a finite volume method of order higher than two, even if we use higher order method in time. In this paper we implement the second order Runge-Kutta me-thod to integrate the semi-discrete form (4) with respect to time.

IV. NUMERICAL RESULTS

This section provides numerical results regarding two dif-ferent strategies for the numerical evolution. The first strategy is imposing a fixed time step in the second order Runge-Kutta integration. The second strategy is imposing a fixed CFL num-ber where in our simulations we use CFL number to be 1.0 in one case and 0.01 in another case. Details about CFL condi-tions and CFL numbers can be found in the References [9, 10, 17].

Numerical settings are as follow. We use SI units for meas-ured quantities, so we omit the writing of units. Errors are quantified using absolute L1 formula as used in [13, 14]. In this paper we consider one test case. Standard test cases are available in the References [4, 18].

As a test case we consider the dam break problem. We as-sume that the topography is given by a flat horizontal bottom

0)( =xB , where 11 ≤≤− x . Therefore we have that stage

equals to water height. The water height is initially given by

><

=.0

,0

,

,

4

10)0,(

x

xxh (5)

The analytical solution of this problem has been found by Stoker [18] and an extension to the debris avalanche problem has been solved by Mungkasi and Roberts [13, 14].

TABLE I

COMPARISON OF STAGE ERRORS BETWEEN IMPOSING A FIXED TIME STEP AND

IMPOSING FIXED CFL NUMBERS. THE FIXED TIME STEP IS 0.05 TIMES THE CELL-WIDTH, WHEREAS FIXED CFL NUMBER ARE 1.0 AND 0.01.

Cell num-ber

Fixed time step CFL=1.0 CFL=0.01

Error RC Error RC Error RC

100 0.0589

0.0582

0.0569

200 0.0308

0.9343

0.0304

0.9359

0.0296

0.9405

400 0.0144

1.0940

0.0143

1.0917

0.0140

1.0823

800 0.0072

1.0014

0.0071

1.0014

0.0070

0.9994

1600 0.0036

0.9925

0.0036

0.9900

0.0035

0.9860

Average rate of convergence

1.0055

1.0047

1.0020

TABLE II COMPARISON OF DISCHARGE ERRORS BETWEEN IMPOSING A FIXED TIME STEP

AND IMPOSING FIXED CFL NUMBERS. THE FIXED TIME STEP IS 0.05 TIMES THE

CELL-WIDTH, WHEREAS FIXED CFL NUMBER ARE 1.0 AND 0.01.

Cell num-ber



100 0.4714

0.4652

0.4520

200 0.2448

0.9455

0.2416

0.9453

0.2341

0.9488

400 0.1177

1.0562

0.1164

1.0533

0.1138

1.0414

800 0.0589

0.9984

0.0583

0.9964

0.0571

0.9938

1600 0.0299

0.9791

0.0296

0.9778

0.0291

0.9707


0.9948

0.9932

0.9887

TABLE III

COMPARISON OF VELOCITY ERRORS BETWEEN IMPOSING A FIXED TIME STEP

AND IMPOSING FIXED CFL NUMBERS. THE FIXED TIME STEP IS 0.05 TIMES THE

CELL-WIDTH, WHEREAS FIXED CFL NUMBER ARE 1.0 AND 0.01.

Cell num-ber



100 0.0732

0.0724

0.0705

200 0.0388

0.9157

0.0384

0.9156

0.0373

0.9166

400 0.0178

1.1276

0.0176

1.1268

0.0172

1.1209

800 0.0088

1.0090

0.0087

1.0088

0.0085

1.0067

1600 0.0044

1.0032

0.0044

1.0035

0.0043

0.9966


1.0139

1.0137

1.0102

Our simulation results are summarized in Tables 1-3. Ta-ble 1 shows error comparison for stage (water surface) be-tween three scenarios of simulations. Errors for discharge (momentum) and velocity are summarized in Table 1 and Ta-ble 2 respectively. From these three tables, the highest conver-gence rate is achieved by setting a fixed time step, rather than imposing a fixed CFL number. We should note that the aver-age rate of convergence for imposing CFL number 1.0 produc-es a very close average rate of convergence to the fixed time step setting. Furthermore imposing CFL number to be 1.0 gives the most efficient computation as it takes the shortest running time. Setting CFL to be too small such as 0.01 gives a low rate of convergence. Of course setting CFL number too



small makes the computation be expensive, so the running computation is long. Here the fixed time step scenario used is

xt ∆=∆ 05.0 , with the time step t∆ and the cell width x∆ .

Fig. 1. The initial condition of the dam break problem (at time 0=t us-

ing 100 cells. Solid line represents the exact solution. Dotted line represents the numerical solution.

Fig. 2. Solution to the dam break problem at time 05.0=t using 100

cells with the fixed time step. Solid line represents the exact solution. Dotted line represents the numerical solution.

Figure 1 shows the initial condition for the test case. The

first subfigure is the stage or water level (free surface). The second and third subfigures are the momentum and velocity respectively. It is clear that initially we have only discontinuity in the stage, while the momentum and velocity are continuous.

Figure 2 shows the stage, discharge and velocity of water af-ter 0.05 seconds of dam break using the fixed time step. The numerical solutions approximate the analytical solution well based on this Figure 2. Here we see discontinuities in the stage, momentum and velocity.

The convergence rate in our simulation is about 1.0 even though we have implemented a second order finite volume method, that is, second order in space and second order in time. This is because the discontinuities of the solution occur. The discontinuity appears in the measured quantities as well as the derivative of the quantities. Again, see Figure 2 for these discontinuities.

It is worth to note that the formal convergence rate of our numerical method is two, because we use a second order me-thod in space as well as in time. However this formal order is

true only when the solution of the shallow water equations is smooth [9, 10]. As our solution in this paper is nonsmooth due to discontinuities, it is not surprising that we obtain that the rate of convergence is less than two, that is, about one.

Even though we have a fixed formal order, the numerical order or rate of convergence is obviously dependent on the numerical strategy that we use. This has been shown in this paper. Taking a fixed time step in the finite volume method gives different convergence rate from taking a varying time step with imposing a CFL number. In addition, imposing a specific CFL number gives different convergence rate from imposing another CFL number.

V. CONCLUSION

We have investigated the CFL effects on the convergence rate of finite volume methods used to solve the shallow water equations. Our simulations indicate that the use of CFL num-ber 1.0 for solving the dam break problem gives the best com-bination between efficiency and accuracy. Note that setting the CFL number greater than 1.0 may make the numerical method unstable when we solve the shallow water equations in general.

REFERENCES [1] J. Balbas and S. Karni, A central scheme for shallow water flows along

channels with irregular geometry, ESAIM: Mathematical Modelling and Numerical Analysis, 43 (2009), 333–351.

[2] F. Bianco, G. Puppo and G. Russo, High order central schemes for hyperbolic systems of conservation laws, SIAM Journal on Scientific Computing, 21 (1999), 294–322.

[3] F. Bouchut, Nonlinear stability of finite volume methods for hyperbolic conservation laws and well-balanced schemes for sources, Birkhauser, Bassel, 2004.

[4] N. Goutal and F. Maurel, Proceedings of the 2nd Workshop on Dam-Break Wave Simulation, No. HE-43/97/016/B, Departement Laboratoire National d’Hydraulique, Groupe Hydraulique Fluviale, EDF, Chatou, 1997.

[5] A. Harten, High resolution schemes for hyperbolic conservation laws, Journal of Computational Physics, 135 (1997), 260–278.

[6] A. Kurganov and D. Levy, Central-upwind schemes for the Saint-Venant system, ESAIM: Mathematical Modelling and Numerical Analysis, 36 (2002), 397–425.

[7] A. Kurganov, S. Noelle and G. Petrova, Semidiscrete central-upwind schemes for hyperbolic conservation laws and Hamilton–Jacobi equations, SIAM Journal on Scientific Computing, 23 (2001), 707–740.

[8] A. Kurganov and E. Tadmor, New high-resolution central schemes for nonlinear conservation laws and convection-diffusion equations, Journal of Computational Physics, 160 (2000), 241–282.

[9] R. J. LeVeque, Numerical methods for conservation laws, 2nd Edition, Birkhauser, Basel, 1992.

[10] R. J. LeVeque, Finite-volume methods for hyperbolic problems, Cambridge University Press, Cambridge, 2004.

[11] D. Levy, G. Puppo and G. Russo, Central WENO schemes for hyperbolic systems of conservation laws, ESAIM: Mathematical Model-ling and Numerical Analysis, 33 (1999), 547–571.

[12] X. D. Liu and E. Tadmor, Third order nonoscillatory central scheme for hyperbolic conservation laws, Numerische Mathematik, 79 (1998), 397–425.

[13] S. Mungkasi, A study of well-balanced finite volume methods and refinement indicators for the shallow water equations, Thesis of Doctor of Philosophy, The Australian National University, Canberra, 2012; Bulletin of the Australian Mathematical Society, 88 (2013), 351–352, Cambridge University Press.



[14] S. Mungkasi and S. G. Roberts, Analytical solutions involving shock waves for testing debris avalanche numerical models, Pure and Applied Geophysics, 169 (2012), 187–1858.

[15] R. Naidoo and S. Baboolal, Application of the Kurganov–Levy semi-discrete numerical scheme to hyperbolic problems with nonlinear source terms, Future Generation Computer Systems, 20 (2004), 465–473.

[16] H. Nessyahu and E. Tadmor, Non-oscillatory central differencing for hyperbolic conservation laws, Journal of Computational Physics, 87 (1990), 408–463.

[17] S. Osher and E. Tadmor, On the convergence of difference approximation to scalar conservation laws, Mathematics of Computation, 50 (1988), 19–51.

[18] J. J. Stoker, Water Waves: The Mathematical Theory with Application, Interscience Publishers, New York, 1957.



Abstract— From several migration models for individual data,

the schedule model has advantages over logistic models and event histories analysis. In terms of data, the schedules model is more simple because does not involve non-migrants such as the logistic model. While the event history analysis requires special survey because it need detailed information. Schedule model show regu-lar features as a peak in young, declining migration in old age, and may be elevated migration in retirement age. These features can characterize migration flows that associated with labour mi-gration, return migration or familial affiliations. This paper using Bayesian approach to apply schedule model to see the pattern of in-migration to East Java by age so that it can be used for devel-opment planning.

Keywords— Migration, Schedule Model, Bayesian, East Java

I. INTRODUCTION

ANY discipline of sciences interested in developing migration model. It is because migrations are a complex phenomenon that involves many dimensions. It requires

a comprehensive understanding which is not limited to particu-lar disciplines. Multidisciplinary modeling approach couple with the right chosen variables would be more beneficial than just using any particular theory approach [2]. There are two aspects that follow the process, those are individuals and re-gions. The individual data or the micro data requires specific modeling to the individual characteristic related to the decision to migrate. While the region data or macro data requires dif-ferent modeling to characterize the region, as the origin and the destination of migration. Figure 1 shows separation some models that are used to elaborate migration viewed from the availability of data.

This paper using individual data from populations census in 2010 by BPS. For individual data there are 3 options, namely logistic model, event history analysis, and schedule models. Each model has its advantages and weakness when applied to migration data in Indonesia. Based on the data used in logistic model, it would be very hard in preparation for analysis. It is due to the difficulty to have the entire population data. Using logistic models on individual data migration, on the other hand, will involve migrants and non-migrants. Indonesia which still relies on census data for the analysis of migrations, therefore, need the use of computational intensive approaches due to the involving large data.

Figure 1. Separation of Selected Migration Models

Event history analysis requires a special survey to see the

migration history of each individual during every individual lifetime, and Indonesia not already have migration surveys. So, the using of schedule model is the most probable to applied.

II. BAYESIAN MIGRATION SCHEDULE MODEL

Schedule model show regular features as a peak in young, declining migration in old age, and may be elevated migration in retirement age. These features can characterize migration flows that associated with labour migration, return migration or familial affiliations [6].This paper using Bayesian approach to apply schedule model with purely parametric model. Let Yx is migration flows at individual years of ages (x=1,2,3,…), Nx is mid year populations, and assume Yx ~ Poi(Nxmx) where mx are migrations rates.

Bayesian Migration Schedule Model: An Aplication to Migration in East Java

Preatin1), Iriawan, N. 1), Zain, I.1), and Hartanto, W. 2) 1) Statistics Departement , Sepuluh Nopember Institute of Technology, Indonesia

2) National Family Planning Coordination Board (BKKBN), Indonesia

M

Migration



1 1

2 2 2 2 2

3 3 3 3 3

4 4

exp( )

exp ( ) exp[ ( )]

exp ( ) exp[ ( )]

exp( )

xm a x

a x x

a x x

a x c

αα µ λ µα µ λ µ

λ

= −+ − − − − −+ − − − − −+ +

The component with parameters ɑ1 and α1 represent child mi-gration, the component with parameters (ɑ2, α2, µ2, λ2) represent young migration which in mainly labour migration, the retirement age represented by the shifted exponential term with parameters (ɑ3, α3, µ3, λ3), and post retirement age represented by parameters ɑ4 and λ4.

α1 = rate of descent of pre-labor force component xj = low peak λ2 = rate of ascent of labor force component xh = high peak α2 = rate of descent of labor force component xr = retirement peakλ3 = rate of ascent of post-labor force component X = labor force shiftα3 = rate of descent of post-labor force component A = parental shift c = Constant B = Jump

Figure 2 Migration Schedule Model

Figure 2 shows migration patterns according to age. Its

graduation was changed by a scheduled model, which is de-fined as a sum of four components: 1. Pre-labor force, a single negative exponential curve with its

rate of decent 1. 2. Labor-force, a left skewed unimodal curve with mean age µ2, rate of ascent λ2, and rate of decent 2.

3. Post-labor force, an almost bell shaped curve, with mean age µ3, rate of ascent λ3, and rate of decent 3.

4. Post-retirement peak, exponential curve with rate of ascent λ4.

5. Constant c. Combination from above components form the 4 types of

schedule models adjusted of data conditions, as shown in figure 3.

Substantively there is some conditions likely to be over dis-persion that excess heterogeneity, this lead to allow hierarchic-al model. The conjugate option for mx as gamma mixing.

Yx ~ Poi(Nxmx) mx ~ Ga(κ, κ/mx)

0 1 1

2 2 2 2 2

3 3 3 3 3

4 4

exp( )

exp ( ) exp[ ( )]

exp ( ) exp[ ( )]

exp( )

xm a a x

a x x

a x x

a x

αα µ λ µα µ λ µ

λ

= + −+ − − − − −+ − − − − −+

Figure 3. Type of Migration Schedules Model

Where κ is an additional positive parameter. The mean mx and the variance mx

2/κ of mx represent that mx declines as κ in-creases.

III. MIGRATION FLOWS IN EAST JAVA

East Java province including the migrants sender to other provinces in Indonesia and abroad. In the province itself there is mobility between district / city or even in-migration from outside. In-migration data in 2010 was 1.5% of the total population.

Figure 4. In-migration Rates by Age and Gender

Figure 4 shows that the in-migration balanced between male

and female, where higher female in-migration in young ages and vice versa in old age. From the image identification shows that schedule models is appropriate to type 2 and type 4, where there is a post-retirement peak. Table 1 and Table 2 contains the estimated parameters and summary measure of fit. Schedule models for male and female have the same conclusion, that fit with type 2 if seen from DIC values, but fit with type 4 if seen from MSE values.

TABLE I MALE SCHEDULE MODEL PROPERTIES

Parameters Type 1 Type 2 Type 3 Type 4

1 a0 0,003712 0,000128 0,003916 0,000004



2 a1 0,027810 0,008710 0,077260 0,004615

3 a2 0,104500 0,069230 0,087320 0,082050

4 a3 0,022260 0,007638

5 a4 0,000000 0,000000

6 alpha 6,15 23,97 4,86 154,10

7 alpha1 0,230700 0,026480 0,459800 0,056460

8 alpha2 0,137100 0,093340 0,181800 0,121400

9 alpha3 0,351200 0,058040

10 lambda2 0,326000 0,646100 0,534000 0,509000

11 lambda3 0,448100 0,024270

12 lambda4 0,207100 0,186900

13 mu2 15,81 13,33 13,84 13,97

14 mu3 29,29 59,45

15 deviance 903,79 875,10 894,40 897,60

16 DIC 994,12 957,22 985,20 964,48

17 mse 0,000021 0,000009 0,000015 0,000006

TABLE 2 FEMALE SCHEDULE MODEL PROPERTIES

Parameters Type 1 Type 2 Type 3 Type 4

1 a0 0,003026 0,000267 0,003106 0,000022 2 a1 0,030100 0,008568 0,069240 0,008067 3 a2 0,097130 0,068920 0,103500 0,133600 4 a3 0,014190 0,002120 5 a4 0,000000 0,000001 6 alpha 4,50 20,34 4,01 141,60 7 alpha1 0,223700 0,031550 0,366500 0,024090 8 alpha2 0,151800 0,116800 0,192300 0,329600 9 alpha3 0,366800 0,280700

10 lambda2 0,511800 0,900600 0,635400 0,419700 11 lambda3 0,517600 0,089270 12 lambda4 0,196700 0,185300 13 mu2 14,20 12,73 13,42 15,43 14 mu3 29,95 55,24 15 deviance 882,30 858,30 878,10 900,70 16 DIC 874,02 942,76 969,89 969,74 17 mse 0,000022 0,000010 0,000023 0,000004

IV. CONCLUSION

Modeling migration must be adapted to the purpose of research and the availability of data. For in-migration in East Java having limited data requires the selection of an appropriate model. For individual data, schedule models is more probable because does not involve non-migrants such as the logistic model and special surveis as event history analysis. Bayesian approach was recommended, because it would be more flexible as data driven approaches, but it requires computational intensive capabilities. Pattern of in-migrations to East Java by ages still characterize as young migration at labor force migrants. Post retirement peak shows return migrations is significant but further research is needed.

REFERENCES [1] n Research (CEFMR) Assuncao, R.M., Schmertmann, C.P., Potter, J.E.,

and Cavenaghi, S.M., “Emprical Bayes Estimation of Demographic Schedules for Small Areas”, Demography, Vol.2, No.3, pp. 537-558, 2005.

[2] Bijak, J., “Forecasting International Migration: Selected Theories, Models, and Methods”, Central European Forum For MigratioWorking Paper No. 04, Warsaw, Poland, 2006.

[3] Bijak, J., Bayesian methods in international migration forecasting in “ International Migration in Europe: Data, Models and Estimates”, J. Raymer and F. Willekens, Chichester, GB, John Wiley, pp. 255-281, 2008.

[4] Bijak, J., “Forecasting International Migration in Europe: A Bayesian View”, Springer, London, 2011.

[5] Butzer, R., Mundlak, Y., and Larson, D.F., “Intersectoral Migration in Southeast Asia: Evidence from Indonesia, Thailand, and the Philippines”, Journal of Agricultural and Applied Economics, Vol.35, pp.105-117, 2003.

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Abstract—A total labeling is called

totally irregular total -labeling of if every twodistinct vertices and in satisfies , and every two distinct edges and in satisfies

, where and . The minimum for which

a graph has totally irregular total k-labeling is called the total irregularity strength of , denoted by . In this paper determined for disjoint union from copies of wheel denoted by .

Keywords—the total edge irregularity strength, the total vertex irregularity strength, total irregularity strength, totally irregular total k-labeling, wheel.

I. INTRODUCTION

athematics is a branch of science known as the Queen of Science. Evident from many other disciplines that em-ploy methods contained in mathematics. One area in

mathematics that great attention is graf.Teori graph theory is part of mathematics that is widely used as a tool to describe or represent a problem so that it is easier to understand, be un-derstood and resolved. Many issues will be clearer to explain if it can be formed into a graph [5].

Until now the use of graph theory is perceived role in vari-ous sectors of other sciences. One of the uses of science graph in other disciplines, namely in the fields of chemistry, includ-ing hydrocarbon compounds that can be formed into a tree graph.

Over time, the growing study of graph theory. One of the topics in graph theory is graph labeling. Labeling graphs was first introduced by Rosa in 1967 [2]. Labeling on the graph is the mapping that carries graph elements to the values [1]. Based on the domain, labeling is divided into three, namely the point of labeling, the labeling, and the labeling of the total. Labeling is the point of labeling with domain the set point, the labeling is labeling with domain the set of sides, and the total labeling is labeling combined with domain the set of points with the set side. One topic of total labeling of a graph is irre-gular total labeling introduced by Marzuki, Salman, and Miller

in a paper entitled On the total Irregularity strength on cycles and paths [3].

Suppose is a graph. Total ling is calleda total k - labeling irregu-lar total of G if any two points and are different in satisfies and any two sides and different in satisfies , where

and . The minimum value of

k so that G has a total k - labeling irregular total called total value of the total (total Irregularity strength) of G and is de-noted by [3]. In this paper, we determined the total irre-gular total labeling disjoint union of wheel graph.

II. THEORY

Irregular total labeling was introduced by Baca, et al in 2007 in a paper entitled On irregular total labelings. In this paper, Baca, et al introduce two types of irregular total labe-ling, ie irregular total labeling and labeling the total irregular point. Definitions and results-labeling studies of labeling is given below.

A. An Edge IrregularTotal Labeling

A total labeling is called an edge ir-regular total -labelingin if every two different edges and in satisfies , where .

The smallestvalue ofksuch that agraph has anedge irregu-lar total -labeling is called the total edgeIrregularitystrengthof agraph isdenoted by [1].

In the paper [1] Baa, et al provides a lower limit of the andthe total edgeIrregularitystrength for some graph, including path and circle graph. The results of these studies are given in the following theorems. Theorem 2.1 [1] Let is a graph with a nonempty and , then

Theorem 2.2 [1] Let is a path, with , then

A Totally Irregular Total Labeling Disjoint Union of Wheel Graph

Diana Kurnia Sari Sudirman1*), Rismawati Ramdani 1), and Siti Julaeha2) 1)Department of Mathematics, SunanGunungDjati State Islamic University Bandung, Indonesia 2)Department of Mathematics, SunanGunungDjati State Islamic University Bandung, Indonesia

*)Diana Kurnia Sari Sudirman:[email protected]

M



Theorem 2.3 [1] Let is a circle graph, with , then

B. A Vertex Irregular Total Labeling

A total labeling is called a vertex ir-regular total -labelingin if every two different vertex and

in satisfies , where .

The smallestvalue ofksuch that agraph has a vertex irregu-lar total -labeling is called the total vertex Irregularity strength of a graph is denoted by [1]. In the paper [1] Baa, et al provides a lower limit of the and the total vertex Irregularity strength for some graph, ie star graph. The results of these studies are given in the following theorems. Theorem 2.4 [1] Let is a graph with and , minimum degree , and maksimum degree , then

Theorem 2.5 [1] Let is a star graph with then

C. Totally Irregular Total Labeling

Marzuki, et al. [3]combine the idea of an edge Irregular total labeling and a vertex irregular total labeling into a new labe-ling called a totally irregular total labeling.

A total labeling is called a totally ir-regular total -labelingin if every two different vertex and

in satisfies and every two different edges and in satisfies

where and .

The smallest value of k such that a graph has a totally irre-gular total -labeling is called the total Irregularity strength of a graph is denoted by [3].

In the paper [3], Marzuki, et al provides a lower limit of . In addition, the same paper has determined the total

irregularity strength of path and circle graphs are summarized in the following theorems. Theorem 2.6 [3] Let is a graph. Then Theorem 2.7 [3] Let be a positive integer number and is a circle graph with n edge, then

Theorem 2.8 [5]

Let n be a positive integer number and is a path graph with n vertex, then

Research on the total irregular total labeling was also per-formed by Ramdani and Salmanin a paperen titled on the total Irregularity strength of some Cartesian product graphs[6].

Inthe paper, given the total irregulariy strength of some Car-tesian product graphs, ie , where is a path graph with n order, is a circle graph with n order and

is a star graph with order. The results are summa-rized in the following theorems. Theorem 2.9 [6] For Theorem 2.10 [6]

For

Theorem 2.11 [6] For Theorem 2.12 [6] For

D. Disjoint Union

Definition 2.13 [2] Two graphs and said disjoint if

. Definition 2.14 [2] Let and are two disjoint graph. Disjoint union from and , denoted are graphwith the set of vertex and the set of edges . Example 2.15 Definition 2.16 [2]

Figure 2.1 Disjoint Union form and

III. RESULT

A. Wheel Graph

Definition 3.1 [2]



Wheel graph with vertices denotedby is a graph formed from the cycle by adding a vertex and connect-ing each point in to the vertex . Example 3.2

Figure3.1Wheel Graph

B. A Totally Irregular Total Labeling Disjoint Union of Wheel Graph

Definition 3.2 [2] graphis a disjoint union from copies of wheel graph .

Figure 3.2 Graph graph has a set of vertex and a set of edges

, where:

and

Theorem 3.3 Let graph is a disjoint union from copies of wheel graph , then

Proof. There are two steps to proof theorem 3.3, ieby determining the lower limit and the upper limit from , as can be seen in the following description

1. Will proof that

graph has vertices and edges. The

smallest degree from graph is

andthe greatest degree from is

.

Based on the theorem 2.1,

Thus

Moreover, based on theorem 2.4

Thus Moreover, based on theorem 2.6

Thus, (3.1)

2. Will prof that Total labeling given the graph as follows: a. Labeling on is as follows:

untuk

b. Labeling on is as follows:

To show that is totally irregular total

labeling, then it will be shown that by labeling , the weights of all vertices

on and weights on all edges are

different.The weight of a vertices and an edges obtained by labeling is as follows:

(i) The weight of the edge for is

(ii) The weight of the edge for

is



(iii) The weight of edge for is

(iv) The weight of edge for is

(v) The weight of edge for is

(vi) The weight of the edge for

is

(vii) The weight of theedge , for is

.

(viii) The weight of the edge for

is

(ix) The weight of the edge for is

.

(x) The weight of the edge for

is

(xi) The weight of the edge for is

(xii) The weight of the edge for

is

(xiii) The weight of the vertices for is

(xiv) The weight of the vertices, for

is



(xv) The weight of the vertices for is

(xvi) The weight of the vertices , for

is

(xvii) The weight of the vertices, for is

(xviii) The weight of the vertices , for

is

(xix) The weight of the vertices , for

(xx) The weight of the vertices , for

is

Based on the formula weigh to the vertices and edges above, it can be seen that the weight of all edges and the weight of all vertices are different.

Thus, the total labelingsatisfy a totally irregular total labe-ling with the biggest labels . Therefore,

(3.2) Based on theequation(3.1) and(3.2), it can be concludedthat

∎ Example 3.4 As an illustration, the following will be given a totally irregu-lar total labeling disjoint union of based formula totally irregular total labeling on theorem 3.3

Figure 3.3 Graph Labeling the edges and vertices of graph as follows

Figure 3.4 A totally irregular labeling of graph Sothere is noequal weight toeach vertices and no equal weight on each edge.



Figure 3.5Weight on all edges graph

Figure 3.6 Weight on all vertices graph

IV. CONCLUSION

In this paper determined for disjoint union from copies of wheel denoted by obtained from theorem 3.3 that .The discussion of thetotalirregulartotallabelingis stillopenfor other researcherstoconductsimilarstudieswithdiffe-renttypes ofgraphs, includinggraph with .

REFERENCES

[1] M. Ba a, S. Jendrol. M. Miller. Dan J. Ryan. On irregular total labellings.Discrete Mathematics 307(2007) 1378-1388.

[2] J.A.Bondydan U.S.R. Murty, Graf Theory with Application, The Macmillan Press Ltd, New York (1976).

[3] J. A. Galian. A Dynamic Survey of Graph Labeling. The Electronic journal of combinatorics18 (2011).

[4] V. E. Levit danE.Mandrescu.The Independence Polynomial of a Graph--A Survey.Dalam proses untuk the 1st International Confe-rence on Algebraic Informatics(2005).

[5] C. C. Marzuki, A. N. M. Salman, dan M. Miller. On the total irregularity strength on cycles and paths. Diterima untuk dipublikasikan di Far East Journal of Mathematical Sciences.

[6] R. Ramdani dan A. N. M. Salman. On the total irregularity strength of some cartesian product graphs,AKCE Int. J. Graphs Comb., 10, No 2 (2013), pp. 199-209.

[7] S. Slamet.PengantarTeori Graf, Universitas Indonesia, Jakarta (1998).

[8] R. J. Wilson. dan J. J. Watkins. Graph:An Introductory Approach, Simultaneously, Canada (1990).



The Implementation of The Meshless Local Petrov-Galerkin on Calculating Volume of River

Sedimentation in The Confluence of Two Rivers

Inu Laksito Wibowo1, Suhariningsih2 and Basuki Widodo3

1 PhD Student in FST UNAIR/Lecturer of Math. Dept. of ITS, 2 Professor in Physics FST UNAIR

3 Professor in Applied Mathematics Department Math of ITS

Abstract---The occurrence of sedimentation in a confluence two rivers can be formulated into mathematical model and simulated numerically, so the morphological changes due to the sedimenta-tion of the river can be unpredictable. Mathematical modeling and numerical simulation results of solutions can be used as a material consideration in the adoption of a policy, so the impact will be caused by the sedimentation of the river can be prevented as early as possible or at least be reduced. In this paper, we consider about a model of sedimentation in the river which is formulated by using control volume and be solved using the me-thod of Meshless Local Petrov- Galerkin (MLPG). Themain pur-pose of meshless method is to get rid of the grid or to reduce the difficulty in making a grid with points. We obtain that the sedimentation distribution in the confluences of two rivers is influenced by the shape of river morphology. The higher of the river velocity the higher the erotion in the river.

Keywords: Sedimentation, Confluence two rivers, MLPG

I. INTRODUCTION

ne of the benefits of river is very important is to store water during the rainy season. Siltation of the river due to sediment deposition causes water or undrained cannot be

accommodated to the maximum, it cause flooding. The process of sedimentation in the river can be constructed

into a mathematical model and numerically simulated, so that the process of morphological changes due to sedimentation of the river can be predicted. Mathematical modeling and numer-ical simulation results that the solution can be used as one con-sideration in making a policy, so the impact will be caused by the presence of river sedimentation can be prevented as early as possible or at least be reduced.

River sedimentation model is built using the approach vo-lume method and it is solved by using the meshless local Pe-trov-Galerkin (MLPG). This method is relatively new, and still being developed in the fluid dynamics problems. Meshless method which is developed in this study is used to resolve those problems. The main purpose meshless method is to elim-inate or to reduce the difficulty in making the grid by using the points (nodes) as his successor. This method is very flexible, accurate and not at all in the use of grid application , either for interpolation purposes or for purposes of calculating the integral . Complexity thing, the MLPG is good when com-pared with the method that uses mesh (Widodo, 2009).This

method is predicted to replace the FEM method in the future (Atlury and Lin, 2001). In this paper, we consider, the method Meshless Local Petrov-Galerkin (MLPG) which is used to solve the model which has been obtained from the Finite Volume Method approach on the sedimentation at the confluence of two rivers. Furthermore, by using the approach of Moving Least Square (MLS) as a function of the shape and Heavyside function as a function of test completion the solution sought. Settlement obtained is then made using the computer program MATLAB program-ming language to be solved numerically using a computer, and by varying the input variables and parameters subsequently simulated to obtain the characteristics of the variables and pa-rameters of the system being modeled. The results of the simu-lation is then visualized in the form of pictures of the calcula-tion results of the numerical simulations and compared with the results of visualization using the software.

II. SEDIMENTATION FORMATION PROCESS

The main function of the river is flowing rain water so that it is possible silting. This is due to deposition of sediment in cer-tain places at the bottom of the river. Sedimentation occurs because of the presence of solid particles (sediment) that is carried on by the flow of water. Sediment transport mechanism is categorized into two, namely bed load and suspended load. Bed load sediment movement is sediment moves on the river bottom by rolling, sliding and jumping around. While the suspended load, consisting of fine granules suspended in water (Widodo 2012). 2.1 Sedimentation Calculation Basic Equation

Bed load is grains / particles / sediment material that gener-ally occurs in the watershed. There are several kinds of ma-thematical formulas that can be used to calculate the amount of sediment in this type of sediment transport. One of the for-mulas / mathematical formula which is popular is formula Meyer - Peter & Müller (Yang, 1996).

In the study Yang (1996), changes in river morphology is assumed to occur only at the bottom of the river and caused by the presence of scour and deposition processes. Changes in the river bed can be calculated using the equation of conservation

o



of mass equation for sediment transport. Yang (1996), Widodo (2012), namely :

10

(1 )bqy

t p x

∂∂ + =∂ − ∂

where: y = height of the river bed, p = porosity, qb= bed load

2.2 Confluence of Watershed

Confluence of two rivers is an interesting phenomenon and very complex. Soburo Komura (Widodo, 2009) conducted a study of the phenomena occurring at the confluence of two streams using the balance equation, which is obtained by using the equations of motion, continuity equation for sediment transport, and the continuity equation for the shear velocity of water flow. The equation obtained is less reflect the real situa-tion on the ground. So that this equation can be used or can be applied to things that are ideal. It is therefore necessary to find or develop a new mathematical model to another or explain the phenomenon approaching real state (Widodo,2009).

2.3 Two forms of Morphology Meeting Watershed

Morphology shape of confluence of two rivers is a natural phenomenon that is very interesting, because we will see the confluence of two rivers form the model that various kinds. Some of these models have been widely studied as a model and a model developed Shazy Shabayek, ie numeca.

In models shaped river Main stream and lateral stream has been shown to result in sedimentation in the riverbed on the research results (Widodo, 2009). In the study described also that sedimentation is not only dependent on the flow of the river upstream but also the morphology of the river, the river mouth will be eroded due to the presence of scours caused as a result of back water gate block of sedimentation (Widodo 2009).

Model confluence of two rivers that form a quarter-circle is arc numeca as depicted at the Figure (2.1).

Figure 2.1 Watershed Model Numeca Bow Quarter Circle.

In the quarter-circle model of domain Numeca river is di-

vided into 2 parts by volume control. The main river (main stream) and two tributaries (lateral stream) on the flow curve is expressed as a volume control 1 while at the confluence straight expressed as volume control 2. For the forces acting on the second volume of this control include hydrostatic force,

the frictional force on the bottom of the river, the frictional forces that occur at the boundary between the two volume con-trol, gravitydue to the influence of Earth's gravity and friction forces on the surface of the river water. Some of the characteristics of the main river will change with the influx of tributary streams. Such changes include the change in mass of the depth, direction , and flow rate , as well as other changes .

Markup (2001) have mathematically derive the equation of conservation of mass and momentum for flow in tributaries entering from the side of the main stream. Mass.and momen-tum equations are:

z QB q

t x

∂ ∂+ =∂ ∂

2

2

| |cos

Q Q z gQ QgA vq

t x A x AC R

β φ ∂ ∂ ∂+ + + = ∂ ∂ ∂

With: A = cross sectional area of the flow B = width of the surface flow Q = flow rate z = height of surface flow v = velocity of lateral flow φ = angle between the main flow and lateral flowφ q = lateral flow width unity C = Chezy coefficient g = gravity 2.4 Method of Meshless Local Petrov - Galerkin (MLPG)

The main purpose of the meshless method is to avoid the use mesh / grid. This method is very useful in problems with the domain boundary that is not continuous or moving, or oth-er difficulties may be found in the use of the finite element method (Atlury and Lin, 2001).

Meshless method is known to be very effective implemented in the field of computational science and engineering, but in terms of speed and reliability still needs to be developed. Inte-gration numerically to determine the convergence of this me-thod numerical solution generated. Nodal shape functions of the Moving Least Square (MLS) is used in this method is very complex, so as to obtain accurate numerical Integration results in weak form is very difficult to do , especially for a method that is included in this type meshlessGalerkin (Ottevanger, 2005). MLPG predicted could replace the finite element me-thod (FEM) in the future (Widodo, 2009).

2.5 Numerical Methods Basic Search Volume

Search volume can be approximated by the approach area of the base multiplied by the height, so that the search is ne-cessary to find the approach area of the base and height func-tion numerically.

III. FRAMEWORK CONCEPT

Numerical methods, in addition to methods meshless local

Petrov - Galerkin (MLPG), there have been widely applied in



solving fluid dynamics problems. One fundamental thing in common and that became the basis of the above methods is the use grid or cells in its application. The use of the grid deter-mines the level of accuracy of these methods. The smaller the grid is created, or in other words the more the number of the grid , the more accurate the output (output) produced , but the more expensive the cost of computation to be done (Atlury and Lin, 2001). Even the grid itself to the manufacture of complex domains is very hard to do. Then Meshless Local PetrovGaler-kin (MLPG) was introduced and applied to the problem known as the ideal fluid Navier Stokes equations (Atlury and Lin 2001) which followed (Atlury and Shen 2002).

Meshless method (without mesh / grid) that developed in subsequent studies used to address the problem of sedimenta-tion. The main purpose meshless method (without mesh / grid) grid is to eliminate or to reduce the difficulty in making the grid by using the points (nodes) as successor (Widodo 2011). This method is very flexible, accurate and not at all in the use of grid application, either for interpolation purposes or for purposes of calculating the integral. This method is known as the MLPG (Meshless Local PetrovGalerkin) method is applied to obtain the distribution pattern of sedimentation with a case study times SurabayaWidodo 2011), and then proceed to the case of the application of MLPG shaped river Numeca. This study followed a combination of curved and straight rivers with MLPG and refined with the application of the Moving Least Square (MLS) (Wibowo 2012) which is then used as an initial study of this dissertation.

Sedimentation volume produced will be very instrumental in determining the gate that creates large blocks scour the river mouth. This phenomenon makes it clear that the large volume of sedimentation factor is an important factor to be addressed in managing a river in relation to keeping the river mouth from fell out.

Sedimentation volume will be constructed from the results of the determination of the location of sedimentation to be made sedimentation area L (A) multiply by a function of posi-tion in the sediment height F (P) and summedto determine the volume of sedimentation can be done with a numerical ap-proach. Shape 3 dimensions are sought and can use some help in getting the visualization software.

IV. BUILDING A MODEL SEDIMENTATION

As has been described in the literature review , that the se-dimentation process can be divided into two parts , namely : the hydrodynamic flow of the river and river morphology . 4.1 Hydrodynamic Watershed

Profile river models numeca quarter-circle arc and the vo-

lume control to be modeled is described as follows:

Figure 4.1 (a) Model Numeca Bow River Quarter Circle, and (b) Volume

Control

4.2 Morphology River

Changes in river morphology is assumed to occur only in the river bed due to scour and deposition processes. Changes in the river bed can be calculated using the equation of conser-vation of mass transport of sediment. As for the formula used to calculate the amount of sediment Meyer - Peter & Muller. In the Application, the second equation is expanded into a two-dimensional equation for. So the formula used to calculate the change of the river bed due to sediment transport and se-diment transport to calculate the amount is as follows : Sediment mass conservation : Lateral Stream (Meandering Flow):

Main Stream :

Sediment transport :

With, Number of bed load sediment

: 8.0

ρs=density of the sediment and ρ = density of water, g = acceleration due to gravity , d50 = median diameter of sediment, µ = 1.0, θc = 0.047,



,

U : velocity of the river flow, h :depth

4.3 Simulation Calculation of Volume Sedimentation

Sedimentation volume will be constructed from the results of the determination of the location of sedimentation to be made sedimentation area L(A) multiply by a function of posi-tion in the sediment height F(P). This volume is calculated from the two creeks to flow Cornering and meet at the creek with a straight flow. 4.3.1 Simulation Flow Creeks Cornering

This volume is calculated from the two creeks to flow Cor-nering obtained by simulating it as follows : Simulation I Initial depth h, = 0.3 Initial velocity v, = 0.9 Initial height of the sediment, =0.3 Time t, = 20 Delta t, = 5

Figure 4.9 Plot Depth River in Simulation I

simulation I, it appears that the flow with initial conditions at depth = 0.3 speed = 0.9 and after the time of the decline in river depth of approximately 0.015832.

Figure 4.10 Plot of Speed on the Flow Simulation I

In Figure 4.10 that seemingly stream with initial condition velocity = 0.9 at all positions (x) increased approximately 0.068586 .

Figure 4.11 Plot Sediment Elevation in Simulation I

In Figure 4.11 shows that the flow with initial conditions

sediment height = 0.3 in all positions (x) and after the time of the change of height of the sediment that is down about 0.166570.

From the results shown above plot (Figure 4.6 - 4:11) shows that the depth h, velocity v, and the sediment height zb under-go different changes at the position (x) after a certain time. When the river flow rate increased from 0.1 into 0.9 visible increase in the depth of the river, a decrease in flow velocity, and rise sediments occur also increases. 4.3.2 Simulation Flow Straight Creeks

Search volumes were then computed sediment yield of peer-temuan two creeks with Cornering the flow then continues on sediments from tributary streams straight. simulation II Initial depth h, = 0.3 Velocity v, = 0.2 Initial height of the sediment, =0.3 Time t, = 5 Delta t, = 1

The angle of the river 1, =

The angle of the river 2, =

The river1 discharge, =0.5

The river 2 discharge, =0.5

Figure 4.12 Plot of the depth of the river simulation II

In the third simulation, shows that the flow with the initial

condition and depth = 0.3 after which time an increase in depth up approximately 2.792678.



Figure 4.13 Plot of flow velocity in simulation II

In Figure 4.13 that seemingly stream with initial conditions a = streamflowtwo = 0.5 and after the time of the change is the speed drops 5.172373.

Figure 4.14 Plot Sediment height on Simulation II

In Figure 4.14shows that the flow with initial conditions se-diment height = 0.3 in all positions (x) and after the time of the change in height of the sediment that is down approximately 2.792678. 4.4 Calculation Of Total Volume Sedimentation

Sedimentation Volume Calculation constructed from the re-sults of the determination of the location of sedimentation to be made sedimentation area L(A) multiply by a function of position in the sediment height F (P) and summed according to the grid (interval) is the sum Reimann as follows : Limit ∑ ∑ L(A) F(P) ∆a∆p = ∫∫ l(a) f(p) dp da

∆p →0 ∆a→0

and used GAUSS Quadrature order to determine the sedimen-tation volume , this technique can produce a numerical 3D shapes using software assistance in getting visualization. For the data: Initial depth h, = 0.3 Velocity v, = 0.2 Initial height of the sediment, =0.3 Time t, = 5 Delta t, = 1 The angle of the river, =

The angle of the river, =

The river discharge, =0.3

Two river discharge, = 0.9 The resulting volume is = 1.5414e +007 = 1.5414 x 107 m3 Sediment volume charts

Figure 4.18 The graph plots the volume of sediment

V. CONCLUSIONS

In this chapter provides the conclusions of the analysis and

discussion that has been done. Moreover, given also the advice to do as a continuation or development of this research. 5.1 Conclusion

• From the analysis and discussion that has been done, it is concludedthat: Sediment distribution patterns along the flow is influ-enced by the shape of its morphology. Streams quar-ter-circle arc -shaped curve or a straight stream to ex-perience the difference at each change of position of the point, either change the depth, speed, and changes in sediment height after a certain time interval.

• River velocity will increase the speed of the bow sec-tion of the river that can allow scouring the bow sec-tion of the river. At confluence, the vector velocity will increase and form a vortex as a result of the con-vergence of two different direction vectors river.

• Sedimentation volume can be constructed from the re-sults of the determination of the location of sedimen-tation to be made sedimentation area L(A) multiply by a function of position in the sediment height F (P) and summed according to the grid (interval) is the sum numerical approaches can be searched and visua-lized in the form of 3 dimensions .

REFERENCES

1. Apsley, D. 2005. “Computational Fluid Dynamic”, Springer. New

York. 2. Asahi. 2003. “Estimation of Sediment Discharge into Account Tri-

butaries to the Ishikari River”, Journal of Natural Disaster Science. Vol 25 No 1 pp. 17-22.

3. Atlury and Lin. 2000.“The meshless local Petrov-Galerkin (MLPG) method for convection-diffusion problems”, CMES. Vol. 1, No. 2, pp. 42-60.

4. Atlury and Lin. 2001.“The meshless local Petrov-Galerkin (MLPG) method for solving incompressible Navier-Stokes Equations”, CMES. Vol. 2, No. 2, pp. 117-142.

5. Atlury and Shen. 2002. “The Meshless Local Petrov-Galerkin (MLPG) Method: A Simple & Less-costly Alternative to the Finite Element and Boundary Element Methods”, CMES, vol.3, no.1, pp.11-51.



6. Atlury and Zhu. 1998. A New Meshless Local Patrov-Galerkin, In Computational mechanics. New York.

7. Koolahdoozan 2003. “Three-Dimensional Geo-Morphological Mod-eling of Astuarine Waters”,International Journal of Sediment Re-search. Vol 18, No.1, pp. 1-16.

8. Ottevanger, W. 2005.Discontinuous Finite Element Modeling of River Hydraulics and Morphology with Application to the Parana River, Master Tesis.Department of Applied Mathemat-ics.University of Twente.

9. Shabayek, S., dkk. 2002. “Dynamic model for sub critical combin-ing flows in channel junction”, Journal of Hydraulic Engineer-ing, ASCE, pp. 821-828

10. Wang. 2004. “River Sedimentation and Morphology Modeling-The State of The Art and Future Development”, Proceedings of the Ninth Symposium on River Sedimentation, Yichang-China.

11. Wibowo, I L and Widodo B, 2013, “Numerical Simulation on Cal-culating Volume Sedimentation On Two Rivers Confluences” Far East Journal of Mathemathical Sciences (FJMS) Vol 76, ISSN 0972-0871, PUSPHA Publishing House India.

12. Yang, C. T. 1996.Sediment Transport, Theory and Practice, McGraw Hill. New York.



Abstract—Let be a graph and k be a positive in-

teger. Total k-labeling of G is a mapping . A total k-labeling of Gis called totally

irregular total k-labeling of G if every two distinct vertices x and y inV satisfies and every two distinct edges and in Esatisfies ,

where = and . The minimum k for

which a graph G has a totally irregulartotal k-labeling is called the total irregularity strength of G, denoted by ts(G). Thefriendship is a graph obtained from wheel by missing every alternate rimedge. In this paper, we consider the total irregularity strength of friendship.

Keywords— friendship, the edge irregularity strength, the total irregularity strength, the vertex irregularit y strength, totally irregular total k-labeling.

I. INTRODUCTION

et be a graph. A labeling of a graph is a mapping that carries graphelements to the numbers (usually to the positive or non-negative integers). A

labeling f is called edge labeling if the domain of f is E, a labeling f is called vertex labeling if the domain of f is V , and if the domain of a labeling f is , then the labeling fis called total labeling. Graph labeling was introduced in 1963 by Sadlacek. There aremany kinds of graph labeling, such as graceful labeling, harmonious labeling, magic labeling, and anti magic labeling.

In 2007, Baa, Jendro, Miller, and Ryan [1] intro-duced irregular total k-labeling.They studied two kinds of irregular total labeling, namely edge irregular total labe-lingand vertex irregular total labeling. Let be a graph. For an integer k, atotal labeling

is called an edge irregular total k-labeling ofG if every two distinct edges and in E satisfy , where =

and . The minimum k

for which a graph G has anedge irregular total k-labeling, denoted by , is called the total edge irregularity-strength of G. Some results about the edge irregular total k–labelingweregiven by Nurdin, Salman, and Baskoro in

[7] and Jendro, Mi kuf, and Sotk in [2].For an integer k, a total labeling is called avertex irregular total k-labeling of G if every two distinct vertices x and y in Vsatisfy , where

. The minimum k for which a graphG has a vertex irregular total k-labeling, denoted by , is called the total vertexirregularity strength of G.Some results about the vertex irregular total k-labeling were given by Nurdin, Baskoro, Salman, and Gaos in [4]-[5]-[8].

Combining both of these notions, Marzuki, Salman, and Miller [3] introduced anew irregular total k-labeling of a graph G called 'totally irregular total k-labeling'.A totally irregular total k-labeling of G is a mapping

such that is distinct for

every and is dis-tinct for every . The minimum k for which a graph Ghas a totally irregular total k-labelng, denoted by , is called the total irregularity strength of G. Marzuki, Sal-man, and Miller [3] provided an upper bound and a lower-bound on . Besides that, they determined the total irregularity strength of cyclesand paths. In [1], Ba a, Jendro, Miller, and Ryanderive a lower and an upper bounds on the total edge irregularitystrength of any graph as follows.

In the same paper, Baa, Jendro, Miller, and Ryan[1] also derive a lower and an upper bounds of the total vertex irregularity strength of any graph with mini-mum degreeand maximum degree , thefollowing bounds hold.

Marzuki, Salman, and Miller [3] given a lower bound of as follows.

Some results about the totally irregular total k-labeling were given by Ramdaniand Salman in [9]. In the paper, they have given the total irregularity strength ofsome Car-tesian product graphs.

On The Total Irregularity Strength Offriendship

Rismawati Ramdani1), A.N.M. Salman2), and Hilda Assiyatun2) 1)Faculty of Sciences and Technologies Universitas Islam Negeri Sunan Gunung Djati Bandung

2) 3)Combinatorial Mathematics Research Group Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung


L



II. MAIN RESULTS

Friendship is a graph with the vertex set

and the edge set

For ilustration, friendship are given in Figure 1.

Figure 1. Friendship Theorem 2.1.Let , then . Proof. has vertices and edges. The minimum degree of is and the maximum degree of is

. From (1) and (2), weget

and . There-

fore, from (3), we get . Next, we will show that . Define a total labeling of as follows:

We can see that f is a labeling from into

. Next,we can check that:

Hence, there are no two vertices of the same weight and there are no two edgesof the same weight. So, f is a totally irregular total -labeling. We conclude that

For ilustration, we give a totally irregular total 6-labeling for in Figure 2.

Figure 2. A totally irregular total 6-labeling for

The weights of all vertices and the weights of all edges under the totally irregulartotal 6-labeling are given in Fig-ure 3.

Figure 3.The weights of vertices and edges under the labe-

ling of Figure 2

III. CONCLUSION

Friendship is a graph which has the total irregularity strength which is equal to its lower bound, so that it com-pletes other graph other graphs classes on paper [9].

REFERENCES

[1] M. Ba a, S. Jendro, M. Miller, and J. Ryan, On irregular total labe-lings,Discrete Mathematics, vol. 307, 1378-1388, 2007.

[2] S. Jendro, J. Mi kuf, and R. Sotk, Total edge irregularity strength of completegraphs and complete bipartite graphs, Discrete Mathe-maticsvol. 310, 400-407, 2010.

[3] C. C. Marzuki, A. N. M. Salman, and M. Miller, On the total irregu-laritystrength on cycles and paths, Far East Journal of Mathemati-cal Sciences, to be published.

[4] Nurdin, E. T. Baskoro, A. N. M. Salman, and N. N. Gaos, On the total vertexirregularity strength of trees, Discrete Mathematics, vol. 310, 3043-3048, 2010.

[5] Nurdin, E. T. Baskoro, A. N. M. Salman, and N. N. Gaos, On the total vertexirregularlabelings for several types of trees, UtilitasMa-thematica, vol. 83, 277-290, 2010.

[6] Nurdin, E. T. Baskoro, and A. N. M. Salman, The total edge irregu-lar strengthof the union of ,JurnalMatematikadanSains FMIPA-ITB, vol. 11,105-109, 2006.

[7] Nurdin, A. N. M. Salman, and E. T. Baskoro, The total edge-irregular strengthof the corona product of paths with some graphs, Journal of CombinatorialMathematics and Combinatorial Compu-ting,vol. 65, 163-175, 2008.

[8] Nurdin, A. N. M. Salman, N. N. Gaos, and E. T. Baskoro, On the total vertex-irregular strength of a disjoint union of t copies of a path, Journal of Combinatorial Mathematics and Combinatorial Computing, vol. 71, 227-233, 2009.

[9] R. Ramdani, A.N.M. Salman, On the total irregularity strength of some Cartesian product graphs, AKCE International. Journal of Graphs and Combinatorics, vol. 10, No.2, pp. 199-209, 2013.



C

Abstract— This paper discusses the existence and unique-

ness of minimizers of a functional in Sobolev spaces with Direct method, and by introducing of Euler-Lagrange equa-tion with the Classical method. Finally, we prove the exis-tence solution of Euler-Lagrange equation in Ω .n⊂ R

Keywords— Euler-Lagrange equation, Existence, Unique-ness

I. INTRODUCTION

ALCULUS of variations is a branch of mathematics that deals with optimization problem to find the ex-

tremum for a functional. Functionals is function of the functions. It can be expressed as integrals and derivatives of the function.

Some methods that used to solve the problem of the calculus of variations are classical and direct methods. In the classical method, the most important tool is the Euler-Lagrange equation, see [2]. The existence and uniqueness of the solution of Euler-Lagrange equation in the finite dimensional has been discussed in [5]-[7]. Unlike in the finite dimensional, the classical method in the infinite dimensional can not be used directly since u% in

( )1 2 orC C is difficult to be proved, see [2].

In this paper, we prove the existence and uniqueness

solution u% of Euler-Lagrange equation in Ω n⊂ R for a functional

( ) ( ), , ,I u F u u x dxΩ

= ∇∫ (P)

where ( ) ( )1: , Ω , , ,nu F C F F u u xΩ→ ∈ × × = ∇R R R and

, 1, 2, , .i

uu i n

x

∂∇ = =∂

L

Moreover, we find the minimizer u% for a functional (P) which satisfy the equation

( ) ( )( )1

, , , , 0, Ωxi

n

u ui i

F u u x F u u x xx=

∂∇ − ∇ = ∀ ∈∂∑% % % %

where u

FF

u

∂=∂

and , 1,2, , .xi

i

ux

FF i n

u

∂= =∂

L We

call such equation as Euler-Lagrange equation in

Ω .n⊂ R

II. PRELIMINARIES

In order to prove existence and uniqueness of minimizers for a functional in Sobolev spaces, we need some theorem as in the following Theorem 1 (Holder inequality)

Let Ω n⊂ R be open and 1 .p≤ ≤ ∞ If ( )Ωpu L∈

and ( )'

Ωpv L∈ where '

1 11,

p p+ = then ( )1 Ωuv L∈

and moreover

'1 p pL L Luv u v≤

Theorem 2 (Poincare inequality)

Let Ω n⊂ R be a bounded open set and 1 .p≤ ≤ ∞

Then there exists ( )Ω, 0pγ γ= > so that

( )1,0

, Ω ,p pp

L Lu u u Wγ≤ ∇ ∀ ∈

or equivalently

( )1,1,0

, Ω .p pp

W Lu u u Wγ≤ ∇ ∀ ∈

Theorem 3 Let Ω n∈ R be convex. The function

:Ωf → R said to be convex if for every , Ωx y∈

and every [ ]0,1 ,λ ∈ the following inequality holds

( )( ) ( ) ( ) ( )1 1 .f x y f x f yλ λ λ λ+ − ≤ + −

Theorem 4 Let : nf →R R and ( )1 ,f C∈ R the

function f is convex if only if

( ) ( ) ( ); , , nf x f y f y x y x y≥ + ∇ − ∀ ∈ R

EXISTENCE AND UNIQUENESS SOLUTION

OF EULER-LAGRANGE EQUATION IN ⊂Ω Rn

Ratna Dwi Christyanti 1*), Ratno Bagus Edy Wibowo 2), Abdul Rouf Alghofari 2) 1) Student of Magister Mathematics, Faculty of Mathematics and Natural Sciences, Brawijaya

University, Malang 2) Department of Mathematics, Faculty of Mathematics and Natural Sciences, Brawijaya University,

Malang



where ( ) ( ) ( ) ( )1 2

, , ,n

f y f y f yf y

x x x

∂ ∂ ∂ ∇ = ∂ ∂ ∂

L and

.;. denotes the scalar product in n.

Theorem 5 (Fundamental Lemma of the Calculus of vari-ations)

Let Ω n⊂ R be an open set and ( )1 Ωlocu L∈ so that

( ) ( ) ( )0

Ω

0, Ωu x x dx Cϕ ϕ ∞= ∀ ∈∫

then 0,u = almost everywhere in Ω.

III. RESULTS

Using Direct method we have Theorem 6 (Existence)

Let Ω n⊂ R be a bounded open set with Lipschitz boundary.

Let ( ) ( )0 Ω , , , ,nF C F F u u x∈ × × = ∇R R satisfy

(A1) ( ), ,u F u u x∇ → ∇ is convex for every

( ), Ω,u x ∈ ×R

(A2) there exist 1p q> ≥ and 1 2 30, ,α α α> ∈R

such that

( ) ( )1 2 3, , , , , Ω.p q nF u u x u u u u xα α α∇ ≥ ∇ + + ∀ ∇ ∈ × ×R R

Let

( ) ( ) ( )1,0 0

Ω

, , : Ωpinf I u F u u x dx u u W m

= ∇ ∈ + =

∫ (1)

where ( )1,0 Ωpu W∈ with ( )0 .I u < ∞ Then there

exists ( )1,0 0 Ωpu u W∈ +% a minimizer of (1).

Proof: We will assume that ( )1 ΩnF C∈ × ×R R and

(A3) ( ) ( ), , ,u u F u u x∇ → ∇ is convex for every

Ω.x∈

(A4) there exist 1p > and 1 30, α α> ∈R such that

( ) ( )1 3, , , , , Ω.p nF u u x u u u xα α∇ ≥ ∇ + ∀ ∇ ∈ × ×R R

(A5) there exist a constant 0β ≥ so that for every

( ), , Ωnu u x∇ ∈ × ×R R

( ) ( )1 1, , 1

p p

uF u u x u uβ − −∇ ≤ + + ∇

and

( ) ( )1 1, , 1

p p

uF u u x u uβ − −∇ ∇ ≤ + + ∇

where ( )1 2, , , , , 1,2, ,

x x x xn i

i

u u u u ux

FF F F F F i n

u∇∂= = =∂

L L

and .u

FF

u

∂=∂

The proof of Theorem 6 are devided into theree steps by

Direct method. Step 1: (Compactness)

Since ( )0I u < ∞ and from (A4), we get

( )0 .m I u−∞ < ≤ < ∞

Let ( )1,0 0 Ωp

vu u W∈ + be a minimizing sequence of

(1), i.e.

( ) ( ) inf , .vI u I u m asv→ = → ∞

From (A4) that for v large enough

( ) 1 31 ,p

p

v Lm I u uα α+ ≥ ≥ ∇ +

and hence there exists 4 0α > so that

4 .pL

u α∇ ≤

Applying Theorem 2 (Poincare inequality), we can find

constants 5 6, 0α α > so that

1,4 5 6 ,pp v WL

u uα α α≥ ∇ ≥ −

and we can find 7 0α > so that

1, 7 .pv W

u α≤

We use the fact that 1,p > there exists

( )1,0 0 Ωpu u W∈ +% and subsequence (still denoted vu )

so that

vu u % di ( )1, ΩpW .asv→ ∞

Step 2: (lower semicontinuity)

We now show that ( )I u% weakly lower semicontinuity,

this mean that

( ) ( ) ( )1, in Ω liminf .pv vv

u u W I u I u→∞

⇒ ≥ % %

Since ( ) ( ), , ,v v v vu u F u u x∇ → ∇ is convex for every

Ω,x∈ then from Theorem 4 we get

( ) ( ) ( ) ( )( )

, , , , , ,

, , ; .

v v u v

u v

F u u x F u u x F u u x u u

F u u x u u∇

∇ ≥ ∇ + ∇ −

+ ∇ ∇ −∇

% % % % %

% % %

(2)

Furthermore, we need to show that

( ) ( )'

, , ΩpuF u u x L∇ ∈% % and

( ) ( )'

, , Ω; .p nuF u u x L∇ ∇ ∈% % R

From (A5) and ( )1, Ωpu W∈% where

''

1 11 ,

1

pp

p p p

+ = = −

we have

( ) ( )'

1,1

Ω

, , 1 p

p p

u WF u u x uβ∇ ≤ + < ∞∫ % % %

and

( ) ( )'

1,1

Ω

, , 1 ,p

p p

u WF u u x uβ∇ ∇ ≤ + < ∞∫ % % %



1β being a constant.

We get

( ) ( )'

, , ΩpuF u u x L∇ ∈% % and

( ) ( )'

, , Ω; .p nuF u u x L∇ ∇ ∈% % R

Using Theorem 1 (Holder inequality) we find that for

( )1, Ωpvu W∈

( ) ( ) ( )1, , Ωv uu u F u u x L− ∇ ∈% % %

and

( ) ( )1, , ; Ωu vF u u x u u L∇ ∇ ∇ − ∇ ∈% % %

We next integrate (2), to get

( ) ( ) ( )( ) ( )Ω Ω

, , , , ; .v u v u vI u I u F u u x u u dx F u u x u u dx∇≥ + ∇ − + ∇ ∇ −∇∫ ∫% % % % % % %

Since 0vu u− % in ( )1, ΩpW (i.e. 0vu u− % di pL

and 0vu u∇ − ∇ % di pL ), then we get

( )( ) ( ) ( )'

Ω

lim , , 0, , , Ωpu v uv

F u u x u u dx F u u x L→∞

∇ − = ∀ ∇ ∈∫ % % % % %

and

( ) ( ) ( )'

Ω

lim , , ; 0, , , Ω .pu v uv

F u u x u u dx F u u x L∇ ∇→∞∇ ∇ −∇ = ∀ ∇ ∈∫ % % % % %

We have

( ) ( )lim . vv

inf I u I u→∞

≥ %

Step 3: Since ( ) ( ) infvI u I u m→ = and

( ) ( )lim vvinf I u I u

→∞≥ %

then

( ) ( )lim inf .vv

m I u I u→∞

= ≥ %

We deduce that ( ) ,I u m=% or in other words u% is a mi-

nimizers of (1). Theorem 7 (uniqueness)

If the function ( ) ( ), , ,u u F u u x∇ → ∇ is strictly con-

vex for every Ωx∈ then the minimizers is unique.

Proof: Let ( )1,0 0, Ωpv w u W∈ +% % be two solutions of

( ) ( )Ω

, , ,I u F u u x dx= ∇∫

and let

( ) ( )Ω

inf , , .u

I u F u u x dx m∈

= ∇ =

∫A

We prove .v w=% %

Denote by 1 1

2 2y v w= +% % % and observe that

( )1,0 0 Ω .py u W∈ +%

Since ( ) ( ), , ,u u F u u x∇ → ∇ is convex for every

Ω,x∈ then

( ) ( ) ( )1 1 1 1 1 1, , , , , , , ,

2 2 2 2 2 2F v v x F w wx F v w v wx F y y x

∇ + ∇ ≥ + ∇ + ∇ = ∇

% % % % % % % % % %

We get,

( ) ( ) ( )1 1.

2 2m I v I w I y m= + ≥ ≥% % %

We therefore get,

( ) ( )Ω

1 1 1 1 1 1, , , , , , 0.

2 2 2 2 2 2F v v x F w wx F v w v wx dx

∇ + ∇ − + ∇ + ∇ =

∫ % % % % % % % %

Since the integrand is, by strict convexity ,F positive

unless v w=% % and v w∇ = ∇% % we deduce that v w=% % al-

most everywhere in Ω.

Theorem 8 (Euler-Lagrange equation)

Let ( )1,0 0 Ωpu u W∈ +% be a minimizer of

( ) ( ) ( )1,0 0

Ω

, , : Ω ,pinf I u F u u x dx u u W m

= ∇ ∈ + =

∫ (3)

where ( )1,0 Ω ,pu W∈ then u% satisfies the weak form of

the Euler-Lagrange equation

( ) ( )( ) ( )1,0

Ω

, , , , ; 0, Ω .pu uF u u x F u u x dx Wη η∇∇ + ∇ ∇ = ∀ ∈∫ % % % % (4)

Moreover,

If ( )2 ΩnF C∈ × ×R R and ( )2 Ω ,u C∈% then u%

satisfies the Euler-Lagrange equation

( ) ( )( )1

, , , , 0, Ω.xi

n

u ui i

F u u x F u u x xx=

∂∇ − ∇ = ∀ ∈∂∑

% % % %

PrProof: The proof of Theorem 8 are devided into two steps by Classical method. Step 1: By using Gateaux derivative, we prove that

( ) ( ) ( ) ( )( )Ω

, , , , ; .u uDI u F u u x F u u x dxη η η∇= ∇ + ∇ ∇∫% % % % %

Letε ∈ R , ( )1,0 ΩpWη ∈ and

( )1,0 0 Ω .pu u Wεη+ ∈ +%

Since

( )( ) ( )0

,d

DI u I ud ε

η εηε =

= +% %

then



( )( ) ( )

( ) ( )

Ω 0

Ω

, ,

, , , , ; .u u

dDI u F u u x dx

d

F u u x F u u x dx

ε

η εη ε ηε

η η

=

∇

= + ∇ + ∇

= ∇ + ∇ ∇

∫

∫

% % %

% % % %

Step 2: Indeed since u% is a minimizers of (3), then

( ) ( ) ( )1,0 , ΩpI u I u Wεη η+ ≥ ∀ ∈% %

and thus

( )( ) 0.DI u η =%

Furthermore, we get

( )( ) ( ) ( )Ω

, , , , ; 0.u uDI u F u u x F u u x dxη η η∇= ∇ + ∇ ∇ = ∫% % % % % (5)

Since

( ) ( )( )Ω Ω

, , ; . , , ,u uF u u x dx F u u x dxη η∇ ∇∇ ∇ = − ∇ ∇∫ ∫% % % %

then

( ) ( )

( ) ( )( )Ω

Ω

, , , , ;

, , . , , .

u u

u u

F u u x F u u x dx

F u u x F u u x dx

η η

η

∇

∇

∇ + ∇ ∇ =

∇ − ∇ ∇

∫

∫

% % % %

% % % %

From (5), we have that

( ) ( )( ) ( )1,0

Ω

, , . , , 0, Ω .pu uF u u x F u u x dx Wη η∇ ∇ − ∇ ∇ = ∀ ∈ ∫ % % % %

From Theorem 5, ( )2 Ωu C∈% satisfies

( ) ( )( ), , . , , 0,u uF u u x F u u x∇∇ − ∇ ∇ =% % % %

or in other words

( ) ( )( )1

, , , , 0, Ω.xi

n

u ui i

F u u x F u u x xx=

∂∇ − ∇ = ∀ ∈∂∑

% % % %

Theorem 9 (Existence solution of Euler-Lagrange equation) If u% satisfies

( ) ( )( )1

, , , , 0, Ωxi

n

u ui i

F u u x F u u x xx=

∂∇ − ∇ = ∀ ∈∂∑

% % % % (6)

and if ( ) ( ), , ,u u F u u x∇ → ∇ is convex for every

Ω,x∈ thenu% is a minimizer of

( ) ( )Ω

, , .I u F u u x dx= ∇∫ (7)

Proof: Let u% be a solution of (4).

Since ( ) ( ), , ,u u F u u x∇ → ∇ is convex for every

Ω,x∈ then from Theorem 4 we deduce that for every

( )1,0 0 Ωpu u W∈ + the following holds

( ) ( ) ( )( ) ( ) ( ), , , , , , , , ;u uF u u x F u u x F u u x u u F u u x u u∇∇ ≥ ∇ + ∇ − + ∇ ∇ −∇% % % % % % % %

Integrating the above inequality. Since

( ) ( ) ( ) ( )( )Ω Ω

, , . . , , ,u uF u u x u u dx u u F u u x dx∇ ∇∇ ∇ − =− − ∇ ∇∫ ∫% % % % % %

then

( ) ( ) ( ) ( )( )( )Ω

, , . , ,u uI u I u F u u x F u u x u u dx∇≥ + ∇ −∇ ∇ −∫% % % % % %

Using

( ) ( )( )1

, , , , 0,xi

n

u ui i

F u u x F u u xx=

∂∇ − ∇ =∂∑% % % %

we get indeed that ( ) ( ).I u I u≥ % We deduce u% that is

a minimizer of (7). Corollary 10 (uniqueness) If u% satisfies

( ) ( )( )1

, , , , 0, Ωxi

n

u ui i

F u u x F u u x xx=

∂∇ − ∇ = ∀ ∈∂∑

% % % % (8)

and if ( ) ( ), , ,u u F u u x∇ → ∇ is stricly convex for

every Ω,x∈ thenu% is a unique minimizer of

( ) ( )Ω

, , .I u F u u x dx= ∇∫ (9)

Proof: The proof of Corollary 10 is analog with Theorem 9, and by Theorem 7 we deduce u% is a unique minimizer of (9).

IV. CONCLUSIONS

From this paper we can conclude that by Direct method, optimization problems in the infinite dimensional

space have solution if ( ), ,u F u u x∇ → ∇ is convex and

there exist 1p q> ≥ and 1 2 30, ,α α α> ∈R such

that

( ) ( )1 1 3, , , , , .p q nF u u x u u u u x R Rα α α∇ ≥ ∇ + + ∀ ∇ ∈ × ×Ω

It is shown that if function ( ) ( ), , ,u u F u u x∇ → ∇ is

strictly convex, then the minimizer of (P) is unique.

Moreover, Euler-Lagrange equation in Ω n⊂ R have

solution if function ( ) ( ), , ,u u F u u x∇ → ∇ is convex.

Furthermore, we shown that Euler-Lagrange equation in

Ω n⊂ R have a unique solution if function

( ) ( ), , ,u u F u u x∇ → ∇ is stricly convex.

REFERENCES [1] Adams R.A., Sobolev spaces, Academic Press, New York, 1975. [2] Dacorogna B., Introduction to the Calculus of Variations, Imperial

College Press, French, 1992.



[3] Sun W. and Yuan Y., Optimization theory and methods, Springer, New York, 2006.

[4] Clarke F.H., The Euler-Lagrange Differential Inclusion, J. Of Diferential Equations. 19 (1975), 80-90.

[5] Bohner M. and Guseinov G.Sh., Double integral calculus of varia-tions on time scales, J. of Computers and Mathematics with Ap-plications. 54 (2007), 45-57.

[6] Christyanti R.D, Euler-Lagrange Equation, Research report, 2011. [7] Orpel A., The existence of minimizers of the action functional

without convexity assumption, J. of the Juliusz Schauder Center. 20 (2002), 179-193



Abstract—Central Java Through Health Spending, Educa-

tion, health and income are the three pillars that are impor-tant in the economic development of a region (World Bank, 1993). By considering the importance of health for the im-provement of the health of a region need to get the govern-ment's attention. Barro model offers economic growth through health channels. With the healthy person's produc-tivity will increase, so that the output will be generated will increase the economic growth of a region. Central Java is one of the provinces in Indonesia, which has a human devel-opment index (HDI) which is lower than the HDI and the Indonesian island of Java. With the improvement of health in Central Java, is expected to boost economic growth in Cen-tral Java, so that with the economic growth of Central Java which will increase Indonesia's economic growth.

Keywords— Economic growth, health

I. INTRODUCTION

evelopment is a tool used to achieve the goals of eco-nomic growth of the nation and is one of the indicators to assess the success of a country's development. Devel-

opment paradigm that is being developed at this time is economic growth measured by the human development that seen with the level of quality of human life in each country. One of the benchmarks used in looking at the quality of human life is the Human Development Index (HDI) which is measured by the quality of education, health and economic (purchasing power). Through the third increase this indicator is expected to increase the quality of human life.

To see the extent of development and human well-being, UNDP has issued an indicator of the Human De-velopment Index (HDI) to measure the success of a coun-try's development and prosperity. Human Development Index (HDI) is a benchmark figure of a region or state welfare is seen by three dimensions: life expectancy at birth, literacy rates and average length of the school, and purchasing power. Life expectancy is an indicator to measure the health, indicator of the adult population literacy rate and the av-erage length of the school to measure education and the last indicator measures the purchasing power of the stan-dard of living. (United Nations Development Programme, UNDP, 1990).

The rate of economic growth in Central Java Prov-ince from 2005 to 2012 has increased. This suggests that the economic development in Central Java Province has increased. It will boost economic development and human

development. Regional economic growth positively and significantly influenced by human development.

TABLE I GROSS REGIONAL DOMESTIC PRODUCT AT CONSTAN

2000 MARKET PRICE 2005-2012

Year Gross Regional Domestic Product at Constan 2000 Market Price

2005 5.35

2006 5.33

2007 5.59

2008 5.61

2009 5.14

2010 5.84

2011 6.03

2012 6.34 Source : BPS-Statistics of Central Java Province

HDI achievement targets in Central Java in 2013 is

expected to experience a significant increase in the amount of 74.3% with a life expectancy indicators (life expectancy) of 73.8 years, the average length of 7.0 years of school, literacy rates for 97.3%, and the per capita ex-penditure of IDR. 626,200. It became a Central Java in order to make the target unable to compete with other regions, especially in Java and outside Java in general, which is expected to improve the competitiveness in terms of quality of human resources.

The elements of human development underlines explicitly targets to be achieved, namely a healthy life and longevity, educated and can enjoy a decent living. This means that human development aims to improve the wel-fare of the community with regard to the quality of human and society. Therefore, human is central to the develop-ment process.

Health, education and income has been regarded as the three pillars of human development in the Human De-velopment Index (HDI) (UNDP, 1990). Health is an im-portant form of human capital. This can increase worker productivity by improving their physical capacity, such as strength and durability, as well as their mental capacity, such as cognitive functioning and reasoning abilities.

Health factors closely related to the quality of hu-man resources (quality of human resources) itself. High and low quality of human resources (HR) will be deter-mined by health status, education and income levels per

APPLICATION BARRO MODEL ON ECONOMIC GROWTH VIA HEALTH IN

CENTRAL JAVA

Caroline Universitas Sultan Fatah Demak, Demak, Indonesia


D



capita (Ananta and Hatmadji, 1985). In economic activi-ties, the three indicators of the quality of human resources will also indirectly impact on the level of productivity of human resources, in this case, especially labor productivi-ty.

TABLE II

HUMAN DEVELOPMENT INDEX IN CENTRAL JAVA PROVINCE 2005-2013

Years

Life Expec-tation (Year)

The aver-age Old School (Years)

Literacy rates (%)

Real ex-penditure / per capita (Rp.000)

HDI

2005 70.60 6.60 87.40 621.40 69.8

2006 70.80 6.80 88.20 621.70 70.25

2007 70.90 6.80 88.62 628.53 70.92

2008 71.10 6.86 89.24 633.58 71.60

2009 71.25 7.07 95.60 624.20 72.10

2010 71.40 7.24 96.10 624.60 72.49

2011 73.20 6.90 96.60 625.30 72.94

2012 73.50 7.00 97.00 625.80 71.72

2013 73.80 7.00 97.30 626.20 71.68 Source : BPS-Statistics of Central Java Tengah Province

The quality of labor, in the form of human capital,

contributing significantly to economic growth. Workers are physically fit and mentally more energetic and robust. They are more productive so that it will get higher wages. They also tend to be absent from work due to illness (or disease in their family). Disease and disability reduce hourly wages substantially.

Todaro and Smith (2006) that health is at the core of the welfare and education is essential to achieve a satis-fying and worthwhile life. Education plays a major role in shaping the ability of developing countries to absorb modern technology and to develop the capacity to create sustainable growth and development. Health is a prerequi-site for improved productivity, while educational success also relies on good health. Its dual role as both input and output cause health and education is very important in economic development and economic growth. So research on the application dieprlukan barro models in promoting economic growth through health channels in Central Java.

II. THEORY

A. Economic Growh Teory The theory of economic growth has a long history

dating back to the late 18 century when the analysis of economic growth was at the center of attention of classical economists such as Smith (1776), Malthus (1798) and Ricardo (1817). These studies identified important causes and mechanisms that affect economic growth. The most important result from them is that the accumulation and investment of the production output is the main driving force behind economic growth.

The much later works of Ramsey (1928), Young (1928), Schumpeter (1934) and Knight (1944), which emphasize the elements of competition, equilibrium dy-namics, diminishing returns, the accumulation of physical and human capital and the monopoly power gained from

technology advances, formed a good basis for the neoclas-sical growth theories and the endogenous growth theories developed after the middle 20 century.

The models of Solow (1956) and Swan (1956) use a production function approach where there are constant returns to scale but diminishing return to each input. The equilibrium will exist if certain conditions are satisfied. The growth rate of the economy is determined exclusively by the exogenous technology. In other words, there will be long-term economic growth only if there are conti-nuous new technologies available. One important finding of the neoclassical model is neoclassical models explain everything except long-term growth. To overcome this modeling deficiency, researches on endogenous growth such as Romer (1986), Lucas (1988) and Romer (1990), which emphasize the roles technology changes and human capital accumulation in the form of education play, help to generate some important results confirming the important roles of technology changes and education in promoting long-term growth. The theory of "conditional conver-gence" which shows that the growth rate of the economy will be faster the further this economy is below its own equilibrium level. The historical facts show that the posi-tive rate of economic growth persists over a century and there is no trend of decline. The property of diminishing return of the inputs determines that the neoclassical mod-els explain everything but long-term growth. To overcome this modeling deficiency, researches on endogenous growth such as Romer (1986), Lucas (1988) and Romer (1990), which emphasize the roles technology changes and human capital accumulation in the form of education play, help to generate some important results confirming the important roles of technology changes and education in promoting long-term growth. A. Economi Growth Via Health with Barro Model

Fogel (1991, 1997, and 2000) have used histori-cal facts to demonstrate that health is a powerful engine of economic growth. Barro (1991), used a-cross sectional framework; that is, the growth and the explanatory were observed only once per country. The main reason extend to a panel set up is to expand the sample information. Al-though the main evidence turn out to ceme from the cross-sectional (between-country) variation, the time-series (within-country) dimension provides some additional in-formation. Barro Model with initial level of GDP,initial level of schooling and initial health status. • Initial Level of GDP

For given of the explanatory variables, the neoclas-sical model predicts a negatif coefficient on initial GDP, which enters in the system in logarithmic form. The coef-ficient on log of initial GDP has the interpretation of a conditional rate of convergence. • Initial Level of Schooling

Education appears in two in system : average years of attaiment for male age 25 and over in secondary and higher schools at the start of each period and an interac-tion between the log of initial GDP and theyears of male secondary and higher schooling. Female schooling is im-portanat for other indicators of economic development, such as fertility, infant morality and political freedom. Specifically, female primary education has a strong nega-tive relation with the fertility rate (see Schultz [1989], Behrman [1990], and Barro [1994]).



• Initial Health Status The population’s overall health status is measured

here by the log of life expectancy at birth at the start of each period. The results are, however, similar with some alternative aggregate indicators of health, such at the in-fant mortality rate, the mortality rate up to age five, or expectancy at age five. • Fertility Rate

If the population is growing, then a portion of the economy’s investment is used to provide capital for new workers, rather than to raisecapital per worker. For this reason, a higher rate of population groeth has a negative effect on y*, the steady-state level of output per effective workers in the neoclassical growth model. Another, rein-forcing, effect is that a higher fertility rate means that in-creased must be devoted to childerearing, rather than to production of goods (see Beckers and Barro [1988]). Fer-tility decision are surely endogenous; previous research has shown that fertility rypically declines with measures of prosperity, especiall female primary education and health status (see Schultz [1989], Berhman [1990], and Barro and Lee [1994]).

Based on the Barro (1996) framework, inspired

by the argument made by Grossman (1972) that health depreciation rate should not be constant, we endogenize the health depreciation rate by considering the following two cases: (1) health depreciation is determined exclusive-ly by health; (2) health depreciation rate is jointly deter-mined by health and education.

In these two cases, the negative effect of health on economic growth is reflected explicitly by the endo-genous health depreciation rate which is a positive func-tion of health. The optimization results show that when the endogenous health depreciation rate is determined only by health, the negative effect of health on economic growth would be too strong to generate endogenous growth in the long-term. In contrast, if we consider the effect of education on lowering the health depreciation rate simultaneously with the positive effect of health on health.

In the Barro (1996b) model, health affects eco-nomic growth by entering the production function direct-ly, which corresponds to part I of Figure 1.

Health affect economic growth through labor productivity. Improvement in health would allow the worker to work more efficiently, increase the amount of effective working hours and lower the probability of being absent from work either by the worker or his/her family members. Better health status would also increase the life expectancy and thus prolong the working ages which would encourage investment in education because the return on education investment is higher with longer effec-tive working time. All these channels would lead to im-provement in labor productivity which results in economic growth.

B. The health depreciation rate

As health depreciation is one component in the health accumulation function, we are interested in endo-genizing the health depreciation rate in order to reflect the negative effect of health in promoting economic growth.

Our idea of endogenous health depreciation rate is sup-ported by Grossman (1972). In the Grossman (1972) pa-per, health has been identified as another important form of human capital, which provides a good starting point for researchers to analyze the relationship between health and economic growth. However, as accepted by Grossman, health depreciation rate should vary over time. To under-stand why the health depreciation rate should not be con-stant, we should first understand the definition of health depreciation rate, which is the cost of maintaining the current level of health.

There are many examples to show why the health depreciation rate should not be a constant. For example, before a major competition like the Olympic Games, an athlete needs to spend time on train-ing, to eat following the instruction of dietitian and to check his/her body fitness regularly. In order to keep the match fitness, the investment is huge. However, after the competition, he/she no longer needs to keep that high lev-el of match fitness and the expenditure to keep his/her non-match fitness level of health would be lower. Another example is that one of the significant indicators of better health is life expectancy. C. The Barro model of health and economic growth

Barro (1996b) proposed a one-sector model which extended the neoclassical model to incorporate the impact of health on economic growth. In his model, health affects economic growth both directly and indirectly. First, health directly enters production function indicating a direct im-pact of health on productivity. In other words, if other determinants of the production function, such as physical capital, labor and schooling, are all constant, an improve-ment in an individual's health would increase the produc-tivity. Second, health also determines the depreciation rate of both health and education. Therefore, health contri-butes to economic growth indirectly through its effect on education. D. The Barro growth model revisited

In the Barro model, health is a private good that is financed totally by the individuals themselves. Invest-ments in health include activities such as the purchase of nutrition products, the leisure time spent on sports, the money paid on doctors and medicines, a regular body check, etc.

The economy is a one sector economy. First, total output Y is determined in a Cobb-Douglas function by physical capital inputs, K,individual's schooling and other education related factors, S, the health capital of individu-al, H, and the amount of labor provided, L:

Y = AKα S β H χ (L)1-α-β-χ where A is the knowledge stock parameter, which represents the exogenously determined technology level. The model assumes that α > 0, β > 0 , χ > 0 and α + β + χ < 1.



Fig. 1 The interaction network between health and growth at Barro Model

That is, in this production function, Barro assumes constant returns to scale with respect to the four inputs (physical capi-tal, education, health and labor) but diminishing returns with respect to each of the inputs respectively. This is a key as-sumption to derive the results of the Barro model. One thing to pay attention to these assumptions is that constant returns to scale with respect to the four inputs imply diminishing returns to scale with respect to the inputs of physical capital, education and health together.

III.D ATA

We construct a panel of Central Java over 1988-2012.

Output data Gross Regional Domestic Product Constant 2000 from BPS-Statisctics of Jawa Tengah Province.

We measure a province’s labor supply by the size of its economically active population using data from BPS-Statisctics of Jawa Tengah Province. Life expectancy date from BPS-Statisctics of Jawa Tengah Province.

IV. ESTIMATION AND RESULTS From the SPSS 16 output display, Model summary

magnitude of R2 is 0.746, meaning 74.6 GDP variation is ex-plained by the variation of the four independent variables POP, LABORFORCE, LABOR and LIFE EXPECTANCY. While the remaining (100% - 74.6% = 25.4%) is explained by other causes outside the model.

TABEL III DESCRIPTIVE STATISTICS

Mean Std. Deviation N

PDRB 8.49E7 2.935E11 19

Pop 3.1136E7 1.00281E10 19

Laborforce 1.7185E7 3.73047E10 19

Labor 1.4683E7 5.92126E9 19

Lifeexpectation 69.7355 4335.65823 19

a. Weighted Least Squares Regression - Weighted by Education

Standard error of estimate (SEE) is 1.478 Milyar . The small-er the value of SEE will make more precise regression models in predicting the dependent variable.

TABEL IV

Model Summaryb,c

Model R R Square Adjusted R

Square Std. Error of the

Estimate

Change Statistics

Durbin-Watson

R Square Change F Change df1 df2

Sig. F Change

1 .896a .803 .746 1.478E11 .803 14.242 4 14 .000 2.065 a. Predictors: (Constant), Lifeexpectation, Laborforce, Labor, Pop b. Dependent Variable: PDRB c. Weighted Least Squares Regression - Weighted by Education

Less illness

More Work Energy

Higher Cognitive Ability

Longer years of working

Higher life expectancy

Lower morbidity

Lower fertility

Higher labor productivity

Longe working hours

Increasing Return on Education

Increased effective labor

Population structure change Health

Goods production function: Y=AF (K, H)



From the ANOVA F test obtained or calculated value F calcu-lated at 14.242 with probability 0.000. Because the probability is much smaller than 0.05 then the regression model can be

used to predict the GDP or POP, LABORFORCE, LABOR and Life Expectancy jointly affect the GDP.

TABLE V ANOVAb,c

Model Sum of Squares df Mean Square F Sig.

1 Regression 1.244E24 4 3.111E23 14.242 .000a

Residual 3.058E23 14 2.184E22

Total 1.550E24 18

a. Predictors: (Constant), Lifeexpectation, Laborforce, Labor, Pop

b. Dependent Variable: PDRB

c. Weighted Least Squares Regression - Weighted by Education TABLE VI

Coefficientsa,b

Model

Unstandardized Coefficients

Standar-dized Coef-

ficients

t Sig.

95% Confidence Interval for B Correlations

Collinearity Statistics

B Std. Error Beta

Lower Bound

Upper Bound

Zero-order Partial Part Tolerance VIF

1 (Constant) -4.290E9 7.221E8 -5.941 .000 -5.839E9 -2.741E9

Pop -3.948 5.198 -.135 -.759 .460 -15.096 7.201 .576 -.199 -.090 .447 2.239

Laborforce -.837 1.027 -.106 -.816 .428 -3.040 1.365 -.339 -.213 -.097 .827 1.209

Labor -1.250 6.512 -.025 -.192 .851 -15.217 12.718 .327 -.051 -.023 .816 1.225

Lifeexpectation 6.496E7 1.226E7 .960 5.299 .000 3.867E7 9.126E7 .888 .817 .629 .429 2.329

a. Dependent Variable: PDRB

b. Weighted Least Squares Regression - Weighted by Education

The coefficient of the independent variable (indepen-

dent) can use unstandarized coefficients and standarized coef-ficients. Unstandarized beta coefficients :

The four independent variables included in the model OLS, Life expectancy variables significant at 0.05, while the other three variables were not significant (because of the above 0:05). Mathematical equation: PDRB = -4.290E9 -3.948 Pop – 0,837 Laborforce – 1.250 Labor + 6.496E7 Life expectancy.

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fiscal policy transmission channels in low-income countries. European Journal of Political Economy, 20 (3), 517-549.

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Abstract— This study aimed to develop Graphical User

Interface (GUI) simulation-based media that involved ma-terial concepts and problem-solving of mathematical proc-esses, particularly pertaining to the transformation of ge-ometry material. The rationale for this is that the language of mathematics that employs symbols and abstract mean-ings are hardly understood by students, and therefore it is regarded as a difficult subject to study. To deal with this condition, it is necessary to develop teaching media that can transform the abstract mathematical concepts into more concrete ones. Generally, the mathematics software for learning simulation does not show the process of solving mathematical problems. The software only generates the final result without helping students to do the thinking process. With this in mind, it is necessary to develop a mathematical learning simulation that contains mathemati-cal processes pertaining to the mathematical problem-solving. To conduct the current research, the researcher used a flow chart design.

Keywords— Abstract, Math Problems, GUI-Based Me-dia, Symbols, Software

I. INTRODUCTION HE specific objective of mathematics teaching at school is to train students with logical, critical, precise, and applicale thinking as well as providing

students with ability to study sciences and technology for further education. The teaching of mathematics has experienced paradigm changes of learning technique. This can be seen from the development of the cooperative models in mathematics teachings which focus on the students as the learning indivduals.

[Herman]1 On the other hand, according to Rastaman &

Rastaman (1997), laboratorium is a supporting media in the process of teaching and learning. Optimum result will be achieved when students are involved both physically and mentally in the teaching process. Through the simulation media being developed, it is expected that teachers can easily explain the abstract mathematical concepts becoming more concrete ones.

The problem to solve is how the Graphic User interface (GUI-based) simulation media in the teaching of mathematics can help convey materials and concepts of mathematics problems solving. This study aims at developing a GUI-based simulation media for mathematics teaching that contain maerials and concepts of mathematics problems solving.

II. LITERATURE RIVIEW

A. The Mathematics Learning Theory of Dienes

According to Dienes, mathematical games are really important because the math operation in the game illu-strates concrete rules that guide and sharpen the mathe-matic understanding of the learners. Thus, the concrete objects in the form of game play an important role in mathematics teaching when manipulated well. The more varied the the concepts introduced, the clearer the un-derstanding of the students is.

B. Graphical User Interface (GUI)

So far, the teaching of mathematics is dominantly per-formed in the traditional way in which everything is written on the board. Nowadays, the teaching process has has progressed where Matlab is used as computation device that help teachers in teaching mathematics. Mat-lab is a programming language with high performance in computation that is highly qualified for technical computation. It is also an interactive system for numeric computation and data visuaization.

C. Simulation Method

This simulation method develops learners’ skills, both mental and pysical/technical skills. The method transfers a real-life situation into a learning activity or learning room as there is difficulty to do a practice in the real situation. For instance, an aviation school student does a flying operation simulaton. The situation faced in the simulation must be created in such that it appears like the real true one (reality replication).

III. RESEARCH METHOD

Research design is the procedure concept to guide the research implementation so that it runs correctly to achieve the goal. This is a developing type of research with the following design:

The Development of Gui Simulation-Based Media for Geometry Transformation

Lilik Hidayati1*)

1) SMKN 2 Lingsar West Lombok, West Nusa Tenggara, Indonesia *) Corresponding author: [email protected]

T



IV. UNITS

Chart 1. Diagram of the Developing of GUI-Application for Maths Teaching Simulation

IV. RESULT AND DISCUSSION

A. Design of the GUI Model

Figure 1.This is GUI Geometry Transformation concept Note the Figure is designed for GUI model. The program is used for calculating geometric transfomation. By input the data into the boxes (A,B,and C). The data is operated by pressing the button (translasi, refleksi, rotasi, dilatasi) and the result will appear in the screen.

B. Scripting

After designing the GUI model, the computation code is added to the m-file. This scripting process can be seen in details in the appendix. Some parts of the stages are as the following: function varargout = translasi(varargin) % TRANSLASI MATLAB code for translasi.fig

% TRANSLASI, by itself, creates a new TRANSLASI or raises the existing % singleton*. % % H = TRANSLASI returns the handle to a new TRANSLASI or the handle to % the existing singleton*. % % TRANSLASI('CALLBACK',hObject,eventData,handles,...) calls the local % function named CALLBACK in TRANSLASI.M with the given input arguments. % % TRANSLASI('Property','Value',...) creates a new TRANSLASI or raises the % existing singleton*. Starting from the left, property value pairs are % applied to the GUI before translasi_OpeningFcn gets called. An % unrecognized property name or invalid value makes property application % stop. All inputs are passed to translasi_OpeningFcn via varargin. % % *See GUI Options on GUIDE's Tools menu. Choose "GUI allows only one % instance to run (singleton)". % % See also: GUIDE, GUIDATA, GUIHANDLES % Edit the above text to modify the response to help translasi % Last Modified by GUIDE v2.5 23-Nov-2013 11:58:56 % Begin initialization code - DO NOT EDIT ........ function edit7_CreateFcn(hObject, eventdata, handles) % hObject handle to edit7 (see GCBO) % eventdata reserved - to be defined in a future version of MATLAB % handles empty - handles not created until after all CreateFcns called % Hint: edit controls usually have a white background on Windows. % See ISPC and COMPUTER. if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) set(hObject,'BackgroundColor','white'); end

C. Implementation and Discussion The result of the developing of the simulation media is to be applied for problem solving in transformational geometry. The samples of problems are chosen on the basis of their representativeness covering items such as translation, reflection, rotation, and dilation.

The project application from the development of of the GUI-based simulation media is in the form of soft-ware compilation in CD application. The GUI - Model is equipped with scripting (computation language). After verification and application test in which the media is considered successful, it is then applied into teaching practice. The data of the implementation/application stage is analyzed to see the relation among variables and also to identify the pattern of the data.

The advantage for teacher: The first, process of teaching leaarning is effective and effesient, the second the target of learning achieved or sucessfull, for students: The first, the whole process appear in this

Start

Designing GUI Model

Success-ful

Project Application

End

Implementation

Scripting

No

Yes



program, the second the program appearing the animation process make the process of teaching reality without leaving the mathematic terms, third constructed the students’ knowledge systematicly.

V. CONCLUSION

Based on the implementation result of the GUI-Model simulation media and the study on the learning theory of Dienes, the teaching media is found to be po-tential to contribute much in increasing the students’ understanding towards the concept of transformational geometry.

REFERENCES

[1] Bloom, Benyamin S., et. all (1971), Handbook on Formative and Summative Evaluation of Student Learning, McGraw-Hill Book Company, New York.

[2] Djamarah, Syaiful Bahri, Drs. Strategi Belajar Mengajar. Jakar-ta: Rineka Cipta. 2002.

[3] Gronlund, Norman E. (1985) Measurement and Evaluation in Teaching, Fifth Edition, Macmillan Publishing Company New York.

[4] Guilford (1973). Fundamental Statistic in Psychology and Education, Tokyo: Mc Graw-Hill Kogakusha.

[5] Herman Hudoyo. 1998. Belajar Mengajar Matematika. Jakarta: Depdikbud P2LPTK.

[6] Hidayati, L., (2012), Aplikasi Berbasis Gui (Grafik User Inter-faces) Untuk Simulasi Limit Fungsi, Jurnal, AVISENA, Vol.4/No.2/ISSN2086-8960/Desember/2012 UNIZAR Mata-ram.

[7] Hidayati, L., (2013), Pengembangan Media Pembelajaran Matematika Berbasis Gui (Grafik User Interfaces), Makalah Seminar Nasional MIPA dan Pendidikan Matematika, UNESA, 2013.

[8] Ibrahim, (2012), Teori Dienis, Makalah tugas mata kuliah Psi-kologi Belajar Matematika, Jurusan Matematika UIN Sunan Kalijaga Yogyakarta.

[9] Muslihati, (2012), Teori Belajar Permainan Dienes Dalam Pem-belajaran matematika, Jurnal, STKIP PGRI Metro.

[10] Purwanto, Drs. Strategi Pembelajaran Matematika. Surakarta: Sebelas Maret University Press. 2003.

[11] Sardiman, A.M. Interaksi dan Motivasi Belajar Mengajar. Cet. IV; Jakarta: Rajawali Pers. 1992.

[12] Sri Wulandari Danoebroto. 2008. “Meningkatkan Kemampuan Pemecahan Masalah Melalui Pendekatan PMRI dan Pelatihan Metakognitif”. Jurnal Penelitian dan Evaluasi Pendidikan. No-mor 1 Tahun XI. 69 – 81.

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