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Page 1: ISSN No. 2320 Journal of Experimental Biology and ... · Journal of Experimental Biology and Agricultural Sciences Journal of Experimental Biology and Agricultural Sciences ... 1).07.15
Page 2: ISSN No. 2320 Journal of Experimental Biology and ... · Journal of Experimental Biology and Agricultural Sciences Journal of Experimental Biology and Agricultural Sciences ... 1).07.15

ISSN No. 2320 – 8694 Peer Reviewed - open access journal Common Creative Licence - NC 4.0 Volume No – 4 Issue No – I February, 2016 Journal of Experimental Biology and Agricultural Sciences

Journal of Experimental Biology and Agricultural Sciences (JEBAS) is an online platform for the advancement and rapid dissemination of scientific knowledge generated by the highly motivated researchers in the field of biological sciences. JEBAS publishes high-quality original research and critical up-to-date review articles covering all the aspects of biological sciences. Every year, it publishes six issues.

The JEBAS is an open access journal. Anyone interested can download full text PDF without any registration. JEBAS has been accepted by EMERGING SOURCES CITATION INDEX (Thomson Reuters – Web of Science database), DOAJ, CABI, INDEX COPERNICUS INTERNATIONAL (Poland), AGRICOLA (USA), CAS (ACS, USA), CABI – Full Text (UK), AGORA (FAO-UN), OARE (UNEP), HINARI (WHO), J gate, EIJASR, DRIJ and Indian Science Abstracts (ISA, NISCAIR) like well reputed indexing database.

[HORIZON PUBLISHER INDIA [HPI] http://www.horizonpublisherindia.in/]

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Editorial Board Journal of Experimental Biology and Agricultural Sciences _______________________________________________________________________________

Editor-in-Chief Sebua Silas Semenya Department of Biodiversity University of Limpopo South Africa Email: [email protected]

Co-Editor in Chief Kuldeep Dhama (M.V.Sc., Ph.D) NAAS Associate, Principal Scientist Division of Pathology, IVRI, Izatnagar, India- 243122 Email: [email protected]

Managing Editor Kamal Kishore Chaudhary (M.Sc, Ph.D) INDIA Email: [email protected], [email protected]

Associate Managing Editor Anusheel Varshney School of Environment & Life Sciences, University of Salford, England, United Kingdom [email protected]

Technical Editors M K Meghvansi Scientist D Biotechnology Division Defence Research Laboratory, Tezpur, India E mail: [email protected] B L Yadav Head – Botany MLV Govt. College, Bhilwara, India E mail: [email protected] Yashpal S. Malik ICAR-National Fellow Indian Veterinary Research Institute (IVRI) Izatnagar 243 122, Bareilly, Uttar Pradesh, India E mail: [email protected]; [email protected]

K L Meena Lecturer – Botany MLV Govt. College, Bhilwara, India E mail: [email protected] Gautam Kumar Room No – 4302 Computer Center – II IIIT-A E mail: [email protected] A. K. Srivastava Principal Scientist (Soil Science) National Research Center For Citrus A Nagpur, Maharashtra, India Email: [email protected]

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Neeraj Associate Professor and Head Department of Botany Feroze Gandhi College, RaeBareli, UP, India

Md.Moin Ansari Associate Professor -Senior Scientist Faculty of Veterinary Sciences and Animal Husbandry Srinagar-190006, J&K, India

Associate Editors Biswanath Maity Carver College of Medicine Department of Pharmacology University of Iowa – Iowa City, USA Email: [email protected] Wu Yao Senior Manager China Development Bank ChaoYang District Beijing, China Email: [email protected] Auguste Emmanuel ISSALI Forestry Engineer Head - Coconut Breeding Department at Marc Delorme Coconut Research Station, Port Bouet, Côte d’Ivoire Regional Coordinator -COGENT Email: [email protected] Omoanghe S. Isikhuemhen Department of Natural Resources & Environmental Design

North Carolina Agricultural & Technical State University Greensboro, NC 27411, USA Email: [email protected] Vincenzo Tufarelli Department of Emergency and Organ Transplantation (DETO) Section of Veterinary Science and Animal Production University of Bari ‘Aldo Moro’ s.p. Casamassima km 3, 70010 Valenzano, Italy Email: [email protected] Sunil K. Joshi Laboratory Head, Cellular Immunology Investigator, Frank Reidy Research Center of Bioelectrics College of Health Sciences, Old Dominion University 4211 Monarch Way, IRP-2, Suite # 300, Norfolk, VA 23508 USA Email: [email protected]

Assistant Editors

A K Trivedi Senior Scientist (Plant Physiology) - NBPGR Nainital (Uttarakhand) INDIA – 263 132 E mail: [email protected] Rajnish Kumar Room No – 4302 (Biomedical Informatics Lab) Computer center – II, IIIT-A, Allahabad E mail: [email protected]

Bilal Ahmad Mir Department of Genetics University of Pretoria South Africa-0002 E mail: [email protected]; [email protected] Amit Kumar Jaiswal School of Food Science and Environmental Health College of Sciences and Health Dublin Institute of Technology, Dublin 1, Ireland E mail: [email protected]

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Gurudayal Ram Assistant Professor Jacob School of Biotechnology and Bioengineering (JSBB) Sam Higginbottom Institute of Agriculture, Technology and Sciences(SHIATS) Allahabad, Uttar Pradesh – 211007 Rajveer Singh Chauhan Division of Phycology Department of Botany University of Lucknow, Lucknow, INDIA E-mail: [email protected] Y. Norma-Rashid (Norma Yusoff) Professor Institute of Biological Sciences – Faculty of Science University of Malaya, 50603 Kuala Lumpur MALAYSIA E-mail: [email protected] Peiman Zandi Department of Agronomy I.A.University Takestan branch,Takestan,Iran E-mail: [email protected]

Oadi Najim Ismail Matny Assistant Professor – Plant pathology Department of Plant Protection College Of Agriculture Science University Of Baghdad, Iraq E-mail: [email protected], [email protected] Girijesh K. Patel Post Doc Fellow 1660 Springhill Avenue Mitchell Cancer Institute University of South Alabama, USA E-mail: [email protected] Anurag Aggarwal MD, DA, PDCC (Neuroanesthesia and Intensive Care), India E-mail: [email protected]

ISSN No. 2320 – 8694 Volume No – 4 Issue No. - I February, 2016 Journal of Experimental Biology and Agricultural Sciences Publisher: HORIZON PUBLISHER INDIA [HPI]

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Welcome Message - Managing Editor (Dr Kamal Kishore Chaudhary, M.Sc, Ph.D) _______________________________________________________________________________

Dear Readers, It is with much joy and anticipation that we celebrate the launch of Volume 4, Issue II of Journal of Experimental Biology and Agricultural Sciences (JEBAS). On behalf of the JEBAS Editorial Team, I would like to extend a very warm welcome to the readership of JEBAS. I take this opportunity to thank our authors, editors and anonymous reviewers, all of whom have volunteered to contribute to the success of the journal. I am also grateful to the staff at Horizon Publisher India [HPI] for making JEBAS a reality. JEBAS is dedicated to the rapid dissemination of high quality research papers on how advances in Biotechnology, Agricultural sciences along with computational algorithm can help us meet the challenges of the 21st century, and to capitalize on the promises ahead. We welcome contributions that can demonstrate near-term practical usefulness, particularly contributions that take a multidisciplinary / convergent approach because many real world problems are complex in nature. JEBAS provides an ideal forum for exchange of information on all of the above topics and more, in various formats: full length and letter length research papers, survey papers, work-in-progress reports on promising developments, case studies and best practice articles written by industry experts. Finally, we wish to encourage more contributions from the scientific community and industry practitioners to ensure a continued success of the journal. Authors, reviewers and guest editors are always welcome. We also welcome comments and suggestions that could improve the quality of the journal. Thank you. We hope you will find JEBAS informative. Dr. Kamal K Chaudhary Managing Editor - JEBAS February 2016

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INDEX ____________________________________________________________________________ Accumulation and distribution of heavy metals in Leucaena leucocephala Lam. and Bougainvillea Albert Einstein D S Juson, Maria Kariza M Martinez, and Johnny A Ching* [doi: http://dx.doi.org/10.18006/2015.4(1).01.06 ] 01.06 Biochemical and physiological analysis of zinc tolerance in Jatropha curca Preeti Badoni, Maya Kumari, Vikas Yadav Patade, Atul Grover* and M Nasim [doi: http://dx.doi.org/10.18006/2015.4(1).07.15 ] 07.15 Diva technology: indispensable tool for the control of Johne’s disease Sujata Jayaraman, Mukta Jain, Kuldeep Dhama, S V Singh, Manali Datta, Neelam Jain, K K Chaubey, S Gupta, G K Aseri, Neeraj Khare, Parul Yadav, A K Bhatia and J S Sohal* [doi: http://dx.doi.org/10.18006/2015.4(1).16.25 ] 16.25 Detection of quantitative trait loci (qtl) associated with yield and yield component traits in sorghum [Sorghum bicolor (L.) Moench] sown early and late planting dates Zenbaba Gutema*, Teshale Assefa and Fuyou Fu [doi: http://dx.doi.org/10.18006/2015.4(1).26.36 ] 26.36 Response of soybean (Glycine max) to molybdenum and iron spray under well-watered and water deficit conditions Ayoub Heidarzade, Mohammadali Esmaeili*, Mohammadali Bahmanyar and Rahmat Abbasi [doi: http://dx.doi.org/10.18006/2015.4(1).37.46 ] 37.46 Mesquite (Prosopis juliflora DC.) has stimulatory effect on nitrate reductase activity in rice seedlings Gowsiya Shaik and Santosh Kumar Mehar* [doi: http://dx.doi.org/10.18006/2015.4(1).47.51 ] 47.51 Effect of different planting dates and defoliation on the properties of sugar beet (Beta vulgaris L.) Mohammad Nabi Ilkaee*, Zohre Babaei, Amirsaleh Baghdadi and Farid Golzardi [doi: http://dx.doi.org/10.18006/2015.4(1).52.58 ] 52.58 Wild edible mushrooms of Nagaland, India: a potential food resource Toshinungla Ao, Chitta Ranjan Deb* and Neilazonuo Khruomo [doi: http://dx.doi.org/10.18006/2015.4(1).59.65 ] 59.65 Phytoplankton community in aquaculture and non-aquaculture sites of Taal Lake, Batangas, Philippines Airill L. Mercurio*, Blesshe L. Querijero and Johnny A. Ching [doi: http://dx.doi.org/10.18006/2015.4(1).66.73 ] 66.73

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Residual effects of compacted digested effluent on growth of dwarf napier grass in warm regions of Japan Hadijah Hasyim, Yasuyuki Ishii*, Ahmad Wadi, Ambo Ako Sunusi, Satoru Fukagawa and Sachiko Idota [doi: http://dx.doi.org/10.18006/2016.4(1).74.84 ] 74.84 Characterization and impact of mycorrhiza fungi isolated from weed plants on the growth and yield of mustard plant (Brassica juncea L.) Halim*, Resman and Sarawa [doi: http://dx.doi.org/10.18006/2016.4(1).85.91 ] 85.91 Development of a protocol for the application of commercial bio-stimulant manufactured from Kappaphycus alvarezii in selected vegetable crops Kosalaraman Karthikeyan and Munisamy Shanmugam* [doi: http://dx.doi.org/10.18006/2016.4(1).92.102 ] 92.102 Inhibition of quorum sensing in Chromobacterium violaceum cv026 by violacein produced by Pseudomonas aeruginosa Any Fitriani*, Dwi Putri Ayuningtyas and Kusnadi [doi: http://dx.doi.org/10.18006/2016.4(1).103.108 ] 103.108 Water quality in aquaculture and non-aquaculture sites in Taal lake, Batangas, Philippines Blesshe L Querijero* and Airill L Mercurio [doi: http://dx.doi.org/10.18006/2016.4(1).109.115 ] 109.115 Effect of aqueous extract of Amaranthus spinosus on hematological parameters of wistar albino rats Bhande Satish S* and Wasu Yogesh H [doi: http://dx.doi.org/10.18006/2016.4(1).116.120 ] 116.120

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_________________________________________________________

Journal of Experimental Biology and Agricultural Sciences

http://www.jebas.org

KEYWORDS

Bio-monitoring

Bougainvillea spectabilis

Leucaena lecocephala

Heavy metals

ABSTRACT

This study was conducted to determine the degree of heavy metal contaminations in the soils around the

perimeter of an industrial park located in the city of Sta. Rosa, Laguna, Philippines that houses light-to-

medium scale manufacturing industries, through accumulation of heavy metals in two plant systems viz.

Bougainvillea spectabilis (bougainvillea) and Leucaena lecocephala (ipil-ipil). Results of study revealed

that the soil samples collected from the study site contained higher concentrations of Cu and Zn

compared to a residential site as non-polluted source, some amount of nonessential mineral like Cd and

Pb was also found from the sample collected from the study area. Findings of the study suggested that

Cu is an immobile element, was highly accumulated in the roots of B. spectabilis, while highest

concentration of Zn was accumulated in the leaves. Moreover, the leaves of L. leucocephala collected

from the study site accumulated significantly higher concentrations of both Cu and Zn as compared to

the leaves of the same plant species collected in a residential site. The non-essential metals, Cd and Pb,

exhibit no significant difference in their accumulation and distribution to different plant parts and

between the industrial and residential sites.

Albert Einstein D S Juson1, Maria Kariza M Martinez

1, and Johnny A Ching

1,2,*

1Biological Sciences Department, College of Science and Computer Studies, De La Salle University-Dasmariñas, City of Dasmariñas, Cavite, Philippines

2Graduate Studies Department, College of Science and Computer Studies, De La Salle University-Dasmariñas, City of Dasmariñas, Cavite, Philippines

Received – December 02, 2015; Revision – December 21, 2015; Accepted – January 21, 2016

Available Online – February 15, 2016

DOI: http://dx.doi.org/10.18006/2015.4(1).01.06

ACCUMULATION AND DISTRIBUTION OF HEAVY METALS IN Leucaena

leucocephala Lam. AND Bougainvillea spectabilis Willd. PLANT SYSTEMS

E-mail: [email protected] (Johnny A Ching)

Peer review under responsibility of Journal of Experimental Biology and

Agricultural Sciences.

* Corresponding author

Journal of Experimental Biology and Agricultural Sciences, February - 2016; Volume – 4(1)

Journal of Experimental Biology and Agricultural Sciences

http://www.jebas.org

ISSN No. 2320 – 8694

Production and Hosting by Horizon Publisher

(http://publisher.jebas.org/index.html).

All rights reserved.

All the article published by Journal of Experimental

Biology and Agricultural Sciences is licensed under a

Creative Commons Attribution-NonCommercial 4.0

International License Based on a work at www.jebas.org.

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_________________________________________________________

Journal of Experimental Biology and Agricultural Sciences

http://www.jebas.org

1 Introduction

Environmental pollution by heavy metals as a result of

increasing industrial activities has become a main global

concern. One of the most predominant environmental pollution

caused by various productions from industries is heavy metals

contamination in the air and soil (Gaur & Adholeya, 2004).

Although living organisms can tolerate numerous ranges of

heavy metals, still at excessive levels several body systems of

organism could be damaged (Chronopoulos et al., 1997).

Because of this hazardous nature of heavy metals to human

health, monitoring of the environmental burden of heavy

metals is an important ecological interest (Onianwa & Ajayi,

2002; Peng et al., 2006). There are two different methods in

order to monitor or assess the extent of pollution caused by

heavy metals, i.e. direct method that measures metal

concentrations in the substrate and indirect method that studies

the presence of metal in some living organisms such as plants

(Hervada-Sala et al., 2003).

Plants can be described as solar driven pumping stations for

those that degrade pollutants or accumulate them from their

immediate environment (Cunningham et al., 1995). Use of

plants in removing toxins from the environment is known as

phytoremediation and is an important means of cleaning up

these toxins. Many plants species were used and have been

reported successful in absorbing contaminants such as lead,

cadmium, chromium, arsenic, and various radionuclides from

the soil (Wang et al., 2002; Sekara et al., 2005; Yazaki et al.,

2006; Ching et al., 2008). There are also plants that used in

bio-monitoring; these plants can be grouped into two viz. bio-

indicator plants and bio-accumulator plants. Bio-indicator are

those plants which are more sensitive to pollutants and shows

visible symptoms of contamination on the leaf and other plant

systems, these plants are generally used as pollution marker,

whereas bio-accumulator plants have built resistance against

these pollutants; they can store pollutants without any visible

damage on their morphology and physiology (Radnai, 1997).

Burhan et al. (2001) suggested that there are about 50 metals

which are of special interest with respect to the toxicological

importance to human health, plants and animals. Essential

elements such as Fe, Zn and Cu are useful to plants at low

concentration but playing a detrimental role in plant

development at higher levels. While trace metals present in the

environment are not only hazardous to ecosystems but can also

cause hazard to human health and plant growth (Shafiq &

Iqbal, 2006). Because of such problems, it was deemed

necessary to determine the accumulation of heavy metals such

as Cu, Zn, Cd, and Pb. Present study was formulated for

accessing the presence of these heavy metals in the soil

samples collected from the perimeter of an industrial park

situate in the city of Biñan, Laguna, Philippines soils. Two bio-

accumulator common plant species viz. Bougainvillea

spectabilis (bougainvillea) and Leucaena lecocephala (ipil-

ipil) were used for the study. Further, this study determined the

contamination level of the industrial area soil and the degree of

heavy metal accumulation in the roots, stem, and leaves of B.

spectabilis and in the leaves of the L. leucocephala collected

around the industrial park.

2 Materials and Methods

2.1 The Study Site

The study site is a 224-hectare industrial park located in the

city of Sta. Rosa, Laguna, Philippines. This industrial park is

an estate houses for light-to-medium scale manufacturing

industries like garments, foods and papers, plastics, ceramics,

paints, electronics, rubber, home appliances and car parts.

2.2 Collection of Soil Samples and Plant materials

Two most common plant species of study area are B.

spectabilis and L. lecocephala selected for the present study.

Plant samples i.e. roots, stems and leaves of B. spectabilis and

leaves of L. lecocephala were collected from the plant found

within 5 meters range around the perimeter of the study site.

Simultaneous to the collection of plant samples, about 0.5 kg

soil samples were also collected from the upper 2 -10 cm of the

surface soil (Ochotorena, 1994). Likewise, soil and plant

samples of the same species were collected from a residential

site in the city of Biñan, Laguna, more than 20 km away from

the study site to serve as basis of comparison from a non-

polluted source (Tsikritzis et al., 2002).

2.3 Processing of Samples and Concentration Analysis

Prior to determination of heavy metal concentration, samples

collected from the different plant parts were oven dried at

150°C, ash of the dried samples were made in the furnace at

450°C (Ochotorena, 1994). One-half gram of dry samples was

digested with 4 ml of 65% HNO3, and 1 ml of 37% HCl for 20

min. After digestion, the remaining soil and sand particles

were removed by filter paper. The digested and filtered

samples were diluted with 0.2% nitric acid. At the same time,

blank solutions of 1 ml hydrochloric acid and 4 ml nitric acid

was also prepared (Tsikritzis et al., 2002).

Soil samples were also oven-dried at 100-105°C.

Representative sample was taken by quartering technique and

was ground to pass a 60-mesh sieve. About 0.5 g of the

sample was weighed into a porcelain crucible and ignited at

450°C in furnace to destroy the organic matter. It was

decomposed twice with 10 ml of a 1:1 mixture of concentrated

HNO3 and HF in a 100 ml polypropylene beaker and was

evaporated to dryness over a water bath. The residue was

dissolved in a 20 ml of 2M HNO3 and was diluted in a 100-ml

volumetric flask (Mitra, 2003).

02 Juson et al

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_________________________________________________________

Journal of Experimental Biology and Agricultural Sciences

http://www.jebas.org

Table 1 Average Concentrations of Heavy Metals (mean ± SD) in the Soils from Industrial and Residential Sites.

Heavy Metal Average Metal concentration (mg/kg)

Industrial Residential

Cu 0.847 ± 0.01a

0.793 ± 0.01b

Zn 3.464 ± 0.04a

2.869 ± 0.04b

Cd 0.690 ± 0.05a

0.688 ± 0.08a

Pb 1.390 ± 0.02a

1.334 ± 0.02a

Metal concentrations are average of three replicates; mean ± SE values followed by the different letter in same horizontal row are

significantly different

Aliquots of the plant and soil solutions were taken for the

concentration analysis of copper (Cu), zinc (Zn), cadmium

(Cd), and lead (Pb) using a graphite furnace atomic absorption

spectrophotometer (AAS). The analysis was performed at the

Chemistry Research Center of De La Salle University-

Dasmariñas in the city of Dasmariñas, Cavite, Philippines.

2.4 Data Analysis

The degree of heavy metal concentrations for each of the plant

sample collected from the study site was measured by

comparing it to the heavy metal concentrations of the same

plant species collected from the residential site. To determine

the significant difference in the heavy metal concentrations

among the collected plant species and the pattern of variations

in the heavy metals content accumulated in the different plant

parts, two-way analysis of variance (ANOVA) was employed.

Whenever there is significant difference, Tukey test was used

as post-statistical treatment. All statistical analyses were done

at 95% level of significance.

3 Results and Discussion

3.1 Concentration of heavy metals in soil sample

Soil samples collected from the industrial site were found to

contain significantly (p<0.05) higher Cu and Zn

concentrations, both considered as essential metals, than those

collected from a residential site (Table 1). Concentrations of

metals in industrial sites have an average of 0.847 mg kg-1

for

Cu and 3.464 mg kg-1

for Zn. While those collected in the

residential sites, concentrations have an average of only 0.793

mg kg-1

and 2.869 mg kg-1

for Cu and Zn, respectively.

However, for the non-essential metals, Cd and Pb, no

significant difference was established between the metal

concentrations in the soils of industrial and residential sites.

Although significantly higher concentration of Cu and Zn was

reported from the samples collected from the industrial site but

it did not exceed from the standards set by the Government of

China, i.e. 250 mg kg-1

for Cu and Zn. These concentrations

were also within the range from soils collected at polluted sites

in China (Wang et al., 2003) but slightly higher than soils

samples collected from agricultural land, pasture lands and

forests of Belguim (Aydinalp & Marinova, 2003). High

concentrations of metals in soil from the industrial site could

be attributed to the industrial activities that pollute the

environment with gases containing these heavy elements. Soil

is contaminated by material from the air and by direct

depositing of pollutants. Most of the industrial plants were

operated without taking into consideration the problem of

pollution and wastes, and consequently they have no

technological ways to manage the problem (Wang et al., 2003;

Ching et al., 2008). Areas near heavy industries, including

smelters and mining sites, are exposed to the atmospheric

deposition of heavy metals, so that such deposition may

contribute significantly to the concentrations of metals in the

soils (Wang, et al., 2003).

3.2 Accumulation of heavy metals in plant sample

Accumulation and distribution of heavy metals in the roots,

stems, and leaves of B. spectabilis collected from industrial

and residential sites are presented in Table 2. Roots were

found to have significantly higher concentrations of Cu as

compared to stems and leaves. While for the Zn, leaves were

found to accumulate the highest concentration followed by

stems and roots. However, Cd and Pb did not show any

significant variations of in the distributions to the different

plant tissues of the plant sample. Only Cu and Zn, the

essential elements, showed significantly higher concentrations

in B. spectabilis collected from industrial site as compared to

the samples collected from the residential site but there was no

visible damage or symptoms of contamination on the examined

plant parts.

Roots worked as a primarily passageway for all fluids and

nutrients spread to the plant tissues, thus it could accumulated

higher concentration of metals. Johansson et al. (2005)

reported that accumulation of Cu varied with plant species,

these researchers reported that in Pistacia terebinthus and

Cistus creticus, most of the Cu was found in the roots, while

Bosea cypria accumulated most of the Cu in the leaves, in this

manner, results of present study are in agreement with P.

terebinthus and C. creticus. Zn is a mobile element and it

primarily enters through the roots of the plant species and

spread throughout the plant system. According to Herrero et

al. (2003), plants have special Zn transporters mechanism to

absorb this metal.

Accumulation and distribution of heavy metals in Leucaena leucocephala lam. and Bougainvillea spectabilis willd. plant systems. 03

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_________________________________________________________

Journal of Experimental Biology and Agricultural Sciences

http://www.jebas.org

Table 2 Accumulation and distribution of heavy metals in plant tissues samples of B. spectabilis collected around the Perimeter of an

Industrial Park and residential area

Heavy

metals

Sites Average Metal Concentration (mg/kg)

Roots Stems Leaves Total

Cu Industrial 1.256±0.02x

0.142±0.004y

0.835±0.02z

2.233a

Residential 0.423±0.01x 0.045±0.01

y 0.399±0.02

x 0.867

b

Zn Industrial 1.208±0.02x 1.544±0.01

x 2.150±0.01

y 4.902

a

Residential 0.313±0.02x 0.540±0.03

x 1.818±0.03

y 2.671

b

Pb Industrial 0.629±0.1x 0.676±0.03

x 0.677±0.05

x 1.982

a

Residential 0.656±0.08x 0.596±0.07

x 0.606±0.04

x 1.858

a

Cd Industrial 1.490±0.04x 0.964±0.006

x 1.484±0.03

x 3.938

a

Residential 1.267±0.02x 1.409±0.04

x 1.321±0.05

x 3.997

a

Metal concentrations are average of three replicates; mean ± SE values followed by the different letter a/b shows significantly different

between the study site and residential site while mean ± SE values followed by the different letter x/y/z shows significantly different

between the various plant tissues

Similarly, Cd, is also a mobile element in the soil and is taken

up by plants primarily through the roots. Cd and Pb strengthen

the effect of each other’s. Further, Cd promotes the

accumulation of Zn, but this process decelerated the number of

Cu and Pb in soil concentrations (Valizadehfard et al., 2012).

Pb is one of the elements that could also be taken by plant

through the aerial way. Since that it could pass through the air,

there was a high accumulation in the leaves of the plant.

Another factor that contributes to the high accumulation of

lead in the leaves only was the slow mobility of the metal

(Ogundiran & Osibanjo, 2008).

Heavy metal accumulation in the leaves of L. leucocephala

collected from industrial and residential sites were presented in

Table 3. Although there was no morphological symptoms of

contamination observed but the concentrations of essential

heavy metals, Cu and Zn, in the leaves of plant samples

collected from the industrial site were reported as significantly

higher (p<0.05) than those collected in the residential site.

However, in case of non-essential metals, Cd and Pb, there was

no significant difference was observed in the metal

concentrations in leaves samples collected from both sites.

These results were congruent to the findings of Rehman &

Iqbal (2009) in the study of metal transfer ratio in L.

leucocephala by using soils of industrial areas of Korangi and

Landhi, Karachi. Results of this study revealed that the

presence of high concentrations of metals in the leaves of the

plant could be attributed to other sources like aerial deposition.

Non-essential metals, like Cd and Pb, have lesser accumulation

as compared to the essential metals, i.e. Cu and Zn. This may

be possible because of slower mobility of these metals

(Yazaki, et al., 2006).

Conclusion

Soil samples collected from the industrial site were found to

have significantly higher levels of essential metals, Cu and Zn,

than those collected from the residential site. While for the

non-essential metals, Cd and Pb, no significant difference was

established between the metal concentrations in the soils of

industrial and residential sites. However, heavy metal

concentrations in the soils collected from the study site were

found to be within the range of non-polluted soil. Higher

concentrations of Cu and Zn was reported in the plant sample

collected industrial site but this higher concentration is not

making any morphological damage, so it can be conclude that

these two plant species worked as a potential bio-accumulators.

On the contrary, the non-essential elements Cd and Pb did not

show any significant variations for both plant samples

collected on both sites. Cu accumulated highest in the roots of

B. spectabilis while its leaves accumulated the highest

concentration of Zn. Heavy metals can also be distributed and

accumulated by means of aerial deposition, thus metals could

be transmitted to the leaves and stems of the plant. Cd and Pb

are evenly distributed in all the tissues of B. spectabilis having

no significant difference on their concentrations.

Table 3 Accumulation and distribution of heavy metals in leaves of L. leucocephala collected from the industrial and residential sites.

Heavy metals Average Metal Concentration (mg/kg)

Industrial site Residential site

Cu 3.418± 0.46x

1.181 ± 0.01y

Zn 3.203 ± 0.1x

1.536 ± 0.07y

Cd 0.679 ± 0.06x

0.677 ± 0.06x

Pb 1.487 ± 0.07x

1.411 ± 0.05x

Metal concentrations are average of three replicates; mean ± SE values followed by the different letter in same horizontal row are

significantly different

04 Juson et al

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The same pattern of accumulation was observed in the leaves

of L. leucocephala.

Furthermore, researches must be carrying out in order to

establish the phytoremediation capability of plants that are

common in industrial sites. Likewise, studies on the

interactions among several metal contaminants affecting the

uptake mechanisms in plants must also be carried out along

with establishing the transformation processes for metal

tolerance of different plant species.

Acknowledgements

The researchers express their deepest gratitude to the Dr. Airill

Mercurio, Ms. Chona Bandelaria and Ms. Jonnacar San

Sebastian of the Biological Sciences Department under the

College of Science and Computer Studies of De La Salle

University-Dasmariñas for their unwavering support and

intelligent inputs in all possible ways.

Conflict of interest

Authors would hereby like to declare that there is no conflict of

interests that could possibly arise.

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KEYWORDS

Antioxidant enzymes

Growth parameters

Lipid peroxidation

Proline content

Zinc accumulation

ABSTRACT

Jatropha curcas L., widely recognized as a viable option for production of bio diesel, has been assessed

for its ability to withstand stress induced by supra-optimal zinc concentrations. In the present study

plants were exposed to varying Zinc (Zn) concentrations (0, 500, 1000, 1500 and 2000 mg/kg), and and

different growth, physiological and biochemical parameters were studied. It was reported that up to

1500 mg/kg Zn, no significant effects on most of the growth parameters of the plants could be seen.

However at 2000 mg/kg Zn, a clear retardation of growth was visible, which was apparently reflected by

the physiological as well as biochemical parameters. These effects were more profound in the aerial

parts of the plant. Atomic Absorption Spectra (AAS) profiles suggested that Zn got mainly accumulated

in the roots after absorption from the soil. Osmotic adjustments indicated significantly increased

accumulation of proline, phenols and reducing sugars with increasing concentration of Zn as compared

to the control. Membrane damage was not observed up to 1000 mg/kg concentration. Jatropha, owing to

its tolerance to supra-optimal Zn concentrations is, thus, a suitable candidate for phytoremediation of Zn

from contaminated soils along with cultivation for biofuel production.

Preeti Badoni1, Maya Kumari

2, Vikas Yadav Patade

3, Atul Grover

1,* and M Nasim

1

1Defence Institute of Bio-Energy Research (DIBER), Goraparao, P.O. Arjunpur, Haldwani 263139. India

2Office of Director General Life Sciences, Defence Research and Development Organization, DRDO Bhawan, Rajaji Marg, New Delhi 110011. India

3Defence Institute of Bio-Energy Research (DIBER) Field Station, Panda Farm, Pithoragarh 262501. India

Received – October 01, 2015; Revision – October 21, 2015; Accepted – January 27, 2016

Available Online – February 15, 2016

DOI: http://dx.doi.org/10.18006/2015.4(1).07.15

BIOCHEMICAL AND PHYSIOLOGICAL ANALYSIS OF ZINC TOLERANCE IN

Jatropha curcas

E-mail: [email protected] (Atul Grover)

Peer review under responsibility of Journal of Experimental Biology and

Agricultural Sciences.

* Corresponding author

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1 Introduction

Heavy metals (HMs), frequently referred to lead (Pb),

chromium (Cr), arsenic (As), zinc (Zn), cadmium (Cd), copper

(Cu), mercury (Hg) and nickel (Ni) occur naturally in the soils.

Beyond a certain concentration, these heavy metals are toxic.

In recent years, their concentrations in soil have become a

concern worldwide (Rascio & Navari-Izzo, 2011; Villiers et

al., 2011). Zn is a necessary cofactor for many biological

reactions, known to limit oxidative degradation of auxin and is

necessary to maintain membrane integrity (Tsonev & Lidon,

2012). Zinc concentration in soils lesser than 125 ppm is

considered optimum for the growth of plants (Hussain et al.,

2010). Plants growing in such edaphic environments display

Zn concentrations in the range 0.02-0.04 mg g-1

dry weight

(Tsonev & Lidon, 2012). Higher concentrations of Zn in soil,

however, have direct effects on the growth and yields of the

plants (Chibuike & Obiora, 2014), and thus adversely affect

the agriculture.

The general symptoms are stunting of shoot, curling and

rolling of young leaves, death of leaf tips (Rout & Das, 2003)

and chlorosis (Rout & Das, 2003). Due to Zn toxicity, the

activity of proteins present in the plasma membrane and

especially the activity of SH groups gets affected which causes

damage to membrane stability. As soon as heavy metals pass

through the plasma membrane, they can immediately interact

with all metabolic processes (Rout & Das, 2003). To avoid Zn

toxicity in plants, the excess quantities of Zn shall be cleaned

up from the soil. Among several methods available for such

clean up, phytoremediation is catching attention in recent

years, as plants survive for longer durations and have potential

to permanently fix the pollutants. Plants have many cellular

mechanisms involved in the detoxification of heavy metals and

thus tolerance to metal stress. These include the binding of

metals to cell wall and extracellular exudates, reduced uptake

or efflux pumping of metals at the plasma membrane, chelation

of metals in the cytosol by peptides such as phytochelatins,

repair of stress-damaged proteins and the compartmentation of

metals in the vacuole by tonoplast located transporters (Hall,

2002). However, this necessitates that plants being used for

phyto-remediation should be non-edible and can grow

effectively at the polluted sites (Nanda & Abraham, 2011).

In view of the above, we have assessed the potential of

jatropha, which is also being projected as a promising bio fuel

crop, to survive and thrive under condition of higher soil

concentrations of Zn. It is a small tree that has naturalized in

most parts of the world and grows in a variety of agro-climatic

areas. Many studies show the potential of J. curcas to recover

and reclaim heavy metal contaminated soil (Yadav et al.,

2009).

2 Materials & Methods

2.1 Plant material and Zn concentrations

Mature, healthy and current harvest seeds of J. curcas strain

DARL-2 were soaked overnight in 0.1% (w/v) Bavistin,

washed several times under running tap before sterilizing with

70% (v/v) ethanol and followed by three washes of sterile

water. Thereafter, seeds were allowed to germinate on moist

filter papers in Petri dishes. After germination, seedlings of

uniform size were selected and transplanted into pots

containing autoclaved mixture of sand and soil in 1:1 ratio.

ZnSO4.5H2O solution was added in the pots to obtain the Zn+2

concentrations of 0 (control), 500, 1000, 1500, 2000 mg/kg of

soils. Experiment was conducted with three replicates each,

and replication had five pots having three plants each. Both

control and treated pots were irrigated at regular interval.

The Zn concentration in soil and in different parts of the plant

(root, stem and leaf) was estimated using Atomic Absorption

Spectrometer (M Series 650294v129, Thermo Electron

Corporation, USA) fitted with an air-acetylene burner,

expressed as mg/g dry weight of the sample.

2.2 Growth parameters

Root length, shoot length, total number of leaves, fresh weight

and dry weight of root and stem were recorded for each

treatment after 4 months.

2.3 Physiological parameters

Total chlorophyll (a + b) and carotenoids were determined

from fresh leaf (100 mg FW) according to Arnon (1949). The

leaf material was ground in a pre-chilled mortar in acetone

(80% v/v). After homogenization, the mixture was filtered and

the volume was adjusted to 10 ml with cold acetone. The

absorbance of the extract was measured at 645, 663, and 470

nm using a spectrophotometer (UV-Vis Dual Beam, Labomed

inc.) and the pigments content were calculated. The

chlorophyll stability indices (CSI) were determined using the

formula:

Total chlorophyll content in stressed leaves / total chlorophyll

content in control leaves X 100

The leaf relative water content (RWC) was determined

according to Patade et al. (2011). Fresh weight (FW) of the

leaf was recorded immediately after plucking from the plant.

After 24 h of saturation with deionized water the turgid weight

(TW) was recorded. Dry weight (DW) was recorded after

drying the leaves for 48 hrs in the hot air oven at 70oC. The

RWC was calculated as:

RWC (%) = [(FW-DW)/ (TW-DW)] X100

Reducing sugar was estimated as described by Miller (1959).

About 100 mg leaf sample was homogenized in 3ml of 80%

ethanol. The homogenate was centrifuged at 6000 g for 10 min

at 48oC and the supernatant was mixed with equal volume of 3,

5-dinitro-salicylic acid (DNSA) reagent. Distilled water was

08 Badoni et al

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used for blank readings. The reactants were mixed by

vortexing and the tubes were placed in a boiling water bath for

10 min after which they were cooled on ice. The absorbance

was measured at 540 nm and the reducing sugars content (mg

g-1

FW) was calculated based on standard curve with glucose as

standard.

Leaf pieces (~1.0 cm2) after washing with distilled water were

transferred to glass culture tubes containing 20 ml distilled

water and incubated for 24 h with intermittent shaking. Electric

conductivity was recorded using EC meter (WTW, Germany).

EC1 was recorded after 24 hrs of incubation of the leaf. Tubes

were capped and then autoclaved at 121oC for 20 min. to

completely kill the tissues and release all electrolytes. EC2 was

recorded after cooling the solution to room temperature.

Membrane damage rate (MDR) was calculated using the

formula (Lutts et al., 1995):

MDR (%) = (EC1 / EC2) X 100

Proline content was determined according to Bates et al.

(1973). 200 mg of leaf was homogenized in aqueous

sulfosalicylic acid (3% w/v). The filtered homogenate was

reacted with equal volume each of acid ninhydrin and acetic

acid for 1 h at 100°C in a water bath. The reaction mixture was

extracted with toluene and the absorbance was recorded at 520

nm using toluene as a blank. Proline concentration (µg g-1

FW)

was determined from a standard curve using L-proline as a

standard.

Lipid peroxidation was determined according to the method of

Heath & Packer (1968). 100 mg of leaf was homogenized in

1.5 ml of 0.25% Thiobarbituric acid (TBA) in 10%

Trichloroacetic acid (TCA). The mixture was heated at 95oC

for 30 min. and then cooled in ice, it was then centrifuged at

10000 g for 10 min. Absorbance of the supernatant was read at

532 nm and 600 nm, keeping 0.25% TBA in 10% TCA as

blank. MDA content was calculated according to its extinction

coefficient of 155 mM-1

cm-1

.

Total phenolic content was estimated according to Folin-

Cioalteu method as described by Ainsworth & Gillespie

(2007). The leaf tissue was ground to a fine powder using

liquid nitrogen. 2 ml of 95% (v/v) ice cold methanol was then

added to the ground tissue and incubated for 48 h at room

temperature in dark. It was then centrifuged at 13000 g for 5

min. Supernatant (100 µl) was taken and mixed with 200 µl of

10% (v/v) F-C reagent to which, 800 µl of 700 mM Na2CO3

was added and again incubated at room temperature for 2 h.

Absorbance was recorded at 765 nm. Total phenolic content

was calculated based on standard curve with gallic acid as

standard and expressed as mM µM-1

gallic acid equivalent.

2.4 Antioxidant enzyme assays

CAT activity was measured in a reaction mixture (1.0 ml)

containing 50 mM phosphate buffer (pH 7.0) and 15 mM H2O2

as described by Maehly & Chance (1959). The reaction was

initiated by adding 50 µl enzyme extract and the activity was

determined by monitoring decrease in absorbance at 240 nm (E

= 39.4mM-1

cm-1

) for 2 min. at intervals of 15 sec, as a result of

H2O2 decomposition. The slope of the rate assay (ΔA) was

used to determine the enzyme activity, which was expressed as

µmol.mg protein-1

min-1

.

APX activity was determined according to Nakano & Asada

(1981). The reaction mixture (2.0 ml) contained 50 mM

phosphate buffer (pH 7.0), 0.5 mM ascorbate, 0.1 mM H2O2

and 0.1 mM EDTA. The reaction was started by adding 100 µl

of crude enzyme. The H2O2 dependent oxidation of ascorbate

was followed by a decrease in the absorbance at 290 nm (E =

2.8 Mm-1

cm-1

). APX activity was measured in terms of

µmol.mg protein-1

min-1

.

GPX activity was determined according to Kar & Feierabend

(1984). The reaction mixture (1.0 ml) contained 50 mM

phosphate buffer (pH 7.0), 0.1 mM EDTA, 10 mM guiacol and

10 mM H2O2. Oxidation of guiacol was monitored by

measuring the increase in absorbance at 470 nm (E = 26.6 Mm-

1cm

-1) for 1 min at interval of 15 s after addition of 50 µl of

crude enzyme. GPX activity was measured in terms of µmol of

tetraguaicol formed mg protein-1

min-1

.

2.5 Statistical Analysis

Mean, standard error and statistical significance of mean

values for different parameters were determined. Analysis of

variance (ANOVA) for all the variables was performed using

Cropstat for Windows (7.2.2007.2 module, IRRI, Phillipines).

3 Results

3.1 Growth responses

Compared to the control, the growth parameters were not

significantly (p>0.05) affected up to 1500 mg/kg Zn (Table 1)

as seen by non-significant differences in various growth

parameters (root length, shoot length, fresh and dry weight of

the root and fresh and dry weight of the stem). However, at

2000 mg/kg Zn all the growth parameters were significantly

(p<0.05) reduced except the root length as compared to the

control (Table 1).

3.2 Zn accumulation in different plant parts

A significant amount of Zn was detected in J. curcas plants

grown at different concentrations of metal. Accumulation was

maximum in the roots, i.e., 8.93 mg/g DW followed by the

stem 3.61 mg/g DW and leaves 0.79 mg/g DW (Figure 1).

About nine folds higher Zn level was detected in the roots at

2000 mg/kg Zn as compared to the control. Similarly the level

of Zn in the stem and leaves of plants at 2000 mg/kg Zn was

11.5 folds and 1.27 folds respectively in relation to the control.

Biochemical and physiological analysis of zinc tolerance in Jatropha curcas. 09

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Table 1 Effect of different concentrations of Zn on growth parameters of J. curcas. Standard error of three treatment means (SE) and

LSD values are given in the last row.

Zn conc.

(mg/kg soil)

Root length

(cm)

Shoot length

(cm)

Total

leaves

Fresh weight

stem (g)

Dry weight

stem (g)

Fresh weight

Root (g)

Dry weight

Root (g)

0 8.233 21.767

9.000

3.625

2.493

0.270

0.056

500 8.483 22.333

8.33

3.785

2.690

0.467*

0.072*

1000 7.050 20.833

9.00

3.699

2.462

0.330*

0.063*

1500 5.267 20.733

10.67*

3.277

2.570

0.234

0.053

2000 4.583 14.600*

8.00

1.778*

1.398*

0.161*

0.024*

SE 0.778 0.293 0.24 0.128 0.093 0.013 0.001

5% LSD 2.295 0.956 0.77 0.418 0.303 0.039 0.005

The values marked with asterisk (*) are significantly different from control at P ≤ 0.05, as determined using Least Significant Difference

(LSD) test.

3.3 Zn concentration in soil before planting and after

harvesting of J. curcas

The results of soil analysis showed that the percent uptake of

Zn increased significantly (p<0.05) at different concentrations

as compared to the control (Table 2). At 2000 mg/kg Zn

concentration, the percentage uptake of Zn from soil increased

by 6.68 folds as compared to the control.

3.4 Chlorophyll and carotenoid contents

The total chlorophyll and carotenoides contents, and the

chlorophyll stability index was increased significantly (p<0.05)

at 1000 mg/kg Zn as compared to the control but at lower (500

mg/kg) and higher (≥ 1500 mg/kg) concentrations of Zn, no

difference in the total chlorophyll content and the chlorophyll

stability index were observed (Table 3).

3.5 Osmotic adjustments

In response to different concentrations of Zn, RWC of the leaf

was not significantly (p>0.05) changed as compared to the

control, but the accumulation of reducing sugars has

significantly (p<0.05) increased in relation to the control.

Significantly (p<0.05) higher content of total phenol (1.44

folds) and proline (2 folds) was observed at 2000 mg/kg Zn as

compared to the control (Figure 2a&b).

Figure 1 Accumulation of Zn in plant parts exposed to different concentrations of Zn. Different letters indicate significant differences at

p>0.05, as determined using Least Significant Difference (LSD) test. Error bars indicate SE of three treatment means.

10 Badoni et al

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Figure 2a Effects of different concentrations of Zn on RWC

measured in leaves of J. curcas. Different letters indicate

significant differences at p>0.05, as determined using Least

Significant Difference (LSD) test. Error bars indicate SE of

three treatment means.

Figure 2b Effects of different concentrations of Zn on reducing

sugar measured in leaves of J. curcas. Different letters indicate

significant differences at p>0.05, as determined using Least

Significant Difference (LSD) test. Error bars indicate SE of

three treatment means.

Figure 2c Effects of different concentrations of Zn on total

phenol measured in leaves of J. curcas. Different letters

indicate significant differences at p>0.05, as determined using

Least Significant Difference (LSD) test. Error bars indicate SE

of three treatment means.

Figure 2d Effects of different concentrations of Zn on proline

content measured in leaves of J. curcas. Different letters

indicate significant differences at p>0.05, as determined using

Least Significant Difference (LSD) test. Error bars indicate SE

of three treatment means.

3.6 Lipid peroxidation and Membrane damage rate

Higher concentrations of Zn has affected the membrane

properties which is revealed by significantly (p<0.05)

increased (1.31 folds) amount of MDA content at 2000 mg/kg

Zn as compared to the control (Figure 3a). However, the

electrical conductivity of the leaves was not significantly

(p>0.05) changed in response to higher Zn concentrations as

compared to the control (Figure 3b).

3.7 Antioxidant enzyme activities

The activities of the antioxidant enzymes (CAT, APX and

GPX) were significantly (p<0.05) increased in response to

higher concentrations of Zn as compared to the control (Figure

4a-c). In relation to the control, significantly (p ≤ 0.05) higher

CAT activity was observed in all the treatments and the highest

increase of 2.8 folds was observed at 1000 mg/kg Zn. The

APX activity was significantly (p ≤ 0.05) increased up-to 1500

mg/kg Zn (2.3 folds) but at 2000 mg/kg Zn the APX activity

was decreased in relation to the control. GPX activity was also

significantly (p ≤ 0.05) increased in all the treatments in

relation to the control and the highest increase of 3.4 folds was

observed at 2000 mg/kg Zn.

Biochemical and physiological analysis of zinc tolerance in Jatropha curcas. 11

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Table 2 Analysis of the soil sample used for growing J. curcas exposed to different concentrations of Zn.

Initial Zn conc. in soil (µg/g) Final Zn conc. in soil (µg/g) % Zn uptake by plant from soil

(Control) 12 10.33

13.89

500 55.00*

89.00*

1000 103.00*

89.70*

1500 112.00*

92.53*

2000 144.00*

92.80*

SE 0.82 1.00

LSD p≤0.05 0.58 0.71

The values marked with asterisk (*) are significantly different from control at P ≤ 0.05, as determined using Least Significant Difference

(LSD) test.

Table 3 Effect of different concentrations of Zn on pigment content in J. curcas. Standard error of three treatment means (SE) and LSD

values are given in the last row.

Zn conc. (mg/kg soil) Chl (a + b) (mg/gFW) Chlorophyll stability index (CSI) Carotenoids (mg/gFW)

0 8.40

100

415.56

500 9.16

109.06

442.47

1000 14.22*

169.33*

633.56*

1500 9. 01

107.28

483.21

2000 7.99

95.06

354.70

SE 0.49 5.99 18.39

LSD 1.61 19.55 59.97

The values marked with asterisk (*) are significantly different from control at P ≤ 0.05, as determined using Least Significant Difference

(LSD) test.

Figure 3a Effects of different concentrations of Zn on MDA

measured in leaves of J. curcas. Different letters indicate

significant differences at p>0.05, as determined using Least

Significant Difference (LSD) test. Error bars indicate SE of

three treatment means.

Figure 3b Effects of different concentrations of Zn on EC

measured in leaves of J. curcas. Different letters indicate

significant differences at p>0.05, as determined using Least

Significant Difference (LSD) test. Error bars indicate SE of

three treatment means.

12 Badoni et al

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Figure 4a Effects of different concentrations of Zn on CAT

activity measured in leaves of J. curcas. Different letters

indicate significant differences at p>0.05, as determined using

Least Significant Difference (LSD) test. Error bars indicate SE

of three treatment means.

Figure 4b Effects of different concentrations of Zn on APX

activity measured in leaves of J. curcas. Different letters

indicate significant differences at p>0.05, as determined using

Least Significant Difference (LSD) test. Error bars indicate SE

of three treatment means.

Figure 4c Effects of different concentrations of Zn on GPX activity measured in leaves of J. curcas. Different letters indicate significant

differences at p>0.05, as determined using Least Significant Difference (LSD) test. Error bars indicate SE of three treatment means.

Discussion

Despite being an essential micronutrient, the threshold of

toxicity due to Zn varies among plant species (Tsonev &

Lidon, 2012). J. curcas, has remarkable ability to withstand

elevated levels of Zn concentration, often accumulating excess

concentrations within the cells. It was reported that shoot

length, total number of leaves, fresh weight of stem, fresh

weight of root, dry weight of stem and dry weight of root were

not affected up to 1500 mg/kg Zn. At 2000 mg/kg Zn,

however, a significant decrease in the growth parameters was

observed, though no significant reductions occurred in the root

length. Growth inhibition is a general phenomenon associated

with most of heavy metals (Luo et al., 2010) and there are

reports which show that higher Zn concentrations results in

biomass decline and inhibition of cell elongation and division

(Tsonev & Lidon, 2012).

Zn acts as a structural and catalytic component of proteins,

enzymes and as a co-factor for normal development of pigment

biosynthesis which could be the reason behind increased

chlorophyll and carotenoid contents at 1000 mg/kg Zn as

compared to the control. Chlorophyll pigments are present in

the chloroplasts of leaves and it has been found that under

stress the amount of chloroplast increases for maintaining the

photosynthesis in plants. Increased content of photosynthetic

pigments was also observed by other workers (Jamil et al.,

Biochemical and physiological analysis of zinc tolerance in Jatropha curcas. 13

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2007; Pinhiero et al., 2008; Rahdari et al., 2012) under

different abiotic stresses.

The RWC in Jatropha plants grown at different concentrations

of Zn was not significantly affected as compared to the control,

indicating ability of Jatropha to osmotically adjust to higher

concentrations (upto 2000 mg/kg) of Zn in soil. Osmotic

adjustment was also inferred in terms of levels of accumulation

of reducing sugars, phenols and proline in leaf tissues. The

accumulation of reducing sugars could be a result of starch

degradation, and helps in adjusting water potential in the

cytosol, i.e., intracellular osmotic adjustment. In case of

accumulation of heavy metals, altered water potential would be

instrumental in adjusting to higher concentrations of

accumulated ions in the vacuole, and would protect integrity of

cellular membranes (Naghavi, 2014).

Proline too is a well documented osmolyte involved in abiotic

stress tolerance including heavy metal stress (Chandra et al.,

2012; Corcuera et al., 2012; Diaz et al., 2014; Pandey &

Gupta, 2015). Elevated levels of proline during stress

conditions could be a result of increased catabolism of the

phenolic compounds (Hamid et al., 2010). Importantly, free

proline chelates the metal ions, forming non-toxic metal-

proline complexes, thereby protecting cellular structures, and

metabolism thereof (Patel et al., 2013). Results of present

study indicated that increase in total phenolic content at higher

concentrations of Zn as compared to the control. Presumably,

the oxidative effects of metal ions and metalloids are prevented

by the antioxidant activity of phenolics that allows them to

scavenge free ions due to their redox properties, thereby

showing elevated levels of proline (Hamid et al., 2010). The

breakdown of phenolics is triggered by the enzymes and a

trailing cascading reactions, what we commonly also refer to

as participation of ‘antioxidant enzymes’, which would involve

hydrogen donors and quenchers of reactive oxygen species

(ROS). H2O2 is an important ROS that disrupts the functions of

the cell. CAT, APX and GPX are important enzymes that

regulate the levels of H2O2 (Hosseini & Poorakbar, 2013). We

have found an increased activity of CAT, APX and GPX

enzymes at different concentrations of Zn as compared to

control suggesting higher abilities of Jatropha to withstand

oxidative stress generated by Zn.

From the above discussion, it is inferred that J. curcas can

remove a significant amount of Zn from the soil and roots are

the primary sink for accumulation of the metal, causing almost

no damage to the plant growth.

Acknowledgements

Authors thank Dr. Shashi Bala Singh, Director and Dr.

Somnath Singh of Defence Institute of Physiology and Allied

Sciences (DIPAS), Delhi for allowing access to Atomic

absorption spectrometer. Preeti Badoni thanks Defence

Research and Development Organization (DRDO) for research

fellowship.

Conflict of interest

Authors would hereby like to declare that there is no conflict of

interests that could possibly arise.

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KEYWORDS

Paratuberculosis

Tuberculosis

Johne‟s disease

Secreted antigens

DIVA

Vaccine

Vaccination

ABSTRACT

Ruminant Paratuberculosis (Johne‟s disease) is categorized as List B disease by OIE. Paratuberculosis is

a disease of socio-economic and public health importance and has significant effect on in the

international trade of animals and animal products. Control of paratuberculosis is priority in many

countries and different countries have designed their own control programs tailored to their farming

practices and geographical conditions. However, the major component shared by these control programs

is “Test and Cull” policy. Due to inability of detecting paratuberculosis in early stages this policy has

globally failed to control the disease and hence there is global urgency in developing control measures.

Vaccination has shown promise in controlling this disease. However, vaccination in present form cannot

be used due to lack of DIVA (Differentiation of Infected from Vaccinated Animals) technology, because

present vaccines interfere with diagnosis of naturally infected paratuberculosis animals and animals

infected with tuberculosis. Therefore markers are needed to be identified for developing DIVA. This

paper summarizes the findings of vaccination trials conducted in different countries and highlights the

importance of vaccination in controlling paratuberculosis and also discusses strategies for developing

DIVA for paratuberculosis vaccines.

Sujata Jayaraman1,#

, Mukta Jain

1,#, Kuldeep Dhama

2, S V Singh

3, Manali Datta

4, Neelam Jain

4, K K

Chaubey3, S Gupta

3, G K Aseri

1, Neeraj Khare

1, Parul Yadav

5, A K Bhatia

6 and J S Sohal

l.*

1Amity Institute of Microbial Technology, Amity University Rajasthan, Kant Kalwar, NH-11C Delhi-Jaipur Highway, Jaipur- 303 002, India

2Division of Pathology, Indian Veterinary Research Institute, Izatnagar, Bareilly-

243122, Uttar Pradesh, India 3Animal Health Division, Central Institute for Research on Goats, Makhdoom, PO - Farah, Mathura- 281122, Uttar Pradesh, India

4Amity Institute of Biotechnology, Amity University Rajasthan, Kant Kalwar, NH-11C Delhi-Jaipur Highway, Jaipur- 303 002, India

5Amity University Science & Instrumentation Centre, Amity University Rajasthan, Kant Kalwar, NH-11C Delhi-Jaipur Highway, Jaipur- 303 002, India

6Department of Microbiology and Immunology, GLA University, Chaumuhan, Mathura, Uttar Pradesh, India

Received – November 23, 2015; Revision – December 14, 2015; Accepted – January 27, 2016

Available Online – February 15, 2016

DOI: http://dx.doi.org/10.18006/2015.4(1).16.25

DIVA TECHNOLOGY: INDISPENSABLE TOOL FOR THE CONTROL OF JOHNE‟S

DISEASE

E-mail: [email protected] (J S Sohal)

Peer review under responsibility of Journal of Experimental Biology and

Agricultural Sciences.

* Corresponding author (# Authors equally contributed to this work)

Journal of Experimental Biology and Agricultural Sciences, February - 2016; Volume – 4(1)

Journal of Experimental Biology and Agricultural Sciences

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ISSN No. 2320 – 8694

Production and Hosting by Horizon Publisher

(http://publisher.jebas.org/index.html).

All rights reserved.

All the article published by Journal of Experimental

Biology and Agricultural Sciences is licensed under a

Creative Commons Attribution-NonCommercial 4.0

International License Based on a work at www.jebas.org.

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1 Introduction

Johne‟s disease (JD) or Paratuberculosis has emerged as wide-

spread, highly prevalent and economically devastating

infectious disease of domestic livestock around the world

(Singh et al., 2014). JD is characterized by persistent diarrhea

with progressive loss of weight. Chronic diarrhoea results in

protein losing enteropathy. Disease is caused by an extremely

fastidious microbe known as Mycobacterium avium subspecies

paratuberculosis (MAP). It is highly resistant to environmental

stress like temperature, drying and is able to persist for years in

farm soil (Singh et al., 2013). In India disease has been widely

reported from all the domestic ruminant species (Singh et al.,

2014). In India MAP has also been reported from wild

ruminants such as blue bulls, hog deer, bison (Singh et al.,

2010a; Singh et al., 2011a) as well as other animals like rabbit

and primates (Singh et al., 2011b; Singh et al., 2012).

Paratuberculosis is a spectral disease where it takes years for

clinical signs to appear in animals. In very early stages (silent

stage) typically there are no signs of disease and none of the

available tests can detect infected animals at this stage, this

stage progresses to sub-clinical disease, where shedding of

MAP in feces can be occasionally seen without any signs of

the disease (Tiwari et al., 2006). This stage often then

progresses to clinical infection. At this stage animal have

intermittent diarrhea and progressive weight loss without

reduction of appetite. Sporadic signs at this stage generally

give way to more severe infection. These animals will give

positive results with fecal culture tests, because of host

shedding massive numbers of organism. Animals in this stage

are also positive on serological assays. Clinical signs continue

for months, which may usually results in death.

Paratuberculosis is increasingly being recognized as significant

problem affecting animal health, farming and the food industry

due to the high prevalence of the disease across the world.

Paratuberculosis can cause significant economic loss in

affected herds, as a result of reduced milk yield, poor milk

quality, poor feed conversion, increased susceptibility to

disease in general, reduced reproductive efficiency, premature

culling and reduced slaughter values. It is estimated that 68.0%

of US dairy herds are infected with JD, costing between $200

million to $1.5 billion per year to dairy industry (Sohal et al.,

2015). A study from India by Vinodhkumar et al. (2013)

estimated loss of Rs 1,840 (US$ 38.33) per infected

sheep/year. Another study from India in a Holstein Frisian

(H/F) dairy farm estimated loss of Rs 1,63,800.0 (US$ 2465)

in 180 days due to JD (Rawat et al., 2014). Besides costly to

animal husbandry, MAP is gaining interest as a zoonotic and

food-borne pathogen. Evidences suggest the involvement of

MAP in human diseases like Crohn‟s disease and type I

diabetes (Sohal et al., 2015). MAP is not killed by

pasteurization and milk has been considered as main source of

infection transmission to humans. MAP has frequently been

isolated from pasteurized milk and milk products (Shankar et

al., 2010). Rising concern of the MAP zoonosis has generated

lot of awareness among veterinarians and medicos seeking to

control this disease in animals. Therefore paratuberculosis

needs immediate attention for control in animals. This paper

discusses the role of vaccination and DIVA (Differentiation of

Vaccinated & Infected Animals) technology in efforts to

control Johne‟s disease.

2 Vaccination as tool of Paratuberculosis Control

Due to predominant subclinical nature of disease and

prolonged course of infection, early diagnosis is not possible

therefore control of paratuberculosis is problematic task. The

most widely practiced control strategy for paratuberculosis is

test and cull policy. However, due to lack of tools to detect

disease in early stages; test and cull policy is not sufficient in

preventing spread of MAP (Bastida & Juste, 2011). Despite

test and cull policies in place disease burden has continued to

increase (Singh et al., 2014). Positivity (number of positive

animals vs number of tested animals) of ruminant species in

India for paratuberculosis (cattle, goat, sheep and buffalo)

increased from 11.6% to 23.3% over the period of 28 years

(1985-2013) (Singh et al., 2014). Therefore alternate strategies

are required if control of paratuberculosis is to be achieved.

Vaccination programs in past have been successfully deployed.

As a result, there is increased interest in use of vaccination

against paratuberculosis. Vaccination is a cost-effective

strategy for paratuberculosis containment (Singh et al., 2007;

Juste & Perez, 2011; Bush et al., 2008; Dhand et al., 2013).

Vaccination reduces morbidity & mortality due to JD, reduces

shedding of MAP in feces, improves clinical signs (reduces

diarrhea & increases body weight), cures intestinal lesions and

enhances flock immunity to JD (Singh et al., 2007; Singh et

al., 2010b; Singh et al., 2013). Studies have confirmed that

vaccination not only reduces the prevalence of JD but also has

economic benefits to farmers (Groenendaal et al., 2015).

Vaccination also provides revival against MAP infection i.e.

therapeutic effects observed in already infected animals (Singh

et al., 2010b).

Benefits of vaccination have been summarized in Table 1.

Therefore vaccination is being considered an economically

attractive tool for controlling Johne‟s disease (Sohal et al.,

2015). Examples are there from countries like Iceland and

Australia where compulsory vaccination programs brought

about sufficient reductions in prevalence of paratuberculosis

(Sohal et al., 2015). Another indirect benefit of

paratuberculosis vaccination is that there is some degree of

cross protection for tuberculosis (de Val et al., 2012). Both

killed and live attenuated vaccines have the same efficiency in

controlling paratuberculosis (Singh et al., 2007) however,

killed vaccines have longer shelf life and are safer.

17 Jayaraman et al

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Table 1 Beneficial effects of vaccination against MAP

S.

No.

Name/ kind of vaccine Country Species Major Observation Reference

1. Laboratory Scale

(Live)

USA Cattle Vaccination reduces fecal

shedding of MAP

Larsen et al., 1974

2. Fromm (Killed) USA Cattle Hurley & Ewing 1983

3. Laboratory Scale

(Live)

Denmark Cattle Jorgensen, 1983

4. Laboratory Scale

(Live)

France Cattle Argente, 1992

5. Phylaxia (Killed) Hungary Cattle Kormendy, 1994

6. Neoparasec (Live) Germany Cattle Klawonn et al., 2002

7. Lio–Johne (Live) Spain Sheep Aduriz, 1993

8. Laboratory Scale

(Live)

Greece Sheep Dimareli–Malli et al., 2013

9. Live Vaccine New Zealand Sheep Gwodz et al., 2000

10. Killed vaccine - Goat Kalis et al.,

2001

11. Weybridge (Live) UK Cattle Vaccination improves

production

Wilesmith, 1982

12. Lelystad (Killed) Netherlands Cattle Kalis et al., 1992

13. Lio–Johne (Live) Spain Sheep Aduriz, 1993

14. Gudair (Killed) Australia Sheep Windsor et al., 2003

15. Neoparasec (Live) New Zealand Sheep Gwozdz et al., 2000

16. Laboratory Scale

(Killed)

Netherlands Cattle Histological improvement

after vaccination

van Schaik et al., 1996

17. Silirum (Killed) Spain Cattle Garcia–Pariente et al., 2005

18. Laboratory Scale

(Killed)

Iceland Sheep Sigurdsson, 1960

19. Lio–Johne (Live) Spain Sheep Aduriz, 1993

20. Mycopar (Killed) USA Sheep Thonney & Smith, 2005

21. Laboratory Scale

(Live)

Norway Goat Saxegaard & Fodstad, 1985

22. Laboratory Scale

(Killed)

USA Goat Kathaperumal et al., 2009

23. Laboratory Scale

(Killed)

India Goat Histological improvements,

reduction fecal shedding,

improves production and

therapeutic effects

Singh et al., 2007; Singh et

al., 2010b

24. Gudair (killed) Australia, New

Zealand and

Spain

Goat and

Sheep

Histological improvement,

reduction fecal shedding,

improves production

Griffin et al., 2009;

Reddacliff et al., 2006;

Eppleston et al., 2004; Corpa

et al., 2000

25. Killed Vaccine Iceland Sheep Reduction in mortality,

improves clinical signs and

reduction in fecal shedding of

MAP

Sigurdsson & Gunnarson,

1983

26. Live Vaccine Cyprus Sheep Reduction in mortality,

improves clinical signs and

reduction in fecal shedding of

MAP

Crowther et al., 1976

DIVA Technology: Indispensable Tool for the Control of Johne‟s disease. 18

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3 Issues of Paratuberculosis Vaccination

Vaccination efforts against paratuberculosis have succeeded

numerous times (Table 1); however, there are several issues in

implementing vaccination programs. First, vaccination against

paratuberculosis may interfere with routine diagnosis of

paratuberculosis. ELISA is the most widely used test for

diagnosing paratuberculosis because of low cost, rapid

turnaround time and high sensitivity. Other tests are costly,

time consuming, have poor sensitivity and requires

sophisticated facilities so are of limited utility when

incorporated into paratuberculosis control programs. However,

presently available ELISAs cannot discriminate vaccinated

individuals from naturally infected individuals. ELISA results

can be a problematic in certifying herds for disease

(paratuberculosis) free status where a compulsory vaccination

has either lowered the prevalence or eradicated the disease

from herd due to the fact that these tests can‟t discriminate

between pathogen and vaccine-induced immunological

responses. Since positive ELISA diagnostic test results for

MAP are often sufficient in triggering herd cull responses,

false positive results can be economically disastrous for cattle

farms. Secondly, vaccination against paratuberculosis will

interfere with diagnosis with tuberculosis and there are

evidences on this (Juste & Perez, 2011).

MAP and M. bovis share antigenic structures; therefore

immune responses generated by vaccination against these two

can interfere with diagnosis of paratuberculosis as well as

tuberculosis. Hence, implementing vaccination against MAP

will not only affect the diagnosis of paratuberculosis but will

also affect the tuberculosis control programs. Considering

these issues most of the countries are hesitant to vaccinate

against MAP. This problem is not just restricted to

paratuberculosis, there are number of animal diseases where

vaccines are available but cannot be used. One example is

FMD; vaccines are available and are quite effective in

controlling clinical disease but are not used in disease free

countries, as it interferes with diagnostic test results. Positive

immunological test results could ruin the disease free status

even of vaccinated healthy livestock populations, which, in

turn, can lead to serious economic losses in a region‟s

agronomy (Meeusen et al., 2007).

4 DIVA Technology

The primary goal of vaccination is to help in elimination of

disease. However, vaccinations elicit immune responses

similar to those found infected animals, thereby rendering

traditional diagnostic screening protocols useless as a means of

determining true herd disease status. Therefore it is essential to

differentiate immune responses due to vaccination compared to

natural infection. The term Differentiation of Infected from

Vaccinated Animals (DIVA) was coined in 1999 by Jan T. van

Oirschot (van Oirschot, 1999). It is now generally used in

place of older term „marker vaccines‟. The DIVA principle has

now also been extended to include killed whole organism

vaccines (Pasick, 2004). The general DIVA principle is that

antibody response produced by vaccination can be

differentiated from the antibody response elicited by natural

infection.

DIVA tests work by detection of immune response against

specific antigens which are present in the infectious agent but

in the absence of vaccine. Successful DIVA technologies has

been developed for animal vaccines like bovine rhinotracheitis

(IBR), pseudorabies, classical swine fever (CSF) etc (Meeusen

et al., 2007). Strategies for developing DIVA based vaccines

for other diseases like bovine tuberculosis (Vordermeier et al.,

2001), avian influenza (Rahn et al., 2015), PPR (Liu et al.,

2014) and bluetongue virus (Calvo-Pinilla et al., 2014) are also

under development. Though there has been great demand to

develop DIVA strategies for paratuberculosis, so far no

progress has been made and to our knowledge there is no

laboratory working on it. Till date, vaccination is the only

practical method for controlling paratuberculosis.

Since popular commercially available paratuberculosis

vaccines are whole cell killed preparations, simple strategies

can be designed to develop DIVA technology to differentiate

infected and vaccinated animals. Killed whole cell vaccines

will generate immune response only against cellular antigens

i.e. vaccinated animals will only have antibodies against

cellular antigens. However, in naturally infected animals will

have antibodies against both cellular and secreted (culture

filtrate antigens) antigens. Immune response against secreted

antigens can be selectively used to differentiate vaccinated and

naturally infected animals (Fig. 1). Therefore secreted antigens

of MAP can serve as markers of differentiation between

vaccinated and naturally infected animals and can be used to

develop DIVA. There have been reports that secreted antigens

are released early during the infection process and elicit

antibody responses (Ahmad, 2010), hence these can be used as

markers for early sero-diagnosis of paratuberculosis in early or

subclinical stages (Facciuolo et al., 2013). Presently available

commercial ELISAs contain a crude antigen mixture termed

PPA, which is prepared by thorough physical disruption of

mycobacterial bacilli followed by removal of cell debris.

The low sensitivity of available conventional ELISA tests can

be attributed to the lack of secreted antigens. Hence, a simple

ELISA based test can be developed using secreted antigens to

diagnose paratuberculosis as well as the same secreted antigens

based ELISA can be used to differentiate vaccinated and

infected animals if used in conjunction with conventional

ELISA protocols (Table 2). If MAP specific secreted antigens

are incorporated in this system then same ELISA regimen can

be used for diagnosis, DIVA marker detection and for

differentiating paratuberculosis & tuberculosis infection. Table

3 summarizes the MAP secreted proteins that can be used to

develop DIVA for killed whole cell vaccines and for

differentiating paratuberculosis and tuberculosis.

19 Jayaraman et al

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Figure 1 Scheme of the immune response that can be used to differentiate vaccinated and naturally infected animals.

Table 2 Scheme of the differentiation of healthy, infected and vaccinated animals

Concluding Remarks

Paratuberculosis is a devastating disease, negatively affecting

the livestock agronomy throughout the world, its presence

triggers trade restrictions and raises serious public health

concerns. Therefore control of paratuberculosis is of the utmost

urgency. Most extensively accepted “Test and Cull” policy is

very costly to farmers and governments; moreover it is not

particularly effective in stemming the physical spread of MAP

from one region to the next. Through the scientific, technical

and farming experiences it is becoming clear that vaccination

is the only practical solution for controlling this disease.

However, in the absence of DIVA technology, vaccination

programs cannot be implemented at national scale, as

vaccinations often elicit immune responses indistinguishable

from those generated by pathogens (using standard test

regimens).

DIVA Technology: Indispensable Tool for the Control of Johne‟s disease. 20

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Table 2 List of secreted MAP proteins that can be used to develop DIVA (in killed whole cell vaccine system) and for differentiating

paratuberculosis and tuberculosis.

S. No. Secreted Protein Function Remark

1. MAP 2609 - Tested for antigenicity by Willemsem et al. (2006)

2. MAP 2942c - Tested for antigenicity by Willemsem et al. (2006)

3. MAP 0210c - Tested for antigenicity by Willemsem et al. (2006); Mon

et al. (2012)

4. MAP 0209 - Tested for antigenicity by Mon et al. (2012)

5. MAP 0187c - Tested for antigenicity by Mon et al. (2012)

6. MAP 1272 Putative invasin, NlpC/P60 superfamily Tested for antigenicity by Mon et al. (2012)

7. MAP 1569/ ModD - Tested for antigenicity by Souza et al. (2011)

8. MAP 0471 - Tested for antigenicity by Facciuolo et al. (2013)

9. MAP 1981c - Tested for antigenicity by Facciuolo et al. (2013)

10. MAP 0196c - Tested for antigenicity by Facciuolo et al. (2013); Mon et

al. (2012)

11. MAP 1693c Peptidyl-prolyl cis–trans isomerase Tested for antigenicity by Mon et al. (2012); Roupie et

al. (2012)

12. MAP 0853 - Tested for antigenicity by Bannantine et al. (2008)

13. MAP 4308c - Tested for immunogenicity by Roupie et al. (2008)

14. CobT Phosphoribosyl transferase Tested for immunogenicity by Byun et al. (2012)

15. MAP 2168c - Tested for antigenicity by Cho et al. (2007)

16. MAP 1022c - Cho et al. (2006)

17. Antigen 85C Mycolyl transferase Tested for antigenicity by Shin et al. (2005)

18. PepA (N-terminal) Serine proteinase Tested for antigenicity by Cho et al. (2007)

19. PepA (C-terminal Serine proteinase Tested for antigenicity by Cho et al. (2007)

20. MAP 3273c - Gurung et al. (2014)

21. AhpC Alkyl hydroperoxide reductase C Tested for antigenicity by Olsen et al. (2001)

22. AhpD Alkyl hydroperoxide reductase D Tested for antigenicity by Olsen et al. (2001)

23. MAP 3680c - Tested for immunogenicity by Carlos et al. (2015)

24. Superoxide dismutase

(Sod)

- Tested for antigenicity by Shin et al. (2005)

25. MAP 0586c Possible transglycosylase SLT domain Tested for immunogenicity by Roupie et al. (2008)

26. MAP 2677c Glyoxylase Tested for antigenicity by Roupie et al. (2012)

27. MAP 3199 - Tested for antigenicity by Leroy et al. (2007)

28. MAP 1272c Putative invasin, NlpC/P60 superfamily Tested for antigenicity by Mon et al. (2012)

29. MAP 2942c - Tested for antigenicity by Gumber et al. (2009)

30. GreA Transcription elongation factor GreA Tested for antigenicity by Mon et al. (2012)

31. MAP 0593c - Tested for antigenicity by Gumber et al. (2009)

32. MAP 2411 - Tested for antigenicity by Kawaji et al. (2012)

33. MAP2168c - Tested for antigenicity by Cho et al. (2007)

34. Ppa Inorganic pyrophosphatase Tested for antigenicity by Gumber et al. (2009)

35. ClpP ATP-dependent Clp protease proteolytic

subunit

Tested for antigenicity by Gumber et al. (2009)

36. Ag85A - Tested for antigenicity by Rosseels et al. (2006)

37. Ag85B - Tested for antigenicity by Rosseels et al. (2006)

21 Jayaraman et al

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This, in turn, greatly impairs determination of livestock herd

infectious disease status- critical for the entire livestock

agronomy. Since killed vaccines have good protective efficacy

against paratuberculosis, the strategy proposed in the paper can

be used to develop DIVA ELISA using the above

comprehensive list of antigens. A careful selection and

screening of secretory antigens is performed, we can develop

an ELISA based test that can be used as routine diagnostic

tool, as a DIVA tool and one that will be able to differentiate

paratuberculosis and tuberculosis. We suggest that the

development and validation of such a test be carried out on

global scale, with many laboratories working in conjunction

with one another, so that an effective strategy can be developed

for combating the worldwide spread of Johne‟s disease and

animal tuberculosis.

Conflict of interest

Authors would hereby like to declare that there is no conflict of

interests that could possibly arise.

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KEYWORDS

QTL

DArT

Yield and yield components

Planting dates

Sorghum

ABSTRACT

Genetic improvement for grain yield is one of the challenges in plant breeding programs. QTL analysis

is often used to dissect complex trait like grain yield for a better genetic manipulation. The purpose of

this study was to map QTLs associated with yield and yield component traits of sorghum grown under

early and late planting dates. A total of 528 recombinant inbred lines (RILs) and their two parents were

sown early and late planting times in an augmented rectangular lattice block design with two

replications to generate field phenotypic data. A total of 379 markers consisting of DArT, SSR and

morphological markers were used to genotype the RILs and the parents. Results revealed the presence of

overall twelve QTL across traits and planting dates. More QTLs were detected for grain yield as

compared to each of the other traits and most QTL associated with grain yield were consistent across

planting dates while QTL associated with yield component traits were not. Stable QTLs detected in this

study might provide valuable information in breeding sorghum for enhanced grain yield in diverse

growing environment.

Zenbaba Gutema1,*

, Teshale Assefa2 and Fuyou Fu

3

1Department of Biology, Northern Virginia Community College, Annandale Campus, 8333 Little River Turnpike, Annandale, VA 22003

2Department of Agriculture, Iowa State University, 2104 Agronomy Hall, Ames, Iowa, USA

3Department of Agronomy, Purdue University, 915 W. State Street, West Lafayette, USA

Received – October 25, 2015; Revision – November 08, 2015; Accepted – January 27, 2016

Available Online – February 15, 2016

DOI: http://dx.doi.org/10.18006/2015.4(1).26.36

DETECTION OF QUANTITATIVE TRAIT LOCI (qtl) ASSOCIATED WITH YIELD

AND YIELD COMPONENT TRAITS IN SORGHUM [Sorghum bicolor (L.) Moench]

SOWN EARLY AND LATE PLANTING DATES

E-mail: [email protected] (Zenbaba Gutema)

Peer review under responsibility of Journal of Experimental Biology and

Agricultural Sciences.

* Corresponding author

Journal of Experimental Biology and Agricultural Sciences, February - 2016; Volume – 4(1)

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1 Introduction

Grain yield genetic improvement is one of the most important

objectives of many plant breeding programs around the world.

However, grain yield genetic manipulation has remained to be

a continued challenge in crop breeding. Sorghum [Sorghum

bicolor (L.) Moench] is the fifth most important cereal crop in

the world (FAO, 2012). It is the staple food for millions of

people in Africa and Asia. It is also one of the major feed crops

in the developed countries. Sorghum is extensively grown in

arid and semi-arid tropical and subtropical regions of the world

(Doggett, 1988). Sorghum cultivation has also been expanded

to the wet, cool temperate regions of the world particularly in

North America. Despite its economic importance, however,

sorghum has not been characterized very well genetically.

The multiplicative and interactive nature among yield

components in the formation of sorghum grain yield makes the

genetic manipulation of this important trait more complex

(Francis et al.,1983; Heinrich et al.,1985; Saeed et al., 1986).

Quantitative trait loci (QTL) analysis approach is useful in

dissecting such a complex trait for a better genetic

manipulation. QTLs linked with robust molecular markers

could be used to increase the efficiency of selection and

genetic gain in grain yield improvement.

QTL analysis has been used to detect genomic regions and

QTL associated with grain yield and its component for some

other crop species (Lu et al., 1997; Lu et al., 2006; Liu et al.,

2008; Xue et al., 2010). In sorghum, QTL associated with yield

and yield component traits have been reported recently

(Shehzad & Okuno, 2015). Further, a number of QTLs have

been also reported recently under certain contrasting conditions

such as photoperiod (Zou et al., 2012) and drought adaptation

(Phuong et al., 2013; Borrell et al., 2014) but till now, planting

dates have not been considered in QTL analysis. Although in

general late planting is considered normal, farmers desire early

planting because of its several advantages including full

utilization of late spring and early summer rainfalls. Early

planting may also help in completing plant life cycle before

cold spell during the growing season (Yu & Tuinstra, 2001).

Most of the current sorghum cultivars are developed under late

planting, however, early planting needs to be considered as

well.

In the present study, large numbers of molecular and field data

were generated on grain yield and its component traits like

plant population, kernel number and kernel weight by using

RIL population sown early and late planting dates. The data

were subjected to QTL analyses utilizing relatively dense

genetic map constructed from mix of molecular and

morphological markers. Several QTL were detected

segregating in this large population for each of the traits.

However, only few QTL were found to be stable across

planting dates. These stable QTL were listed and characterized

for further studies targeting sorghum breeding for better grain

yield.

2 Materials and Methods

2.1 Genetic materials and experimental design

Sorghum recombinant inbredlines (RILs) lines were derived

from a cross between SRN-39 (an African caudatum) and Shan

Qui Red (SQR), (a Chinese germplasm line). The two parents

differ for a number of characteristics including yield and yield

components. About 1000 random F2 plants of this cross were

selfed and advanced up to the F5 generation by the single seed

descent method. Selfed seeds from each inbred line were used

to grow F5:6 progeny rows which were selfed and bulked (Cisse

& Ejeta 2003). Subsequent generations were maintained

through repeated cycles of selfing and bulking to F5:9 seeds

from which a total of 528 RI lines were sampled for the

purpose of this study. Seeds of these parents were maintained

in the on-going sorghum breeding nurseries.

The RILs and the parents were grown in an augmented lattice

design with 24 sub-blocks within two replications. The parents

were repeated in each sub-block thus each replication

constituted 576 total entries each with a two-row plot. Rows of

six meter length, spaced 75 cm apart were drill-seeded at the

rate of approximately 10 seeds per 30 cm at a depth of 2.5 cm

by a John Deer Max Emerge planter modified for small

research plots. The seedlings were hand-thinned to a spacing of

6 plants per 30 cm. The nurseries were planted during the 2005

and 2006 crop seasons at Purdue University Agronomy Center

for Research and Education (ACRE) in West Lafayette,

Indiana. The nurseries were planted early and late planting

dates each year. The first planting dates were at least three

weeks earlier than the late (normal) planting and they were

chosen to enhance performance differences among the RILs

under cool and wet soil conditions. Relatively large data were

generated for QTL analysis on sorghum grain yield and yield

components. Three yield components: plant population size,

kernel number, and kernel weight were assessed.

Weather conditions were measured at hourly intervals via a

weather station close to the test plots. Weather data during the

early and late planting dates of the experiments are

summarized in Table 1. The weather conditions were

contrasting between early and late planting dates especially

during the early growing period (times during seedling growth)

of the nursery. The average rainfall was lower in 2005 than in

2006.

27 Gutema et al

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Table 1 Climatic condition of study site at Purdue University Agronomy Center for Research and Education, West Lafayette, Indiana in

2005 and 2006 crop seasons.

Planting time Planting Date Temperature (0C) Rainfall (mm)

Soil Air

Min Max Min Max Average

Early May 4, 2005 11.9 20.0 9.3 20.8 54.1

Late May 23, 2005 19.2 28.1 18.0 26.0 61.5

Early April 27, 2006 9.6 26.0 6.3 26.9 89.4

Late May 30, 2006 19.7 28.3 18.3 27.3 86.0

Data courtesy of Purdue University Weather Station.

2.2 Phenotypic data measurements and analyses

The middle five meters of the two rows in each of the RILs

entries were used for yield and yield component traits

measurements in this study. Plants per plot (3.75 m2) were

counted at maturity to determine plant population size.

Panicles of counted plants were harvested manually at the end

of the growing season for each plot, threshed and weighed for

grain yield (kg/plot) for QTL analyses but converted to t/ha for

analyses of variance (ANOVA), which was used to examine

significant differences among the RILs for these traits. One

hundred counted seed weights were measured and kernel

number per plant was estimated from data on kernel weight,

grain yield and number of plants per plot. Values were

averaged per replication for each of the traits for QTL

analyses.

The ANOVA was carried out using GLM procedure in SAS V.

9.1 (SAS Institute, Cary, NC) using the following general

model: Y = µ + G + L R + ε, (Knoll & Ejeta, 2008) where

Y is the dependent variable, μ is the experiment mean, G is a

genotypic effect, R is a replicate, L is a lattice block (nested in

rep), and ε is the error. Broad sense heritability was estimated

following method of Cisse & Ejeta (2003).

2.3 Genomic DNA isolation

Leaf tissue for genomic DNA isolation was harvested from

field grown seedlings at three weeks after planting.

Approximately one gram sections were cut from the uppermost

second and third leaves and bulked. Harvested leaf tissue was

kept on ice during harvest and lyophilized upon returning to

the laboratory. The lyophilized samples were kept at -80oC

until grinding to a fine powder with a UDY Cyclone Mill. A

CTAB based DNA extraction method according to Saghai-

Maroof et al. (1984) was used with only minor modifications.

2.4 Genotyping

The 528 RILs and the two lines were genotyped with three

types of markers vise-a-vise morphological markers scored in

the field or laboratory, SSR markers scored in our laboratory

and Diversity Array Technology (DArT™) markers scored at

DArT Plc., Yarralumla, Australia. A total of 359 DArT™

markers that were polymorphic between the parents, 15 SSR

markers and five morphological markers (seedling color, plant

color, seed color, presence/absence of a pigmented testa layer

and pericarp color) were used to construct a dense sorghum

genetic linkage map using the maximum likelihood method of

Join tMap 4 (Van Oijen, 2006).

2.5 QTL analysis

Single-trait analyses were undertaken for each environment

data using composite interval mapping (CIM) (Zeng, 1994)

with QTL Cartographer version 1.17ji (Basten et al., 2005).

The CIM was undertaken with conditional settings of 10 cM

(default) control intervals, five control markers set by QTL

Cartographer stepwise regression and forward/background

(FB) stepwise regression to account for the genetic background

variation. The five most significant (default) markers were

utilized and the default threshold level of 2.5 LOD in QTL

Cartographer 1.17ji (Basten et al., 2005) was used to declare a

QTL.

3 Results

3.1 Linkage map construction

A genetic linkage map consisting 11 linkage groups was

constructed in which chromosome A was found to be broken

into two linkage groups according to Bhattramakki et al.

(2000). We used SSR markers from this published map to

anchor the relatively new DArT markers. The map spans 1175

cM with an average marker distance of 3.14 cM with no major

gap.

3.2 Parental lines phenotypic analysis

The parental lines displayed significant divergence for all the

traits except for grain yield (Table 2). The SQR parent had

more plants per plot as compared to SRN-39 under early cold

planting. On the other hand, SRN-39 had better field

establishment under late planting as expected for this Africa

originated cultivar adapted to higher temperatures. However, it

did not exceed the SQR even under improved temperatures,

suggesting the importance of SQR for field establishment

under any planting condition. Contrary to plant population,

SRN-39 had more kernels as compared to SQR under both

early and late plantings.

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Table 2 Summarized mean performance of parental lines in field experiments planted early and late during 2005 and 2006 crop seasons

at the Agronomy Center for Research and Education testing site of Purdue University, West Lafayette, Indiana.

Planting time Parental line Traita

Plant population

(count)

Kernel number Kernel weight

(gm)

Grain yield (t/ha)

Early SRN-39 24.08±10.3 2699.49±120 3.71±0.3 4.31±0.8

SQR 44.37±4.2 1584.00±70 2.84±0.4 5.21±0.4

Late SRN-39 32.23±6.2 1622.64±60 3.75±0.2 5.11±0.3

SQR 43.05±3.2 1521.01±45 2.86±0.3 4.46±0.2

Overall mean SRN-39 28.16±8.2 2161.07±90 3.73±0.3 4.71±0.7

SQR 43.71±4.0 1552.51±65 2.85±0.4 4.84±0.3 a/Traits measured in augmented rectangular lattice block design with the parents repeated in each of 24 lattice blocks replicated twice in

four field experiments conducted over two years.

SRN-39’salso had heavier kernels than SQR under both

planting dates. The result of higher kernel number by SRN-39

under early planting as compared to SQR when it planted

under late normal planting was probably in response to sparse

plant population under cold early planting that killed some of

its seedlings. Kernel weight was not significantly different in

the planting dates for this parent. SQR yielded more grain than

SRN-39 under early planting but not under late planting.

Overall, SQR had higher yield than SRN-39 despite of SRN-

39’s outperformance both in kernel number and kernel weight.

The results might be a reflection of higher impact of plant

population size on the development of grain yield in sorghum

as compared to kernel number or kernel weight.

3.3 Phenotypic analysis and broad-sense heritability

Results in Table 3 showed wide variations and transgressive

segregations for all traits among the RILs at both early and late

planting dates. Phenotypic mean values for grain yield and

other traits in this RIL population appeared to be more or less

normally distributed (Figure 1). Mean agronomic performances

were lower than each parent except for plant population trait

where the RILs mean performance was higher than the ‘low’

parent SRN-39. The results indicated the extreme sensitivity of

the African origin SRN-39 parent to cold weather and\or the

efficiency of cold tolerance genes from Chinese highland SQR

parent.

The RILs had better performance under late (normal) plantings

for plant population size as well as grain yield traits as

expected. However, for kernel number, on average, RILs had

better performance under early plantings like the parents. For

kernel weight, similar performance was observed across

planting times although lower kernel weight was measured for

2006 late planted nursery. In general, mean agronomic

performances of the RILs were better during 2005 crop season

as compared to 2006. The air and soil temperatures during

2005 plantings improved quickly resulting into higher

performance across planting dates during this season. In

contrary, continued colder weather in 2006 crop season

plantings killed seedlings of several entries particularly in early

planted nursery and resulted into erratic field establishment

which in turn resulted into lower agronomic performances for

most traits. The highest overall mean agronomic performance

was noted for 2005 late planted nursery, while the lowest was

noted for 2006 early planted nursery, reflecting two possible

extreme growing conditions for sorghum in our study. Overall,

high standard deviations were recorded for all the traits except

for kernel weight.

Table 3 Mean agronomic performance, range and heritability values of yield and yield component traits of sorghum measured in 528

RILs planted early and late cropping season.

Year Planting time Traita

Plant population (count) Kernel number Kernel weight (gm) Grain yield (t/ha)

2005 Early 40.7±13.1 1257.5±47 3.0±0.4 3.7±1.1

Late 43.9±10.6 1210.9±45 3.1±0.5 4.1±1.3

2006 Early 17.6±9.8 2171.7±1120 3.0±0.4 2.7±1.4

Late 36.0±7.8 1297.2±40 2.8±0.4 3.4±1.1

Mean±Stdevb

34.6±15.0 1484±789.2 2.3±0.4 3.5±1.3

Range 1.0 – 78.0 39.7- 9102.1 1.3 – 4.7 0.004 - 7.7

Heritability (H2) 0.64 0.68 0.93 0.77

aTraits measured in augmented rectangular lattice block design with 24 lattice blocks replicated twice in four field experiments

conducted over two years. bStdev = Standard deviation.

29 Gutema et al

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Broad-sense heritability values were in general high and the

high values illustrate the utility of this large RILs population in

our study in determining greater genetic influence on the

expression of traits as compared to the environment. The

highest heritability value was calculated for kernel weight

(0.93), followed by grain yield (0.77). Kernel number (0.68)

and plant population (0.64) had relatively low heritability

indicating more environmental influence in the expression of

these two traits of present study.

The ANOVA showed that there was highly significant

variation for entries in each of the two years experiment for all

the traits (Table 4). Planting date also had a significant effect.

Furthermore, the interaction of planting date with the RI lines

was highly significant in both years showing high genotype-

by-environment interaction. The observed high phenotypic

variability of all the traits in terms of ranges and ANOVA

results indicated that the population was suitable for QTL

analyses.

Figure 1 Phenotypic value distribution for 528 RILs planted in an augmented rectangular lattice design with 24 lattice blocks replicated

twice in four field experiments conducted over two years.

Detection of quantitative trait loci (qtl) associated with yield and yield component traits in sorghum [sorghum bicolor (l.) moench] sown early. . . 30

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Table 4 Analysis of variance for grain yield and yield component traits of sorghum.

Year Source dfa Plant

Populationb

Kernel

numberb

Kernel

weight

Grain

yield

2005 Planting Date 1 43.558 0.218 15.281 5.297

Rep(Planting Date) 2 0.891 0.134 0.339 0.219

Lattice block (Planting date x Rep) 92 0.388 0.027 0.029 0.058

RIL 526 2.854 0.067 0.665 0.289

Planting Date x RIL 526 0.299 0.013 0.035 0.045

Error 960 0.152 0.007 0.019 0.021

2006 Planting Date 1 1991.5 19.944 22.226 31.31

Rep (Planting Date) 2 16.856 1.671 0.028 0.725

Lattice block (Planting date x Rep) 92 0.531 0.028 0.025 0.053

RIL 526 2.307 0.091 0.547 0.399

Planting Date x RIL 526 0.824 0.028 0.035 0.090

Error 960 0.182 0.011 0.015 0.030 aDegrees of freedom.

bError variances for plant population and kernel number were not strictly homogenous in this analysis. ***, All

mean squares are significant at α = 0.0001.

3.4 Phenotypic correlation among traits

Pearson correlation coefficients were computed among traits

values (Table 5). The largest but negative sign correlation

coefficient (r=0.57) was observed between plant population

and kernel number, the negative sign of the coefficient

suggesting the opposing effect of these two traits in the

formation of final grain yield in sorghum. The second largest

but with positive sign of correlation coefficient (r=0.51) was

between plant population and grain yield, indicating positive

impact of plant population on grain yield of sorghum.

The other negative coefficient (r=0.26) was observed between

plant population and kernel weight, again indicating the

opposing effect of these two traits in the formation of sorghum

grain yield. Significant positive correlations coefficients were

also observed for both kernel number and kernel weight with

grain yield. These results were as expected as both of these

traits actually contribute to the measure of grain yield.

Interestingly, kernel number and weight also had a positive

correlation.

3.5 QTL detection

Sixteen data sets were organized and analyzed for QTL

detection. Using the default 2.5 LOD threshold to declare a

QTL in QTL Cartographer, a total of 12 QTL were detected

across traits and planting dates as summarized in Table 6.

Seven chromosomes (linkage groups) of the ten sorghum

chromosomes were mapped with one or more QTL (Figure 2).

Twenty two DArT and two SSR markers were closely linked

with QTLs of the traits (Table 7). Compared planting time

wise, in general, the number of QTL detected tended to be

higher in late (normal) planted nurseries as compared to early

planted nurseries.

3.6 Plant population

Two QTLs were detected for plant population (Table 6). These

QTL were detected in both early and late planted nurseries of

2005 crop season but no QTL detected for plant population in

our extended cold season experiments of 2006 where cold had

significant impact on seedling establishment. The phenotypic

variation explained by these QTLs ranged from two to three

percent and the additive effects for both QTL were 3.2. The

increasing alleles of the QTLs expressed under early planting

time was contributed by the Chinese cold tolerant SQR line

while the alleles for QTLs expressed under late planting

contributed by African originated SRN-39 genotype both of

these results being as expected.

3.7 Kernel number

Three QTL were detected for kernel number across planting

dates and years (Table 6). Unlike in plant population trait, two

QTLs were detected in severe cold conditions of 2006 early

planted nursery. From phenotypic data analysis, kernel number

trait performance was best in this nursery and the result might

suggest the importance of good phenotypic performance in the

detection of more QTL. The phenotypic variations explained

by the QTLs were four percent and were the same for all the

QTL. The additive effects were103 and 227 for alleles

contributed by the parent SRN-39 and 159 for alleles

contributed by the parent SQR.

3.8 Kernel weight

Like in plant population, only two QTLs were detected with

the default 2.5 LOD threshold used for other traits but with

slight modification of this threshold to 2.4, four more QTLs

were detected for kernel weight (data not shown).

31 Gutema et al

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Table 5 Pearson correlation coefficients (R) among yield and yield component traits values from data on 528 RI lines replicated twice in

four field experiments conducted over two years.

Traits Plant population Grain yield Kernel weight Kernel number

Plant population 1.00

Grain yield 0.51*** 1.00

Kernel weight -0.26*** 0.10*** 1.00

Kernel number -0.57*** 0.15*** 0.10*** 1.00

***, Significant at α = 0.0001.

Table 6 Number of QTL detected with data collected early and late (normal) planted nurseries for grain yield and yield component traits

of sorghum as analyzed through composite interval mapping (CIM).

Year Planting Date Number of QTL detected

Total

Plant population Kernel number Kernel weight Grain yield

2005 Early 1 - - 2 3

Late 1 1 - 1 3

2006 Early - 2 - - 2

Late - - 2 2 4

Total 2 3 2 5 12

Despite similar phenotypic performance of kernel weight trait

across most of our environments, QTLs detected for kernel

weight were limited to 2006 late planted nursery. Both parents

contributed to the increasing alleles.

3.9 Grain yield

A total of five QTLs were detected for grain yield although

from the partially overlapping position three QTLs seemed to

be the same. No QTL detected for grain yield in 2006 cold

early planted nursery in a manner this environment affected

QTL detection for plant population. From phenotypic data

analysis, plant population has significant impact on grain yield

which might have again reflected on the detection of QTL for

grain yield in this environment. Three QTLs were detected in

three of four environments indicating the most consistence of

grain yield QTL in this study. Two of the QTL were found on

two different chromosomes (C and E) but three QTLs were all

found on the same chromosome (F). A LOD score as high as

4.5 were noted for one of these QTL showing high evidence

for grain yield QTLs in our analysis. The phenotypic variations

explained by the QTL ranged from two to five. Unlike yield

component traits where both parents contributed the increasing

alleles, all the increasing alleles were contributed by SRN-39

for grain yield.

4. Discussion

4.1 Phenotypic traits analysis

Traits from seedling to crop maturity can impact grain yield of

sorghum (Heinrich et al., 1985; Saeed et al., 1986). Present

study shows that plant population (density) is the most

important trait among yield component traits of sorghum to

increase grain yield per unit area. The far reaching importance

of plant population sizes in grain yield per unit area was

recognized for maize long ago (Tokatlidis & Koutroubas

2004). In sorghum, the importance of plant population size

might be overlooked because sorghum plants try to

compensate grain yield loss due to sparse population by

tillering (Stickler & Pauli, 1961). Observation on parental lines

demonstrated this assertion. We observed that SRN-39

responded to sparse population by tillering and producing more

and heavier kernels, but surprisingly it did not produce higher

grain yield than SQR. Clearly, SQR produced more plant

population per unit area than SRN-39, which resulted into

higher grain yield. The results indicated the importance of

plant population for increasing grain yield in sorghum,

especially under cold early planting. Furthermore, our RILs

correlation analyses show that plant population is positively

and highly correlated with grain yield, suggesting that an

increase in plant population per unit area would results into an

increase in grain yield as well. On the other hand, it was

observed that plant population is negatively correlated with

other yield components such as kernel number and kernel

weight. This result suggested the counter effect of increased

plant population on grain yield through these two yield

component traits. As the number of plants per plot increases,

smaller panicles (thus fewer kernel number/panicle) with

smaller kernel size (thus lesser kernel weight) are produced

because of plant to plant competition. Therefore, optimum

plant population (density) should be established by empirical

field data to maximize grain yield for a given cultivar under a

given production condition (Francis et al., 1983).

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Figure 2 Genetic linkage map showing significant QTL associated with grain yield and yield component traits of 528 RIL and their

parents grown early and late planting dates.QTL named as q=QTL, followed by abbreviation name of the trait: YE=grain yield,

KN=kernel number, KW=kernel weight and PP=plant population, and linkage group number (LG) and then serial number of the QTL

per linkage group.

Table 7 List of QTL mapped for plant population, kernel number, kernel weight and grain yield analyzed using CIM.

Trait QTLa Flanking marker

b Position

c Additive effect R

2d (%) LOD score Source

e

Plant population qPP-LG02 DM164-DM233 130 3.24 3 3.0 SQR

qPP-LG11 DM290-DM37 99 3.16 2 2.7 SRN-39

Kernel number qKN-LG03 DM320-DM143 50 159 4 3.3 SQR

qKN-LG05 DM275-Txp145 46 227 4 3.5 SRN-39

qKN-LG07 DM271-DM1 13 103 4 3.4 SRN-39

Kernel weight qKN-LG06 DM276-DM130 22 0.10 3 3.3 SRN-39

qKN-LG08 DM11-DM71 50 0.08 3 2.7 SQR

Grain yield qYE-LG07 DM28-DM61 50 0.23 4 3.4 SRN-39

qYE-LG08 DM278-DM138 45 0.22 5 3.6 SRN-39

qYE-LG11-1 DM157-DM57 30 0.32 5 4.5 SRN-39

qYE-LG11-2 Txp258-DM145 15 0.16 4 3.5 SRN-39

qYE-LG11-3 DM157-DM57 22 0.14 2 2.6 SRN-39 aQTL named as q=QTL, followed by abbreviation name of the trait: YE=grain yield, KN=kernel number, KW=kernel weight and

PP=plant population, linkage group number (LG) and serial number of the QTL per linkage group. bLeft and right flanking markers;

DM=DArT marker. cShows absolute positions of test locations from left telomere in centiMorgans.

dVariation explained by the QTL.

eShows source parents for the increasing allele.

DM3260.0DM210.3Txp21117.7DM31626.4DM2339.1DM14254.4DM29955.2DM15561.3DM7961.6DM24164.3DM13965.5DM28768.6DM17468.9DM23472.6DM22774.3DM33380.6DM988.9DM28599.7DM35100.3DM340101.5DM72109.8DM95109.9DM99111.1DM102112.4DM334114.2DM38118.0DM267118.2DM212120.4DM352122.2DM203127.3DM193128.3DM164128.9DM195129.6DM233132.7DM69133.8DM122134.2DM260136.3DM259138.6DM129144.5DM208DM288DM209

153.0

DM322155.1DM243164.3DM18166.2Txp8169.5DM183174.5DM292DM256

174.7

DM261174.9

qPP-LG02

LG2

DM1730.0DM1095.1DM1818.0DM20713.9DM8522.4Txp21722.8DM6523.4DM19923.6DM137DM156DM41

23.7

DM8823.9DM16224.2DM635.3DM31038.5DM32839.1DM32042.0DM3642.2DM24643.7DM14357.2DM2057.3DM26958.3DM5877.8DM11978.1DM29185.3DM21593.1DM251DM258

100.8

qKN-LG03

LG3

Sedlcolor0.0

DM26310.3DM16512.7Plancolor19.2DM3220.5DM5221.5DM23623.9

DM18436.0

DM27541.0

DM32758.8Txp14561.7DM20266.8DM33571.9DM16874.9DM8180.0DM8980.4DM21984.4

DM34198.1DM29599.5DM15101.9

DM313110.6DM3113.3

DM192122.6

DM350131.7

DM346140.4DM330142.5

DM282152.5qKN-LG05

LG5

DM2380.0DM2714.0DM305DM226DM249

5.4

DM122.0

Txp15928.9

DM28034.3Txp31236.1

DM2844.7

DM18752.1

DM6163.5DM35964.4DM28466.6DM18867.6DM13571.2

DM23780.8

DM14894.1DM30100.2DM230103.1DM323106.1DM166108.5DM298108.9DM302115.2

DM347125.0

qKN-LG07qYE-LG07

LG7

DM460.0DM2551.8DM1348.0Txp25812.2DM15716.2DM14528.8DM5029.2DM21829.5DM5731.6DM11339.2DM12139.5DM11840.3DM35642.0DM248DM178

42.1

DM27042.2DM22345.9DM4248.1DM24756.2DM30162.2DM167DM128

81.9

DM1988.9DM34389.3DM10889.6DM25791.8DM26595.6DM29097.4DM1297.7DM3798.9DM5100.4DM228106.5DM49110.2

qPP-LG11

qYE-LG11-1

qYE-LG11-2

qYE-LG11-3

LG11

DM2440.0DM339DM26

8.2

DM448.4DM16DM252

8.7

DM14010.2DM17912.9Txp28524.7DM325DM25

33.9

Txp6943.3DM27846.6DM1148.5DM13849.7DM7152.8DM13255.9DM34259.3

DM20474.1

DM23279.9

DM32990.0DM34892.4DM21196.9

DM221106.8

DM22112.8

DM355124.6DM303130.6DM114133.5DM262134.7

qKW-LG08

qYE-LG08

LG8

DM2810.0DM30412.5DM27614.1DM283DM151

14.4

DM19114.5DM19418.1DM4018.3DM34418.6DM13027.7DM731.7DM17232.6DM14132.8DM9333.3DM31838.5DM30854.3DM27458.2DM27363.4DM6867.9DM16082.3DM28985.4DM22994.1DM896.3DM32196.6DM19097.2DM8097.3DM8497.5DM9297.8DM12398.0Txp43104.6DM222106.1DM136111.3

qKW-LG06

LG6

33 Gutema et al

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It was also observed that kernel number and kernel weight had

highly significant positive correlation within themselves and

with grain yield suggesting both of these traits could be equally

important in breeding high-yielding sorghum cultivars. These

two interdependent traits could be manipulated simultaneously

or separately in selection of sorghum with high grain yield.

However, kernel number was found to be influenced by

planting time, while kernel weight was not. Our data showed

that more kernels produced under early planting as compared

to late planting. This result might be due to better opportunity

of longer growing season under early plantings or smaller plant

population size. When cold weather killed seedlings of several

RI entries during early planting, we observed that the

remaining seedlings use the opportunity of having more space

to grow more profusely producing larger panicle and more

kernels as we observed for SRN-39 parent also. The results

indicated the importance of planting time for maximum kernel

number. Contrary to kernel number, kernel weight seemed to

be lightly affected by planting time. Based on Saeed et al.

(1986), kernel weight might be affected more by moisture

availability in the soil during critical period of grain filling

rather than planting time. In present study, although less

rainfall was received in the first year of our experiments as

compared to the second year, there was no moisture stress that

could affect grain fill or kernel weight at any time.

In addition to yield increase per unit area, yield stability across

diverse growing environments is also an important

consideration for plant breeders. In present study, both plant

population and kernel number traits seemed to be unstable

across environments. On contrary, grain yield and kernel

weight seemed to be more stable. Based on its stability and

high heritability, kernel weight might be one of the most

important traits to attain stable grain yield in sorghum under

diverse growing environments.

More stable grain yield could be obtained by late planting

because optimum plant population can be established under

late planting. However, in our study we found that late planted

nurseries exhibited rapid growth rate, which could promote

lodging during late seedling growth because of higher

temperature. This again demonstrated the importance of

optimal time of planting to attain optimum plant populations

under early planting cold conditions. This can be realized with

the development of sorghum cultivars with cold tolerance

genes suited to early planting (Knoll & Ejeta, 2008).

Although the extended growing time of present early planting

did not seem to translate to higher overall mean grain yield,

there were several individual entries that had higher grain yield

under early planting, consistent with Knoll & Ejeta (2008)

observations for sorghum hybrids. The observation of extreme

values (both minimum and maximum) under early cold

planting was a recurring result for most of the traits measured

in our study including panicle size and plant height (data not

shown). This indicates both the challenges and the

opportunities for sorghum genetic improvement under cold

early planting.

4.2 QTL Analysis

Considering the quantitative nature of the traits in this study,

divergence in the parents for most of the traits and

performance of the RILs, surprisingly few QTL were detected

in this study for each trait. However, in QTL analysis, several

factors such as data quality, phenotypic performance, marker

density, population size and growing environment can

contribute to the power of QTL detection.

Considering data quality, obviously good quality data such as

limited number of missing plot would be necessary to detect

more QTL. In our study, missing plots were not more than

three in our over 4600 plots and in general our data can be

regarded as of a high quality. Other than limited number of

missing plots, field agronomic performance by the genotype is

also important to detect QTLs. The detections of more QTLs

with our better performing nurseries might justify this

assertion. However, from phenotypic data analysis, it was

surprising that QTLs were detected rather in the least

performing environment for kernel weight trait. It might be

suggested that other data structures were also important in

detection of more QTLs rather than mere phenotypic

performance scored for kernel number. In addition,

environment can affect the number of QTLs being detected. In

some extreme cases, QTL may not even detect as for most

traits in 2006 cold early planted nursery. Besides phenotypic

data, genotypic (marker) data are an important issue in the

power of QTL detection (Darvasi et al., 1993; Doerge et al.,

1997; Doerge, 2002). In this study, relatively large and dense

genetic map constructed from various markers was used. Our

study confirms the utility of DArT marker system for sorghum

as demonstrated in other most recent publications (Mace et al.,

2008; Mace et al., 2009; Mace & Jordan, 2011). Besides

marker size, population size could affect the number of QTL

detected (Beavis, 1994). In sorghum, Rami et al. (1998)

detected no QTL with their limited population size. In our

study, relatively large population size was utilized and to our

knowledge, we deployed the largest (N=528) RI lines

population ever used to analyze QTL in sorghum.

From the above discussions, the few number of QTL detected

in present analyses might be the actual number of QTL

segregating in this population. The relatively high heritability

values calculated for each trait and large LOD score

particularly for grain yield trait in our study might give certain

confidence in the number of QTLs detected. In our study, apart

from our interest in the exploration of the number of QTLs

expressed under contrasting environments, identification of

QTLs associated with sorghum grain yield and its component

traits deemed useful in breeding sorghum for higher grain

yield.

As described in phenotypic data analyses, plant population is

crucial in attaining high grain yield in sorghum and therefore,

identification of robust QTLs associated with this trait is of

particularly interest as previously addressed by Knoll & Ejeta

(2008). However, due to the fact that plant population develops

Detection of quantitative trait loci (qtl) associated with yield and yield component traits in sorghum [sorghum bicolor (l.) moench] sown early. . . 34

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early in the season when environmental conditions are less

conducive for sorghum plant, QTLs associated with plant

population size accessed through stand count at maturity are

environmentally sensitive and may not be detected at all as in

our 2006 experiments. Kernel number per panicle is also

basically set during early growth as it is related to plant

population (density). However, there is flexibility for sorghum

plant to realize this trait more as a function of time in the

growing season. This makes QTL associated with kernel

number trait important for sorghum breeding aimed at higher

grain yield. Regarding kernel weight, QTLs detected were few

and limited to single environment in our study. However, when

we decreased the QTL detection threshold LOD just slightly

from the default 2.5 to 2.4, four more QTL were detected (data

not shown) and these QTLs were consistent across planting

dates (environments). These results might substantiate our

findings in phenotypic data analysis and suggest the

importance of kernel weight QTL in breeding sorghum for

yield stability across diverse growing environments. In

phenotypic data analysis, we observed the environment to have

little impact on kernel weight as evidenced by small mean

standard deviation and high heritability for this trait.

Consistent with the most quantitative nature of grain yield trait,

the highest number of QTLs was detected for grain yield in our

study despite little divergence in the parents for this trait.

Rather many loci could contribute to the overall grain yield

making breeding for grain yield a challenging task but from

our finding, it is likely that one major QTL significantly

contributed the greatest portion of grain yield as in oligogenic

traits. Surprisingly our results showed that QTLs for grain

yield were more stable across planting dates as in kernel

weight. The results were consistent with the observations

reported by Stuber et al. (1992) in corn. In our study, it might

be speculated that the effect of environment on grain yield was

buffered through physiological functions of the yield

components since grain yield is rather the result of complex

interaction of the individual components (Saeed et al., 1986).

In conclusion, our findings indicated that although grain yield

trait is complex, one major QTL that significantly contribute to

grain yield could be identified and utilized in breeding

sorghum for higher grain yield. In addition, the robust SSR

markers detected flanking grain yield and its component in our

study could be used in breeding sorghum to improve grain

yield under different planting conditions if validated with

further research.

Acknowledgments

The financial support for this study was provided by the

International Sorghum and Millets Research Network

(INTSORMIL) of USAID.

Conflict of Interest

The authors declare that there is no conflict of interests that

could possibly arise.

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KEYWORDS

Micronutrient

Soybean

Water deficit stress

Yield parameters

ABSTRACT

Effects of foliar application of molybdenum and iron either individually or in combination on the yield

properties of soybean crop were investigated under water deficit condition at research farm of Sari

Agricultural Sciences and Natural Resources University, Sari, Iran during the cropping season of 2014.

This experiment was laid out in factorial arrangement based on completely randomized block design

with three replications. The two irrigation regimes were used (Irrigation after 65 and 130 mm

evaporation from Class A pan) as primary factors while spray application of micronutrient (water as

control Fe, Mo and Fe + Mo) were considered as the secondary factors. Result of study revealed that

drought stress and micronutrient sprays have effect on all studied parameters such as pod number, seed

number, yield and weight of 1000 seeds. Also, the interactions were statistically significant among

studied parameters. According to the results drought stress severely impressed the number of pods, total

seed numbers, seed yield, seed protein and seed oil. Furthermore, using of micronutrients spray

particularly Fe+Mo on soybean crop is environmentally acceptable strategy and reduced the damages

caused by water deficit condition.

Ayoub Heidarzade1, Mohammadali Esmaeili

1,*, Mohammadali Bahmanyar

2 and Rahmat Abbasi

1

1Department of Agronomy, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

2Department of Soil Sciences, Sari Agricultural Sciences and Natural Resources University, Sari, Iran

Received – December 16, 2015; Revision – December 29, 2015; Accepted – January 31, 2016

Available Online – February 20, 2016

DOI: http://dx.doi.org/10.18006/2015.4(1).37.46

RESPONSE OF SOYBEAN (Glycine max) TO MOLYBDENUM AND IRON SPRAY

UNDER WELL-WATERED AND WATER DEFICIT CONDITIONS

E-mail: [email protected] (Mohammadali Esmaeili)

Peer review under responsibility of Journal of Experimental Biology and

Agricultural Sciences.

* Corresponding author

Journal of Experimental Biology and Agricultural Sciences, February - 2016; Volume – 4(1)

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ISSN No. 2320 – 8694

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1 Introduction

Soybean (Glycine max) is a common legume plant and

cultivated for more than 3000 years in Southeastern Asia

(Dwevedi & Kayastha, 2011). Soybean stands first in the world

as edible oil and occupies important place in the economy.

Climatic and edaphic factors severely affect its production;

according to Turner (1991) performance of this crop is highly

affected by the availability of trace elements such as

Molybdenum and Iron. It has been also well reported that

deficiency of micronutrients such as Fe, Mn and Zn affect the

soybean production (Khudsar et al., 2008; Caliskan et al.,

2008).

Furthermore, various researcher reported that the application of

essential micronutrients such as Zinc, Iron and Magnesium

improve the yield and yield components of crops (Davis, 1983;

Fox & Guerinot, 1998; Ekhtiari et al., 2013). Normally

fertilization carried out in soil but in this condition very less

amount of nutrient reached to the plant system and remaining

amount waste through leaching in soil, it also cause land and

water pollution. Foliar fertilization is better option to avoid

leaching and in this quick translocation of nutrients carried out

in different parts of the plant system (Neumann. 1982).

According to Ghasemian et al, (2010) micronutrient spray can

enhance resistance against the environmental stress.

Geographical region Iran is characterized by arid climatic

conditions with high pH and mean temperature, here plants

mostly affected by different abiotic stresses.

In this condition drought stress is the main limiting abiotic

factor for crop production and decline the efficient use of dry

and semi dry lands. Furthermore, these water stress conditions

also severely affect the absorption of micronutrients by plants.

Water deficient conditions affect the water potential and turgor

pressure of the cells and this can disturbs the normal plant

physiological mechanisms (Hsiao, 1973). These changes

induced various effects on growth and yield parameters of the

crops (Reisdorph & Koster, 1999). Many studies showed

inhibitory effects of drought stress on different plant

properties, such as grain yield in maize (Ebrahimian &

Bybordi, 2011); growth and productivity in sunflower, fresh

and dry weights in shoot and flowers of marigold (Tagetes

erecta L.) and Asian red sage (Asrar & Elhindi, 2011; Liu et

al., 2011) and yield reduction due to limited growth in bread

wheat (Abbas et al., 2009). In soybean, biological nitrogen

fixation carried out by bacterium Bradyrhizobium sps., it was

reported that drought condition adversely affect the biological

nitrogen fixation in this crop.

Low soil fertility and limited availability of macro and micro

nutrients are the most important constraints under drought

conditions. Diagnosis and development of new strategies are

required which can help in inducing drought tolerance or

reduced the determinable effect of drought on crop, these

techniques will also help in the full utilization of the available

resources and convert semi-arid land to arable regions (Bruce

et al., 2002). Role of trace elements in crops production under

drought stress conditions have been less studied by researchers.

The aim of the present study was to investigate the response of

soybean in term of yield, yield components, seed oil and

protein yield to foliar spray of micronutrient (Fe and Mo)

under drought condition.

2 Materials and Methods

2.1 Study area and Experimental setup

In order to investigate the effects of micronutrient spray on

yield parameters of soybean under drought stress condition,

present study was conducted at research farm of Sari

Agricultural Sciences and Natural Resources University during

the cropping season of 2014. Each experimental plot had 5

meters long and 3 meters (3m×5m) wide and 6 ridges spaced

50 cm apart. Soil samples were collected and its

physicochemical properties were analyzed in soil science

laboratory, Department of soil science, Sari Agricultural

sciences and natural Resources University. All the

physicochemical properties were analyzed by the method

described by Blakemore et al. (1987).

Table 1 Physicochemical and mechanical properties of the

experimental area soil.

Depth 0-30cm

Texture Clay silt

EC dS/m 1.4

pH 7.5

T.N.V% 19.3

O.C% 3.48

Pppm 12.3

Kppm 367.3

N (%) 0.251

Uniform healthy soybean seeds (033 cultivar) were purchased

from Iran's Oilseed Research and Development Company

Deputy of Sari, Iran. Selected seeds were used for hand sowing

in the month of June, 2014 after removing the trashes and

impurities. The experiment was laid out in factorial

arrangement based on completely randomized block design

with three blocks. Two irrigation regimes were used viz

irrigation after 65 mm (as normal irrigation) and 130 mm (as

water deficit) evaporation from Class A pan along with the

simultaneous application of spray fertilization of the selected

micronutrient either singly or in combination (water as control,

Fe, Mo and Fe + Mo). For this purpose FeSO4 and

(NH4)6Mo7O24 (Merck, Darmstadt, Germany) were used as

Fe (400 ppm) and Mo (4 ppm) spray treatment, respectively.

38 Heidarzade et al

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The foliar spray of the selected micronutrient was carried out

for three times viz. at the start of stem elongation, flower buds

formation and pod set stage (according to the drought stress

treatment). Urea and ammonium phosphate were applied as

nitrogen and phosphate sources at 150 kg/ha-1

and 100 kg ha-1

,

respectively.

The plots were irrigated by tape irrigation method and the

applied water amounts were controlled by water meters and the

following equation:

d = FC− θ D/100

Where, FC%= field capacity ; θ%=soil moisture content; Dcm =

soil depth; dcm = irrigation water depth. Soil moisture content

was determined by oven drying method (ASTM D2216-10,

2010).

During foliar application, the plots were surrounded by

polyethylene to avoid the drift of solutions. Soybean was

manually harvested completely from each plot (15 m2) at full

physiological maturity of pods (80-90% dry weight). Total

grain yields after drying in oven (65°C for 72h) were adjusted

to 12% moisture content. Harvest index (HI) by dividing the

total grain yield on total biomass was calculated:

HI = (GY/BM) × 100%

Where, GY= grain yield and BM= biomass

2.2 Estimation of total protein content of soybean seeds

Estimation of total protein content was based on the total

nitrogen contents. Nitrogen content of soybean seeds was

measured by Total Kjeldahl Nitrogen (TKN) method as

mentioned by Isaac & Johnson (1976) (Kjeltec Auto1030

Analyzer, Foss Tecator AB, Hoganas, Sweden). For nitrogen

determination the pure seeds were dried (Fan Azma Gostar,

24060, Iran) for 72 h in oven. Total reduced nitrogen was

determined by using a micro Kjeldahl procedure with sulfuric

acid, digestion catalyst and conversion of organic nitrogen to

ammonium form according to the Total Kjeldahl Nitrogen

(TKN) method. Nitrogen content is then multiplied by a factor

to arrive at protein content. The average nitrogen (N) content

of proteins that found by the above method led to use of the

calculation N × convert factor (5.71) (King-Brink & Sebranek,

1993).

2.3 Estimation of Total seed oil content

Total oil content of soybean seeds was determined by using the

soxhlet device; the pure seeds of each treatment were dried and

weighted before insert into the device. The chloroform was

used as solvent, it is a popular solvent seed oil extraction,

particularly for lipids of intermediate polarity and when mixed

with methanol it becomes a general extraction solvent. So the

dried and powdered seed samples were inserted into the

soxhlet device and the extraction was completed by

evaporating the solvent.

2.4 Statistical analysis

Analysis of variance was performed for studied traits by using

the general linear model (PROC GLM) procedure in Statistical

Analysis System (SAS) and the mean comparisons were

evaluated based on Least Significant Differences (LSD).

3 Results and Discussion

Drought stress and micronutrient sprays were significantly

affected all studied soybean parameters. Also, the interactions

were statistically significant among studied traits with the

exception of seed number/pod (table 2).

3.1 Pod number/ plant

Number of pods per plant is an important growth characteristic

in soybean and could be helpful in determining the final plant

performance during the growing period (Ohashi & Nakayama,

2009). During the growth stage, plants were highly affected by

water availability and micronutrient application which directly

become visible at pods initiation and forming (table 3). Water

deficits plots produced a lower number of pods/plant.

Table 2 ANOVA for soybean parameters in response to different micronutrient spray under two irrigation regimes.

Oil yield

(kg ha-1

)

Protein yield

(kg ha-1

)

Seed yield

(kg ha-1

)

1000 grain

weight (g)

Seed number/

plant

Pod number/

plant

d.f S.O.V

** ** ** ns ** ** 2 Block

** ** ** * ** ** 1 W.S. (A)

** ** ** ns ** ** 3 M.S. (B)

** ** * ** ** ns 3 AB

795.33 5391.83 27099.61 0.16 33.78 2.46 14 Error

23 Total

5.49 5.57 5.28 0. 39 5.27 3.34 CV

Whereas W.S. – Water stress condition; MS- Micronutrient Spray, **;* and ns indicated significant difference at 0.01 and 0.05

probability level and non significant respectively

Response of soybean (Glycine max) to Molybdenum and Iron spray under well-watered and water deficit conditions. 39

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Table 3 Means of main effects of drought stress and micronutrient spray on pod numbers.

Treatments Pod number/ plant

Water status Normal 54.57a

Stress 39.12b

Micronutrient spray Control 40.86c

Fe 42.36c

Mo 50.35b

Fe + Mo 53.81a

Data with the same letters are not significantly different according to LSD (0.05) probability levels.

This could be explained by a reduction in flower production

and by an increase in flower abortion (Ekhtiari et al., 2013).

According to the results drought stress severely impressed the

number of pods so that the plants which were exposed to water

limitation showed highly reduction in term of pod numbers

compared to normal condition (more than 28% reduction).

Throughout the different spray of microelements the maximum

positive effects on pod numbers was reported from Fe + Mo

spray treatment (with 53.81 pods/plant) (table 3).

Translocation of assimilates from the source leaf to the pods in

soybean plant depends on many factors, such as deficiency in

water supply and photosynthetic rate (Chen et al., 1993;

Ohashi et al., 2000; Nobuyasu et al., 2003). According to

Ohashi et al. (2009) drought stress reduced the plant dry

weight by decrease pod dry matter accumulation, also they

found that, stress conditions reduced the rate of photosynthesis

significantly, but these conditions induced greater partitioning

of assimilates from the leaf compared to the well water

condition. However, these assimilates did not move to the

reproductive parts and accumulated in the vegetative

structures, mostly in the stem. Findings of present study are in

agreement with the Ekhtiari et al. (2013) who suggested that

water deficit showed highly inhibition in soybean seed yield.

Furthermore, Ohashi et al.(2009) reported different responses

of pod thickness and dry matter to drought stress during the

grain filling stage in soybean plants. Kaiser et al. (2005)

suggested the significant effect of micronutrient such as Mo

especially on soybean production. Low soil fertility and limited

availability of macro and micro nutrients are the most

important constraints under drought conditions. Various

studies suggested the reduction in soybean production due to

deficiency of micronutrients especially Fe, Mo and Zn (Kaiser

et al., 2005; Khudsar et al., 2008; Caliskan et al., 2008).

3.2 Seed /plant

The average number of seeds for each plant is relatively

constant in normal condition and almost controlled by genetic

factors, but it can be rapidly change under adverse

environmental conditions (Ohyam et al., 1992). The

importance of seed number in final performance of legumes

especially in soybean production was investigated by many

authors (Kobraee et al., 2011; Yasari & Vahedi, 2012; Yadavi

et al., 2014; Abdel-Latif & Haggan, 2014) all of them

suggested a positive correlation between seed numbers per

plant and the final seed performance. However, the average

number of seeds was significantly affected by irrigation and

micronutrient levels (table 2). According to the results obtained

from mean comparison (fig. 1), the number of seeds per plant

in each micronutrient spray treatment was reduced under water

deficit (Irrigation after 130 mm evaporation from pan class A)

condition as compared to control. Minimum reduction due to

drought stress among above parameter was related to Fe+Mo

treatment (7% reduction in compare control), in other words,

there was no significant difference with the control.

Figure 1 Response of soybean seed numbers to micronutrient spray under different water status

40 Heidarzade et al

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Water deficiency reduces water potential and turgor pressure in

plant which lead to difficulty in performing normal

physiological function especially during the reproductive

period (Lisar et al., 2012). Also, in present study highest seed

numbers per plant was obtained when Fe+Mo spray was

applied under normal irrigation (more than 173 seeds in each

plant) and the lowest was belong to water spray treatment in

drought stress condition (less down 65 seeds/plant) (fig. 1).

The role of molybdenum in plant growth and seed setting has

been well documented before (Arnon & Stout, 1939;

Anderson, 1942; Davies, 1945; Mitchell, 1945; Fido et al.,

1977; Chatterjee & Nautiyal, 2001; Kaiser et al., 2005). Result

of study suggested that by using some good strategies such as

foliar application of essential micronutrient especially Mo+Fe

help in normal functioning of specific plant enzymes to

participate in reduction and oxidative reactions, it could

reduced the damage caused by drought stress in plants

particularly in soybean.

Figure 2 Impact of micronutrient spray under different water

status on the weight of 1000 soybean seed.

Figure 3 Response of soybean seed yield to micronutrient

spray under different water status.

3.3 Effect of foliar spray on 1000 grain weight

Weight of 1000 Grain is an important yield contributing factor,

which plays an important role in showing the potential of a

crop variety. The average weight of 1000 grain was in ranges

of 101.6-103.2g and statistically analysis revealed that water

limitation significantly affected this parameter, in contrast the

micronutrient sprays didn’t (table 2). However, the interactions

had significant effects on this parameter (fig. 2). According to

the mean comparison the highest 1000 grain weights was

obtained in control treatment (water spray with stress) with

103g but it was not significantly different from the Fe+Mo

spray treatment (102.3g). Since the high genetically

dependence of grain weight to the variety, this factor affected

in lower range by drought stress and foliar application of

micronutrients compared with other yield properties. It seems

that, micronutrient treatments could increase the soybean seed

weight under water deficit condition and conversely, under

normal irrigation it didn't work. Drought stress occurring

during the critical growth stages of soybean (flowering to early

pod expansion period) ultimately decreases individual seed

weight but spray of micronutrients may rectify this effect

(Royo et al., 2000).

3.4 Seed yield

Seed yield is a final performance which resulted by integrated

effects of many complex morphological and physiological

processes occurring throughout the growth and development of

a crop. Due to water deficiency seed yield in soybean was

reduced if water limitation occurs during the critical growth

stage of growth especially at the time of pod set stage. Mean

comparisons of seed yield which influenced by drought stress

and micronutrient spray are considered in figure 3, it showed

higher reduction in seed yield through the stress condition (fig.

3). The highest positive effect on final seed yield (with 4885

kg h-1) was related to Fe+Mo treatment in normal irrigation

which was significantly higher than other treatments (figure 3).

The deleterious effects of micronutrient deficiency on seed

yields and quality have been reported clearly before (Cakmak,

2002; Welch & Graham, 2004; Kaiser et al., 2005; Malakouti,

2007). Among various abiotic stresses, drought is one of the

major environmental constraints limiting crop productivity

worldwide (Masoumi et al., 2010; Khamssi et al., 2011;

Batlang et al., 2013). About 25 % of the world’s agricultural

land is affected by drought stress (Jajarmi, 2009).

Various researchers reported the inhibitory effect of the

drought stress on the rate of photosynthesis and growth,

particularly seed yield of soybean plants severely affected (De

Souza et al., 1997; Griffin & Luo, 1999; Earl, 2002). In the

present study the seed yield was limited by water shortage but

the micronutrient sprays could partially mitigate these adverse

effects (fig. 3), for instance; spray of Fe+Mo could

compensated the yield loss due to drought stress and

accordingly no significantly difference was observed between

mentioned treatment under drought stress and control (water

spray) under normal irrigation (figure 3). Drought stress

decreases soybean yield by decline in yield components,

although there is a differential responses in yield components

to changes in environmental conditions.

Response of soybean (Glycine max) to Molybdenum and Iron spray under well-watered and water deficit conditions. 41

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According to Whan et al (1991) partitioning and translocation

of assimilates is dependent to water availability in soil

(Schnyder, 1993; Wardlow & Wilenbrink, 1994; Mohapatra et

al., 2003; ), thus Soybean yield and its components were

markedly reduced in non-irrigated plants compared with

irrigated plants (Andriani et al., 1991; Frederick et al., 2001;

Kerbauy, 2004 ). According to water deficit at early of

flowering and pod set increased flower and pod failure

(Osborne et al., 2002). Environmental condition during the

reproductive phase has a major impact on final yield (Levitt,

1980).

3.5 Protein yield

Soybeans had been cultivated widely due to exceptional

protein content (contains all 8 essential amino acids), due to

the presence of high protein content it is consider as vegetarian

meat (Dwevedi & Kayastha, 2011). Drought stress negatively

affected many physiological processes such as photosynthesis;

transpiration; accumulation and assimilates allocation (Ohashi

et al., 2006). Water status and micronutrient spray indicated

high significant effects on protein yield, also the interaction

was significant too (table 2). As drought stress by restriction in

micro and macro nutrients uptakes cause the huge yield lose in

crops(Ohashi et al., 2006) as well as application of some trace

elements such Molybdenum can play a great role in uptake the

above nutrients (Kaiser et al., 2005). The results from mean

comparison (figure 4) showed substantial different between

treatments.

Figure 4 Effect of micronutrient spray under water deficient

condition on the yield of soybean seed protein.

Although, there were a huge different between the lowest and

highest amount of protein yield, and obtained from water

spray through the water stress and Fe+Mo spray under normal

irrigation treatment respectively. Also there were no significant

difference in protein yield between Fe+Mo in drought stress

and normal irrigation. On the other hand reduction in total

protein content has been offset by foliar application of Fe+Mo

combination. Various studies have been proven the role of

molybdenum in biological nitrogen fixation and plant nitrogen

metabolism (Mendel & Haensch, 2002; Williams & Frausto da

Silva, 2002). Furthermore, molybdenum increased the

nitrogenase activity and fix higher nitrogen by forming larger

root nodules (Parker & Harris, 1977; Adams, 1997; Vieira et

al., 1998). All this ultimately leads to increase nitrogen uptake

and transformation to vital metabolites such as proteins (Kaiser

et al., 2005).

Figure 5 Impact of micronutrient spray under different water

status on soybean seed yield (The bar demonstrated the oil

percentages)

3.7 Oil yield

Like protein, content of seed oil is also a major parameter

which determining the nutritional value of soybean, seeds of

soybean contains about 20% oil (Dwevedi & Kayastha, 2011).

In present study the mean comparison of seed oil yield and

percentage (fig. 5) indicated that drought conditions have

negative impact on the oil yield but the oil percentage

increased. Meanwhile unlike to other studied parameters (seed

yield and protein content) seed oil percentage reduced (fig. 5).

Chung et al. (2003) suggested that soybean seed protein

content is negatively correlated with the amount of seed oil

percentage. Further, it has been demonstrated that water

availability will affect seed oil yield and quality (Ku et al.,

2013). Nevertheless the highest positive effect on seed oil yield

in both water status treatments was related to Fe+Mo spray

application which had dramatically difference by the other

micronutrients spray. Dornbos & Mullen (1992) performed a

differential irrigation experiment on soybean cultivars and

reported a 4.4% increase in protein content and 2.6% decrease

in oil content under severe drought stress. Similar type of

findings was obtained by Vollmann et al. (2000) when they

evaluated the response of soybean cultivars to drought

condition. Result of this study confirmed a negative correlation

between seed protein and seed oil contents as well as the effect

of drought on seed protein and seed oil contents. They

suggested that, variations in contents of seed protein and oil

were attributed largely to the differential rainfall during the

seed filling stage.

42 Heidarzade et al

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Conclusions

Findings of the present study well demonstrated the positive

effects of micronutrients spray particularly Fe+Mo treatment

on various growth parameters of soybean plant. Further, it was

well reported that seed protein and seed oil were strongly

affected by water stress conditions. Seed oil percentage

response conversely to drought stress as compare to other

parameters but this act couldn’t alter the final performance of

oil yield. Also high reduction in pod numbers during the

reproductive stage due to its sensitivity to water limitation was

the main cause of the final yield loses. Overall, when plants

like soybean are not supplied with an optimum amount of Fe

and Mo due to environmental limitation, growth inhibition and

physiological changes will be appear more quickly, depending

on the strength and duration of the imposed stress.

Conflict of Interest

Authors would hereby like to declare that there is no conflict of

interests that could possibly arise.

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KEYWORDS

Prosopis juliflora

Aqueous leaf extract

Rice

Nitrate reductase

ABSTRACT

Prosopis juliflora is an invasive and allelopathic plant widespread in most parts of the world. P. juliflora

is known to influence the growth of plants growing in its vicinity by the release of the allelochemicals

during the decomposition of its litter. The negative effect is manifested by inhibition of the various

physiological processes of the target plants. In the present study the effect of aqueous leaf extract of P.

juliflora on the nitrogen metabolism of rice seedlings was assessed through the means of estimation of

nitrate reductase (NR) activity of rice seedlings. For the study, aqueous leaf extract of dry mature leaves

was prepared. From this, three concentrations viz., 1%, 10%, and 25% of the leaf extract were prepared

by diluting with distilled water, while distilled water served as control. Rice seeds were incubated in

different concentrations of extract for 10 days. Germination data was recorded and used for calculating

the germination indices. After 10 days of exposure to the extract, seedlings were harvested and

measurements for root and shoot length, fresh weights of root, shoot, and seed was taken and nitrate

reductase activity of the seedlings was assayed. Germination and phenotypic results showed no negative

affect by the extract. The activity of NR significantly increased with increase in the concentration of the

extract. Our study revealed that the activity of NR was promoted by the extract addition.

Gowsiya Shaik1 and Santosh Kumar Mehar

1, 2,*

1Department of Botany, Sri Venkateswara University, Tirupati, AP, India

2Dept. of Botany, J.N.V. University, Jodhpur, India

Received – October 19, 2015; Revision – November 08, 2015; Accepted – January 31, 2016

Available Online – February 20, 2016

DOI: http://dx.doi.org/10.18006/2015.4(1).47.51

MESQUITE (Prosopis juliflora DC.) HAS STIMULATORY EFFECT ON NITRATE

REDUCTASE ACTIVITY IN RICE SEEDLINGS

E-mail: [email protected] (Santosh Kumar Mehar)

Peer review under responsibility of Journal of Experimental Biology and

Agricultural Sciences.

* Corresponding author

Journal of Experimental Biology and Agricultural Sciences, February - 2016; Volume – 4(1)

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ISSN No. 2320 – 8694

Production and Hosting by Horizon Publisher

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1 Introduction

The word Allelopathy was coined by Plant Physiologist

Mollisch (1937). The word Allelopathy (allelon + pathos) is

derived from the Greek allelon, „of each other‟, and pathos, „to

suffer‟; hence it means: the injurious effect of one upon

another. The term thus relates the scientific knowledge which

concerns the production of biomolecules by one plant, mostly

secondary metabolites, that can induce suffering in, or give

benefit to, another plant. It therefore involves the biochemical

interaction among plants. A plant influencing other plants

growing in its vicinity, by the release of chemicals in the

rhizosphere or through the litter, is known as allelopathic. The

formal definition for the allelopathy is “any direct or indirect

harmful or beneficial effect by one plant (including

microorganisms) on the other, through production of chemical

compounds that escape into the environment” (Mollisch, 1937;

Rizvi & Rizvi, 1992). There are hundreds of plants which were

reported to be allelopathic in nature (Mollisch, 1937; Sen &

Chawan, 1970; Rizvi & Rizvi, 1992; Thoyabet et al., 2009;

Weston & Duke, 2003). The chemicals that are released from

the allelopathic plant are known as allelochemicals.

Allelochemicals in majority are secondary metabolites,

released into the environment as exudates, volatiles and/or

residues of plant tissue decomposition (Weston & Duke,

2003). These allelochemicals when released into the

environment have been shown to possess a broad activity

spectrum on biological systems in surroundings (Corcuera et

al., 1993; Wink et al., 1998). The effects of allelochemicals‟

action has been detected and reported at different levels of

plant viz., molecular, structural, biochemical, physiological

and ecological levels (Gniazdowska & Bogatek, 2005).

Prosopis juliflora is reported to influence the growth of other

plants (Rizvi & Rizvi, 1992; Thoyabet et al., 2009). Phenolic

compounds present in this plant have biological toxicity

towards many plants and can cause disturbances in various

processes by interfering with the enzymology of the target

plants. Our previous works on germination and seedling

growth of rice by the P. juliflora extracts has showed no

negative influence at lower concentrations, instead the effects

were stimulatory (Mehar, 2011; Shaik & Mehar, 2014; Shaik

& Mehar, 2015). In the present investigation, the effect of P.

juliflora on the nitrogen metabolism of rice plants was

analyzed by assessment of variation in the activity of NR when

the rice seedlings are exposed to P. juliflora litter extract.

2 Materials and methods

2.1 General methodology

Karnool Sona variety of rice was used as the test plant in the

study. Collection of P. juliflora leaf material and processing is

detailed in our earlier studies (Shaik & Mehar, 2014; Shaik &

Mehar, 2015). 2D-DM (2 day dry mature) leaves‟ extract of P.

juliflora was used as source of allelopathic material at three

concentrations viz., 1%, 10% and 25%, while distilled water

served as control.

2.2 Setup of the experiment

Seeds were surface sterilized using 0.1% HgCl2 for 60 seconds

and kept for incubation in the different petriplates containing

10ml of the above treatments (during the initial wetting of the

filter papers). In each petriplate, 10 seeds were placed and the

triplicates of the each treatment were maintained. The

incubation period was 10 days at the room temperature of 28o

C ±2.

2.3 Data Collection

Germination data collected throughout the incubation period

was used for calculating germination indices i.e. total

germination (GT), speed of germination(S), speed of

accumulated germination (AS), co-efficient of the rate of

germination (CRG) as per Chiapusio et al. (1997). After 10

days of exposure to allelopathic extract, Root length (RL) and

Shoot length (SL) were measured.

2.4 Nitrate reductase assay

Rice seeds germinated in extracts were used for nitrate

reductase assay according to the method of Hageman &

Hucklesby (1971) and Evans & Nason (1953) after 10 days of

exposure to allelopathic extract.

2.5 Statistical analysis

All the germination indices‟ values for all the treatments were

compared with control using Mann-Whitney U-Test. RL and

SL were compared to control using paired t-test. Activity of

nitrate reductase was compared with control using t-test.

Mann-Whitney U-Test and T-test were performed using SPSS.

3 Results

3.1 Germination

Result of study indicated that leaf extract did not show any

inhibitory effect on the germination of rice seeds. The Gt, S,

AS and CRG were 100% in 1% and 10% extracts but at higher

concentration i.e. 25% some reduction was reported in the Gt,

S, AS and CRG and it was 83.3%, 66.7%, 66.7% and 71.1%

respectively. All the germination indices were comparable to

control at all the concentration (Table 1, values are as

percentage of control).

Growth of the plant was not significantly reduced by any of the

extract concentrations. SL (Figure 1a) and RL (Figure 1b) were

not affected by the exposure to extract. In comparison with

control, the change in the root and shoot lengths of the seedling

were statistically not significant.

48 Shaik and Mehar

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Table 1 Germination indices values of different treatments (values are the percentage of the control).

Treatments Gt S AS CRG

1% 100NS

100 NS

100 NS

100 NS

10% 100 NS

100 NS

100 NS

100 NS

25% 83.33333 NS

66.66667 NS

66.66667 NS

71.11111 NS

Gt=Total germination, S=Speed of Germination, AS=Speed of accumulated Germination, CRG=Co-efficient of the rate of Germination,

NS=Not Significant

3.2 Nitrate Reductase activity

In the present investigation, NR activity was maximum at 25%

followed by the 10% and 1%. It was significantly (p≤0.001)

promoted in comparison to control. There was linear

relationship between the concentration of the extract and the

NR activity of rice seedlings (R2 =0.966 (y=0.013x+0.039;

Figure 2.). This indicates the extracts have stimulatory affect

on the activity of the NR enzyme.

Discussion and Conclusions

Seed germination assays are the preliminary screening to check

the effect of allelopathic extracts. In the present study,

germination indices clearly indicated that the there is no

inhibition of the germination when exposed to the allelopathic

extracts of the P. juliflora. Even the higher concentration, 25%

was not inhibitory. Results of present study are in contrast to

the reports of Siddique et al. (2009).

A B

Figure 1 Boxplots for shoot length (A) and root length (B), the treatments labeled A, B and C represent 1%, 10% and 25% of P. juliflora

extract

Figure 2 NR activity of Rice seedlings

Mesquite (Prosopis juliflora DC.) has stimulatory effect on nitrate reductase activity in rice seedlings. 49

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Although there are reports of the negative effect of P. juliflora

extracts on the germination (Noor et al., 1995; Nakano et al.,

2001), it also needs to be considered that the effect is

dependent on the concentration and type of the material being

used. It was reported that, seed characteristics such as seed size

and seed coat permeability can influence the uptake and effects

of allelochemicals in seeds and interference of the

allelochemicals varies accordingly (Marianne et al., 2000).

Seed germination assays (Barnes & Putnam, 1987; Pérez,

1990; Chase et al., 1991) showed that species with small seeds

were more sensitive, and hence were inhibited more than larger

seeded species when exposed to similar concentration of the

allelochemicals. Seedling parameters also had no significant

reduction. In this manner findings of present study are

contradictory to the findings of Thoyabet et al. (2009) and

Sen& Chawan (1970). Thoyabet et al. (2009) has reported the

reduced root length by the extracts of leaves of P. juliflora.

Similarly, Sen& Chawan (1970) reported the inhibition in

germination and early seedling growth. As in the case of

germination, effect on the seedling parameters is also

dependent on the type of donor plant, test plant and the

concentration of the extracts being used.

Along with the suppressing effects on the germination and

seedling parameters the allelochemicals are reported to

suppress the activity of the respiratory and photosynthetic

enzymes, and therefore, there is perceived scope for the

inactivation of the NR by the allelochemicals.

Nitrogen and sulphur are very important nutrients for plant

growth (Fazili et al., 2010) and play important role in amino

acid biosynthesis, and regulate the protein synthesis (Harris et

al., 2000). According to Fazli et al., (2005), the increased

amount of nitrogen and sulphur nutrition affected lipid

accumulation, acetyl-CoA concentration and acetyl-CoA

carboxylase activity. Nitrate reductase is a key enzyme in the

nitrogen metabolism. Nitrate is assimilated through a pathway

involving nitrate uptake steps and by two reductive steps

catalyzed by the enzymes NR and nitrite reductase (NiR). But,

here we have found no inhibitory effect on the NR activity;

instead with increase in the concentration of the extracts, the

NR activity has been promoted.

Based on the above results, here we conclude that extract had

no negative effect on the germination of rice seeds, and its

seedling growth. Besides, the activity of nitrate reductase was

promoted by the extract addition which further suggests that

the addition of litter has stimulatory effect on the nitrogen

metabolism in rice and its overall growth.

Conflict of interest

Authors would hereby like to declare that there is no conflict of

interests that could possibly arise.

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Mesquite (Prosopis juliflora DC.) has stimulatory effect on nitrate reductase activity in rice seedlings. 51

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KEYWORDS

Defoliation stage

Sugar Percentage of Root

Root Yield

White Sugar Yield

ABSTRACT

The experiment was conducted in spring 2014 to evaluate the effect of defoliation on the quantitative

and qualitative characteristics of sugar beet in Motahari research station located in the Kamal Shahr

region in Karaj, Iran. The study was conducted in split plot factorial with completely randomized block

design with four replications. The main factors included two planting dates viz 23 April, 2014 (suitable

planting time) and 18 May, 2014 (Late planting) and the sub-factors included five levels of defoliation

including stage of early cotyledon growth to two true leaves (2 leaves), the stage of plant deployment

(about 12 leaves), mid-growth (about 32 leaves) and late season of growth (about 54 leaves) and another

sub-factors included five levels of defoliation intensity of leaves included 25%, 50%, 75% and 100% of

defoliation and non-defoliation stage (control) as randomized and factorial were considered. Result of

study revealed that different planting dates have significant effect on the sugar percentage of root. In

addition, the treatment of defoliation stage could have a significant effect on root yield and white sugar

yield (p>0.01).The treatment of defoliation intensity had a significant effect on all three traits (p>0.01).

In general, increase in the defoliation intensity negatively affects the root yield and significantly reduced

the white sugar yield (compared to control). Among the various stages of defoliation, middle stages of

the defoliation have least effect on the evaluated traits which indicated more sensitivity of this treatment

during the growing season of plant.

Mohammad Nabi Ilkaee1,*

, Zohre Babaei1, Amirsaleh Baghdadi

1 and Farid Golzardi

2

1 Department of Agronomy, Karaj Branch, Islamic Azad University, Karaj, Iran 2 Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran.

Received – December 31, 2015; Revision – January 14, 2016; Accepted – January 30, 2016

Available Online – February 20, 2016

DOI: http://dx.doi.org/10.18006/2015.4(1).52.58

EFFECT OF DIFFERENT PLANTING DATES AND DEFOLIATION ON THE

PROPERTIES OF SUGAR BEET (Beta vulgaris L.)

E-mail: [email protected] (Mohammad Nabi Ilkaee)

Peer review under responsibility of Journal of Experimental Biology and

Agricultural Sciences.

* Corresponding author

Journal of Experimental Biology and Agricultural Sciences, February - 2016; Volume – 4(1)

Journal of Experimental Biology and Agricultural Sciences

http://www.jebas.org

ISSN No. 2320 – 8694

Production and Hosting by Horizon Publisher

(http://publisher.jebas.org/index.html).

All rights reserved.

All the article published by Journal of Experimental

Biology and Agricultural Sciences is licensed under a

Creative Commons Attribution-NonCommercial 4.0

International License Based on a work at www.jebas.org.

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1 Introduction

There are several biotic and abiotic factors which able to

damage the leaves of sugar beet plants and subsequently

reduce the yield. Among these, some of the already noted

factors are cold and frost, drought, hail, wind, pests and

diseases which can effectively reducing the leaf area (Jadidi et

al., 2010). Sugar beet production under semi-arid conditions of

growth has been mainly decreased due to the limited access to

water (Morillo-Velarde & Ober, 2006). Water shortage may be

a significant limiting factor and can reduce the quality of sugar

beet root in the future due to climate changes (Jones et al.,

2003). In the Middle East, low rainfall during the months of

July and August, when water demand is maximum,

supplemental irrigation is necessary to avoid lowering the

quality of sugar beet root. Furhter, sugar beet is a drought

tolerant species (François & Maas, 1994) and part of the plant

foliage (defoliation) limited the water demand and respond to

the environmental stress (Vesk & Westoby, 2003).

Since the leaves of plants are the main area of receiving

sunlight and photosynthetic. Therefore, any reduction in the

surface of leaves or their low efficiency can be considered as

main factors in reducing the ability of plant to assimilate

carbon dioxide and on the other hand decrease the

translocation of assimilates into the storage or vegetative

organs and subsequently impairs the plant yield. For this

reason, the estimation of yield loss caused by defoliation has

an important role in farm management (Muro et al., 2001;

Ashley et al., 2002;Abdi et al., 2007). Data obtained from the

simulated hail damages as defoliation in the United States,

Canada, England, India and Spain suggest that damages

resulted from hail on the quantity and quality of sugar beet

depend on the severity of the damage in stage and period of

development (Alimoradi, 2001). Every year pests, diseases and

hail caused considerable damage to sugar beet farms in the

country. Further, there are no scientific patterns to estimate the

damages caused by hail and other factors reducing leaf area in

sugar beet farms. Though some effort has been carried out for

the estimation of these damages but these are often not

accurate. That’s why this study was conducted to eastimate the

damages caused by hail factors. With this effect of leaf area

reduction via defoliation on qualitative and quantitative yield

of sugar beet was also determined. The effects of time and

intensity of defoliation on root yield and other quantitative and

qualitative characteristics of root were also studied. In

Montana, Morris (1950) found that complete defoliation in late

June or early July in sugar beet reduced yield to a quarter of

the normal conditions and 50% defoliation can reduce yield to

one sixth of normal conditions (Morris, 1950). Under similar

condtions, Afanasiev et al. (1960) reported that more than 75%

defoliation caused reduction in root yield to less than 6% and

the yield of the plant to less than 20%.

According to the results of the research, complete defoliation

caused 80% reduction in weight of the leaves between 23% to

27% decline in the yield of root. In fact, when sugar beet is

confronted with semi-arid conditions, high temperature, light

and salinity stress is not easily separable and more complex

situation is created (Munns, 2002; Chaves et al., 2002).

According to the reports of Jones et al. (1955) in the UK on

sugar beet, 50%, 75% and 100% defoliation at 4 or 8- leaf

stages decreased the root yield to 5%, 10% and 27% ,

respectively. The results of artificial defoliation at 120 and 144

days after planting in India showed that defoliation treatments

of 25%, 50%, or 75% did not reduce the root yield of sugar

beet, but 100% defoliation at 120 days after planting

significantly decreased the root yield (Singh et al., 1980).

Defoliation to 100% in late June or early July or middle of

September or the middle of October decreased the average root

yield in the 3-year period to 23, 27, 20 and 10%, respectively

(Stallknecht & Gilbertson, 2000).

2 Materials and Methods

The experiment was conducted in Motahari research station

located in Kamalshahr of Karaj during 2014. The station is

located at Latitude 35° 15' N and longitude 50°51' E with an

altitude of about 1300 meters above sea level. This area with

180-150 dry days is considered of the hot and dry

Mediterranean climate zones. Split plot factorial experiments

were carried out in a randomized complete block design with

four replications. Treatments of planting dates as main plots

were placed on two levels, including 23 April, (appropriate

time of planting) and 18 May, (at late planting ). Treatments of

defoliation as sub plots were the early growth of cotyledon to

two true leaves (2 leaves), establishment stage (about 12

leaves), middle of growth (about 32 leaves) and the end of the

growing season (about 54 leaves) along with five levels of

defoliation intensity factors including the removal of 25, 50, 75

and 100 percent of leaves and non-defoliation (control) as

factorial and random. Row to spacing was 50 cm and the

length of the rows was 8 m and each sub-plots had four lines

and the final harvest took place taking into consideration

marginal effects. The amount and time of use from nutrient

elements were imposed based on soil test results. During the

growing season, pests and weeds were controlled at critical

times. Irrigations time was also determined based on the

amount of evaporation between 80 and 90 ml from class A

pan. Plant density on the farm was considered about 100

thousand plants per hectare.

Defoliation per plot was treated on the green leaves with at

least 75 per cent healthy leaves. Defoliation was imposed using

scissors and its intensity was based on experimental treatments

on each leaf, independently. During the growing season, the

process of light absorption was performed using a radiometer

in intervals of 20 days, since the beginning of the treatment.

The total numbers of leaves were counted every 15 days to

determine the time of treatment in the control plot. The final

harvest was simultaneously carried out in all the plots in late

October or early November, 2014. In the running time of each

treatment, dry weights of leaf, petiole, stem and storage root as

well as leaf area were determined at a level of about 1 square

53 Ilkaee et al

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meter after 4 weeks and at harvest time; and the trend of

changes was examined in different treatments. Also root yield,

sugar content of root and white sugar yield were measured at

the end of the growing season. Sugar content of roots was

measured by polarimetry (Kunz et al., 2002). Also, according

to the results, the reduction in sugar yield caused by leaf

damage was determined in each growth stage. Data obtained

from this study was analyzed using SPSS software and

ANOVA analysis method and LSD test was used for mean

comparison (P<0.05).

3 Results and Discussion

3.1 Root yield

According to the results of variance analysis, the root yield of

sugar beet was not affected by different planting dates. The

main effects of defoliation treatments, defoliation intensity and

interaction between planting date and the defoliation was

reported on the root yield and these were significantly

different(P<0.01) to each other (Table 1). Among these,

lowest root yield was obtained in the middle stage of growth

with an average of 48.10 tons per hectare which showed

significant differences compared with other treatments. The

highest root yield (57.40 tons per hectare) was also achieved in

the early stages of growth (Figure 1). This factor can be

attributed to the fact that at the beginning of growth, the pace

of growth in many plants is directly associated with the amount

of light received by their leaf area (Monteith, 1977; Gallagher

& Biscoe, 1978). In fact, when the plant encounters in the early

stages with a treatment of defoliation has more time for the

next leaf production and capture more light, resulting in

increased yield so that plant is almost associated with higher

levels of leaves until the middle stages of growth. Compare the

average interaction between planting date and defoliation

showed that the highest ratio of root yield was observed in the

treatment of early planting date and the initial defoliation stage

(64.72 tons per hectare) and the lowest root yield was in the

treatment of late planting date and in the middle of the

defoliation stage (42.27 tons per hectare) (Figure 3). It is also

remarkable that the ratio of root yield was reduced by

increasing the intensity of defoliation. So that the highest root

yield (61.89 tons per hectare) was obtained in control and the

lowest rate (51.05 tons per hectare) was in treatment of 100%

defoliation (Figure 2); it could be due to reduced ability to

absorb light by plant with increasing the intensity of

defoliation. Result of the present study are in agreement with

the findings of Morris (1950,) also found that with increasing

the intensity of defoliation, the root yield will be greatly

reduced. Further, Afanasiev et al. (1960) reported that

complete defoliation led to a reduction of 23 to 27 percent of

yield in sugar beet. Similar types of results was obtained by

Singh et al. (1980), they suggested that complete defoliation at

120 days after planting can severely reduce root yield; but

unlike the results of this study, Singh and colleagues did not

observe any reduction in root yield in the treatments of 25%,

50% and 75% defoliation. Jones et al. (1955) in their research

on sugar beet in the United Kingdom reported that 50%, 75%

and 100% defoliation in the 4 and 8-leaf stages decreased root

yield, respectively, to 5%, 10% and 27%.

3.2 Sugar content of root

According to the results of Table 1, plants that were planted at

late planting date had higher sugar content, so that the ratio of

these traits showed significant differences compared to the

early planting which the ratio in late May planting was 15.16%

while in early planting it was 14.03% (Figure 4). This can be

attributed to the fact that, unlike the earlier planted plants,

plants that were planted later have spent most of their energy

for storage of sugar in the root. In fact, early planting has

probably led to more opportunities for more suitable vegetative

growth. According to present research, there is a negative

correlation between root weight and sugar content in sugar beet

was reported by various researchers (Abdollahian Noghabi,

1992; Ashraf Mansouri, 2000; Ebrahimian, 1993; Habibi,

1993; Beigy, 2007; ). Also in the studies of Bazrafshan et al.

(2008) highest percentage of sugar in late planting date (25th

June) was 14.98% and 17.72% on 20th May (Sarmast-Garusi,

2011).

Table 1 Analysis of variance for treatments of planting date, defoliation stage and intensity of defoliation on physiological traits in sugar

beet.

Source of variations (S.O.V.) Degree of Freedom (d.f.) Root yield Sugar content of root White sugar yield

Replication 3 439.01 1.85 4.50

Planting dates(Pd) 1 4058.45 ns

51.05* 15.13

ns

Error Pd 3 697.07 3.03 16.62

Defoliation(D) 3 788.96 **

1.47 ns

13.21 **

Defoliation intensity(Di) 4 714.76 **

2.31**

14.84**

Pd×D 3 198.56 * 0.44

ns 2.29

ns

Pd×Di 4 49.53 ns

0.91 ns

0.31 ns

D×Di 12 96.21 ns

1.02 ns

1.23 ns

Pd×D×Di 12 65.75 ns

1.00 ns

0.96 ns

Error 114 66.61 0.6 1.15

C.V. (%) 14.92 5.31 16.79

n.s: not significant. *, **: Statistically significant at P < 0.05, 0.01, respectively.

Effect of different planting dates and defoliation on the properties of sugar beet (Beta vulgaris L.) 54

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Figure 1 The effect of defoliation stages on root yield of sugar

beet

Figure 2 The effect of the intensity of defoliation treatment on

root yield of sugar beet

In this regard, have stated that on April planting, the sugar

content of root was more compared to late planting date due to

the production of larger roots (Chaves et al., 2002). The

intensity of defoliation had a significant effect (P<0.01) on

sugar content in roots (Table 1). The most sugar content

(15.04%) was related to the control and the lowest percentage

(14.35%) was observed in treatments of 100% defoliation

(Figure 5). According to the table of mean comparisons, there

was a significant difference among treatments of control and

defoliation. This means that defoliation, reduced sugar content

in sugar beet root, but the intensity of defoliation had no

significant effect on reducing the traits. The results of these

experiments were in agreement with the reports of Kamandi et

al. (2008). Also in accordance with the results of Sarmast-

Garusi (2011) in the maximum reduction of sugar content was

found in complete defoliation treatment. In this study, sugar

content was not affected by the mean defoliation stage. These

results indicate that plant leaves for their development use the

large amount of sugar stored without recycling opportunities.

3.3 White sugar yield

According to the results, it can be stated that unlike planting

date which had no significant effect on the yield of white

sugar, some factors, such as intensity and stages of defoliation

caused significant difference (P<0.01) on this trait (Table 1).

Among the intensity of defoliation treatments, the highest yield

of white sugar (6.57 tons per hectare) was belonged to 25%

defoliation and the lowest rate (5.88 tons per hectare) was

obtained in 100% defoliation (Figure 7); but generally

complete defoliation treatments caused a significant decrease

in yield of white sugar compared to the control. In fact, there

was an inverse relationship between high intensity of

defoliation and white sugar yield that can be attributed due to

less absorption of light by leaves.

Figure 3 The effect of interaction between treatments of planting

date and defoliation stage on root yield of sugar beet

Figure 4- The effect of planting date on the sugar content in sugar

beet root

55 Ilkaee et al

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Figure 5- The effect of the intensity of defoliation on the sugar

content in sugar beet root

Figure 6- The effect of defoliation stages on the yield of white

sugar in sugar beet

Figure 7- The effect of the intensity of defoliation on the yield of white sugar in sugar beet.

According to Stallknecht & Gilbertson (2000) 100%

defoliation leads substantially to decrease in the amount of

sugar yield. Further, according to the results of Kamandi et al.

(2008), white sugar yield was declined with increasing of

defoliation compared with the control (non- defoliation). In

this study, it was observed that the least amount of white sugar

yield was achieved in the middle stage of growth, which this

decrease can be expected due to the lowest of produced root

yield. The greatest amount of white sugar yield was belonged

to the early stage treatments with 6.79 tons per hectare and the

lowest rate was observed in the middle stage treatments with

5.55 tons per hectare (Figure 6). According to the results of

Muro et al. (1998) in defoliation treatments in the middle of

growth had the greatest impact on reducing of root yield and

consequently the yield of white sugar.

Acknowledgements

The authors would like to thank respectable authorities on

Motahari research station in Kamalshahr of Karaj, as well as

Department of Agronomy, Faculty of Agriculture, Islamic

Azad University of Karaj, Iran that helped me out in carrying

out this study.

Conflict of interest

Authors would hereby like to declare that there is no conflict of

interests that could possibly arise.

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KEYWORDS

Food security

Natural resource

Socio-economic value

Underutilized

WEM

ABSTRACT

Wild edible mushrooms (WEM) are known for its medicinal and nutritional value across the globe.

WEM have become one of the most prized after food especially in the developed countries where people

are mostly health conscious. The present study throws light on the diverse flora of WEMs of Nagaland

and how it can be income generator for the tribal people with proper research in this aspect. Till now,

the knowledge of distinguishing between edible and non-edible varieties is only confined to people who

go for mushroom hunting. As such the indigenous knowledge remains with only those few people

involved. The current data can pave the way for future research work and also make people aware of the

many varieties of WEMs available in the state. A total of 33 WEMs were collected and identified during

the peak mushroom season of the state i.e. from end May to September of every study year.

Toshinungla Ao, Chitta Ranjan Deb*

and Neilazonuo Khruomo

Department of Botany, Nagaland University, Lumami 798 627, Nagaland, India

Received – November 27, 2015; Revision – December 24, 2015; Accepted – January 31, 2016

Available Online – February 20, 2016

DOI: http://dx.doi.org/10.18006/2015.4(1).59.65

WILD EDIBLE MUSHROOMS OF NAGALAND, INDIA: A POTENTIAL FOOD

RESOURCE

E-mail: [email protected] (Chitta Ranjan Deb)

Peer review under responsibility of Journal of Experimental Biology and

Agricultural Sciences.

* Corresponding author

Journal of Experimental Biology and Agricultural Sciences, February - 2016; Volume – 4(1)

Journal of Experimental Biology and Agricultural Sciences

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ISSN No. 2320 – 8694

Production and Hosting by Horizon Publisher

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1 Introduction

Wild edible fungi have been collected and consumed by people

for thousands of years. Wild edible fungi are important sources

of nutrition and medicines. Around 2000 species of

mushrooms are considered safe for human consumption and

about 650 of these possess medicinal properties (Rai et al.,

2005). Mushrooms have a long association with humankind

and provide profound biological and economical impact. Since

time immemorial, wild mushrooms have been consumed by

man (Das, 2010). Edible mushrooms have high content of

proteins, vitamins, minerals, fibers, trace elements and low/no

calories and cholesterol (Murugkar & Subbulakshmi, 2005).

Mushrooms have been used in folk medicine for thousands of

years and are considered to be Neutralceuticals while others

can produce potent Neutriceuticals (Ribeiro et al., 2005). Due

to its traditional usage, Trametes versicolor has been

considered among the 25 major medicinal macrofungi

worldwide (Boa, 2004) and polysaccharo-peptides purified

from this species, show experimental immune-modulatory and

anti-cancer effects (Cheng & Leung, 2008). Besides,

mushrooms are known to be rich sources of various bioactive

substances like anti-bacterial, anti-fungal, anti-viral, anti-

parasitic, anti-oxidant, anti-inflammatory, anti-proliferative,

anti-cancer, anti-tumour, cytotoxic, anti-HIV, hypo-

cholesterolemic, anti-diabetic, anti-coagulant, hepato-

protective compounds, among others (Wasser & Weis, 1999;

Ajith & Janardhanan, 2007).

Mushrooms are a boon for progress in developing countries

like India with rich biodiversity especially in the field of food,

medicine and unemployment (Wani et al., 2010). World

production of mushroom exceeds 3 million tons worth a

market value of U.S $ 10 billion. Netherlands, Poland, Ireland

and Belgium are major exporting countries of fresh

mushrooms in the world. China is the largest exporter of

preserved mushrooms and Netherlands and Spain are the other

major countries (Harsh & Joshi, 2008). Germany, U.S.A and

France are considered to be major importing countries of

prepared and preserved mushrooms. Till 2008 India ranked 6th

as an exporter of mushrooms. India has a great potential to be

an important producer of mushroom in the future and currently

ranks 54th in the world in producing mushrooms. Edible

mushrooms are valuable sources of nutrients and bioactive

compounds in addition to its rich flavors and culinary features.

Recently mushrooms have become increasingly popular as

functional foods for its potential beneficial effects on human

health (Guillamon et al., 2010). Modern pharmacological

research confirms large parts of traditional knowledge

regarding the medicinal effects of mushrooms due to their

antifungal, antibacterial, antioxidant and antiviral properties

(Wani et al., 2010). Wild edible mushrooms are not well

documented in many countries, poorly studied and

underutilized though they are rich source of non wood forest

product. There is no systematic survey and study on mushroom

harvest, its market and income generation potential (Tibuhwa,

2013). The FAO of the UN has emphasized the adoption of

mushrooms as an ideal food for developing countries and its

contribution to global food security.

Wild edible mushrooms are used as food and medicine by the

indigenous tribes of Similipal Biosphere Reserve (SBR) of

Odisha, India. More than ten ethnic groups of SBR were found

to be mycophilic and have extensive traditional mycological

knowledge (Sachan et al. 2013). The mushrooms identified in

the SBR are native to many parts of India which were reported

by some authors in the North-Eastern hills of India (Verma et

al., 1995; Singh et al., 2007; Tanti et al., 2011); North Western

Himalayas (Atri et al., 1997) and Kanyakumari district

(Davidson et al., 2012). The northeast region of India is known

for its rich biodiversity. The high humidity during monsoon

period provides ideal agro-climatic conditions for the growth

of mushrooms. The people of Nagaland are highly known for

coveting wild edible mushrooms. Mushrooms are highly prized

delicacy of the state. Very few works has been done on wild

edible mushrooms in Nagaland (Kumar et al., 2013). In most

of these reports the mushroom resources are ill presented. The

purpose of the present study was to bring to light the rich

diversity of WEMs of Nagaland and its potential as a valuable

food resource.

2 Materials and Methods

Survey Area

Nagaland is located in the North Eastern region of India with

total geographic area of 16,579 sq Km. Nagaland shares

borders with Myanmar in the East, Assam in the West,

Arunachal Pradesh and a part of Assam in the North and

Manipur in the South. It lies between 93o15ˊ to 95

o15ˊ E and

25o10ˊ to 27

o4ˊ N. According to the meteorological data of the

state the average annual rainfall ranges between 2000-2500

mm while, temperature during summer ranges from 16-31oC

and drops as low as 4oC during winter. During the present

study regular surveys and collection were carried out in various

districts and market areas of Nagaland from October 2013–

May 2015 during the peak mushroom season of the state.

Forest areas and market places of Mokokchung, Zunheboto,

Kohima, Tuensang, Phek and Wokha were surveyed during

this period. Local markets were surveyed to know about the

wild varieties sold during the season and regular mushroom

collectors were interviewed to gain more knowledge about the

hunting areas.

Wild edible mushrooms were collected in silver foil/collection

boxes and brought to the laboratory for identification.

Mushrooms with leathery texture were preserved in 4% (v/v)

formaldehyde solution and mushrooms with soft texture were

preserved in 2% (v/v) formaldehyde solution and maintained

as herbarium specimens.

60 Toshinungla et al

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Table 1 List of Wild Edible Mushrooms (WEM) found in Nagaland, India.

Name of Species (Family) Habitat Season of collection Accession No

Auricularia auricula-judae (Bull.) Quél (Auriculariaceae) On dead stumps and branches of sub-tropical and temperate trees especially Alnus species. End May-November NUBOT-TA-AA-01

A. polytricha (Mont.) Sacc (Auriculariaceae) In clusters on rotten or dead and decaying stumps and twigs September-November NUBOT-TA-AP-02

Cantharellus cibarius (Fr.) (Cantharellaceae) Found under Lithocarpus in sub-tropical forests End June-October NUBOT-TA-CC-03

Lactarius piperatus (L.) Pers. (Russulaceae) Under sub-tropical semi-evergreen forests June-October NUBOT-TA-LP-04

Lactarius volemus (Fr.) (Russulaceae) Under sub-tropical semi-evergreen forests including pine June-October NUBOT-TA-LV-05

Lentinula edodes (Berk.) Pegler (Omphalotaceae) On trunks of Oak trees June-July NUBOT-TA-LE-06

Hericium cirrhatum (Pers.) Nikol (Hericiaceae) On trunks of semi-evergreen and temperate trees June-July NUBOT-TA-HC-07

Dacryopinax spathularia (Schwein) G. W. Martin (Dacrymycetaceae) On dead and decaying logs in large groups June-July NUBOT-TA-DS-08

Schizophyllum commune Fr. (Schizophyllaceae) On branches of dead wood and cut timber April-August NUBOT-TA-SC-09

Strobilomyces strobilaceus (Scop.) Berk (Boletaceae) Grows in association with semi-evergreen and coniferous trees June-September NUBOT-TA-SS-12

Amanita strobiliformis (Paulet ex Vittad.) (Amanitaceae) Under sub-tropical semi evergreen forest trees June-August NUBOT-TA-AS-19

Boletus edulis Bull. (Boletaceae) Under coniferous and semi-evergreen forest types August-September NUBOT-TA-BE-22

Tricholoma imbricatum (Fr.) P. Kumm. (Tricholomataceae) In coniferous woods, especially with pine July-August NUBOT-TA-TI-27

Pleurotus pulmonarius (Fr.) Quél. (Pleurotaceae) In clusters on cut timber and fallen logs June-September NUBOT-TA-PP-28

Clavaria fragilis Holmsk. (Clavariaceae) Grows in clusters on ground amongst leaf litters and in fields August-November NUBOT-TA-CF-35

Tremella fuciformis Berk. (Tremellaceae) On dead or fallen branches of broadleaved trees September-November NUBOT-TA-TF-37

Lentinus squarrosulus Mont. Singer (Polyporaceae) On dead stumps of trees like Oak June-August NUBOT-TA-LS-40

Hygrocybe conica (Schaeff.) P. Kumm (Hygrophoraceae) In grass in fields after burning the area June-July NUBOT-TA-HC-41

Russula heterophylla (Fr.) Fr. (Russulaceae) Under Lithocarpus and Castanopsis in sub-tropical forests October-January NUBOT-TA-RH-44

Suillus luteus (L.) Roussel (Suillaceae) Under coniferous especially pine September-November NUBOT-TA-SL-46

Xerocomellus chrysenteron (Bull.) Šutara (Boletaceae) Under sub-tropical semi-evergreen forests including pine July-November NUBOT-TA-XC-48

Suillus pictus (Peck) A.H. Sm. & Thiers (Suillaceae) Under sub-tropical semi-evergreen forests June-November NUBOT-TA-SP-49

Laccaria tortilis (Bolton) Cooke (Hydnangiaceae) On bare soil in damp woods August-November NUBOT-TA-LT-51

Melanoleuca grammopodia (Bull.) M. (Tricholomataceae) Found to grow on leaf mulch or composted soil in fields June-October NUBOT-TA-MG-61

Aleuria aurantia (Pers.) Fuckel (Pyronemataceae) Found to grow in groups on soil amongst grasses or on bare soil or at roadside August-November NUBOT-TA-AA-62

Macrolepiota albuminosa (Berk.) Pegler (Agaricaceae) Grows on termite mounds in grassy fields May-August NUBOT-TA-MA-63

Termitomyces heimii Natarajan (Lyophyllaceae) Found to grow on termite mounds and clayey soil May-August NUBOT-TA-TH-64

Lentinus sp. (Polyporaceae) Grows on tree trunks and dead barks of Oaks End May-June NUBOT-TA-L-69

Termitomyces eurhizus (Berk.) R. Heim (Lyophyllaceae) Grows in groups on ground in termite mount soil July-August NUBOT-TA-TE-71

Lycoperdon perlatum Pers. (Agaricaceae) Grows in fields, roadsides, in woods and amongst fallen leaf litter End April-September NUBOT-TA-LP-72

Laetiporus sulphureus (Bull.) Murr. (Polyporaceae) Grows on dead stumps as well as living tree trunk of hardwoods and oaks July-September NUBOT-TA-LS-73

Coprinus comatus (O.F. Müll.) Pers. (Agaricaceae) Grows amongst grasses in sub-tropical forests May-October NUBOT-TA-CC-74

Pleurotus citrinopileatus Singer (Pleurotaceae) Grows on trunks of hardwood June-August NUBOT-TA-PC-75

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Figure 1 Some of the wild edible mushrooms of Nagaland; a. Auricularia polytricha, b. Aleuria aurantia, c. Auricularia judae, d.

Cantharellus cibarius, e. Coprinus comatus, f. Dacryopinax spathularia, g. Laetiporus sulphureus, h. Lactarius volemus, i. Laccaria

tortilis, j. Lactarius piperatus, k. Lentinula edodes, l. Lycoperdon perlatum, m. Macrolepiota albuminosa, n. Pleurotus citrinopileatus, o.

Pleurotus pulmonarius, p. Schizophyllum commune, q. Termitomyces eurrhizus, r. Termitomyces heimi, s. Tremella fuciformis, t.

Tricholoma imbricatum.

A part of the collected materials were dried at 40-72oC using

blowing hot air and kept for future references, characterization

and documentation. The habitat, odor, morphology, spore print

and adaptation to the environment studied prior to the

preservation of the collected macro fungi. Identification of the

collected mushrooms was done by standard microscopic

methods (Roy & De, 1996) and by studying the macroscopic

and microscopic characters (David, 1986; Das, 2009; Philips,

2006). The mushroom specimens were deposited in the

herbarium of Department of Botany, Nagaland University,

Lumami, India with the accession numbers as mentioned in

Table 1.

62 Toshinungla et al

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3 Results

A total of 33 Wild Edible Mushroom (WEM) species

belonging to Auriculariaceae, Cantharellaceae, Russulaceae,

Polyporaceae, Hericiaceae, Dacrymycetaceae,

Schizophyllaceae, Boletaceae, Amanitaceae,

Tricholomataceae, Pleurotaceae, Clavariaceae, Tremellaceae,

Hygrophoraceae, Suillaceae, Hydnangiaceae, Pyronemataceae,

Agaricaceae and Lyophyllaceae were collected and identified

as per various literatures (Table 1). Besides edible mushrooms,

9 species are used for medicinal purpose to cure different

diseases (Table 2). Figure 1 shows some of the common wild

edible mushrooms of Nagaland. Market surveys revealed that

WEM are highly coveted food resource in Nagaland. The local

people prepare soups, chutney, salads and various side dishes

from mushrooms. During the season, there is high demand of

edible varieties of WEMs and these are sold ranging from 50-

250 INR per packet at local markets. The prize varies

depending on popularity, taste and demand. Some popular

varieties available at local markets during the season are

Schizophyllum commune, Lentinus edodes, L. squarrosulus,

Termitomyces heimi, T. eurhizus, Auricularia auricula-judae,

Lactarius volemus, and Pleurotus pulmonarius. S. commune

and L. edodes are sold in dried form throughout the year till

stocks last with the local people.

Discussions

Nagaland is one of the North Eastern states of India which is

agro-climatically very rich and supports the growth of many

wild mushrooms. Unfortunately till date there is no systematic

survey of wild edible mushrooms in the state. Indigenous

knowledge possessed by the local people about WEMs will

provide significant opportunities to develop micro-enterprises

and entrepreneurship. This can be a means of achieving

sustainability. Mushroom hunting is not gender oriented in the

state i.e. both men and women are equally involved. Folk

taxonomy through traditional knowledge and experience is

usually used to identify edible mushrooms from poisonous

ones. Naming of the species is done in local dialect to keep

memory and transfer the knowledge from one generation to the

next. The study promotes awareness to harvest and exploit this

underutilized local resource, which will provide nutritious food

and employment opportunities especially to the disadvantaged

groups (i.e. unemployed and old people) (Kumar et al., 2013;

Sachan et al. 2013; Tanti et al., 2011; Tibuhwa, 2013).

The exploitation of WEM would contribute significantly in

boosting the economy and at the same time, food security is

checked. Mushrooms are a source of income generator

especially for rural areas. The cultivation of WEM hardly

causes any effect on the environment in fact they act as

ecological indicators. As such the study calls for awareness

and cooperation from forest conservers to allow mushroom

gatherers to freely collect this non wood forest resource which

is highly underutilized. The present work also highlights the

ethno-medicinal potential of the state. The uses (nutritional and

medicinal values, neutriceuticals and neutraceutical

compounds) of WEM is likely to be lost if these are not

properly documented and screened. Further studies need to be

carried out in order to assess the ethno-medicinal potential of

WEMs for discovery of novel compounds for their

pharmaceutical applications.

The present work may lead to the creation of a database for

WEM of the state as no such work has been carried out in

depth. The first phase of this study enumerates the wild edible

mushrooms of Nagaland. Works on nutritional analysis,

molecular profiling of wild edible mushrooms is in progress.

During recent times, cultivated mushrooms have gained much

attention because of the many health benefits of mushrooms

but unfortunately in remote regions of the world like Nagaland

no such markets are available for the local people to enjoy the

highly popular cultivated mushrooms. In such circumstances,

the wild edible mushrooms which are available in the state

should be brought to light so that the people can reap the

benefits of consuming edible mushrooms like the rest of the

world. Moreover, with proper research and infrastructure

facilities, WEM can be commercialized which can play a key

role in the socio-economic upliftment of the people.

Table 2 Medicinal uses of WEM as described by other researchers.

Name of the species Medicinal uses

Auricularia auricula-judae Anti-tumor, anticoagulant, hypocholesterolemic

Auricularia polytricha Anti-coagulant, hypocholesterolemic

Pleurotus pulmonarius Anti-HIV, hyperglycemic

Cantharellus cibarius Anti-microbial

Schizophyllum commune Anti-cancer (drug- Schizophyllan)

Lentinula edodes Anti-tumor, anti-HIV, natural antidote

Lactarius piperatus Anti-tumor, anti-bacterial, anti-oxidant

Lycoperdon perlatum Antimicrobial and Antifungal (lycoperdic acid)

Lentinus squarrosulus Used as neutraceutical

Sources: Chang & Miles, 2004; Das, 2010; Patel et al., 2012; Sachan et al., 2013; Sharma & Atri, 2014.

Wild edible mushrooms of nagaland, india: a potential food resource. 63

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Acknowledgement

The authors are thankful to the University Grants Commission,

Govt. of India, New Delhi for financial help through the UGC-

SAP (DRS-III) program to the Department of Botany. The

infrastructure and facility used from the Institutional Biotech

Hub, Department of Botany, Nagaland University are duly

acknowledged.

Conflict of Interest

Authors would hereby declare that there is no conflict of

interests.

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KEYWORDS

Phytoplankton

Taal Lake

Aquaculture

Importance value

ABSTRACT

This study was aimed to assess the phytoplankton communities between the aquaculture and non-

aquaculture sites of the Taal Lake in Batangas, Philippines for a sampling period of 10 months from

August 2013 to May 2014. Total of 39 phytoplankton genera under Division Chlorophyta, Cyanophyta,

Chrysophyta and Pyrrophyta were reported from the study site. Among these, 36 genera were observed

from the aquaculture sites while only 30 genera from the non-aquaculture sites. Results of the density

revealed that availability of phytoplankton was significantly higher in the aquaculture than the non-

aquaculture sampling stations for all major phytoplankton divisions. Highest monthly density was also

recorded during the summer months of March to May 2014 and lowest in the month of January 2014

due to sulphur upwelling. The most dominant phytoplankton based on importance value for both

sampling sites was Microcystis followed by Merismopedia in aquaculture sites and Oscillatoria in non-

aquaculture sites, all under division Cyanophyta, indicating the organic pollution and eutrophication of

Taal Lake.

Airill L. Mercurio1,*

, Blesshe L. Querijero2 and Johnny A. Ching

1

1Biological Sciences Department, College of Science and Computer Studies, De La Salle University-Dasmariñas, City of Dasmariñas 4115, Cavite, Philippines.

2Animal Biology Division, Institute of Biological Sciences, College of Arts and Sciences, University of the Philippines, Los Baños 4031, Laguna, Philippines.

Received – November 25, 2015; Revision – December 20, 2015; Accepted – January 21, 2016

Available Online – February 20, 2016

DOI: http://dx.doi.org/10.18006/2016.4(1).66.73

PHYTOPLANKTON COMMUNITY IN AQUACULTURE AND NON-

AQUACULTURE SITES OF TAAL LAKE, BATANGAS, PHILIPPINES

E-mail: [email protected] (Airill L. Mercurio)

Peer review under responsibility of Journal of Experimental Biology and

Agricultural Sciences.

* Corresponding author

Journal of Experimental Biology and Agricultural Sciences, February - 2016; Volume – 4(1)

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ISSN No. 2320 – 8694

Production and Hosting by Horizon Publisher

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All rights reserved.

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Creative Commons Attribution-NonCommercial 4.0

International License Based on a work at www.jebas.org.

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1 Introduction

Taal Lake covers an area of 268 km2, lies 60 km south of

Manila, in the province of Batangas. The lake provides

multiple services to various users, including fisheries, of which

aquaculture has flourished rapidly over the years. The lake is

also used for recreation, tourism, navigation, and as water

source for the nearby cities and agricultural fields (Mutia,

2001). Aquaculture had been the major source of livelihood of

the fisher folk living in the lakeshore since 1970 (Gibertas,

2008). Fish cage operators rely on the water of Taal Lake for

intensive fish production (Vista et al., 2006).

In aquaculture, phytoplankton contributes to primary

productivity that helps in maintaining fisheries (Brraich &

Saini, 2015). Phytoplankton serve as food of zooplanktons

which are usually being fed to fish larvae reared in fish

hatcheries (Moncheva & Parr, 2010; Diwowo, 2013).

Phytoplanktons also play an important role in material

circulation and energy flow in aquatic ecosystem. Its presence

often controls the growth, reproduction capacity, and

population characteristics of other organisms (Ariyadej et al.,

2008). It is also an important biological indicator of the water

quality (Edward & Ugwumba, 2010; Brraich & Saini, 2015).

The current study compared the phytoplankton community in

two separate areas in Taal Lake, the fish cage farming sites and

non-aquaculture sites during a 10-month sampling period from

August 2013 to May 2014 as indicator of possible effect of

aquaculture activities on lake productivity.

2 Materials and Methods

2.1 Phytoplankton Collection and Measurement

Phytoplankton samples for quantitative and qualitative

analyses were collected from three sampling stations for the

aquaculture sites and one sampling station for the non-

aquaculture sites. Each sampling station has three sub-

sampling stations; total of 12 sub-sampling stations for the

study site. The aquaculture sampling stations, located in the

municipalities of Talisay and Laurel, and the non-aquaculture

station in the municipality of Tanauan were identified using

GPS Garmin E-trex® Global Positioning device (Fig. 1).

One liter lake water was collected and 10 ml of it was taken in

a test tube for centrifugation (Hermile®) for 5 min at 4,000 rpm

to concentrate its algal component. The supernatant was

decanted and 1 ml precipitate was placed in a vial preserved

with one drop of Lugol’s iodine. The isolated phytoplanktons

were identified and photographed using photomicroscope

(Nikon®) at 400x. Phytoplankton frequency, abundance and

density were determined for the identification of dominant

species based on importance value.

Figure 1 Sampling stations for aquaculture and non-aquaculture sites in Talisay and Laurel, Batangas, Philippines (Map created by the

author using Q-GIS).

67 Mercurio et al

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The phytoplankton was counted using Haemocytometer

method (Martinez et al., 1975). Final population of

phytoplankton was calculated by using following formula:

The importance value of the phytoplankton species was

determined by adding the relative values of the frequency,

density and abundance. The phytoplankton species which has

the highest importance value was considered as the most

dominant. The frequency, abundance and density, and their

relative values are calculated by the standard formulas given

by Umaly, 1988.

Where, RF is relative frequency, RA is relative abundance and

RD is relative density.

2.2 Quantitative Analysis of isolated Phytoplankton

Quantitative analysis of the importance value of phytoplankton

was done using the summation of the relative values of the

frequency, density and abundance of the different species of

plankton collected from the lake. Mann-Whitney U test at 95%

level of confidence was used to determine the significant

difference of phytoplankton density between the aquaculture

and non-aquaculture sites of Taal Lake.

3 RESULTS AND DISCUSSION

3.1 Phytoplankton Communities

A total of 39 genera of phytoplankton belonging to four major

divisions namely, Chlorophyta, Chrysophyta, Cyanophyta and

Pyrrophyta were observed in the aquaculture and non-

aquaculture sites in Taal Lake. The detail of isolated genera

has been provided in Table 1. Among the total isolated genera,

36 were isolated from aquaculture sites while only 30 genera

from the non-aquaculture sites.

Results presented in table 2 revealed that the density of

phytoplankton is significantly higher in the aquaculture than

the non-aquaculture sampling stations for all major

phytoplankton divisions.

Both organic and inorganic matter from commercial

aquaculture operations have been implicated in phytoplankton

production (Bunting, 2013).

Figure 1 Representative phytoplankton in aquaculture and non-aquaculture sites in Taal Lake, Batangas, Philippines.

Phytoplankton community in aquaculture and non-aquaculture sites of Taal Lake, Batangas, Philippines. 68

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Table 1 Collected phytoplankton in aquaculture and non-aquaculture sites of Taal Lake

No. Genus Aqu. sites Non-aqu. sites No. Genus Aqu. sites Non-aqu. sites

Division- Chlorophyta Division- Chrysophyta

1 Actinastrum X 1 Coconeis

2 Ankistrodesmus 2 Coscinodiscus

3 Asterionella 3 Cyclotella

4 Chlorella X 4 Cymbella X

5 Chlorococcum X 5 Diatoma

6 Chodatella 6 Fragilaria

7 Closterium 7 Gomphonema X

8 Coelastrum 8 Melosira

9 Cosmarium 9 Navicula X

10 Crucigenia X 10 Nitzschia

11 Eudorina 11 Synedra X

12 Kirchneriella 12 Tabellaria

13 Oocystis Division - Cyanophyta

14 Scenedismus 1 Anabaena

15 Schroederia X 2 Chroococcus

16 Selenastrum 3 Merismopedia

17 Staurastrum 4 Microcystis

18 Tetraedron 5 Oscillatoria

19 Treubaria Division - Pyrrophyta

20 Westella X 1 Glenodinium X

2 Peridinium X

In aquaculture, usually only a fraction of the fish feed is being metabolized. Feeds that were not taken by the fishes tend to settle at the

bottom and decomposed which results to the growth of phytoplankton and bacteria (Glibert et al., 2002).

Table 2 Density of phytoplankton (per ml of water) by division in aquaculture and non-aquaculture sites of Taal Lake.

Division Aquaculture Non-aquaculture

Chlorophyta 12,189a 6,668

b

Chrysophyta 7,304a 2,748

b

Cyanophyta 193,711a 107,800

b

Pyrrophyta 200a 0.0

b

Different letters as superscript in the same row indicate significant difference (p<0.05) between the average total counts of phytoplankton

communities in aquaculture and non-aquaculture sites of Taal Lake.

3.1.1 Division Chlorophyta

Of the 39 genera of phytoplankton, 20 genera were observed

under Division Chlorophyta. The Chlorophyta or the green

algae appear bright grass green because their chlorophyll is not

concealed by large amounts of accessory pigments. They

exhibit a surprising level of nutritional variation. Among

these, Coelastrum has highest density at both sites, with mean

value of 2,608 cells per ml in aquaculture and 2,307 cells per

ml in non-aquaculture sites. Unlike other green algae,

Coelastrum exhibit asexual reproduction by autocolony

formation. Coelastrum cells are connected to one another by

blunt processes to form hollow coenobia that will give rise to

autocolonies without the involvement of flagellate zoospores

(Graham & Wilcox, 2000). Actinastrum, Crucigenia, and

Westella were observed only in non-aquaculture sites because

these are rare species and prefer low level of nutrients.

Chlorella, Chlorococcum and Schroederia were observed only

in aquaculture sites indicating preference for high level of

nutrients. Schroderia has the lowest density of 175 cells per ml

in aquaculture sites. Ankistrodesmus has the lowest density of

200 cells per ml in non-aquaculture sites.

3.1.2 Division Chrysophyta

Under Division Chrysophyta, total 12 genera namely Coconeis,

Coscinodiscus, Cyclotella, Cymbella, Diatoma, Fragilaria,

Gomphonema, Melosira, Navicula, Nitzschia, Synedra and

Tabellaria were observed. In the aquaculture sites, the

phytoplankton with the highest average total count was the

genus Melosira with mean value of 4,528 cells per ml while

genus Cymbella has the lowest value of 100 cells per ml.

69 Mercurio et al

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Similarly, the highest number of phytoplankton that was

observed in the non-aquaculture sites are Melosira with a value

of 1,288 cells per ml, but the lowest number of phytoplankton

are the Coscinodiscus with an a density 100 cells per ml.

Among the 12 genera, four of them were not observed in the

non-aquaculture sites which include Cymbella, Gomphonema,

Navicula and Synedra. Melosira has the highest density

among the phytoplankton under this Division because

Melosira takes advantage of the high nutrient levels promoting

its growth and benefits from low levels of competition and

grazing by having resting stage in the sediments (Horne &

Goldman, 1994).

3.1.3 Division Cyanophyta

Among the four major divisions, Cyanophyta has the highest

phytoplankton density. Five genera i.e. Anaebena,

Chroococcus, Merismopedia, Microcystis and Oscillatoria

were observed under Division Cyanophyta. Microcystis has the

highest total count of 10,666 and 8,579 cells per ml in both the

aquaculture and non-aquaculture sites, respectively. Among

the studied population, the lowest number of phytoplankton

was Anaebaena with an average density of 900 cells per ml in

the aquaculture sites and Chroococcus with only 1,570 cells

per ml in the non-aquaculture cites. Microcsytis is the most

dominant species that was observed in both aquaculture and

non-aquaculture. Microcystis become dominant since they

produce secondary chemicals called microcystins which causes

blooms in freshwater that protects them from zooplanktons and

grazers (Carmichael et al., 1988; Krebs, 2009). Cyanobacteria

or blue green algae are “nuisance algae” which becomes

abundant when nutrients are plentiful. Cyanobacteria can even

tolerate low oxygen conditions and concentrations of H2S.

They prefer alkaline conditions and pH may rise up to 9

(Vincent, 2009).

The energy required for the vertical movement of the blue

green algae is small, especially for those species that use

carbohydrate ballast such as the Microcystis to balance more

permanent gas vacuoles and to regulate their position in the

water column. The ballast is used up overnight and the algae

float to the surface in the morning to resume the cycle (Horne

& Goldman, 1994).

3.1.4 Division Pyrrophyta

Only two genera of dinoflagellates were observed under

Division Pyrrophyta, namely Glenodinium and Peridinium.

These were present only in the aquaculture sites and observed

during the month of May. Both of them have an average count

of 100 cells per ml. Dinoflagellates grow best in summer

because they can actively swim to favorable light and

nutrients. Their requirements are complex and require high

organic substrates. Their population may decline due to

zooplankton grazing. The active swimming of large

phytoplankters such as dinoflagellates requires also large

amounts of energy unlike small phytoplankton (Horne &

Goldman, 1994).

3.2 Monthly Density of Phytoplankton

The monthly density of the observed phytoplankton in both

aquaculture and non-aquaculture sites during the 10-month

sampling period is shown in Fig. 2. Results showed that the

phytoplankton density is at its peak during summer i.e. March

to May. Results are in agreement with the findings of Horne &

Goldman (1994) those have reported that phytoplankton grows

best in summer due to higher light intensity. Light is a

fundamental aspect of phytoplankton ecology since they are

the primary producers in aquatic areas. They convert light

energy into biomass through photosynthesis (Graham &

Wilcox, 2000). On the other hand, the phytoplankton density

is lowest on the month of January and this may be attributed to

the sulphur upwelling that transpired last January 16, 2014

(BFAR, 2014). Sulfur upwelling in Taal Lake usually occurs

from November to February when the northeast wind disturbs

the sediments in the lake, resulting in the upwelling of

hydrogen sulfide which is a poisonous gas (BFAR, 2014). This

affected the density of the phytoplankton.

Figure 2a Monthly density of observed phytoplankton (over-all).

Phytoplankton community in aquaculture and non-aquaculture sites of Taal Lake, Batangas, Philippines. 70

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Figure 2b Monthly density of observed phytoplankton (division wise)

3.3 Importance Value of Phytoplankton

The dominant phytoplankton species in both aquaculture and

non-aquaculture sites of Taal Lake were identified by

determining their importance value (Table 3). This was carried

out by adding the relative frequency, relative abundance and

relative density per genus (Umaly,1988).

Table 3 Dominant Phytoplankton Species based on Importance Value.

Dominant species Relative frequency Relative abundance Relative density Importance Value

Aquaculture sites

Microcystis 22.273 22.271 22.271 66.815

Merismopedia 16.722 16.720 16.720 50.162

Melosira 9.454 9.454 9.454 28.362

Oscillatoria 6.348 6.348 6.348 19.044

Coelastrum 5.445 5.445 5.445 16.335

Non-aquaculture sites

Microcystis 19.340 18.831 19.340 57.511

Oscillatoria 14.090 13.720 14.090 41.900

Anaebena 10.709 10.427 10.709 31.845

Merismopedia 10.089 9.832 10.089 30.010

Westella 7.214 7.025 7.214 21.453

71 Mercurio et al

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Microcystis has highest importance value for both aquaculture

and non-aquaculture sites which indicates that it is the most

dominant species. Merismopedia rank as the second dominant

species in aquaculture sites, while Oscillatoria in non-

aquaculture sites. Other identified dominant species in

aquaculture sites include Melosira, Oscillatoria, and

Coelastrum, while Anabaena, Merismopedia, and Westella for

the non-aquaculture sites. These findings were in agreement

with the study of Xing et al. (2013), wherein one of the

dominant phytoplankton observed in Baiyangdian Lake during

spring was Microcystis, which is an indicator of

eutrophication. Ansari et al. (2008) also reported that

Microcystis and Oscillatoria dominate Unkal Lake in

Kartanaka, India. Occurrence of bloom of Microcystis and

Oscillatoria indicates organic pollution and eutrophication of

Unkal Lake. Further, highest importance value belonging to

division Cyanophyta indicates organic pollution and

eutrophication of Taal Lake.

Conclusions

Phytoplanktons serve as an indicator of water quality and

trophic condition of a lake. The phytoplankton community in

aquaculture and non-aquaculture sites of Taal Lake, Batangas,

Philippines was assessed. During the sampling period, a total

of 39 genera of phytoplankton under divisions Chlorophyta,

Chrysophyta, Cyanophyta and Pyrrophyta was observed in the

study site. Statistically, phytoplankton density in aquaculture

sites is significantly higher than non-aquaculture sites. The

monthly density of phytoplankton was observed to be highest

during the summer months of March to May 2014 and lowest

in the month of January 2014 due to sulphur upwelling.

Dominant phytoplanktons were also determined based on

importance value. In both aquaculture and non-aquaculture

sites, Microcystis ranks first thereby is the most dominant

species, followed by Merismopedia in aquaculture sites and

Oscillatoria in non-aquaculture sites. All the three

phytoplankton with the highest importance value belong to

division Cyanophyta indicating organic pollution and

eutrophication of Taal Lake. Thus, regular environmental

monitoring of the lake is hereby recommended.

Acknowledgements

The authors acknowledge the University Research Office

(URO) of De La Salle University-Dasmariñas (DLSUD) for

providing financial support, Dir. Esmeralda Paz-Manalang of

Regional Fisheries Office, Ms. Nenita S. Kawit of the Inland

Fisheries Research Station, Mr. Aljon S. Andrade of BFAR

RFO IV-A in Tanauan, Batangas, and Mr. Victor H. Mercado,

PASu TPVL, the TLAAI and LGU’s of Talisay and Laurel,

Batangas.

Conflict of interest

Authors would hereby like to declare that there is no conflict of

interests that could possibly arise.

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KEYWORDS

Digested effluent

Dwarf Napier grass

Aftereffect

Compaction effect

Plant production

ABSTRACT

In regional intensive livestock farming, animal waste has a significant dispersal problem. One of the

solutions is to process animal wastes to digested effluent of manure (DEM) by the operation of biogas

plants. The objectives of the present study were to determine the residual effects of DEM in field

application at three levels in the previous two years and the effect of compacted DEM amended with

solid manure in a pot trial on production in a dwarf variety of late-heading type Napier grass

(Pennisetum purpureum Schumach). Plant growth attributes (plant height, tiller number, mean tiller

weight and plant dry weight) in the year examined tended to increase with DEM application rate the

previous two years, while the difference between the previous year’s treatments was less than 25%, the

aftereffect of manure application on normal Napier grass was more than 4 times higher. Pot-cultured

growth attributes were highest for liquid DEM and chemical fertilizer, followed by a mixture of DEM

with manure under the same total nitrogen supply, and decreased significantly with a decrease in the

DEM-amended mixture application. Thus, it is concluded that DEM would have a limited residual effect

as in the chemical fertilizer plot and a smaller effect than manure application, and a mixture of DEM

amended with solid manure should facilitate supplying DEM to forage crops by compaction.

Hadijah Hasyim1, Yasuyuki Ishii

2,*, Ahmad Wadi

3, Ambo Ako Sunusi

4, Satoru Fukagawa

5 and

Sachiko Idota2

1 Interdisciplinary Graduate School of Agriculture and Engineering, University of Miyazaki, Miyazaki, Japan

2 Faculty of Agriculture, University of Miyazaki, Miyazaki, Japan

3 Polytechnic Agriculture Negeri Pangkep, Segeri Mandalle, Indonesia

4 Department of Animal Sciences, Hasanuddin University, Makassar, Sulawesi Selatan, Indonesia

5 Nagasaki Agricultural and Forestry Technical Development Center, Shimabara, Nagasaki, Japan

Received – January 08, 2016; Revision – January 27, 2016; Accepted – February 15, 2016

Available Online – February 20, 2016

DOI: http://dx.doi.org/10.18006/2016.4(1).74.84

RESIDUAL EFFECTS OF COMPACTED DIGESTED EFFLUENT ON GROWTH OF

DWARF NAPIER GRASS IN WARM REGIONS OF JAPAN

E-mail: [email protected] (Yasuyuki Ishii)

Peer review under responsibility of Journal of Experimental Biology and

Agricultural Sciences.

* Corresponding author

Journal of Experimental Biology and Agricultural Sciences, February - 2016; Volume – 4(1)

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ISSN No. 2320 – 8694

Production and Hosting by Horizon Publisher

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All rights reserved.

All the article published by Journal of Experimental

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International License Based on a work at www.jebas.org.

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1 Introduction

In integrated dairy production, large quantities of high quality

herbage are required to reduce the costs of nutrient supply for

herbivore production. Livestock wastes should be treated

promptly to produce fermented manure (Annicchiarico et al.,

2011). Adequate plant nutrient supply from organic fertilizers

is essential to increase the potential of herbage production in

eco-friendly livestock farming (Barnes et al., 1995; Whitehead,

2000). Plant growth is determined by defoliation intensity,

fertilizer input and mineral nutrient loss in the cropping

systems (McFarland et al., 1998). Biogas plants produces

methane gas to generate electricity from livestock manure and

end product is digested effluent of manure (DEM), which is

assessed as the promising organic fertilizer (Cornes, 2006;

Nest et al., 2015). High rate of herbage production is essential

by supplying high rate of fertilizer input, which can reduce the

cost of application using DEM, replacing chemical fertilizer

application (Hasyim et al., 2014).

Napier grass (Pennisetum purpureum Schumach) has a wide

genetic variation from normal to dwarf genotypes in the tropics

and subtropics (Barnes et al., 1995; Rengsirikul et al., 2011) as

well as in the warm regions of Japan (Wadi et al., 2003;

Khairani et al., 2013). Dwarf varieties of late-heading type

(dwarf) Napier grass was introduced from Thiland into

southern Kyushu, Japan in 1996 (Ishii et al., 1998; Mukhtar et

al., 2003). Dwarf Napier grass tended to have higher tiller

density and leaf percentage than normal Napier grass (Ishii et

al., 1998), can survive in winter at the coastal southern Kyushu

(Utamy et al., 2011) and is suitable to grazing of beef cows

(Ishii et al., 2005).

In previous study by same authors (Hasyim et al., 2014),

growth of dwarf Napier grass was positively associated with

DEM application via leaf area development without disturbing

the chemical environments of soils neighboring the examined

areas. However, the residual effect of DEM application on

plant growth in the field the following year in the absence of

fertilization has not been determined. Liquid DEM requires

transport from biogas plants to forage fields; however, due to

its low concentration of nutrients, compaction of DEM

amended with cattle manure and dried under plastic house

should facilitate the supply of DEM to the field.

The objective of this study was to determine the aftereffects of

DEM at three levels of field application in the previous two

years and the effect of compacted DEM amended with solid

manure in a pot trial on plant production in dwarf Napier grass

in southern Kyushu, Japan.

2 Materials and Methods

2.1 Aftereffects of DEM application to fields in the previous

two years

2.1.1 Plant culture

The experiment was conducted on Andosols at 31 m above sea

level in Kibana Agricultural Research Station, University of

Miyazaki in southern Kyushu, Japan (131.41°E, 31.83°N).

Previous two cropping years, field was fertilized by different

fertilizing treatments (2007 to 2008). Lime (200 g m-2

) and

fermented cattle manure (600 g m-2

) were basally dressed on 8

May, 2007. Dwarf Napier grass was grown by transplanting

individual rooted tillers with 2 plants m-2

(0.5 m × 1.0 m of

spacing) on 10 May, 2007, and Italian ryegrass (cv. Ace, Snow

Brand Seed Co. Ltd. Sapporo, Japan) was sown into the inter-

row spaces as an intercrop after harvesting dwarf Napier grass

in autumn on 30 October, 2007 and 27 October, 2008. The

plots (13.5 m2/plot) were set into a randomized blocked design

with 3 replications and were divided into 4 treatments, which

had 3 levels of DEM application at 5.04, 2.52, and 1.26 g Nm-2

at each application, considered high (H), medium (M), and low

(L) levels, respectively, and chemical compound fertilizer (C)

application at the same rate of N (5.04 g N m-2

time-1

) as H

level with additional 4 times and 3 times of split application

per season of dwarf Napier grass and Italian ryegrass,

respectively. In the season of 2009, no fertilizer application

was carried out to examine the after (residual)-effect of the

previous two years’ DEM application on plant growth of dwarf

Napier grass under the twice-cutting practice per season.

2.1.2 Plant measurement

Dwarf Napier grass plants were sampled 2 times at the harvest

on 18 July and 4 November, 2009, and divided into various

plant fractions such as leaf blade (LB), stem inclusive of leaf

sheath (ST), and dead part (D) to determine dry matter weight

(DMW). Some plant growth attributes, such as plant height and

tiller number were also investigated for randomly selected 3

plants (1 plant from each replication) at each fertilization level

and mean tiller weight (MTW) was calculated by plant DMW

divided by plant tiller number.

2.2 Compaction effect of DEM amended with solid manure in

the pot trial

2.2.1 Plot design and plant culture

Dwarf variety of late-heading type Napier grasses were

transplanted at one shoot per pot to 1/2000 a Wagner pot, 25-

cm of diameter and 30-cm of depth, filled with Andosols on 15

May, 2009 and grown outdoors for 4-5 months in an

experimental field of the Faculty of Agriculture, University of

Miyazaki. The plots were arranged by a completely

randomized block design with 3 replications, where cattle

manure enriched with digested effluent of manure (solid DEM)

supplied at transplanting with 3 levels, 5.04, 10.08, 20.16 g N

m-2

yr-1

for low (L), medium (M) and high (H) level,

respectively, amended with liquid DEM (DEM) and chemical

compound fertilizer (C), split-supplied 4 times at 5.04 g N m-2

application-1

(the same N supply as the H level, 20.16 g N m-2

yr-1

of solid DEM).

75 Hasyim et al

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Table 1 Mineral and water concentration of solid and liquid digested effluent of manure (DEM)

Solid DEM Liquid DEM

Composition Solid DEM (mg/g FM)) Characteristics Liquid DEM

Water 79 pH 8.3

TN 20.4 EC (mS/cm) 17.7

TC 393.7 Composition (mg/L) -

Na 5.0 NH4+ 3296

Mg 5.6 K+ 1582

P 10.1 Mg2+

30

K 31.9 Ca2+

234

Ca 21.1 Na+ 299

Fe 1.1 PO4- 75

Mn 0.2 SO4- 79

Zn 0.2 NO3- 38

The mineral composition and water content of solid and liquid

DEM are listed in Table 1. The spacing of pots was 50 cm ×

100 cm and whole plots were surrounded by bordering plants

grown in soil. Watering was done every day with a plastic vase

set at the outlet of each Wagner pot to protect from runoff of

nutrients. Plants were defoliated at 25 cm above the soil

surface twice, on 27 August and 27 October, 2009.

2.2.2 Plant measurements

Changes in growth attributes, such as plant height and length,

and tiller number, were measured every week, and the dry

matter weight (DMW) of each plant fraction, LB, ST and D of

herbage and stubble parts, underground stem part (UG) and

root (R), and leaf area (LA) were determined at defoliation and

the leaf area index (LAI) was calculated. After harvest, pots

were rearranged to fill the empty spaces left by defoliation.

Plant organs from harvested plants were separated and dried at

70°C to determine DMW. Plant LA was measured with an

AAM-8 automatic area meter (Hayashi Denkoh Co. Ltd,

Tokyo, Japan). Wintering ability of this species was

determined by the percentage of overwintering plants that had

one or more regrown tillers from stubble, and the tiller number

and plant height of regrown plants the following spring on 3

June, 2010.

Figure 1 Changes in the mean air temperature (○), minimum air temperature (●), total solar radiation (SR, △) and precipitation (PRE, □)

in the growing season in 2009 (Data from Japan Meteorological Agency, 2015).

0

5

10

15

20

25

30

0

100

200

300

400

500

600

700

800T

emp

(℃

), S

R (

MJ

/m2/d

ay)

PR

E (

mm

)

2009

J

Month

F M A M J J A S O N D

Residual effects of compacted digested effluent on growth of dwarf napier grass in warm regions of Japan 76

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Figure 2 Changes in plant height (A), tiller density (B), mean tiller weight (C) and plant dry matter weight (DMW, D) in dwarf Napier

grass following the previous two-years’ fertilizing treatments in 2009. Solid DEM manure: L level (5.04 gN/m2/year), M level (10.08

gN/m2/year), H level (20.16 gN/m

2/year), chemical compound fertilizer: C level (20.16 gN/m

2/year) and liquid DEM level (20.16

gN/m2/year). ns: P > 0.05.

2.3 Statistical analysis

Analysis of variance was carried out using SPSS software

(version 15.0) by one-way analysis procedures for growth and

yield attributes of dwarf Napier grass in a randomized

complete design. Mean separation was tested using the least

significance difference method at the probability of 5%.

3 Results

3.1 Climatic conditions

Climatic conditions in 2009, based on data from Miyazaki

Meteorological Observatory, were not very different from

those in a normal year, except for a higher mean temperature

from late August to late October and higher precipitation from

late July to early August and from mid- to late September

(Figure 1). The maximum daily mean air temperature and solar

radiation values occurred in late August. Since air temperature

and solar radiation decreased from late September until the end

of the year, this decrease might have severely suppressed plant

growth.

3.2 Seasonal changes in growth attributes and DMW

Seasonal changes in plant growth attributes, such as plant

height, tiller density, mean tiller weight and plant DMW were

determined two times at the time of cutting i.e. in mid-July and

early November of 2009 (Figure 2). Every growth attribute

tended to increase with the increase in the previous two years’

DEM application rate at both the first and second cuttings, but

did not differ among the treatments. Therefore, no application

rate-dependent plant growth in dwarf Napier grass was

observed in the following year of 2009.

3.3 Seasonal changes in growth attributes and DMW

Seasonal changes in plant growth characters, such as plant

height, plant length and tiller number, were monitored every

week for all fertilizer levels during the growing season in 2009

(Figure 3). For all levels of fertilization, plant height and plant

length increased linearly with time. The increase in plant

height and plant length over time was faster in late June to

mid-July, which may have been due to the higher mean air

temperature in this period. The differences in plant height and

plant length among levels of fertilization were small and not

significant from May to June, 2009 but expanded from July to

October, 2009. Tiller density reached the maximum in early

July and turned to decrease thereafter at all levels of

fertilization.

0

20

40

60

80

100

120

140

L M H C L M H C

I (18 July) II (4 Nov.)

Pla

nt

hei

ght

(cm

)

Cutting time and treatment

(A)ns

0

20

40

60

L M H C L M H C

I (18 July) II (4 Nov.)

Till

er d

ensi

ty(N

o./

m2)

Cutting time and treatment

(B)ns

ns

0

2

4

6

8

10

L M H C L M H C

I (18 July) II (4 Nov.)

Mea

n t

iller

w

eight

(g/t

iller

)

Cutting time and treatment

(C)

ns

ns

0

100

200

300

400

500

L M H C L M H C

I (18 July) II (4 Nov.)

DM

W (

g/m

2)

Cutting time and treatment

(D)

ns

ns

ns

77 Hasyim et al

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Figure 3 Changes in plant height (A), plant length (B) and tiller number (C) in dwarf Napier grass under different fertilizing treatments in

2009. Low (▲), middle (■) and high (●) rate of solid DEM application, chemical compound fertilizer (○) and liquid DEM (◇)

application. Symbols with different letters denote significant difference between treatments on the same date at the 5 % level. ns: P >

0.05.

0

20

40

60

80

100

120

140P

lant

hei

ght

(cm

)

MonthMay June July Aug. Sep. Oct.

(A)

nsns

nsns

a

ab

ab

ab

b

c

c

abc

a

a

a

a

b

c

bc

b

a

a

d

c

a

a

b

d

cd

c

b

a

a

cb

b

a

a

d

c

baa

d

bc

b

aa

d

c

baa

dc

baa

b

ns

b

ab

aab

ab

ab

ab

ab

a a

ab

a

b

a

c

b

ababa

aab

c

b

b

b

a

aaa

b

a

aaa

0

20

40

60

80

100

120

140

Pla

nt

length

(cm

)

MonthMay June July Aug. Sep. Oct.

(B)

ns ns ns

ab

abab

cb

aa

bcab

bcbb

baa

aa

c

b

d d

c

baa

d

c

baa

bd

bc

baa

dbc

b

aa

d

c

b

aa

d

bc

baa

d

cb

aa

dcb

aa

a

ab

ab

ab

b

b

a

ab

abab

a

b

ab

aa

b

ab

aaa

a

aa

a

bc

b

a

c

b

ab

aba

abab

a

aa

a

0

50

100

150

200

250

Til

ler

den

sity

(N

o.

m-2

)

MonthMay June Aug.July Sep. Oct.

(C)

ns

aa

babc

c

ab

aa

abb

a

bc

ab

bcc

aa

b

bcc

aa

b

cc

aa

b

c

c

aab

b

c

c

ab

b

a

c

c

ab

b

a

c

c

b

b

a

a

a

b

b

a

a

a

c

c

b

ab

a a

ab

b

d

c

a

c

b

e

d

c

c

b

b

a

b

d

c

b

a

d

c

b

b

aa

d

c

b

b

d

c

a

b

a

d

c

b

b

a

d

c

b

b

a

d

c

b

b

a

Residual effects of compacted digested effluent on growth of dwarf napier grass in warm regions of Japan 78

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Table 2 Percentage of overwintered plants (POP), tiller density and plant height of regrown plants in dwarf Napier grass as affected by

the previous years’ fertilizing treatments on 3 June, 2010

Treatment POP (%) Tiller density (No./m2) Plant height (cm)

L 100 27.0c ± 4.9✝ 47.7

c ± 3.0

M 100 43.7b ± 4.8 57.2

b ± 4.4

H 100 67.3a ± 7.1 62.7

ab ± 8.8

C 100 67.7a ± 4.3 57.5

b ± 5.5

DEM 100 69.3a ± 9.1 64.7

a ± 4.8

✝: Mean ± standard deviation (n = 6). As for treatments, refer to Figure 2. Figures with different letters denote significant difference

between treatments on the same attribute at the 5 % level.

The tiller density increased with increased level of N

fertilization, and tended to be largest in the C level from July to

August in the first-cut plants and in the DEM level in the

second-cut plants, although the difference in tiller density

between fertilization with the same N level (H, C and DEM)

was not significant (P > 0.05) in the first cutting.

Changes in MTW of the herbage part and LAI with cutting

time were compared among the 5 treatments. At both cutting

times, MTW of the herbage increased with increased N

fertilization level, and the rate of increase of MTW tended to

be larger at the first cutting than the second cutting. The

differences in MTW at the same N level (H, C and DEM) were

small and not significant at either cutting time (Figure 4A).

The LAI increased significantly with increase in N application

level in the first-cut plants, and differences in LAI at the same

N level (H, C and DEM) were not significant in the second-cut

plants (Figure 4B).

Seasonal changes in annual total dry matter yield (TDMY)

were compared among the 5 levels of fertilization at both

cutting times. The TDMY increased with increased N

application level at both cutting times, while the difference in

TDMY between the C and DEM levels was smaller than

differences compared to the other 3 levels at both cutting times

(Figure 5A).

The ratio of top to underground weight (T/(R+UG)) tended to

increase with increased N application level at both cutting

times, reaching about 2, while there was no significant

difference in T/(R+UG) among the levels examined, except for

the lowest ratio in the L level (Figure 5B).

The relationship of CGR to LAI was positive and linear among

the 5 fertilizer levels in both cutting periods, and the regression

coefficient was higher for second-cut plants than first-cut

plants (Figure 6A). The relationship of LAI to NAR was

negative and linear among the 5 fertilizer levels in the first

cutting, while the regression of LAI on NAR was not

significant (P > 0.10) in the second cutting (Figure 6B).

Therefore, annual TDMY in dwarf Napier grass was positively

and linearly related with the annual total N input among the 5

fertilizer levels (r = 0.923, P < 0.05), as shown in Figure 7.

Figure 4 Changes in mean tiller weight (A) and leaf area index (LAI, B) in dwarf Napier grass under different fertilizing treatments in

2009. Treatment: low (L), middle (M) and high (H) rate of solid DEM application, chemical compound fertilizer (C) and liquid DEM

(DEM) application. Symbols with different letters denote significant difference between treatments on the same date at the 5 % level.

0

2

4

6

8

10

12

L M H C DEM L M H C DEM

I (27 Aug.) II (27 Oct.)Mea

n t

ille

r w

eigh

t (g

tille

r-1)

Cutting time and treatment

(A)

b

a

c

b

a

a

c bcab

b

0

2

4

6

8

10

L M H C DEM L M H C DEM

I (27 Aug.) II (27 Oct.)

LA

I (m

2m

-2)

Cutting time and treatment

(B)

ab

d

c

b

aaa

a

b

a

0

2

4

6

8

10

12

L M H C DEM L M H C DEM

I (27 Aug.) II (27 Oct.)Mean

tille

r w

eig

ht

(g t

ille

r-1)

Cutting time and treatment

(A)

b

a

c

b

a

a

c bcab

b

0

2

4

6

8

10

L M H C DEM L M H C DEM

I (27 Aug.) II (27 Oct.)

LA

I (m

2m

-2)

Cutting time and treatment

(B)

ab

d

c

b

aaa

a

b

a

79 Hasyim et al

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Figure 5 Changes in total dry matter yield (TDMY; A), and top to root ratio (T/(R+UG); B) in dwarf Napier grass (DL) under different

fertilizing treatments in 2009. Treatment: low (L), middle (M) and high (H) rate of solid DEM application, chemical compound fertilizer

(C) and liquid DEM (DEM) application. Symbols with different letters denote significant difference between treatments on the same date

at the 5 % level.

Overwintering ability, determined in the following spring on 3

June, 2010, is shown in Table 2. The number of overwintering

plants was 100% in all treatments; however, tiller density and

plant height of overwintered plants increased with increased

solid DEM application, becoming nonsignificantly different at

the H level of solid DEM from the C and liquid DEM levels.

4 Discussions

4.1 Aftereffects of liquid DEM and solid manure application

on plant growth in the following year

Plant growth in the following year under no additional

fertilization was evaluated by annual TDMY following liquid

DEM application, as shown in Table 3A, and results were

compared with the aftereffects of manure application on plant

growth in the following year under no fertilization for normal

Napier grass (cv. Merkeron) reported in Sunusi et al. (2006), as

shown in Table 3B. The ratio of annual TDMY in the

following year remained stable in the range 0.7-0.8 for dwarf

Napier grass for the tested liquid DEM application levels,

while it increased from 0.2 to 1.0 for normal Napier grass

depending on manure application level. Even though the total

N application level differed significantly between liquid DEM

and manure application, the residual effects of DEM on plant

growth in the following year appear to be limited since no

positive effect was observed with a higher rate of liquid DEM

application.

4.2 Characteristics of solid and liquid DEM compared with

manure and chemical fertilizer application

The present study attempted to solve the major difficulty in

transporting liquid DEM to forage fields by using solid DEM,

which was processed by amending liquid DEM with cattle

manure without disturbing the fermentation process for the

manure. As shown in Table 1, solid DEM concentrated the

mineral nutrients compared with liquid DEM and facilitated

supplying this organic fertilizer to forage crops by compaction.

The response of plant growth to the application rate of solid

DEM was almost linear, which means that the increase in

application rate of solid DEM led to a positive linear increase

in the growth of dwarf Napier grass in terms of plant height,

tiller number, LAI and TDMY, especially at the second

cutting, as shown in Figures 3-5.

0

500

1000

1500

2000

2500

3000

3500

L M H C DEM L M H C DEM L M H C DEM

27 Aug. 27 Oct. Annual total

TD

MY

(g

m-2

)

Date and treatment

H S UG R(A)

bacde

aababbcc

aab

bccdd

a

abab

bc

aab abc b

aabc bc

abab

dc

ab

a

d bc

c b b a ac

b

a a

bcd

c

b

aa

d

c

b

a a

0

1

2

3

L M H C DEM L M H C DEM L M H C DEM

27 Aug. 27 Oct. Annual total

T/(R

+UG

)

Date and treatment

(B)

a aa

a

ba a a

a

b b

aaaa

0

500

1000

1500

2000

2500

3000

3500

L M H C DEM L M H C DEM L M H C DEM

27 Aug. 27 Oct. Annual total

TD

MY

(g

m-2

)

Date and treatment

H S UG R(A)

bacde

aababbcc

aab

bccdd

a

abab

bc

aab abc b

aabc bc

abab

dc

ab

a

d bc

c b b a ac

b

a a

bcd

c

b

aa

d

c

b

a a

0

1

2

3

L M H C DEM L M H C DEM L M H C DEM

27 Aug. 27 Oct. Annual total

T/(R

+UG

)

Date and treatment

(B)

a aa

a

ba a a

a

b b

aaaa

Residual effects of compacted digested effluent on growth of dwarf napier grass in warm regions of Japan 80

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Figure 6 Relationships of crop growth rate (CGR; A) and net assimilation rate (NAR; B) with leaf area index (LAI) in dwarf Napier

grass in 2009. I and II denote the first and second cutting periods, respectively. Low (▲), middle (■) and high (●) rate of solid DEM

application, chemical compound fertilizer (○) and liquid DEM (◇) application.

However, if plant growth was compared at the same N level

(the H level) with chemical (C) and liquid DEM application,

LAI and TDMY tended to be higher in the C and liquid DEM

levels than in the H level of solid DEM, as shown in Figures

4B and 5A. The N recovery rate for conventional solid manure

is around 50% in a sole maize cropping season (Idota et al.,

2013), whereas liquid DEM contains mostly inorganic forms

of N (NH4+ and NO3

-) as well as chemical fertilizer, which

should act as a fast effective fertilizer supporting the growth of

plants and leads to a higher N recovery rate than manure

application (Idota et al., 2013). Based on the annual TDMY in

dwarf Napier grass, the H level of solid DEM application

showed 73% and 69% of the yielding ability relative to liquid

DEM and C, respectively. Thus, an increase in application rate

of solid DEM from the H level may be necessary to achieve

the same dry matter yield (DMY) with liquid DEM and C

application, and estimates of the cumulative or residual effect

of solid and liquid DEM application on subsequent plant

growth in the year following application is needed, as in the

case of manure application (Sunusi et al., 2006).

4.3 Response of growth and partitioning in dwarf Napier

grasses to different levels of solid DEM application

An increase in solid DEM application rate led to a positive

increase in plant growth attributes, in terms of plant height,

tiller number, LAI and TDMY in dwarf Napier grass. In

addition, a diminishing return of DMY in response to solid

DEM application occurred from the M to H level compared

with the L to M level (Figure 5). This indicates that dry matter

productivity is most responsive to nitrogen supply up to 200 kg

N ha-1

year-1

under these experimental conditions. Several

experiments have evaluated the effect of high amounts of

manure on forage crops on farms (Kagata et al., 1999; Sunusi

et al., 1999; Idota et al., 2005; Hasyim et al., 2007).

Figure 7 Relationship between annual total dry matter yield (TDMY) of dwarf Napier grass and annual total N input among different

ferilizing treatments in 2009. Low (▲), middle (■) and high (●) rate of solid DEM application, chemical compound fertilizer (○) and

liquid DEM (◇) application

I : y = -0.0972x + 2.64r = -0.908 (P < 0.05)

II : y = -0.525x + 8.96r = -0.413 (P > 0.10)

0

2

4

6

8

10

0 2 4 6 8

NA

R (

g m

-2d

ay-1

)

LAI (m2 m-2)

I

II

(B)

I : y = 1.87x + 1.03r = 0.999 (P < 0.01)

II : y = 6.08x + 3.33r = 0.872 (P < 0.10)

0

5

10

15

20

25

30

0 2 4 6 8

CG

R (g m

-2d

ay-1

)

LAI (m2 m-2)

I

II

(A)

10

10

I : y = -0.0972x + 2.64r = -0.908 (P < 0.05)

II : y = -0.525x + 8.96r = -0.413 (P > 0.10)

0

2

4

6

8

10

0 2 4 6 8

NA

R (

g m

-2d

ay-1

)

LAI (m2 m-2)

I

II

(B)

I : y = 1.87x + 1.03r = 0.999 (P < 0.01)

II : y = 6.08x + 3.33r = 0.872 (P < 0.10)

0

5

10

15

20

25

30

0 2 4 6 8

CG

R (g m

-2d

ay-1

)

LAI (m2 m-2)

I

II

(A)

10

10

y = 128.0x + 253.8r = 0.923 (P < 0.05)

0

500

1000

1500

2000

2500

3000

3500

0 5 10 15 20 25

TD

MY

(g m

-2)

N input (g m-2 yr-1)

81 Hasyim et al

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Table 3 Ratio of dry matter yield (DMY) under no fertilization in the following year (2009) to that for the previous liquid DEM

application under 4 application levels in 2008 (A), compared with the ratio to the previous manure fertilizing year under 3 application

levels in 1995 (B).

(3A) Aftereffect of liquid DEM application (3B) Aftereffect of solid manure application✝✝

Treatment DMY in

2008

(g/m2)✝

Total N

application in

2008 (g/m2)✝

Ratio of

DMY2009 to

DMY2008

Treatment DMY in

1994

(g/m2)

Total N

application in

1994 (g/m2)

Ratio of

DMY1995 to

DMY1994

L 625 5.0 0.79 Low 605 0.0 0.22

M 794 10.1 0.65 Middle 1884 55.1 0.47

H 939 20.2 0.67 High 2653 110.5 0.97

C 899 20.2 0.66 ✝: Data from Hasyim et al. (2014). ✝✝: Data from Sunusi et al. (2006).

In Napier grass and related species, under field conditions, the

critical level of nitrogen application was determined to be 600

kg ha-1

year-1

in Miyazaki for normal Napier grass (cv. Wruk

wona) and king grass (Wadi et al., 2003) and 564 kg ha-1

year-1

for king grass and Napier grass (cv. Hawaii and cv. Africa)

grown in Indonesia (Siregar, 1989). Thus, in subtropical and

tropical conditions, the ceiling nitrogen input that effectively

increased DMY in Napier grass and related species is between

600 and 800 kg ha-1

year-1

. The critical level of nitrogen input

could vary depending on environmental stress conditions such

as low temperature, low solar radiation, drought or

waterlogging of soil, limiting dry matter production (Ahmad &

Butt, 1985). In the present pot experiments, these factors could

shift the critical level of nitrogen input to a lower level.

In terms of dry matter partitioning, dwarf Napier grass tended

to transport photosynthetic assimilates to the root and

underground stem more favorably than to the aboveground

parts compared with normal Napier grass. This suggests that

the lower herbage yield of the dwarf variety compared to the

normal one could be due to a difference in top to root ratio. In

contrast, in normal Napier grass, the top to root ratio can

change to above 10 under field conditions, which may be one

of the major reasons for high dry matter productivity of normal

Napier grass (Ito & Inanaga, 1988).

4.4 Growth parameters and their relationship to DMY as

affected by manure application level

Both LAI and CGR increased with increased solid DEM

application rate, and the increase in CGR was positively

correlated with the increase in LAI. The higher regression

coefficient of CGR with LAI at the second cutting than at the

first cutting may be due to the higher NAR at the same LAI.

Therefore, the high solid DEM application rate was presumed

to be one of the primary factors that widened the variation in

the regression between LAI and CGR. However, the increase

in LAI was concurrent with the decrease in NAR, which led to

a diminished return of DMY in both types of Napier grass.

According to Ito & Inanaga (1988), the increase in CGR of

Napier grass cv. Merkeron before the end of September in

Miyazaki was roughly proportional to the increase in LAI

when the LAI was less than 12. Therefore, the level of

fertilization affected CGR through a primary effect on LAI in

dwarf Napier grass.

Conclusions

Two types of DEM processed by a biogas plant, solid and

liquid DEM, could be used as effective organic fertilizers

capable of producing DMY of 22 and 30 t ha-1

year-1

,

respectively, showing yielding ability comparable with

chemical fertilizer at 32 t ha-1

year-1

. Residual effects of liquid

DEM application were limited, and application of solid DEM

can concentrate the mineral nutrients in liquid DEM. A

mixture of DEM amended with solid manure should facilitate

supplying DEM to forage crops due to the benefits of

compaction.

Conflict of interest

Authors would hereby like to declare that there is no conflict of

interests that could possibly arise.

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KEYWORDS

Mycorrhiza fungi

Grasses weed

Mustard plant

Ultisols

ABSTRACT

Present study was aim to determined the impact of mycorrhiza fungi isolated from grasses weed on the

growth and yield of mustard in Ultisols. Study was conducted in net house located in Sindang Kasih

Village, Dictrict West Ranomeeto; Regency South Konawe Province of Southeast Sulawesi in the

month of June to September 2014. Mycorrhiza fungi infection observated on plants root done in the

Laboratory of the Faculty of Forestry and Environmental Science, Halu Oleo University, Kendari,

Indonesia. Study was conducted in completely randomized block design (CRBD) with five treatments,

each treatment was replicated with 5 replications. The variables observed for results were characteristic

of mycorrhiza fungi, plant height, number of leaves, leaf area, fresh plant weight, dry plants weight,

shoot root ratio, percentage of mycorrhiza fungi infection to plant roots. Results of study revealed that

the treatments contains mycorrhiza fungi propagules @ 100 g per polybag show superiority over all the

tested treatments in improving plant growth characteristics and yield of mustard plant.

Halim1,*

, Resman2 and Sarawa

3

1Specifications Weed Science, Department of Agrotechnology, Faculty of Agriculture, Halu Oleo University, Southeast Sulawesi, Indonesia,

2Specifications Soil Science, Department of Agrotechnology, Faculty of Agriculture, Halu Oleo University, Southeast Sulawesi, Indonesia

3Specifications Agronomy, Department of Agrotechnology, Faculty of Agriculture, Halu Oleo University, Southeast Sulawesi, Indonesia

Received – October 01, 2015; Revision – November 02, 2015; Accepted – February 20, 2016

Available Online – February 20, 2016

DOI: http://dx.doi.org/10.18006/2016.4(1).85.91

CHARACTERIZATION AND IMPACT OF MYCORRHIZA FUNGI ISOLATED

FROM WEED PLANTS ON THE GROWTH AND YIELD OF MUSTARD PLANT

(Brassica juncea L.)

E-mail: [email protected] (Halim)

Peer review under responsibility of Journal of Experimental Biology and

Agricultural Sciences.

* Corresponding author

Journal of Experimental Biology and Agricultural Sciences, February - 2016; Volume – 4(1)

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ISSN No. 2320 – 8694

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1 Introduction

Mustard (Brassica juncea L.) crop is one of the most important

vegetable crops which play an important role in the improving

living standards of farmers. Haryanto (2006) reported the

nutritive value of this crop and suggested that each 100g of

green mustard leaf lettuce contains 2.3g protein, 4.0g

carbohydrates and 0.3g of fat. Furthermore, it is also a good of

sources of vitamins and 100g of mustard leaves had 1.94mg of

vitamin A, 102mg of vitamin C and 0.09mg of vitamin B.

Furthermore, Margianto (2008) suggested that these nutrient

and vitamins are essential for the human body. Annual

production of this crop from Indonesia is still below its

maximum potential. Various factors such as type of soil,

farmer practices and low fertility are responsible for the low

production of this crop in Indonesia. Statistical data of

agricultural department suggested that average production of

mustard plants in Ultisol in Southeast Sulawesi only 3.74 tons

ha-1

(BPS, 2010).

The dominant soil types in Southeast Sulawesi is Ultisols

which is characterized by pH 5.77, 1.92% organic carbon,

0.17% Nitrogen, 12.75 ppm Phosphorus and 0.22 me100g-1

Potassium (Halim & Rembon, 2013; Halim et al., 2015).

Various efforts based on principles of conservation and

ecofriendly natural sources to improve soil fertility of this

Ultisols were carried out. Use of mycorrhiza for improving soil

fertility is a common practice for many crops. Halim (2013)

reported that some kinds of grasses weeds were naturally

infected by mycorrhiza fungi. Mycorrhiza isolated from these

types of weed grasses had positive impact on the soil fertility.

Similar types of results was also reported from the another

research conducted by same authors (Halim et al., 2014). They

also reported that roots of broad-leaved weeds, grasses weed

and sedges weed are infected by mycorrhiza fungi.

2 Materials and Methods

2.1 Study area and Experimental setup

Present study was conducted from June to September 2014 in

net house, village Sindang Kasih, District West Ranomeeto,

South Konawe Regency, Southeast Sulawesi Province and

Laboratory of the Faculty of Forestry and Environmental

Science, Halu Oleo University Kendari, Indonesia. Plant were

grown in polybag (40 cm x 50cm) and study was conducted in

completely randomized block design (RCD) with five

treatments i.e. without mycorrhiza fungi propagules (M0),

mycorrhiza fungi propagules@ 25 g per polybag (M1),

mycorrhiza fungi propagules@ 50 g per polybag (M2),

mycorrhiza fungi propagules @ 75 g per polybag (M3) and

mycorrhiza fungi propagules@ 100 g per polybag (M4), each

treatment was replicated with 5 replications.

2.2 Preparation of planting media

The soil has been taken from the study area field, it cleared

from debris such as twigs, roots, leaves and small rocks.

Cleared soil sifted into a polybag with a weight of 10kg soil

and recommended dose basic NPK fertilizer along with

organic manure was added to each polybag. The mustard seed

sown for 7 days in media seedbed mixture of rice husk, soil,

sand, with the volume ratio 1: 0.5:1 was transferred to the

polybags. Mycorrhiza fungi isolated from the roots of Imperata

cylindrica that had previously been propagated on maize

(Halim, 2012) were transferred to the each polybags.

2.3 Observation of Variables

Isolated mycorrhiza fungi were characterized with the standard

identification key for mycorrhiza. Various growth parameters

such as plant height, number of leaves and leaf area were

measured on the intervals of each seven days which start from

the 7th days after planting and continue upto 28 days after

planting (DAP). The total leaf area was measured by using the

formula proposed by Sitompul & Guritno (1995):

L= p x l x k x j

Note: L= leaf area, P = length of leaf, l = plant fresh weight (g

plant-1

), k = the coefficient of leaf area (0.78), j = number of

leaves.

The fresh weight of the plants (g plant-1

) was measured after

harvest while the dry weight of the plant (g plant-1

) was

measured after harvesting drying plant in oven at a temperature

of 80 0C for 48 hours. Further, Ratio leaf area was measured

(cm2 g

-1) by the using formula proposed by Sumarsono (2010).

Ratio leaf area = L/ W

Whereas L = leaf area (cm2), W = dry weight the plant (g)

Shoot root ratio of the mustard plant was measured by using

the formula proposed by Gardner et al. (1991).

Shoot root ratio = shoot dry weight/ root dry weight

2.4 Percentage of the mycorrhiza fungi infection

The Observations were carried out using a dissecting

microscope at a magnification of 40X. Furthermore,

mycorrhiza fungi infection was calculated by using the formula

proposed by Brian & Schults (1980).

Where IP= the percentage of mycorrhiza fungi infection; r1=

the number of root infected examples and r2= the number of

root not infected examples

86 Halim et al

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Table 1 Effect of mycorrhiza fungi on the average height of mustard (cm) at the age 7-14, 14-21 and 21-28 DAP.

Treatment Average plant height

7-14DAP 14-21DAP 21-28DAP

without mycorrhiza fungi (M0) 3.17d 6.20

c 8.26

d

25 g mycorrhiza fungi (M1) 4.20d 8.70

b 10.35

c

50 g mycorrhiza fungi (M2) 5.58c 9.45

b 11.47

bc

75 g mycorrhiza fungi (M3) 7.06b 10.90

ab 12.40

ab

100 g mycorrhiza fungi (M4) 8.89a 12.60

a 13.47

a

SEM value 1.04 1.19 2.78

DRMT 0.05%

2 1.34 2.19 1.60

3 1.41 2.30 1.68

4 1.45 2.37 1.74

5 1.48 2.42 1.77

Here, DAP = day after planting, SEM = standard error mean, the numbers followed by the same superscript letters in the same column

are not significantly differ on DRMT 0.05%

2.5 Data Analysis

Data of each variable were observed were analyzed by

variance of analysis. If the F count is greater than the F table,

then continued with Duncan Range Multiple Test (DRMT) at

0.05% confidence level.

3 Result

3.1 Characterization of Mycorrhiza Fungi

Two species of mycorrhiza fungi viz Gigaspora sp. and

Glomus sp. were isolated from the selected weed species.

(Halim, 2009), among these Gigaspora sp. was identified by

the presence of a single brown color terminal spore. These

spores have globular or spherical shape with more than one

layer. A complementary tool in the form of bulbous suspensor

was also reported for this species. While the Glomus sp. are

characterized by the presence of single or bunch of ripe hyaline

white or brownish yellow. Spores are located on the terminal

gametangium located on the undifferentiated hyphae in a

sporocarp (Halim, 2012). Mostly these spores are formed on

the external hyphae near the root zone.

3.2 Effect of mycorrhizal application on the growth attributes

3.2.1 Plant Height

The effect mycorrhizal fungi on average plant height are

represented in table 1; results of study revealed that application

of mycorrhizal fungi increase the height of mustard plants.

This improvement in the plant height is continued upto the 21

DAP; after this plants are not showing much improvement in

height and no significant difference was reported between 21

and 28 DAP. Highest dose of mycorrhiza (100g) shows

superiority over the other treatments and 7 DAP is was 8.89cm

which reached 13.4cm at 28DAP.

Gigaspora sp. Glomus sp.

Figure 1 Spore form of Gigaspora sp and Glomus sp.

Characterization and impact of mycorrhiza fungi isolated from weed plants on the growth and yield of mustard plant (Brassica juncea L.) 87

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Table 2 Effect of mycorrhizal fungi on the average number of mustard plant leaves at the age of 7- 28 DAP.

Treatment Average Leaves Number

7DAP 14DAP 21DAP 28DAP

without mycorrhiza fungi (M0) 3.00c 4.40

b 5.40

c 7.20

c

25 g mycorrhiza fungi (M1) 3.60bc

4.60b 5.80

bc 7.80

bc

50 g mycorrhiza fungi (M2) 3.40ab

4.60b 7.20

a 9.20

a

75 g mycorrhiza fungi (M3) 3.80ab

5.00ab

6.80ab

8.60ab

100 g mycorrhiza fungi (M4) 4.00a 5.60

a 7.20

a 9.20

a

SEM value 0.16 0.24 0.62 0.52

DRMT 0.05%

2 0.52 0.64 1.03 0.95

3 0.55 0.67 1.09 0.99

4 0.57 0.69 1.12 1.02

5 0.58 0.71 1.14 1.05

Here, DAP = day after planting, SEM = standard error mean, the numbers followed by the same superscript letters in the same column

are not significantly differ on DRMT 0.05%.

3.2.2 Number of Leaves

The effect mycorrhiza fungi to the average number of leaves of

mustard plants shown in Table 2. The trends of leaves numbers

are similar to plant height and it increased with the increasing

of day of planting and dose of mycorrhiza fungi. The highest

average number of leaves per mustard plant at age of 7 DAP

was 4.00 and it was reported in the treatment M4; this number

reached 9.20 per plant in the same treatment at the interval of

28DAP. At 28DAP, treatment M2 is also showing similar leaf

number and it is significantly not differ from the M4.

3.2.3 Total Leaf Area

Total leaf area was calculated by the method given by

Sitompul & Guritno (1995). The effect mycorrhiza fungi to the

total leaf area of mustard plants shown in Table 3. Total leaf

area also increased with the increasing the dose of mycorrhizal

application and days. Highest total leaf area was reported from

the treatment containing 100g mycorrhiza at 28DAP.

3.3 Fresh and dry weight of mustard Plant

The effect mycorrhiza fungi to the average fresh and dry

weight the plants are calculated on the harvesting of plants.

Average fresh and dry weights are represented in Table 4. The

value of fresh and dry weight increased with the increasing the

dose of mycorrhiza fungi application, lowest fresh and dry

weight was reported from the treatment without mycorrhiza

application (M0) while the highest plant was reported from the

highest dose of mycorrhiza fungi application (M4).

The treatment M4 is showing 37.35 and 51.58% higher fresh

and dry weight respectively as compared to the treatment

without mycorrhiza. Treatment M3 and M4 is not showing any

significant difference (DMRT = 0.05%).

Table 3 Effect of mycorrhiza fungi on the total leaf area of mustard plants at the age 7 - 28 DAP.

Treatment Total leaf area (cm)

7DAP 14 DAP 21 DAP 28 DAP

without mycorrhiza fungi (M0) 21.36c

77.75d 341.69

d 776.88

d

25 g mycorrhiza fungi (M1) 24.52c 128.11

c 462.99

cd 995.38

c

50 g mycorrhiza fungi (M2) 27.01bc

142.46c 582.07

bc 1266.39

b

75 g mycorrhiza fungi (M3) 32.72b 228.74

b 711.03

ab 1364.30

ab

100 g mycorrhiza fungi (M4) 42.86a 288.55

a 812.68

a 1532.68

a

SEM value 0.87 0.24 1.25 20.5

DMRT 0.05%

2 6.22 42.10 143.50 198.60

3 6.53 44.19 150.60 208.50

4 6.73 45.52 155.20 214.80

5 6.86 46.45 158.30 219.10

Here DAP = day after planting, SEM = standard error mean, the numbers followed by the same superscript letters in the same column are

not significantly differ on DRMT 0.05%.

88 Halim et al

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Table 4 Effect of the mycorrhiza application of the average fresh and dry weight (g plant -1

) of the mustard plant.

Treatment Fresh weight (g

plant -1

)

DMRT

(0.05%)

Dry weight (g

plant -1

)

DMRT 0.05%

without mycorrhiza fungi (M0) 71.39c 2 = 18.47 3.99

c 2 = 0.86

25 g mycorrhiza fungi (M1) 81.70bc

3 = 19.39 5.96b 3 = 0.90

50 g mycorrhiza fungi (M2) 96.32ab

4 = 19.97 6.41b 4 = 0.93

75 g mycorrhiza fungi (M3) 107.00a 5 = 20.38 7.62

a 5 = 0.95

100 g mycorrhiza fungi (M4) 113.95a 8.24

a

SEM value 5.39 0.43

Here DAP = day after planting, SEM = standard error mean, the numbers followed by the same superscript letters in the same column

are not significantly differ on DRMT 0.05%.

Table 5 Average shoot root ratio of the mustard plant under the influence of mycorrhiza application.

Treatment Shoot root ratio DRMT 0.05%

without mycorrhiza fungi (M0) 2.01c 2 = 0.64

25 g mycorrhiza fungi (M1) 2.58c 3 = 0.67

50 g mycorrhiza fungi (M2) 3.24b 4 = 0.69

75 g mycorrhiza fungi (M3) 3.61b 5 = 0.71

100 g mycorrhiza fungi (M4) 4.39a

SEM value 0.24

Here DAP = day after planting, SEM = standard error mean, the numbers followed by the same superscript letters in the same column are

not significantly differ on DRMT 0.05%.

3.4 Shoot Root Ratio

The effect mycorrhiza fungi to the average shoot root ratio of

the mustard plant shown in Table 5. The trends are similar to

the growth parameters and highest shoot root ratio was

obtained from the treatment containing 100g mycorrhiza

culture, and it was 52.21 percent higher than the polybag

without mycorrhiza. With the increasing dose of mycorrhiza,

shoot root ration also increased and a significant difference

was reported between all the tested doses.

3.5 Percentage of mycorrhiza colonization

The average percentage of mycorrhiza fungi colonization in

the mustard plant roots are listed in Table 10. Highest dose of

mycorrhiza (M4) shows the highest colonization (42%) and this

was followed by the treatment M3, M2 and M1 respectively.

Highest colonization provides higher nutrition to the mustard

plant and because of this plant shows superiority in all the

studied attributes.

Discussions

The results of research showed that the application of

mycorrhiza fungi significantly affected all the variables of

mustard plants. The possible reason of this was the availability

of sufficient nutrients which favor the plant growth. According

to the Simarmata & Herdiani (2004) biological fertilizers such

as mycorrhiza fungi can increase the availability of nutrients

for plants in marginal land of Indonesia, which in turn

increased crop production. Findings of present study are in the

agreement with the findings of these authors. Further,

Marschner & Dell (1994) stated that the infection of

mycorrhiza fungi change the growth and activity of plant roots

by the formation of mycelia on the external surface which

caused increase in the absorption of nutrients and water. These

higher nutrients increase the plant height, number of leaves and

plant weight. Similar type of growth with respect to plants

height, number of leaves and leaf area of plants was obtained

by Mayerni & Hervani (2008). These researchers reported that

mycorrhizal infection increases the metabolism of plant growth

which could mainly take place in vegetative phase.

Husin (1997) reported that by changing plant metabolic

activities, mycorrhiza fungi influence the production of growth

hormones such as auxin and gibberellins. Among these auxin

prevent the aging of plant roots, so in this condition roots can

function longer and absorption of nutrients will also higher.

While the giberelin performing the function of enlargement

and stimulate the cell division. The ability of mycorrhiza fungi

in absorption of phosphate is not only determined by the fungal

colonies in the roots and development in the soil, but also

determined by ability of external hyphae. In fact, the

percentage mycorrhiza fungi infections on plant roots are not

always comparable to effect on crop yields. Mycorrhiza fungi

infection and the effect decreases with increasing phosphate

available in the soil.

Characterization and impact of mycorrhiza fungi isolated from weed plants on the growth and yield of mustard plant (Brassica juncea L.) 89

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Table10 The average percentage of mycorrhiza fungi infection in the mustard plant roots.

Treatment Percentage mycorrhiza fungi colonization DRMT 0.05%

without mycorrhiza fungi (M0) 0 e 2 = 4.93

25 g mycorrhiza fungi (M1) 10 d 3 = 5.18

50 g mycorrhiza fungi (M2) 18 c 4 = 5.33

75 g mycorrhiza fungi (M3) 26 b 5 = 5.44

100 g mycorrhiza fungi (M4) 42 a

SEM vaue 21.64

Here DAP = day after planting, SEM = standard error mean, the numbers followed by the same superscript letters in the same column are

not significantly differ on DRMT 0.05%.

Further, Turk et al. (2006) also confirmed that mycorrhiza can

improve the availability of Phosphorus in soil that experienced

scarcity of Phosphorus. Phosphorus uptakes in plants affect the

physiological and morphological conditions of the plant which

led to increase the production of energy in the plant body and

fresh and dry weight of plant increases (Nuhamara, 1994).

These observations confirmed the finding of present study

where higher fresh and dry plant weight was obtained by the

application of mycorrhiza.

The shoot root ratio described the patterns of plants growth as

a resultant of plant responses to the environment. Though,

shoot root ratio determined by genetic factors but it is also

strongly influenced by environmental factors such as soil and

climate. Sutedjo & Kartasapoetra (1997) suggested that if one

factor has stronger influence than any other factor, the factor

will be closed off from each of the factors that have different

properties and real work to support the production of plant.

The roots which have greater absorption area will have a

chance to absorb more nutrients, therefore the plants associated

with mycorrhiza fungi will able to improve its capacity to

absorb nutrients and water. In addition, these plant has 2-4

times higher metabolic rate as compared to the plants that do

not colonized by mycorrhiza fungi (Sieverding, 1991).

Similarly, Rasouli-Sadaghiani et al. (2010) reported that the

higher dose of mycorrhiza fungi increased the uptake of

several nutrients. Results of this study confirmed that the

higher dose of mycorrhiza fungi in the planting hole caused

higher colonization of mycorrhiza fungi in plant roots.

Acknowledgements

The author would like to thank to the Ministry of National

Education, Republic of Indonesia for the financial assistance

through the scheme of National Priorities Research Grant

Master Plan for the Acceleration and Expansion of Indonesian

Economic Development 2011-2025 in 2014. The author also

thank to the Rector of Halu Oleo University and the Chairman

of the Research Institute of Halu Oleo University for providing

us moral support and space carry out this study.

Conflict of interest

Authors would hereby like to declare that there is no conflict of

interests that could possibly arise.

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tanaman. Gadjah Mada University Press. Jogyakarta.

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kedelai (Soy beans). Fakultas Peternakan Universitas

Diponegoro. Semarang.

Sutedjo M, Kartasapoetra M (1997) Pupuk dan pemupukan.

Rineka Cipta Jakarta.

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KEYWORDS

Application protocol

Seaweed extract

Kappaphycus alvarezii

Vegetable crops

Yield and quality

ABSTRACT

The field study was conducted to develop a protocol for application of commercially manufactured bio-

stimulant (Brand name: AquaSap) from seaweed Kappaphycus alvarezii. Efficacy of the bio-stimulant

was tested at 5% through foliar application in selected important vegetable crops. 3 to 4 applications

were applied based on the crop cycle of the plant. Total 27 vegetable crops were studied during 2012 to

2015 and observed their response towards bio-stimulant applied in terms of general health of the plant,

growth, yield and quality of the vegetable produce. 11% to 52% of yield increases were observed with

improved quality in all 27 crops studied. Therefore seaweed bio-stimulants will have enormous

potential to organic vegetable production in future.

Kosalaraman Karthikeyan and Munisamy Shanmugam*

Research and Development Division, AquAgri Processing Private Limited, B5, SIPCOT Industrial Complex, Manamadurai - 630 606. Sivaganga District, Tamil Nadu, INDIA

Received – January 12, 2016; Revision – January 27, 2016; Accepted – February 20, 2016

Available Online – February 20, 2016

DOI: http://dx.doi.org/10.18006/2016.4(1).92.102

DEVELOPMENT OF A PROTOCOL FOR THE APPLICATION OF COMMERCIAL

BIO-STIMULANT MANUFACTURED FROM Kappaphycus alvarezii IN SELECTED

VEGETABLE CROPS

E-mail: [email protected] (Muniyasamy Shanmugam)

Peer review under responsibility of Journal of Experimental Biology and

Agricultural Sciences.

* Corresponding author

Journal of Experimental Biology and Agricultural Sciences, February - 2016; Volume – 4(1)

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ISSN No. 2320 – 8694

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1 Introduction

Seaweed personifies not only as an alternative to conventional

chemical fertilizers but also chronically used in agriculture,

horticulture, cookies, ice-cream and jelly mix (Sumkiman et

al., 2014). Further, it was well reported that seaweed extract

contain nutrient of major and minor element, vital amino acid,

essential vitamins and plant growth regulators which stimulate

the growth and quality yield of crops. Application of seaweed

liquid extract stimulate different aspects of plant like good

health, development of root system, absorption of mineral,

enlargement of shoot, increased rate of photosynthesis and

crop yield (Sridhar & Rengasamy, 2010). Seaweed liquid

extract have newly gained importance as foliar spray for lots of

crops including various variety of grasses, flowers, cereals,

vegetables and spices (Pramanick et al. 2013 & 2014).

Further, Zodape (2001) tried various modes of seaweed extract

application such as a foliar spray, application to soil and

soaking of seeds before sowing and reported that extract not

only enhances the germination of seeds but also increases

uptake of plant nutrients and gives resistance to frost and

fungal diseases.

The aqueous extracts of the alga Codium fragile was effective

in increasing root length and it is 18.0% longer than the control

in soybeans (Anisimov & Chaikina, 2014). Furthermore, Pise

& Sabale (2010) treated fenugreek with 50% of seaweed and

reported improvement in the concentration of carbohydrate,

proteins, free amino acids, polyphenols and nitrogen content

while comparing with control plants. Similarly, yield and

nutrient content value were found higher in banana when

treated with 5% of bio-stimulant (AQUASAP) of

Kappaphycus alvarezii (Karthikeyan & Shanmugam, 2014).

Vegetables are herbaceous plants and produce large amount of

biomass within short period (Chatterjee & Thirumdasu, 2014).

Vegetables are very essential to human health as they are rich

in dietary fibre and source of essential vitamins, minerals, trace

elements, vitamins and antioxidants.

In India, vegetable production was around 146.55 million tons

from an area of 8.5 million hectare during 2010-2011. The 4

major vegetables viz. potato (28.9%), tomato (11.3%), onion

(10.3%) and brinjal (8.1%) contribute 58.6% of total vegetable

production. Other important vegetables are cabbage (5.4%),

cauliflower (4.6%), okra (3.9%), peas (2.4%) and okra

contribute 73% of total world production (Vanitha et al.,

2013). The bio-stimulant manure from red seaweed K.

alvarezii is well-off in potash with other primary nutrients like

N, P, K and secondary nutrients like Cu, Zn, Fe, Mo, Mn, etc.,

in addition and to significant amount of plant growth

regulators (Zodape et al., 2009; Prasad et al., 2010;

Karthikeyan & Shanmugam, 2014). The present investigation

describes the dosage and application protocol of bio-stimulant

manufactured from K. alvarezii (AQUASAP is brand name of

AquAgri) on some selected 27 vegetables crops for yield and

quality improvement.

2 Materials and Methods

The trial was carried out at R&D plot of AquAgri Processing

Private Limited and in the farmers’ field in Manamadurai,

Sivagangai Dt., Tamil Nadu, India. (Latitude is 9º42´56´´N

and longitude 78º28´2´´E). The annual normal rainfall

received by the district is 850 mm. The experiment trial was

conducted in 8 plots with 6 m x 4m for each vegetable crop

studied. The healthy seeds were selected and sowed carefully

into the field and the trial crops were irrigated periodically and

chemical fertilizers were applied to crops as per the

recommendation of National Horticulture Board, India. Bio-

stimulant (Aquasap) was collected from the stock of AquAgri

Processing Private Limited and 5% solution was prepared and

used.

2.1 Application protocol of bio-stimulant Aquasap for

vegetable crops

Bio-stimulant (Brand name: AquaSap) manufactured from K.

alvarezii was applied to the crops tested in the present

investigation through foliar application. Three doses viz.

vegetative, pre-flowering and post flowering stages were given

to short-term plants whereas four doses were applied to long-

term crops. Table 2-6 shows the application protocol for 27

crops tested in this study. The physico-chemical and nutritive

value of the bio-stimulant (AquaSap) has been given in table 1.

2.1.1 Tomato (Solanum lycopersicum L.)

The trial on tomato (Co3 hybrid) was conducted in June 2012

(Table 2.) The seeds were sowed in nursery beds, then nursery

plants were collected after 25th day of sowing and their roots

were dipped at 0.7% of bio-stimulant Aquasap for 10min

before transplantation. The first spray was given on 10th day of

transplantation, second and third doses were sprayed on 25-30d

(pre-flowering stage) and on 45-50d (flowering stage)

respectively and last dose was applied at first picking stage

(Table 3).

2.1.2 Lady’s finger (Okra) (Abelmoschus esculentus

(L) Moench) and Brinjal (Solanum melongeana L.)

The experiment of lady’s finger (var. US 7902) and brinjal Co2

hybrid was also conducted in 2012 (Table 2). The okra seeds

were soaked in 1% of bio-stimulant for 10 min. and the soaked

seeds were sowed into the field directly. Treated seeds were

also sowed in nursery beds and nursery plants (35d old) were

collected, treated their roots with 0.7% of bio-stimulant for 10

min before transplantation. The application of bio-stimulant

through foliar was given at the vegetative stage (15-20d),

second spray at flowering stage (35-40d) and final spray was at

first fruits picking stage (50-55d) (Table 3).

93 Karthikeyan and Shanmugam

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Table 1 Physico-chemical properties and Nutritive status of Aquasap bio-stimulant from seaweed K. alvarezii.

Parameters Units Results Parameters Units Results

Physical properties of AquaSap Nutritive Value (Amino Acid )

Alanine g/100g 0.014

Organic Matter (%) gm/100g 0.65 Arginine g/100g 0.0003

Specific Gravity - 1.14 Aspartic acid g/100g 0.0019

Electrical Conductivity dSm-1

63.3 Cystine g/100g 0.0017

pH (1% solution) - 6.68 Glycine g/100g 0.065

Moisture content (%) gm/100g 94.82 Histidine g/100g 0.0007

Total Ash (%) gm/100g 4.53 Isoleucine g/100g 0.0022

Macro and Micro Nutrient contents AquaSap Leucine g/100g 0.0022

Lysine g/100g 0.019

Parameters Units Results Tryptophan g/100g 0.007

Nitrogen (N) gm/100g 0.007 Methionine g/100g 0.0007

Phosphorous (P) mg/kg 3.57 Phenylalanine g/100g 0.0028

Potash (K) gm/100g 1.50 Proline g/100g 0.053

Sodium (Na) gm/100g 0.26 Serine g/100g 0.0013

Calcium (Ca) gm/100g 0.03 Threonine g/100g 0.0006

Silica (Si) gm/100g 0.02 Tyrosine g/100g 0.0016

Chlorine (Cl) gm/100g 2.15 Valine g/100g 0.0026

Magnesium (Mg) mg/kg 0.04 Glutamic acid g/100g 0.0022

Iron (Fe) gm/100g 16.95 Nutritive Value (Vitamins)

Sulphur (S) gm/100g 0.03 Vitamin - A IU/100g 3363.44

Boron as (B) mg/kg 768 Vitamin – E IU/100g 0.21

Copper (Cu) mg/kg 1.1 Vitamin – C mg/100g 22.52

Zinc (Zn) mg/kg 2.15 Vitamin – B1 mg/100g 0.007

Manganese (Mn) mg/kg 5.93 Vitamin –B5 mg/100g 301.1

Cobalt (Co) mg/kg 0.92 Vitamin – B6 mg/100g 3170.2

2.1.3 Chillies (Capsicum annuum L. Var. annuum)

Hybrid chilli (US 612) was selected for present study and its

seeds were sowed directly into field. The seaweed bio-

stimulant was applied at vegetative stage (40-45d), at

flowering stage (90-100d) and last dose was given at first fruits

picking stage (125-130d). In the case of transplanted plant,

nurseries were created (40d old) and treated their roots at 0.7%

of bio-stimulant for 10min before transplantation. During

growing period, first dose of bio-stimulant was given at 20-25th

day of transplantation and second and last applications were

given at 60-65th day (i.e. flowering stage) and at 80-85

th day of

transplantation (Table 3) respectively.

2.1.4 Capsicum (Capsicum annuum L)

Trial on capsicum (var. Arka Mohini) was carried out in

January 2014 (Table 2). The bio-stimulant AquaSap was

applied at vegetative (30-35d), flowering stage (60-65d) and

fruits picking stage (90-95d). But in the case of transplants

raised from 40d old nurseries whose roots were treated with

0.7% of bio-stimulant for 10min before transplantation, first

dose was given at 20-25d (vegetative stage), 60-65d (flowering

stage) and at 80-85d (first fruits picking stage) day of

transplantation (Table 3).

2.1.5 Variety of Gourds

The experimental study on nine varieties of gourds, i.e., Ash

gourd (var. MAH-1), Pumpkin (Arka Chandan), Snake gourd

(Covai -951), Ridge gourd (US 66), Bottle gourd (WARAD

MGH-4), Bitter gourd (US 475), Cucumber (local variety),

Watermelon (Ankur Kashish) and Chow chow (Green Fruits)

(Table 2) was conducted during 2012 to 2014. The seeds were

soaked in 1% of bio-stimulant for 30 min. and the seeds were

sowed in the study field. The application of bio-stimulant was

given at vegetative stage (20-25d), flowering stage (60-65d)

and first fruits picking stage (80-85d). In the case of chow

chow, mature fruits were planted in the field, and bio-stimulant

was first applied at vegetative phase of 25-30 days of

plantation, pre-flowering phase (3rd

month) and final dose was

given at flowering phase (5th month) (Table 3). In the

cucumber, first spray was done at germination phase (10-15th

day), followed by second spray at 35-40th day (vegetative

stage) and final dose was applied at flowering initiation stage

(65-70d) (Table 4).

Development of a protocol for the application of commercial bio-stimulant manufactured from Kappaphycus alvarezii in selected vegetable crops. 94

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2.1.6 Cole crops, Root and Tuber vegetables

2.1.6.1 Potato (Solanum tuberosum L.)

The potato (var. Kufri Jyoti) trial was conducted in August

2013 (Table 2). Bio-stimulant AquaSap was applied at plant

establishment stage (20-25d) vegetative phase (50-55d), early

root development stage (80-85d) and last dosage was given at

maturity stage (100-105d) (Table 5).

2.1.6.2 Cabbage (Brassica oleracea var. capitata L)

Maharani- F1, a hybrid variety of cabbage was taken for trial

in January 2013 (Table 2). 40 days old nursery plant was

created and root treatment was given at 0.7% of bio-stimulant

for 10min before transplantation. Field application of bio-

stimulant was applied at plant establishment stage (10-15d),

second dose was sprayed at head initiation stage (35- 40d) and

last spray was sprayed at head development phase (70-75d)

(Table 5).

2.1.6.3 Cauliflower (Brassica oleracea var. botrytis)

In January 2013, cauliflower (var. Shobha F1) was taken for

trial (Table 2). The root of nursery plants (35d) raised was

treated with 0.7% of bio-stimulant for 10min and transplanted.

During crop cycle, first spray of bio-stimulant aquasap was

given at plant establishment stage (10-15d), the second dose at

curd initiation stage (25-30d) and last dose was given at curd

development stage (45-50d) (Table 5).

2.1.6.4 Beetroot (Beta vulgaris L) and Carrot (Daucus

carota L)

The studies on beetroot (Vally Queen) as well as carrot (Pusa

Kesar) were conducted in 2013 and bio-stimulant Aquasap was

applied at vegetative stage (25-30d), early root development

stage (55- 60d) and root maturity stage (80 -85d) (Table 5).

2.1.6.5 Radish (Raphanus sativus L) and Knol-Khol (Brassica

caulorapa)

Radish (Roshni) and knol-khol (Early White) trial was

undertaken in January 2013. The bio-stimulant was given at

10-15th (vegetative stage), 25-30

th (early root development

stage) and at 40 - 45th day of sowing (root maturity stage)

(Table 5).

2.1.7 Other vegetable crops

2.1.7.1 Lima Bean (Phaseolus lunatus L) and Dolichos Bean

(Lab lab purpureus var. typicus)

The experiment on Lima (Co2) and Dolichos (Ankur Goldy)

beans were conducted during 2012. The seeds were soaked in

1% of bio-stimulant for 10min then sowed into the field.

During crop period, three spray of bio-stimulant were given

viz. at vegetative phase (20-25d), flowering stage (40-45d),

pod formation stage (60-65d) and last spray was given at first

picking stage (80-85th day of sowing) (Table 6).

2.1.7.2 Soybean (Glycine max (L.) Marr.)

The experiment on soybean (JSS 355) was conducted in July,

2012. The seeds were soaked in 1% of bio-stimulant for

10min and then the seeds were carefully sowed in the field.

The crop was applied with bio-stimulant Aquasap for four

times viz. at 20-25d, 40-45d, 60-65d and at 80-85th day of

sowing (Table 6).

2.1.7.3 Moringa (Moringa oleifera L.)

The efficacy trial of AquaSap on drumstick (PKM-1) was

conducted in 2012 (Table 2). The seeds were soaked in 1% of

bio-stimulant for 10min and during crop period bio-stimulant

aquasap was applied at nurseries stage (25-30th), pre-flowering

phase (3rd

month), flowering phase (4th month), and at fruits

development stage (5th month of plantation) (Table 6).

2.1.7.4 Small Onion (Allium cepa var. aggregatum)

Trial on small onion (var. Co-ON-5) was conducted in June

2013. The seaweed bio-stimulant at 5% was sprayed as foliar

application at establishment stage (10-15d), vegetative stage

(25-30d), bulb formation stage (40-45d) and bulb development

stage (60-65d) as shown in Table 6.

2.1.7.5 Bellary Onion (Allium cepa var. cepa)

Effect of bio-stimulant AquaSap on Bellary onion (var. Prema-

178) was studied in June 2013. The application of 5% bio-

stimulant was given on 10-15th (sowing establishment stage),

35-40th (vegetative stage), 60-65

th (bulb formation stage) 75-

80th (bulb development stage) day of sowing (Table 6).

3 Results and Discussion

All vegetable crops investigated in the present study responded

well at 5% dose of bio-stimulant Aquasap (from seaweed of

K. alvarezii). Highest yield was found in moringa with

52.83% over control followed by lady’s finger, chillies,

cabbage, garden lab lab, bellary onion, small onion, ash gourd,

and snake gourd with 45.84%, 37.30%, 36.74%, 33.03%,

32.53%, 30.74%, 30.15%, and 30.02% respectively with

improved quality (Table 2). Improved yield and quality in

crops applied with seaweed liquid fertilizers have been well

documented (Khan et al., 2009). Zodape et al. (2008) had

observed that okra yielded 20.94% more over control with

application of 2.5% extract of K. alvarezii and in tomato the

increment when treated with 5% extract was 60.89% Zodape et

al., (2011). The yield of brinjal with Eucheuma seaweed

powder increased marginally higher to 41.1% (Eswaran et al.,

2005). Similar kind of result with eggplant was reported when

it treated with 2% extract of Ascophyllum nodosum (Bozorgi,

2012). Babu & Rengasamy (2012) observed that when chillies

were treated with 1% and 2% of SLF of K. alvarezii, it

95 Karthikeyan and Shanmugam

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increased the crop yield to 23% and 15% respectively when

compare to control.

The high yield was also observed in ash gourds with 30.15%

over control followed by pumpkin, snake gourd, ridge gourd,

bottle gourd, bitter gourd, cucumber, watermelon and chow

chow with 28.56%, 30.02%, 11.98%, 28.03%, 26.64%,

24.19%, 25.89%, and 17.34% respectively (Table 2). Ahmed

& Shalaby (2012) recommend that liquid extract of E.

intestinelis (green alga), G. pectinutum (red alga) or

commercial seaweed liquid extract (Algreen) in addition to

manure is suitable product for better vegetative growth and

yield of cucumber plants. The seaweed extract of Ascophyllum

nodosum (3g/l) applied on watermelon plant, increased the

fresh weight, fruits diameter and peel thickness than control

plant (Abdel-Mawgoud et al., 2010).

Higher crop yields were observed in cabbage (36.74%),

cauliflower (29.61%), beetroot (28.84%), knol-khol (28.80%),

radish (26.08%), potato (23.90%) and carrot (14.21%) when

compared to control plants (Table 2). The seaweed extracts

powder Alga 600 and Seaforce-2 when applied on potato; it

increased the dry tuber weight to 14.67% when compared with

control (Sarhan, 2011). Abetz & Young (1983) observed that

the yield and size of cauliflower increased when treated with A.

nodosum extract.

The yield of treated plants in moringa, dolichos bean, bellary

onion, small onion, lima bean and soybean were 52.83%,

33.03%, 32.53%, 30.74%, 25.38% and 22.10% respectively

(Table 2). Soybean treated with extract of Kappaphycus at

15% and 12.5% showed highest grain yield of 57% and 46%

respectively compared to the control and maximum straw yield

was also found with treatment of 15% extract (Rathore et al.,

2009). Similar kind of result in onion bulb (22.0%) when

treated with Eucheuma seaweed powder (Eswaran et al., 2005)

and high yield with improved quality of onion was found when

treated with extract of A. nodosum (Dogra & Mandradia,

2012).

Table 2 Effect of bio-stimulant from seaweed K. alvarezii on yield of some vegetable crops.

Cultivar name Variety name Plantation type Date of plantation Yield increase over

control (%)

Tomato Co3 Hybrid Seeds 03.06.12 20.94

Lady’s finger US 7902 Hybrid Seeds 22.05.12 45.84

Brinjal Co2 Hybrid Seeds 03.06.12 24.53

Chillies US 612 Hybrid Seeds 01.01.14 37.30

Capsicum ARKA MOHINI Seeds 01.01.14 29.28

Ash gourd MAH-1 Hybrid Seeds 12.12.14 30.15

Pumpkin ARKA CHANDAN Seeds 12.12.14 28.56

Snake gourd COVAI 951 F1 Hybrid Seeds 20.07.12 30.02

Ridge gourd US 66 Hybrid Seeds 20.07.12 11.98

Bottle gourd WARAD MGH-4 Seeds 20.07.12 28.03

Bitter gourd US 475 Hybrid Seeds 20.07.12 26.64

Cucumber Local Seeds 25.12.13 24.19

Watermelon ANKUR KASHISH Hybrid Seeds 25.12.13 25.89

Chow-chow Green fruits Fully matured fruits 15.08.13 17.34

Potato KUFRI JYOTI Seeds 15.08.13 23.90

Cabbage MAHARANI- F1 Seeds 09.01.13 36.74

Cauliflower SHOBHA -F1 Seeds 09.01.13 29.61

Beetroot VALLY QUEEN Seeds 18.01.13 28.84

Carrot PUSA KESAR Seeds 15.08.13 14.21

Radish ROSHNI Seeds 18.01.13 26.08

Lima Bean Co2 Seeds 17.07.12 25.38

Doli Chos Bean ANKUR GOLDY Seeds 17.07.12 33.03

Soybean JSS 355 Seeds 17.07.12 22.10

Moringa PKM-1 Seeds 02.05.12 52.83

Small onion CO-ON-5 Seeds 15.06.13 30.74

Bellary onion PREMA - 178 Seeds 15.06.13 32.53

Knol-khol EARLY WHITE Seeds 18.01.13 28.80

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Figure 1 Some of the vegetable crops studied in the present investigation with their vegetable yield

97 Karthikeyan and Shanmugam

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Table 3 Dosage and application protocol of seaweed bio-stimulant used for home garden vegetable crops.

Crop name Root dip I Dose II Dose III Dose TBS

Tomato b Root of nurseries was dipped

in 0.7% of bio-stimulant for

10min before transplantation.

10th day (After

transplantation)

25-30th day

(Pre-flowering

stage)

45-50th day

(Flowering phase)

21

(1+5+5+5+5)

Lady’s finger c

(Okra)

- 15-20th day

(Germination stage)

35-40th day

(Flowering

stage)

50-55th day (First

fruits picking

stage)

16

(1+5+5+5)

Brinjal Root of nurseries was dipped

in 0.7% of bio-stimulant for

10min before transplantation.

15-20th day

(Germination stage)

35-40th day

(Flowering

stage)

50-55th day (First

fruits picking

stage)

16

(1+5+5+5)

Chillies Sowing 40-45th day

(Vegetative stage)

95-100th day

(Flowering

stage)

125-130th day

(Fruits picking

stage)

15

(5+5+5)

Transplantation: Root of

nurseries was dipped in 0.7%

of bio-stimulant for 10min

before transplantation.

20-25th day (Days

after

transplantation)

60-65th day

(Flowering

stage)

80-85th day

(First fruits

picking stage)

16

(1+5+5+5)

Capsicum

(Sweet pepper

/ Bell pepper)

Sowing 30-35th day

(Vegetative stage)

60-65th day

(Flowering

stage)

90-95th day

(Fruits picking

stage)

15

(5+5+5)

Transplantation: Root of

nurseries is dipped in 0.7% of

bio-stimulant for 10min before

transplantation.

20-25th day (Days

after

Transplantation)

60-65th day

(Flowering

stage)

80-85th day

(First fruits

picking stage)

16

(1+5+5+5)

a Recommended dosage of bio-stimulant: 5%;

b IV dose at 75-80th day (Picking phase);

c Seed treatment; Seeds were soaked for 10min

in 1% of bio-stimulant before sowing, TBS - Total bio-stimulant required Per acre (L).

3.1 Effect on plant disease control

Twenty seven vegetable crops studied in the present

investigation looked healthy and generally free from disease as

compared to their control plants. Extract of seaweed have been

reported to increase resistance of plant against pest and

diseases, increase plant growth and quality yield (Jolivet et al.,

1991; Verkleij, 1992; Pardee et al., 2004; Hong et al., 2007;

Jeyaraj et al., 2008). Similarly, Sultana et al. (2011) had

reported that number of liquid seaweed extract found to control

root rotting fungi like Rhizoctonia solani, Macrophomina

phaseolina, Fusarium species and root kot nematode

(Meloidogyne spp.) on a variety of crops. The resistance to

frost and fungal disease were reported when seaweed extract

was applied to some crops (Zodape, 2001). Ara et al. (1996)

had observed that extract of Sargassum spp. controlled the root

rot disease in sunflower plant. Seaweed fertilizer was found to

boost the resistibility adjacent to disease and in addition to

reduce the insect attack (Zahid, 1999). Dogra & Mandradia

(2012) had found that extract of A. nodosum significantly

reduced the downy mildew severity over control in onion plant

and it had also been reported that seaweed extract of

Asparagopsis taxiformis found to act against phytopathogens

(Manilal et al., 2009). Lynn (1972) had observed that seaweed

extract of A. nodosum protected Capsicum annuum and sweet

pepper from stress to frost, microbial diseases and insect attack

and increased the shelf life of fruits and better seed

germination.

3.2 Effect of seed Treatment

Seed and root treatment had improved the viability of plantlets

and grow vigorously as compared to control plants in the

present study and it is in agreement with the literatures reports.

The introductory soaking of Triticum aestivun seeds in 20%

extracts of Sargassum wightii for 24 hrs gave an 11% increase

in seed germination, a 63% enhance in number of lateral roots

and 46% increase in shoot length in comparing to control

(Kumar & Sahoo, 2011). 100% seed germination was observed

in lowest concentration of SLF in black gram (Venkataraman

Kumar et al., 1993) and SLF promote the seed germination as

well as yield of the vegetable crops (Narasimha Rao & Reshmi

Chatterjee, 2014). Treatment at 0.05% of concentrated extract

of Laminaria digitata on Plantago lanceolata, Trifolium

repens and Avena strigosa had given higher germination

percentage (Thorsen et al., 2010).

Development of a protocol for the application of commercial bio-stimulant manufactured from Kappaphycus alvarezii in selected vegetable crops. 98

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Table 4 Dosage and application protocol of seaweed bio-stimulant used for gourds vegetable crops.

Crop name Seed treatment I Dose II Dose III Dose TBS

Ash gourd Seeds were soaked for 30min in 1% of bio-stimulant

and incubated it for 6 days for before sowing

20-25th day

(Vegetative stage)

60-65th day (Flowering

stage)

80-85th day

(Picking stage)

16

(1+5+5+5)

Pumpkin Seeds were soaked for 30min in 1% of bio-stimulant

and incubate it for 6 days for before sowing

20-25th day

(Vegetative stage)

60-65th day (Flowering

stage)

80-85th day

(Picking stage)

16

(1+5+5+5)

Snake gourd Seeds were soaked for 30min in 1% of bio-stimulant

before sowing

20-25th day

(Vegetative stage)

60-65th day (Flowering

stage)

80-85th day

(Picking stage)

16

(1+5+5+5)

Ridge gourd / Ribbed

gourd

Seeds were soaked for 30min in 1% of bio-stimulant

before sowing

20-25th day

(Vegetative stage)

60-65th day (Flowering

stage)

80-85th day

(Picking stage)

16

(1+5+5+5)

Bottle gourd Seeds were soaked for 30min in 1% of bio-stimulant

before sowing

20-25th day

(Vegetative stage)

60-65th day (Flowering

stage)

80-85th day

(Picking stage)

16

(1+5+5+5)

Bitter gourd Seeds were soaked for 30min in 1% of bio-stimulant

before sowing

20-25th day

(Vegetative stage)

60-65th day (Flowering

stage)

80-85th day

(Picking stage)

16

(1+5+5+5)

Cucumber Seeds were soaked for 30min in 1% of bio-stimulant

before sowing

10th day (Germination

phase)

35-40th day

(Vegetative stage)

65-70th day

(Flowering initiation

to first picking stage)

16

(1+5+5+5)

Watermelon Seeds were soaked for 30min in 1% of bio-stimulant

before sowing

20-25th day

(Vegetative stage)

60-65th day (Flowering

stage)

80-85th day

(Picking stage)

16

(1+5+5+5) a Recommended dosage of bio-stimulant 5%, TBS - Total bio-stimulant per acre (Lit),

Table 5 Dosage and application protocol of seaweed bio-stimulant used for cole, root and tuber vegetable crops.

Crop name I Dose II Dose III Dose TBS

Chow chow

(Chayote)

25 - 30th day (Vegetative phase) 3rd month (Pre-flowering phase) 5th month (Flowering phase) 15 (5+5+5)

Potato b 20-25th day (Plant establishment stage) 50-55th day (Vegetative phase) 80-85th day (Early root development Phase) 20 (5+5+5+5)

Cabbage c 10-15th day (Plant establishment stage) 35-40th day (Head initiation stage) 70-75th day (Head development phase) 16 (1+5+5+5)

Cauliflower c 10-15th day (Plant establishment stage) 25-30th day (Curd initiation stage) 45-50th day (Curd development phase) 16 (1+5+5+5)

Beetroot 25-30th day (Vegetative stage) 55-60th day (Early root development stage) 80-85th day (Maturity stage) 16 (1+5+5+5)

Carrot 25-30th day (Vegetative stage) 55-60th day (Early root development stage) 80-85th day (Maturity stage) 15 (5+5+5)

Radish 10-15th day (Vegetative phase) 25-30th day (Early root development stage) 40-45th day (Maturity stage) 15 (5+5+5)

Knol-khol 10-15th day (Vegetative phase) 25-30th day (Early root development stage) 40-45th day (Maturity stage) 15 (5+5+5) a Recommended dosage of bio-stimulant 5%,

b IV Dose at 100-105

th day (Root maturity stage),

c Root dip: Transplantation: Root of nurseries was dipped in 0.7% of bio-stimulant for

10min before transplantation, TBS - Total bio-stimulant per acre (Lit).

99 Karthikeyan and Shanmugam

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3.3 Effect of foliar application

Seaweed extract applied as foliar application found to

significantly enhance the yield, growth and quality of crops

(Pramanick et al., 2013). Seaweed liquid extract have gained

importance to different range of crops like cereals, grasses,

vegetables, species and flowers when applied through foliar

application (Crouch & Van Staden, 1992). Seaweed extract is

important to find out the organic sources for seed and foliar

treatments for effective maintenance of vigour and viability

(Dwivedi et al., 2014). The maximum yield of tomato (Zodape

et al., 2011) and banana (Karthikeyan & Shanmugam, 2014)

had been observed when using foliar application of K. alvarezii

extract. Similar kind of result was observed by Pramanick et

al., (2013) that the foliar application of seaweed sap improved

the nutrient uptake capacity of crops.

In the present investigation, it was also observed that emerging

of first flower appeared in all treated plants at least 5-10d

earlier than control and similar kind of observation had been

recorded in the literature. Dwivedi et al., (2014) reported that

seaweed extracts not only increase the vegetative growth of the

plant but it also triggers the early flowering, fruiting in crops

and ultimately on seed yields. Seaweed extracts are

ecologically safe, non-polluting, non-toxic, and harmless to

human beings, animals and birds (Dhargalkar & Pereira, 2005).

In addition to reducing the cost of inorganic fertilizers,

application of seaweed bio-stimulants improves soil health,

enhances the yield and quality of produce in organic vegetables

production thereby increasing the domestic and international

market (Chatterjee & Thirumdasu, 2014).

Conclusions

It can be concluded from the present study that 27 vegetable

crops tested had responded well to bio-stimulant (Aquasap)

manufactured from seaweed K. alvarezii. The average yield

increased from 11.98% to 45.84% with much improved

vegetable quality. Therefore, the protocol used in this study

will be useful to the farmers to produce organic vegetables.

Acknowledgement

The authors are very grateful to Mr. Abhiram Seth, MD, Mr.

Arun Patnaik, CEO and Mr. Tanmaye Seth of AquAgri

Processing Private Limited for their constant encouragement,

guidance and allocation of budget to carry out the present

investigations. The authors also wish to thank farmers who

agreed to carry out and monitor the trials in their farm.

Conflict of interest

Authors would hereby like to declare that there is no conflict of

interests that could possibly arise.

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KEYWORDS

Anti-QS

Pseudomonas aeruginosa

Extract

Chromobacterviolaceum

CV026

Violacein

ABSTRACT

Quorum sensing (QS) regulates various activities of bacteria such as biofilm forming, virulence factors,

swarming, and pigment production. Bacterium Chromobacterium violaceum produced violacein has also

been regulated by quorum sensing system. The aim of this study was to evaluate the QS inhibition

activity buviolaceum produced by the extract of Pseudomonas aeruginosa isolated from root of

Vetiveria zizanioides. P. aeruginosa cultured on King’s B agar, then was extracted using ethyl acetate as

solvent. The extract was used to test anti-QS properties on C. violaceum CV026 at different

concentration viz 0.0 mg/mL (control), 2.5 mg/mL, 3.0 mg/mL and 3.5 mg/mL. Violacein content of

culture was measured by a spectrophotometer at a wavelength of 585 nm. The extract at 2.5, 3.0, and 3.5

mg/mL concentration had a significant effect on the reduction of violacein production by 31.6 %, 35.8

% and 70.3 %, respectively. While using an extract at the same concentration level there was no

negative effect on the number of C. violaceum CV026 cells found. The results of study suggest that

among the various tested concentrations, 3.5 mg/mL extract inhibits QS in C. violaceum CV026 via

violacein production. Thus, the extract of P. aeruginosa has a very high potential to develop anti-QS.

Any Fitriani*, Dwi Putri Ayuningtyas and Kusnadi

Department of Biology Education, Indonesia University of Education, 40154, Indonesia

Received – January 02, 2016; Revision – January 27, 2016; Accepted – February 20, 2016

Available Online – February 20, 2016

DOI: http://dx.doi.org/10.18006/2016.4(1).103.108

INHIBITION OF QUORUM SENSING IN Chromobacterium violaceum CV026 BY

VIOLACEIN PRODUCED BY Pseudomonas aeruginosa

E-mail: [email protected] (Any Fitriani)

Peer review under responsibility of Journal of Experimental Biology and

Agricultural Sciences.

* Corresponding author

Journal of Experimental Biology and Agricultural Sciences, February - 2016; Volume – 4(1)

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1 Introduction

Quorum sensing (QS) is a cell to cell communication

mechanism that allows bacteria to control gene expression in

order to respond to the cell density. Cell density can stimulate

synthesis of small molecules (autoinducers) and QS regulates

many activities such as bioluminescence, biofilm formation,

virulence factor, and swarming (Defoirt et al., 2013). Anti-QS

is a phenomenon of cell-cell communication related to

molecules as intermediate. Molecules as an anti-QS agent

could replace an inducer molecule which is responsible for the

induction of protein expression to produce a vilurent agent.

These anti-QS molecules can be used as a medicine.

C. violaceum is Gram-negative bacteria found in nature and

that causes infections in human and animals. This bacterium

can cause septicemia and abscess in lungs and in the liver

(Petrilo et al., 1984). QS in C. violaceum regulates violacein

pigment production, antibiotic, hydrogen cyanide and some

enzymes. C. violaceum has many genes involved in violacein

production such as vioD, vioC, vioA genes which is arranged

in an operon system mediated by N-Acyl Homoserine Lactone

(AHL) (August et al., 2000). QS plays an important role in

determining virulence factors and it has lead scientists to find a

new target in developing treatment for disease caused by

bacterial infections (Vasari et al., 2013). Actinobacteria,

Firmicutes, Cyanobacteria, Bacteroidetes and Proteobacteria

produce anti-QS enzymes (Kalia, 2013). Noncognate AHLs as

intermadiates of AHL biosynthetic pathway, a dicyclic

peptides, produced by bacteria as anti-QS or Quorum

Quenching (QQ) molecules (Bauer & Robinson, 2002).

Medicinal plants have some endophytic bacteria which

produces secondary metabolites which work as antimicrobial

agents (Strobel, 2003). Nowadays, endophytes are a source for

novel natural products in modern medicine, industry and

agriculture (Yu et al., 2010). Most novel natural products

possesing antimicrobial activities have been isolated from

endophytes.

Endophytic microbes have been isolated from various plant

tissues such as roots, leaves and stems. Fitriani et al. (2013)

have isolated and characterized 17 isolates from the root of

Vetiveria zizanioides on Luria Bertani agar media. Among

these 5 isolates have been identified with polyketide synthase

gene by polymerase chain reaction analysis. Based on 16S

rRNA analysis, one of them is Pseudomonas aeruginosa

(Fitriani et al., 2013). P. aeruginosa is Gram-negative, aerobic

and rod shaped bacteria belonging to the family

Pseudomonadaceae. Further, Allu et al. (2014) isolated and

characterized endophytic P. aeruginosa from red fruit pepper.

Antimicrobial properties of the isolated bacterial strain were

characterized against fungal pathogen Colletotrichum. Based

on the study, biocidal properties of the P. aeruginosa were

established and it was also suggested that isolated bacterium

could grow in an artificial medium. The aim of this study was

to evaluate the QS inhibition activities in term of violaceum

production inhibition by the extract of P. aeruginosa isolated

from root of V. zizanioides.

2 Materials and Methods

2.1 Preparation of bacterial culture

C. violaceum CV026 was obtained from Research Centre

Microbial Diversity, Bogor Agricultural University, Indonesia,

while the P. aeruginosa was isolated from the root of V.

zizanioides. Isolated P. aeruginosa was maintained on the

King’s B agar (Pepton 2.0%; KH2PO4 0.15%; MgSO4 0.15%;

glycerol (85%) 1.5% (v/v) and 2.0% Bacto agar (Difco, Spark,

USA) at 37oC. The culture was rejuvenated after every two

weeks. While C. violaceum CV026 was cultured in Luria

Bertani (LB) agar and incubated at 27oC. Culture was

rejuvenated for the interval of every 4 days. Before the

treatment, culture was inoculated to LB broth and incubated at

27oC for 18-24 h in water bath and shaker.

2.2 Extraction of Endophytic P. aeruginosa

Extraction of P. aeruginosa was carried out by the method

described by Niyaz Ahamed (2012) with some modification.

Briefly, P. aeruginosa were cultured in 10 mL of King’s B

agar (Pepton 2.0%; KH2PO4 0.15%; MgSO4 0.15%); glycerol

(85.0%) 1.5% (v/v); Bacto agar 2.0% (Difco, Spark, USA)

broth with 37oC in temperature and 120 rev/min of shaking for

24 hours. The overnight culture was then inoculated into 90

mL of the same medium and condition. After their stationary

phase, cultures were moved into centrifuge tube and

centrifuged at 10000 rev/min for 10 minutes. Supernatants

were then moved into separating flask and added by ethyl

acetate (Merck, New York, USA) (1:1 v/v). Separating tube

was hand-shaken constantly for about 15 minutes and left for

20 minutes. Then the upper middle layer that formed in the

contained organic matter was taken. Extract was concentrated

by vacuum evaporator with 50oC in temperature.

2.3 Anti-QS assay against C. violaceum CV026

Analysis of the anti-QS assay of P. aeruginosa extract was

conducted as reported by Krishnan et al. (2012) with

modification. One colony of C. violaceum CV026 was

inoculated in a 50 mL LB broth medium and incubated for 18-

24 h at 27oC with 110 rev/min agitations. Four mL of culture

(OD600=1.2) mixed with 20 mL LB (10.0 g/L Triptone, 5.0 g/L

Yeast Extract, 5.0 g/L NaCl) (Difco, Spark, USA) molted agar.

N-hexanoyl-L-homoserine-lactone (C6-HSL) (Sigma, St.

Louis, USA) dissolved in DMSO 100%, was also added to

agar with 1.2 μg/mL final concentration. The agar was mixed

and poured into a Petri dish and wells were made in the center

of the solidified agar plate. Three replicates were then made. A

40 µL of P. aeruginosa extract at 0.0, 2.5, 3.0, 3.5 mg/mL

concentration was put into the well, respectively. The plates

were kept in the incubator for 18-24 h at 27 oC and checked for

inhibition of the violacein.

104 Fitriani et al

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Table 1 Culture composition for each flask related violacein quantification.

Treatment Culture composition Volume Final Concentration Total Volume

Control +

LB medium 1.998 mL - 2 mL

CVO26 (OD600 0.1) 1.1857 mL 1 x 108 CFU/mL

HSL (1 mg/mL) 2.4 µL 0.12 µL/mL

DMSO 1% 140 µL 0.07 % (v/v)

Control - LB Medium 1998 mL - 2 mL

CVO26 (OD600 0.1) 1.860 mL 1 x 108 CFU/mL

DMSO 1% 140 µL 0.07% (v/v)

Extract LB Medium 1.998 mL - 2 mL

CVO26 (OD600 0.1) 1.857 mL 1 x 108 CFU/mL

HSL (1 mg/mL) 2.4 µL 0.12 µL/mL

Extract1 140 µL 0.0; 2.5; 3.0; 3.5 mg/mL

2.4 Violacein Quantification

Violacein production was analyzed by using

spectrophotometry as described by Choo et al. (2005). Table 1

showed composition medium for each flask. One mL culture

from each flask was centrifuged at 13000 rev/min for 5 min.

The supernatant was discarded while the pellet was washed 2

times using buffer phosphat. One mL DMSO was mixed with

the pellet and vortexed vigorously for 30s until the violacein

dissolved completely. The cultures were then centrifuged at

13000 rev/min for 10 min. The absorbance of supernatant was

read using the spectrophotometer (585 nm) to measure of

violacein concentration.

2.5 Analysis of C. violaceum CV026 Cell Viability

The viability of C. violaceum CV026 cell was assessed by a

total plate count (TPC). One hundred mL of C. violacein

CV026 culture with P. aeruginosa extract was centrifuged at

13000 rev/min for 10 min to remove remaining extract in the

culture. Later on the pellets were washed 2 times in 100 mL

PO4-2

and excess of P. aeruginosa extract was discarded. The

culture was added to 100 mL Muller Hinton Broth (MHB).

One mL of diluted culture to factors of 10-1

– 10-8

and one mL

of culture from factors 10-6

– 10-8

pour into Muller Hinton

Agar (MHA). The plates were incubated at 27oC for 18-24 h.

The total of viable bacteria was then counted (Choo et al.,

2005).

2.6 Antibacterial Assay

An antimicrobial assay was analyzed against C. violacein

CV026 using the total plate count (TPC) procedure. One mL

culture was serially diluted to factors of 10-1

– 10-8

and pourn

into an MHA medium. The mixture was then left to be

solidified and incubated at 27oC for 18-24 hours. The total

viable bacterial cell was then counted. To ensure this test, a

paper disc diffusion assay was also taken. 100 µL overnight

culture of C. violaceum CV026 (OD600=0.1) was spread on

MHA. Paper discs containing extracts with current

concentrations (0.0, 2.5, 3.0, 3.5 mg/mL) were loaded onto the

plate. Plates were incubated in 27oC for 24 hours. Zone of

inhibition around the disc was then observed (Sasidharan et al.,

2011).

3 Results and Discussion

Reduction in violacein productions in C. violaceum CV026 are

concentration dependent and reduced with the increasing P.

aeruginosa extract concentration. Highest concentration of P.

aeruginosa extract (3.5mg/ml) resulting in lower production of

violacein as shown in Figure 1. P. aeruginosa extract could

have influenced the mechanism of QS in C. violaceum through

decreasing violacein production. The extract could have also

interfered autoinducer production or interferred with the

expression of the gene.

Figure 1 Decreasing violacein production by P. aeruginosa

extract

Table 2 shows the result of quantification of violacein

production in the presence of P. aeruginosa extract. Violacein

production by cultures in the presence of P. aeruginosa extract

was significantly different than the culture without P.

aeruginosa extract (control). Highest inhibition in the violacein

production was reported at the highest concentration (3.5

mg/mL).

Inhibition of Quorum sensing in Chromobacterium violaceum CV026 by Violacein produced by Pseudomonas aeruginosa. 105

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Table 2 Decreasing Total Violacein production by Pseudomonas aeruginosa Extract using Spectrophotometer.

Absorbance 585 ± SD

2.5 mg/mL 3.0 mg/mL 3.5 mg/mL

E+CV+C6-HSL 0.995 ± 0.17b 0.488 ± 0.1

c 0.132 ± 0.019

d

Control (CV+C6-HSL) 1.23 ± 0.08a

Control (CV- C6-HSL) 0.0285 ± 0.002e

Statistical analysis using Mann-Whitney U test; Different superscripts shows a significant difference (P < 0.05); E:Extract; CV:

Chromobacterium violaceum; HSL: Homo Serine Lactone

According to Kalia & Pirohit (2011) violacein production

might have reduced because of the activity of gene producing

QS signal which can either inhibit or reduce violacein

production. They also reported that structure of the signal was

disrupted and the receptor sites with antagonist signal

analogues were blocked. Heulier et al. (2006) suggested that P.

aeruginosa has N-(3-oxo-dodecanoyl)-homoserine lactone (3-

oxo-C12-HSL) and N-butanoyl-homoserine lactone (C4-HSL).

They regulate the expression of a lot of genes in accordance

with the induction of the transcription of lasI and rhlI, as

autoinducers.

Figure 2 Viable cell of C. violaceum CV026 in the presence of

P. aeruginosa extract

Table 3 shows total population of C. violaceum (log CFU/mL)

in culture with extracts and controls. Total C. violaceum

CV026 population did not show any significant difference in

media with addition 0.0, 2.5, 3.0 and 3.5 mg/ml. Results of the

study reveales that P. aeruginosa extract in media did not

influence the viable cells and that the extract did not cause cell

death.

The comparison of total bacteria also can be observed from

figure 2. The decreasing violacein was not followed by

decreasing of total viable bacteria. Meanwhile, antibacterial

assay which carried by disc diffusion method, showed a similar

result that P. aeruginosa had no inhibitory activity against C.

violaceum CV026. The result of these assays indicates that

extract of P. aeruginosa has potency as anti-QS in C.

violaceum.

GC-MS analysis had been carried and revealed that the extracts

of P. aeruginosa have potential as anti-QS compounds.

According to Pratiwi (2013) P. aeruginosa extract contains 3-

(1- phenyl - 2,3dihydro - 1H - isoindol - 2 -yl) propan-1-ol and

1H-Isoindole-1,3(2H)-dithione. Both compounds are indole

derivatives and similar type of indole derivatives had been

isolated from Escherichia coli by Li & Young (2013) and

established anti-QS activity against C. violaceum. Similarly,

Romano et al. (2014) reported the mechanism of this

compound in inhibiting quorum sensing by inhibiting the

activity of vioA gene which is one of many genes contained in

vioABCD operon that is very crucial for violacein production

in C. violaceum. Similar research showed that endophytic

fungus Penicillium isolated from the stem of the milk thistle

(Sylibum marianum) produces polyhydroxy anthraquinones as

quorum sensing inhibitor (Figueroa et al., 2014).

Table 3 Antimicrobial Assay Against C. violaceum CV026

Total viable bacteria (Log CFU/mL ± SD )

2.5 mg/mL 3.0 mg/mL 3.5 mg/mL

E+CV026+C6-HSL 8.86 ± 0.16a 8.81 ± 0.41

a 8.65 ± 0.32

a

Control (C6-HSL) 9.18 ± 0.028a

Control (-C6-HSL) 9.11 ± 0.073a

Statistical analysis by Duncan test; Different superscripts shows a significant difference (P < 0.05); E - Extract; CV: Chromobacter

violaceum; HSL: Homo Serine Lactone

106 Fitriani et al

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Overall, this study shows that P. aeruginosa isolated from the

root of V. zizanioides produces secondary metabolites which

has anti-QS properties. Furthermore, P. aeruginosa extract

could inhibit violacein production in C. violaceum culture

without killing the cell. Further research will purify the extract

to get a single compound and be analyzed as an anti-QS

compound.

Acknowledgements

The authors are grateful for the Indonesia University of

Education, Bandung, Indonesia for their financial assistance

for this research experiment.

Conflict of Interest

Authors would hereby like to declare that there is no conflict of

interests that could possibly arise.

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KEYWORDS

Fish cages

Taal Lake

Physico-chemical

characteristics

Water quality

Aquaculture

ABSTRACT

Aquaculture activities are often blamed for the degradation of water quality of aquatic ecosystem.

Present study was conducted to determine the water quality of Taal Lake at two different study sites viz.

one under intense fish cage farming activities and the other without aquaculture activities. The study

aims to assess the effect of aquaculture activities on selected water quality parameters, which include

transparency, temperature, pH, nitrates, phosphates, salinity, total dissolved solids (TDS) and dissolve

oxygen (DO). The study was conducted over a ten-month period in 2013-2014. Results of the study

revealed no significant differences in water temperature, pH, salinity, transparency and DO between the

aquaculture and non-aquaculture sites of the lake, although DO and transparency were consistently

lower in the aquaculture sampling stations throughout the 10-month sampling period. DO dipped to

critical level (<4 ppm) for aquatic organisms in the months of January and February. Nitrates,

phosphates and TDS were significantly higher in the area with fish cage farming activities as compared

to the non aquaculture site. Further, the study also reports the efforts of stakeholders to sustain fish cage

farming in the lake which include participative, multi-sectoral action planning, information and

education, policy formulation, regulation and licensing.

Blesshe L Querijero1,*

and Airill L Mercurio2

1Animal Biology Division, Institute of Biological Sciences, College of Arts and Sciences, University of the Philippines, Los Baños 4031, Laguna, Philippines

2Biological Sciences Department, College of Science and Computer Studies, De La Salle University-Dasmariñas, City of Dasmariñas 4114, Cavite, Philippines

Received – January 06, 2016; Revision – January 27, 2016; Accepted – February 20, 2016

Available Online – February 20, 2016

DOI: http://dx.doi.org/10.18006/2016.4(1).109.115

WATER QUALITY IN AQUACULTURE AND NON-AQUACULTURE SITES IN

TAAL LAKE, BATANGAS, PHILIPPINES

E-mail: [email protected] (Blesshe L Querijero)

Peer review under responsibility of Journal of Experimental Biology and

Agricultural Sciences.

* Corresponding author

Journal of Experimental Biology and Agricultural Sciences, February - 2016; Volume – 4(1)

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ISSN No. 2320 – 8694

Production and Hosting by Horizon Publisher

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1 Introduction

Among various resource based activities in and around the

lake, including its watershed areas, aquaculture activities are

often blamed for the degradation of water quality (Pullin,

1993). Aquaculture also supports food security and livelihood.

Reducing vulnerability of aquaculture production due to water

quality deterioration by fishing is a national priority for a

sustainable fish production (Jacinto, 2011). Taal Lake has an

area of 268 km2 with an aquatic surface area of 236.9 km

2 and

a maximum depth of 198 m (Castillo & Gonzales, 1976). It is

the third largest freshwater lake in the Philippines. It provides

multiple services to various users, among these fisheries is one

of the most dominant one with floating fish cages for tilapia

Oreochromis niloticus and milkfish Chanos chanos. Intensive

aquaculture and human activities caused deterioration of the

water quality of the lake and fish kills have become more

common incidents in Taal Lake (Jacinto 2011, Macandog et

al., 2014). In 2011 these incidences of fish kills in Taal Lake

were reported during May to June and killed more than 2,000

tons of farmed fish. Over 7,000 illegally operated fish cages

were dismantled by a multi-agency task force (BFAR 2014).

The changes in the physico-chemical properties of the water of

Taal Lake were reported in several studies (Zafaralla et al.,

1992, Alcañices et al., 2001, Vista et al., 2006, Rosana et al.,

2008, Papa & Mamaril, 2011). According to Galera &

Martinez (2011), the water surface temperature, pH, total

dissolved solids, total suspended solids, color and dissolved

oxygen of Taal lake in 2009 conformed the class C water

standards (DENR AO 34, 1990); and was therefore safe for

aquaculture use, and for primary contact recreation such as

bathing, swimming and skin diving.

This study was undertaken to determine and compare the

physico-chemical characteristics of Taal Lake in aquaculture

and non-aquaculture sites and tried to find out the effects of

fish cage farming on water quality as valuable inputs for policy

decisions pertaining to aquaculture production in the lake. The

study also aimed to document the stakeholders’ efforts to

sustain aquaculture production in the lake and prevent

degradation of water quality.

2 Materials and Methods

2.1 Assessment of Water Quality

Selected physico-chemical characteristics of Taal lake water at

the aquaculture and non-aquaculture sites of the lake were

determined monthly for 10 months from August 2013 to May

2014 using standard procedures. The physical parameters

include water transparency and temperature while the chemical

parameters include pH, nitrates, phosphates, salinity, total

dissolved solids (TDS) and dissolved oxygen (DO) were

studied throughout the study period. Figure 1 shows the

sampling sites, the sampling stations for the aquaculture sites

were located in three Barangays i.e. Sampaloc, Aya and

Berinayan. Among these, Sampaloc and Aya are in the

municipality of Talisay while Berinayan in Laurel

municipality. Both municipalities are located in Batangas

province, Philippines. The non-aquaculture sampling stations

were located in Barangay Wawa, Tanauan municipality,

Batangas (Figure 1). Each Barangay had three sampling

stations to serve as triplicate sampling stations or a total of 12

sampling stations. Sampling was done once every month. A

GPS Garmin E-trex® Global Positioning Device was used to

identify and mark the sampling stations.

Figure 1 Overview of Taal Lake, Philippines and the water sampling stations

110 Blesshe and Airill

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Measurement of physico-chemical parameters of water was

carried out in situ, at the surface and at 10 m depth. Water

samples for chemical analysis were collected from the surface.

In aquaculture sites, measurements of parameters were done

inside fish cages. All of the physico-chemical parameters were

taken in triplicates from each of the sampling stations.

Sampling was carried out between 9 A.M to 2 P.M.

Among physical characteristics, the water temperature was

determined using standard laboratory thermometer at the

sampling site only while the water transparency was measured

by a 3.3 kg metal plate Secchi disk. For chemical

characteristics, dissolved oxygen was measured by using DO

meter (YSI 550A®) with 10 meters length cable, similarly pH,

salinity, conductivity and total dissolved solids (TDS) were

also measured by same length holding SCT pH meter (YSI

EC300A®). The nitrates and phosphates were measured using

HACH DR/890 portable colorimeter. The nitrate and

phosphate contents of the lake water were measured using

nitrates and phosphates reagents tested in a 10 ml vials

collected from each sampling stations (Umaly, 1988).

Quantitative data on water quality parameters were compared

using Mann-Whitney U test at 95% level of confidence to

determine significant difference between aquaculture and non-

aquaculture sites.

2.2 Fish Farmers’ Practices and Stakeholders’ Efforts

Affecting Water Quality

A total of 20 key respondents were interviewed for their

current fish farming practices for exploring the effect of

farming practices on the water quality such as fish stocking

densities and feeding management. Interviewed key informants

were fish cage owners, caretakers, members of the Taal Lake

Aquaculture Alliance Incorporated (TLAAI), municipal

agricultural and fisheries officers of the selected municipalities

and from relevant Philippine government agencies such as the

Bureau of Fisheries and Aquatic Resources (BFAR-IVA) and

the Taal Volcano Protected Landscape (TVPL) of the

Department of Environment and Natural Resources,

Philippines (DENR).

3 Results and Discussion

3.1 Assessment of Water Quality

Results of the study revealed no significant differences in

water transparency, temperature, DO, pH, and salinity between

aquaculture and non-aquaculture sites throughout the 10-month

sampling period, except in nitrates, phosphates levels and in

total dissolved solids where significant differences existed

(Table 1 and Figure 2). DO and transparency were consistently

lower in the aquaculture sampling stations although not

significantly different from the non-aquaculture sampling

stations.

3.1.1 Phosphates

The levels of phosphate was reported higher than the standard

DENR (0.05 – 1.0 mg/L) recommended for Class C waters

(aquaculture purpose) for both study sites during the entire

sampling period. Average monthly phosphate levels during the

10-month sampling period were significantly higher

(2.17mg/L) in the aquaculture areas than in the non-

aquaculture areas (1.91 mg/L) (Figure 2). Higher phosphates

level was observed during the months of September to October

2013, it may be because of heavy rains during these months.

Zafaralla (1993) and Alcañices et al. (2001) observed an

increase in nutrient concentration during the entire wet season

and in this manner results of present study are in agreement

with the findings of these researchers.

Table 1 Average values of the physico-chemical parameters of the water in aquaculture and non-aquaculture sampling stations in Taal

Lake from August 2013- May 2014.

Parameter Units Standard Level for Class

C Water*

Mean (+ SD)***

Aquaculture Non-aquaculture

Phosphates mg/L 0.05 (0.1)** 2.17a +0.45 1.91

b +0.23

Nitrates mg/L 10 2.93a +0.53

2.197

b +0.61

Total Dissolved Solids (TDS) mg/L 1000 1127.37a +60.70

1066.41

b +56.90

Dissolved Oxygen (DO) (minimum) mg/L 5.0 5.67a +1.40

6.73

a +1.72

pH - 6.5-8.5 8.45a +0.47

8.25

a +0.66

Salinity ppt 0.82a +0.42

0.82

a +0.23

Transparency Meter 2.52a +1.38 3.13

a +1.71

Water Temperature °C 3 oC rise 28.35

a +1.98 28.14

a +1.96

*Class C water is described as water for fishery propagation and growth of fish and other fishery products; for recreation and industrial

supply for manufacturing processes (DENR AO 34, 1990), **Values in parenthesis are considered maximum values for lakes and

reservoirs (DENR AO 34, 1990), *** Average values with the different letters as superscript on the same row indicated significant

difference (p>0.05) among the treatments

Water quality in aquaculture and non-aquaculture sites in Taal lake, Batangas, Philippines 111

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According to Hilario & Perez (2013) intensive fishing is a

point source of dissolved inorganic nutrients principally

nitrogen and phosphorus, and that wind stress was responsible

for the slow nutrient transport in Taal lake. Further, Lucas &

Southgate (2012) reported that phosphorus occurred in water

primarily as phosphate ion and in combination with organic

matter which phytoplankton assimilated and caused their

bloom. Other point sources of phosphates and nitrates in lakes

were domestic wastes that include washing detergents and

faecal matter, and agricultural run-off with fertilizers and

liquid manure from livestock.

3.1.2 Nitrates

Nitrates levels inside fish cages ranged from 1.76 mg/L to 3.69

mg/L and were significantly higher than in non-aquaculture

site. These values are higher than previous studies in Taal lake

(Zafaralla et al., 1992; White et al., 2007; Rosana et al.,

2008). In an earlier study, Dela Vega (2001) reported that for

every 1,000 kg of feed used, an estimated 47 kg N and 9 kg P

were lost into the water. The unconsumed food from fish cage

aquaculture settled at the bottom of the lake.

Figure 2 Physico-chemical parameters (phosphates, nitrates, total dissolved solids, dissolved oxygen, water transparency, and water

temperature) in fish cage farming areas (aquaculture) and in the non- aquaculture area in Taal Lake, Philippines from August 2013 to

May 2014.

112 Blesshe and Airill

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3.1.3 Total dissolved solids (TDS)

Freshwater usually have a TDS concentration less than

1000mg/L. The TDS concentrations at both sites were above

1000 mg/L and it was significantly highest during May with

the start of the rainy season. These dissolved solids are

inorganic substances which are available in ionic form.

According to Lucas & Southgate (2012) rainfall and soil

particles that are washed into the water from run-off are also

sources of TDS.

3.1.4 Water Transparency

Although water transparency was not significant different

between these two sites, the monthly water transparency at

non-aquaculture sites were consistently higher than those from

the aquaculture sites. It was reported highest in the months of

January and February, 2014, which coincided with

phytoplankton die off starting January to March 2014. Present

study showed lower transparency value (1.15 m to 5.56 m) as

compared to the previous studies of Zafaralla et al., 1992 (7.8

m), Alcañices et al., 2001 (5m), Vista et al., 2006 (4-6m).

Findings of present study are in agreement with the findings of

Rosana et al. (2008), those have reported 3.44 m transparency

in open water areas while this value was 2.92 m in cage

farming areas. Water transparency value less than 2.0 m is

considered a eutrophic lake (US EPA 1974). Taal lake

transparency values were lower than 2.0 m for several months

during the 10-month sampling period so it can be considered as

eutrophic lake (Figure 2).

3.1.5 Dissolved Oxygen

Lowest DO level was recorded in the month of January 2014,

on the same month when water transparency value was found

to be the highest. This can be attributed to the start of

phytoplankton die-off in the lake. DO levels below 4ppm were

observed in January –February, 2014 in aquaculture site. DO

measurements were taken during the mid-morning hours yet

DO levels were below 5 mg/L. This implies that there exists

only a small margin of safety before the fish are exposed to a

critical DO level of below 4mg/L. DO level may become even

critically low in late evening or during early morning hours in

the absence of photosynthesis, and in the presence of high

standing fish biomass inside cages and it may result to fish kill.

Phytoplankton die-off occurred in January-February, 2014,

followed by fish kill on the same months. Increasing the level

of DO above 6mg/L in aquaculture sites and 8mg/L in non-

aquaculture sites in March-May 2014 may be due to decrease

in the overall fish standing stock in the lake as a result of fish

kill and the light to moderate phytoplankton density during

these months (Mercurio et al., 2016).

3.1.6 Water Temperature

Water surface temperature ranged from 26oC to 31

oC, without

significant differences between the two study sites. Similar

type of result was reported by Papa & Mamaril (2011) for Taal

lake. Lowest water temperature was reported during February

2014 while the highest was during the month of May 2014.

3.1.7 pH

The pH ranged from 7.5 to 9.14 and did not statistically differ

between the two study sites. The lake is known to be of

volcanic origin with annual water overturn and occasional acid

sulphate emission which could result to low water pH

condition. The 9.1 pH value observed in the present study were

similar to the findings of Rosana et al. (2008).

3.1.8 Salinity

Salinity in Taal lake was 0.8-0.9 ppt, uniform throughout the

entire sampling period in both study sites and it considered as

freshwater.

3.2 Fish Farmers’ Practices and Stakeholders Efforts Affecting

Water Quality

Results of the study on farming practices showed efforts of fish

farmers and stakeholders to reduce organic loading in the lake.

Fish farmers were required to attend a government sponsored

seminar on Good Aquaculture Practices (GAP) before permit

to operate a fish cage was given to prospective fish cage

farmers. Regular consultation and information campaign on

good aquaculture practices particularly on reducing feed losses

were also conducted by various stakeholders. A consultative

Unified Rules and Regulation on Fisheries (URRF) in Taal

Lake was drafted and approved on July 2014 as part of the

Taal Volcano Protected Landscape (TVPL) management plan.

URRF sets a limit to 6,000 floating cages in Taal lake,

distributed to the various municipalities and this distribution

was as follows: Talisay – 2,000 cage units; Laurel – 1,350;

Agoncillo – 1,500; San Nicolas – 1,000; Mataas na Kahoy –

120; Cuenca – 20. A cage unit measures 10m x 10m x 10m.

For the circular type, a diameter of 16 m and depth of 10 m is

allowed. URRF also mandated the use of extruded floating

feed starting March 2015 to reduce feed loss.

The recommended maximum stocking density for tilapia in

cages in Taal Lake is 50 pcs/m3 or 50,000/ cage and for

milkfish is 14 pcs/m3 or 14,000/ cage. Unfortunately, some

fish farmers opted to have higher fish stocking density than

recommended. Overfeeding increases production cost and

nutrient loading to the environment (Dela Vega & Querijero,

2005; Bunting, 2013). White (2013) emphasized the

importance of significantly reducing the production Feed

Conversion Ratio (FCR) values to minimize feed costs and

feed loss. Feed costs account for more than 60 percent of total

production costs. Feed loss and fish wastes from intensive fish

cage farming may have negatively affected the quality of water

particularly the levels of nitrates, phosphates, transparency and

Water quality in aquaculture and non-aquaculture sites in Taal lake, Batangas, Philippines 113

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dissolved oxygen in the aquaculture sites as shown in the

present study.

Conclusions

Aquaculture activities like fish cage farming affect water

quality as indicated by the significantly higher levels of

nitrates, phosphates and TDS and the consistently low DO and

transparency values in aquaculture sites compared to non-

aquaculture sites. To sustain aquaculture production in Taal

lake, stakeholders need to continue their collaborative, multi-

sectoral action planning, information and education campaign,

regulation and licensing that are backed up with sound data on

water quality and feedback.

Acknowledgements:

The authors acknowledge the University Research Office

(URO) of De La Salle University-Dasmariñas (DLSUD) for

providing financial support; and the Bureau of Fisheries

Region IVA, particularly Ms. Nenita S. Kawit of the Inland

Fisheries Research Station; the local government units of

Talisay and Laurel; the PASu TVPL Office and Mr. Victor H.

Mercado, and the TLAAI for kind assistance.

Conflict of Interest

Authors would hereby like to declare that there is no conflict of

interests that could possibly arise.

References

Alcañices MM, Pagulayan RC, Mamaril AC (2001) Impact

assessment of cage culture in Lake Taal, Philippines. In:

Santiago CB, Cuvin-Aralar ML, Basiao ZU (Eds.)

Conservation and Ecological Management of Philippines

Lakes in Relation to Fisheries and Aquaculture. SEAFDEC-

PCAMRD-BFAR, Philippines, Pp.153.

Bunting SW (2013) Principles of sustainable aquaculture:

Promoting social, economic and environmental resilience.

Routledge. New York.

Bureau of Fisheries and Aquatic Resources (BFAR) (2014)

Available on www.bfar.da.gov.ph access on 28th December

2015.

Castillo B, Gonzales C (1976) Hydrology of Taal Lake.

Fisheries Research Journal of the Philippines 1: 62-75.

De la Vega JT (2001) Feeds and feeding management of

tilapia in cages. Paper presented at the 4th Southern Luzon

Zonal R and D Review, DAP Tagaytay City Philippines.

Dela Vega A, Querijero BL (2005) Industry Perspective:

Practices and Needs in Feeds and Feeding Management

including Financing Schemes in Cage and Pen Operations

(Bolinao, Pangasinan Experience). In: Querijero BL, Pagdilao

CR, Ilagan S (Eds.) Guidelines for Establishment of Fish

Cages and Other Structures in Lakes and Coastal Waters.

PCAMRD Book Series No. 36/ 2005, Pp.90-101.

Department of Environment and Natural Resources (DENR),

Philippines, Administrative Order No. 34. Series of 1990

(1990) Revised Water Usage and Classification/ Water Quality

Criteria.

Galera IC, Martinez FV (2011) Monitoring and evaluation

of the water quality of Taal Lake, Talisay, Batangas,

Philippines. Journal of Academic Research 1 : 229-236.

Hilario JE, Perez TR (2013) Predicting transport of nutrients

from three tributary rivers of Taal lake Philippines. The

Philippine Agricultural Scientist 96 : 60-70.

Jacinto GS (2011) Fish kill in the Philippines. Science

Diliman 23 : 1-3.

Lucas JS, Southgate PC (2012) Aquaculture: Farming Aquatic

Animals and Plants. Blackwell Publishing. John Wiley and

Sons, UK. Pp. 648.

Macandog DM, Dela Cruz CPP, Edrial JD, Reblora MA,

Pabico JP, Salvacion AR, Marquez TL, Macandog PBM, Perez

DKB (2014) Eliciting local knowledge and community

perception on fishkill in Taal Lake through participatory

approaches. Journal of Environmental Science and

Management 17: 1-16.

Mercurio AL, Querijero BL, Ching JA (2016) Phytoplankton

community in aquaculture and non-aquaculture sites of Taal

lake, Batangas, Philippines. Journal of Experimental Biology

and Agricultural Sciences 4: 66-73. doi:

http://dx.doi.org/10.18006/2015.4(1).66.73.

Papa RS, Mamaril AC (2011) History of the biodiversity and

limno-ecological studies on Lake Taal with notes on the

current state of Philippine limnology. Philippine Science

Letters 4(1):1-10.

Pullin RSV (1993) An overview of environmental issues in

developing-country aquaculture. In: Pullin RSV, Rosenthal H,

Maclean JL (Eds.) Environment and Aquaculture in

Developing Countries. ICLARM Conference Proceeding

31/1993. Pp.1-19.

Rosana MR, Clemente JP, Casao EA, Regpala RR, Kawit NS,

Panisales VD (2008) Primary productivity, phytoplankton and

the development of eutrophic state of Taal Lake, Southern

Luzon, Philippines. In: Inland Fisheries Research Station

Project Report, Bureau of Fisheries and Aquatic Resources

Region IV-A, Ambulong, Tanauan City, Batangas, Philippines.

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Umaly RC (1988) Limnology: Laboratory and Field Guide

Physico-chemical factors and Biological factors. National

Bookstore, Philippines.

United States Environmental Protection Agency (US EPA)

(1976) Quality criteria for water. Washington, D.C. USA.

Vista A, Norris P, Lupi F, Bernstern R (2006) Nutrient

loading and efficiency of tilapia cage culture in Taal Lake,

Philippines. The Philippine Agricultural Scientist 89: 48-57.

White P (2013) Environmental consequences of poor feed

quality and feed management. In: Hasan MR, New MB (Eds.)

On-farm feeding and feed management in aquaculture. FAO

Fisheries and Aquaculture Technical Paper No 583, Rome,

FAO Pp. 553-564.

White P, Christensen GN, Palerud R, Legovic T, Rosario WR,

Lopez N, Regpala RR, Gecek S, Hernandez J (2007)

Environmental monitoring and modelling of aquaculture in risk

areas of the Philippines. Available on

http://aquaculture.asia/files/D8_EMMA_Taal_final_report.pdf

on 6th December, 2016.

Zafaralla M, Santos R, Torreta N, Regalado M, Orozco R

(1992) Influence of water quality and phytoplankton

community structure in Taal Lake. Fisheries Research Journal

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Water quality in aquaculture and non-aquaculture sites in Taal lake, Batangas, Philippines 115

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KEYWORDS

Hematology

Amaranthus spinosus

WBC

PCV

MCV

MCHC.

ABSTRACT

Amaranthus spinosus is known for its various remedies against many ill conditions. Present study has

been carried out to verify the toxic effects of whole plant’s aqueous extract against albino rats at

hematological levels. The aqueous extract was administered orally at 125, 250, 500 and 1000 mg/Kg

body wt./day respectively for 60 days; against distilled water administered control. Results of study did

not show any mortality in all the treatment groups throughout the study period. The significant reduction

in RBC, hemoglobin, PCV and MCHC and significant increase in WBC and MCV was observed at the

dose level 1000 mg/Kg body wt/day. After 90 days of treatment withdrawal, the hematological

parameters regained to control levels. The toxic effects found to be dose dependent as hematological

parameters were altered only at higher dose level.

Bhande Satish S* and Wasu Yogesh H

P.S.G.V.P. M’s, Shri. S. I. Patil Arts, G. B. Patel Science and S. T. K. V. S. Commerce College, Shahada, Dist. Nandurbar (425 409)

Received – January 07, 2016; Revision – January 27, 2016; Accepted – February 20, 2016

Available Online – February 20, 2016

DOI: http://dx.doi.org/10.18006/2016.4(1).116.120

EFFECT OF AQUEOUS EXTRACT OF Amaranthus spinosus ON HEMATOLOGICAL

PARAMETERS OF WISTAR ALBINO RATS

E-mail: [email protected] (Bhande Satish S)

Peer review under responsibility of Journal of Experimental Biology and

Agricultural Sciences.

* Corresponding author

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1 Introduction

Amaranthus spinosus Linn. (Family: Amaranthaceae) is

commonly known as spiny amaranth, prickly amaranth or

thorny amaranth. It is widely distributed throughout the tropics

and warm temperate regions of Asia as a weed in cultivated as

well as fallow lands (Mishra et al, 2012). In India, it is

commonly known as ‘‘Kate Wali Chaulai (Kanatabhajii)” and

used as vegetable and cultivated throughout India. It is widely

used in folk lore medicinal system of India. The whole plant

and parts of the plant contains medicinally active constituents

such as alkaloids, flavonoids, glycosides, phenolic acids,

steroids, amino acids, terpenoids, lipids, saponins, betalains, b-

sitosterol, stigmasterol, linoleicacid, rutin, catechuic tannins

and carotenoids (Kumar et al., 2014). Extracts of A. spinosus

have been reported to show diuretic, antidiabetic, antipyretic,

anti-snake venom, antileprotic, anti-gonorrheal antioxidant,

anti-cholesterolemic, antipyretic, anti-inflammatory,

spermatogenic, antitumor, antifertility, immunomodulatory,

anti malarial, hepatoprotective activities (Kirtikar & Basu,

2001; Olumayokun et al., 2004; Hilou et al., 2006; Tatiya et

al., 2007; Sangameswaran & Jayakar, 2008; Ashok et al.,

2010; Ilango et al., 2010; Joshua et al., 2010; Girija et al.,

2011; Jhade et al., 2011; Barku et al., 2013; Bavarva &

Narasimhacharya, 2013) and also effects on hematology and

biochemical changes in the epididymis (Murugan et al., 1993;

Olufemi et al., 2003).

Although, it is noticeable that A. spinosus showed various

effects, there remains considerable concern over the safety to

develop it as a drug. Evaluation of haematological parameters

can be used to resolve the extent of toxic effect of extracts on

the blood of an animal. It can also be used to clarify blood

relating functions of a plant extract or its products (Yakubu et

al., 2005).Thus, present study has been carried out to verify the

toxic effects of aqueous extract of A. spinosus on

hematological parameters using albino rats as a model.

2 Materials & methods

2.1 Plant Material

A. spinosus as a whole plant was collected from in and around

places of Nandurbar district in Maharashtra state and identified

in Department of Botany of P.S.G.V.P. M’s, Shri. S. I. Patil

Arts, G. B. Patel Science and S. T. K. V. S. Commerce

College, Shahada, Dist. Nandurbar.

2.2 Extraction preparation

Whole plant was air dried under shaded conditions and

coarsely powdered. Hundred gram of powder was refluxed

with 600 ml of water at 100oC for 24 hours. The extract was

filtered through double layer 100 m nylon wire mesh and

concentrated at 50oC to obtain crude aqueous extract.

2.3 Experimental Animals

Normal healthy male Wistar rats (Rattus norvegicus) weighing

200-240g were used in the present investigation. The animals

were maintained as per the guidelines for care and use of

Animals for Scientific Research proposed by Indian National

Science Academy, 2000 at Department of Zoology, in group of

three animals in polypropylene rat cages under 12:12 hrs. light-

dark schedule and fed with rat pellet diet and water was

provided ad libitum.

2.4 Experimental Design

The animals were divided into four group viz. Group I, Group

II, Group III and Group IV, consisting 10 animals in each

group. The aqueous extract of A. spinosus was administered

orally at 125, 250, 500 and 1000 mg/Kg body wt./day

respectively for 60 days. Control animals were administered

with distilled water. Following completion of respective

treatment schedule, all the animals were withdrawn from the

treatment for a further period up to 90 days. Five animals from

each group were sacrificed the next day following the 60th day

of treatment and remaining five after 90 days of treatment

withdrawal.

2.5 Hematology

Blood samples were collected by cardiac puncture and used for

total red blood corpuscles [RBC], white blood corpuscles

[WBC] analysis as prescribed by Lynch et al. (1969), while

hemoglobin (Crosby et al., 1954) and red cell indices viz.,

packed cell volume (PCV), mean corpuscular volume (MCV),

mean corpuscular hemoglobin (MCH) and mean corpuscular

hemoglobin concentration (MCHC) was studied by the

methodology given by Natelson (1951).

2.6 Statistical analysis

Student’s t-test was employed for the statistical comparison.

3 Results

No animal mortality was recorded in the treatment groups

throughout the study period. Treatment with aqueous extract of

A. spinosus did not show any appreciable changes in

hematological parameter in Group I and II animals after 60

days of treatment period. There was significant reduction in

RBC, hemoglobin, PCV and MCHC in the treatment group IV.

However, significant increase (p<0.001) was observed in WBC

and MCV in the treatment Group IV. The levels of MCH did

not show alterations in all the treatment groups.

After 90 days of treatment withdrawal, the hematological

parameters regained to control levels (Table1- 4).

117 Bhande and Wasu

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Table 1 Hematology of rats treated orally with aqueous extract of A. spinosus @ 125 mg/Kg body wt./day (Values with SEM of 5

animals).

Treatment Schedule RBC

(106/mm

3)

WBC

(103/mm

3)

Hemoglobin

(g/dl)

PCV

(%)

MCV

(µ3)

MCH

(µ.µg)

MCHC

(%)

Control 8.22±0.14 10.6±0.36 15.8±0.22 38.5±0.17 46.9±0.60 19.3±0.53 41.1±0.70

Treatment 60 days 8.18±0.08 10.7±0.71 15.5±0.37 38.4±0.24 46.9±0.27 19.0±0.57 40.6±0.84

TW 90 days 8.36±0.15 10.4±0.50 15.7±0.86 38.0±0.41 46.0±1.16 18.7±1.09 40.9±1.93

All the values are the mean of five replicates, TW – Treatment withdrawal

Table 2 Hematology of rats treated orally with aqueous extract of A. spinosus @ 250 mg/Kg body wt./day (Values with SEM of 5

animals).

Treatment Schedule RBC

(106/mm

3)

WBC

(103/mm

3)

Hemoglobin

(g/dl)

PCV

(%)

MCV

(µ3)

MCH

(µ.µg)

MCHC

(%)

Control 8.22±0.14 10.6±0.36 15.8±0.22 38.5±0.17 46.8±0.60 19.3±0.53 41.1±0.70

Treatment 60 days 8.16±0.12 10.9±0.29 15.4±0.32 38.6±0.12 47.1±0.78 18.9±0.62 40.9±0.38

TW 90 days 8.20±0.15 10.8±0.50 15.6±0.86 38.8±0.41 47.4±1.16 19.2±1.09 40.8±1.93

All the values are the mean of five replicates, TW – Treatment withdrawal

Discussion and Conclusions

In the present study, toxic effects of aqueous extract of A.

spinosus in male albino rats have been investigated. The results

indicate that the extract did not lead to any deleterious effects

in the animals treated at 125 and 250 mg/Kg body wt./day. The

absence of significant changes may suggest that the extract

does not have toxic effects at these dose regimens in albino

rats. The significant decrease was observed in the PCV in 500

and 1000 mg/kg. treatment group while RBC, hemoglobin,

PCV and MCHC were decrease significantly at 1000 mg/kg.

b.w./day . It was reported that administration of 50% ethanolic

and methanolic extract of A. spinosus at 0.5 gm/kg. b.w. for 7,

14 and 21 days and 250 mg/kg for 5, 7 & 14 days respectively

showed significant decrease in RBC count (Olufemi et al.,

2003; Srivastava et al., 2011). This reduction in number of

RBC may be due to the hemolytic activity of the extract at

higher dose. Similarly, Choudhury (2012) reported the

presence of glycosides & saponin in the aqueous extract of A.

spinosus. It is earlier reported that haemolysis of red blood

cells is caused by saponin (Lawrence et al., 1997). Also,

aqueous crude extracts of A. cordifolia, P. amarus, P.

muellerianus and S. virosa administered at 2ml/100 gm. body

wt./day for 14 days, caused a significant reduction in PCV, Hb

concentration and RBC, (Adedapo et al., 2007). It is earlier

reported that the oral ingestion of medicinal compounds or

drugs can alter the normal range of haematological parameters

(Ofuya & Ebong, 1996; Ajagbonna et al., 1999).

Table 3 Hematology of rats treated orally with aqueous extract of A. spinosus @ 500 mg/Kg body wt./day (Values with SEM of 5

animals).

Treatment Schedule RBC

(106/mm

3)

WBC

(103/mm

3)

Hemoglobin

(g/dl)

PCV

(%)

MCV

(µ3)

MCH

(µ.µg)

MCHC

(%)

Control 8.22±0.14 10.6±0.36 15.8±0.22 38.5±0.17 46.8±0.60 19.3±0.53 41.1±0.70

Treatment 60 days 7.96±0.33 11.4±0.68 15.2±0.19** 37.7±0.16*** 47.2±0.34 19.2±0.72 40.8±0.50

TW 90 days 8.11±0.26 10.9±0.54 15.5±0.37 37.9±0.40* 46.7±1.33 19.5±0.44 40.9±0.70

All the values are the mean of five replicates, TW – Treatment withdrawal, ** represents significance level at p<0.01, *** represents

significance level at p<0.001.

Effect of Aqueous Extract of Amaranthus spinosus on Hematological Parameters of Wistar Albino Rats. 118

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Table 4 Hematology of rats treated orally with aqueous extract of Amaranthus spinosus @ 1000 mg/Kg body wt./day (Values with SEM

of 5 animals)

Treatment Schedule RBC

(106/mm

3)

WBC

(103/mm

3)

Hemoglobin

(g/dl)

PCV

(%)

MCV

(µ3)

MCH

(µ.µg)

MCHC

(%)

Control 8.22±0.14 10.6±0.36 15.8±0.22 38.5±0.17 46.8±0.60 19.3±0.53 41.1±0.70

Treatment 60 days 6.50±0.22

***

13.7±0.88

***

13.1±0.41

***

35.6±0.62

***

54.8±2.20

*** 20.1±1.10

35.9±0.66*

**

TW 90 days 7.58±0.53* 11.4±0. 86* 15.4±0.53* 37.2±1.28* 49.2±3.71* 20.8±2.09 49.9±1. 86

All the values are the mean of five replicates, TW – Treatment withdrawal, ** represents significance level at p<0.01, *** represents

significance level at p<0.001.

Administration of aqueous extract of A. spinosus showed

significant increase in WBC and MCV at the dose level 1000

mg/Kg body wt./day. The level of MCH did not show

alterations in all the treatment groups. Similarly, the

administration of aqueous ethanolic extract of M. indica stem

bark at a dose of 5000 mg/kg body weight for 14 days induced

significant increase in WBC and the differential leukocytes

counts in the tested animals (John et al., 2012). These results

suggested that the extract may have immunological properties

at higher dose level. The immuno-stimulating activity of wild

A. spinosus water extract was investigated on spleen cells from

female mice. The isolated B lymphocytes, but not T

lymphocytes, could be stimulated by wild A. spinosus in a dose

response manner. The water extract of A. spinosus directly

stimulates proliferation of B lymphocytes in vitro (Lina et al.,

2005). It is earlier accounted that the water extract of the plant

have significant immunostimulating activity (Lagos, 1986).

Nevertheless, the effects of aqueous extract of A. spinosus at

1000 mg/Kg body wt/day are temporary because every

hematological parameter regained to control levels after 90

days of treatment withdrawal and no mortality of animals was

observed throughout the study period. The toxic effects of

aqueous extract of A. spinosus was found to be dose dependent

as hematological parameters were altered only at higher dose

level (1000 mg/Kg body wt./day), while there was no toxic

effects at lower dose level. The effective dose and toxicity may

change with the solvent used in extraction protocol. It is

suggested that the safety dose level for aqueous extract of A.

spinosus could be 500 mg/Kg body wt/day in albino rats.

Acknowledgements

Authors are very much thankful to UGC (WRO-Pune) New

Delhi, India for providing financial assistance to present work.

Conflict of Interest

Authors would hereby like to declare that there is no conflict of

interests that could possibly arise.

References

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