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INDEX _____________________________________________________________________________

Evaluation of maize hybrids to terminal drought stress tolerance by defining drought indices

http://dx.doi.org/10.18006/2016.4(Issue6).610.616

Effect of biological fertilizer on the growth and nodules formation to soya bean (Glicine max

(L.) merrill) in ultisol under net house conditions


Farmer’s management practices to maintain the genetic diversity of sorghum (Sorghum

bicolor L. moench) in south of Chad


Influence of organic and mineral fertilizers on chemical and biochemical compounds content

in tomato (Solanum lycopersicum) var. Mongal F1


Cultivation of Rosmarinus officinalis in hydroponic system


Nutritional quality of maize in response to drought stress during grain-filling stages in

mediterranean climate condition


Analysis of off-season cucumber production efficiency in Punjab: a DEA approach


Effect of nitrogen and sulfur on the quality of the cotton fiber under mediterranean

conditions


Nutritive evaluation of azolla as livestock feed


Physio-biochemical and molecular characterization for drought tolerance in rice genotypes at

early seedling stage


JEBAS

_________________________________________________________

Journal of Experimental Biology and Agricultural Sciences

http://www.jebas.org

KEYWORDS

Grain filling stage

Grain yield

Stress indices

Zea mays

ABSTRACT

Terminal drought stress is one of the most important environmental stress factors which can cause a

significant reduction in maize productivity. Therefore, to identify the best selection indices for drought

tolerance in maize under terminal drought conditions, this research was conducted in two field

experiments with some maize hybrids in two cropping seasons (2014 and 2015) under two moisture

levels (normal irrigation and water deficit-water stress) at grain filling stage. Results of study revealed

that, yield and major yield traits of hybrids adversely affected due to terminal drought stress, it also

causing a reduction in productivity with compare normal irrigation conditions. Water stress significantly

affected on maize hybrids and there were high variation among hybrids, which could be befits for

screening the genotypes. The special attention was paid to hybrids 71May69, Aaccel and Calgary were

showed less reduction of grain yield under terminal drought stress. Concerning the genotypes with high

stress susceptibility index (SSI) and tolerance index (TOL) were considered as high susceptible to

drought and only suitable for irrigated conditions. Accordingly, the positive relationship between stress

indices, drought resistance index (DRI) , geometric mean productivity (GMP) , harmonic mean (HM),

mean production (MP), stress tolerance index (STI) and Yield index (YI) , and grain yield could be

used as the best selection indices for identifying the tolerant hybrids under terminal drought stress.

Celaleddin Barutcular1, Ayman EL Sabagh

2,*, Omer Konuskan

3, Hirofumi Saneoka

4 and Khair

Mohammad Yoldash1

1Department of Field Crops, Faculty of Agriculture, Cukurova University, Adana,Turkey

2Department of Agronomy, Faculty of Agriculture, Kafrelsheikh University, Egypt

3Department of Field Crops, Faculty of Agriculture, Mustafa Kemal University, Turkey

4Plants Nutritional Physiology, Graduate School of Biosphere Science, Hiroshima University, Japan

Received – October 14, 2016; Revision – November 06, 2016; Accepted – November 10, 2016

Available Online – November 13, 2016

DOI: http://dx.doi.org/10.18006/2016.4(Issue6).610.616

EVALUATION OF MAIZE HYBRIDS TO TERMINAL DROUGHT STRESS

TOLERANCE BY DEFINING DROUGHT INDICES

E-mail: [email protected] (Ayman EL Sabagh)

Peer review under responsibility of Journal of Experimental Biology and

Agricultural Sciences.

* Corresponding author

Journal of Experimental Biology and Agricultural Sciences, November - 2016; Volume – 4(Issue-6)



ISSN No. 2320 – 8694

Production and Hosting by Horizon Publisher India [HPI]

(http://www.horizonpublisherindia.in/).


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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http://crossmark.crossref.org/dialog/?doi=10.18006/2016.4(Issue6).610.616&domain=pdf&date_stamp=2016-11-13

_________________________________________________________



1 Introduction

Maize (Zea mays L.) one of the most important summer crops

in Turkey and about 18% of its demand will fulfill by imported

(FAO, 2013). Improving of Maize for drought-stress tolerance

is one of the most important obstacles as the global need for

food, fiber, and fuel increases. Seed companies are succeeding

and endorsing drought-tolerant genotypes, but the mechanism

of physiological drought-tolerance mechanisms for genotypes

are not well understood (Roth et al., 2013).

Environmental stresses adversely affect the growth and

productivity of plants (Islam et al., 2011).The performance of

crops are highly complex phenomenon under water stress

condition and negative affected (Reynolds et al., 2006).

Therefore, research on irrigation and water management has

concentrated on crop productivity responses to water provide

(Chen et al., 2010; Köksal, 2011). It is a fact that when drought

stress starts to influence on the plant at reproductive stage, the

plant reduces the demand of carbon by reducing the size of

sink. As a result, reduction in leaf size, stem extension and root

proliferation, flower may drop pollen may die and ovule may

abort (Blum, 1996; Farooq et al., 2009). The production of

grain yield reduced 60% due to stress condition at grain filling

stages (Khodarahmpour & Hamidi, 2012). The yield reduction

under drought stress was greater at the reproductive stage than

at the vegetative and grain filling stages (Fatemi et al., 2006).

Drought stress tolerance development is difficult due to the

phenomenon of well-built interactions between genotypes and

the environment conditions. Therefore, based on yield loss

under water stress conditions with compare to normal

conditions, various drought indices were determined that have

been used for identification of drought tolerant genotypes

(Mitra, 2001), others investigation recorded in a target stress

condition (Mohammadi et al., 2011). While others experiments

yet have chosen a mid-point and think in selection under both

favorable with combined stress conditions (Sio-Se Mardeh et

al., 2006). Several selection criteria are suggested to designate

genotypes on the basis of their performance in stress and non-

stress conditions (Fernandez, 1992).

Genotypes Identification for water stress tolerance at grain

filling stage for higher production is very necessary for crop

breeding (Menezes et al., 2014). Various previous

investigations revealed that, the advantage of these indices for

classified genotypes with more stable of productivity under

water-limited conditions (Golabadi et al., 2006). Several

indices have been recorded as benefits to identify maize to

drought stress tolerance (Moradi et al., 2012). The

identification of genotypes for drought tolerance is more

difficult due to the interactions between genotypes and the

environment and there is not having enough knowledge about

the role of mechanisms to stress tolerance, therefore, several

scientists have used various techniques for assessment role of

genetic variations in drought tolerance (Fernandez, 1992).

Thus, by keeping in view the above facts, the present study

was undertaken to assess the selection criteria for identifying

drought tolerance in maize hybrids and to distinguish high

yield maize hybrids which are compatible with stressful and

optimal conditions in the Mediterranean condition.

2 Materials and Methods

2.1 Plant material and growing conditions

The current study was conducted at agricultural experimental

area of Cukurova University, Adana, Turkey, during 2014 and

2015 growing season of the second crop. Climatic conditions

of this region have been presented in (Table 1).The

methodologies have been followed as described previously by

EL Sabagh et al. (2015). The design of experiment was

randomized complete block design in a strip-split plot manner

with four replications. The material of experimental was

comprised of 7 hybrids of maize viz. Sancia, Indaco, 71May69,

Aaccel, Calgary, 70May82 and 72May80. These hybrids were

evaluated at grain filling stage under two moisture levels

(normal irrigation and water deficit-water stress), application

method and amount of water and time has presented in (Table

1). Each plot was of 10m in length and 5.6 m width including

plant stand (Intra row: 70 cm, Inter row: 17 cm). Hybrids were

sown during first and the second year on 28 June, 2014 and 12

June, 2015, respectively. Regular agronomic practices which

are necessary for of the maize crop are carried out. During

experiments, nitrogenous fertilizer was utilized within two

times of planting, 100 kg N and P2O5 ha-1

(20-20-0) and V6-

growth stage 200 kg N ha-1

(Urea).

2.2 Sampling and measurements of grain yield traits

At harvesting time, data on various yield components was

collected by using standard procedures, the number of plants

and ears were counted separately.Yield components plant

height (cm), ear height (cm), ear-up stem length (cm), ear

diameter (mm), kernel number (row-1

), kernel row (ear-1

),

kernel number (m-2

), grain weight (mg), grain yield (g m-2

),

biomass (g m-2

) and harvest index (%) were measured.

2.3 Measurements of indices

Drought tolerance indices such as, tolerance index (TOL),

mean production (MP) were calculated according to the

method give by Rosielle & Hamblin (1981). While the

geometric mean productivity (GMP), mean productivity (MP)

and stress tolerance index (STI) was measured according to the

method given by Fernandez (1992). Further, yield index (YI)

and yield stability index (YSI) was calculated as stated by

Bouslama & Schapaugh (1984) and Gavuzzi et al. (1997).

Stress susceptibility index (SSI) was measured according to the

method give by Fischer & Maurer (1978) and drought

resistance index (DI) was calculated according to Bidinger et

al. (1987).

611 Barutcular et al

_________________________________________________________



Table 1 Amount of irrigation and climatic traits during 2014 and 2015 growing season.

Growing period Max.T. (°C) Min.T. (°C) Mean T. (°C) SR (cal cm-2

) MH (%) FI (mm) DI (mm)

2014 growing season

Sowing-Anthesis 33.5 25.1 28.6 535 70.1 240 240.0

Anthesis-PM 32.5 22.6 27.0 428 64.9 287.4 191.4

Sowing-PM 33.1 24.1 27.9 491 67.9 531.1 435.1

2015 growing season

Sowing-Anthesis 32.9 23.5 27.7 578 68.8 337.1 313.1

Anthesis-PM 35.2 24.6 29.4 464 63.4 476.9 308.9

Sowing-PM 34.1 24.1 28.5 518 66.2 814.0 622.0

(T) temperature; (SR) Solar radiation; (MH) Mean humidity; ( FI) Full irigation: Rain + Irrigation, mm; (DI)Deficit irrigation: Rain +

Irrigation, mm.(Source: Meteorological Service of Turkish State)

2.4 Statistical analysis

All data collected for two years average and obtained results

were calculated to analyses of variance according to Gomez &

Gomez (1984). Significant means were separated by the Least

Significant Difference (LSD) at the 0.05 significance level

(P≤0.05).The estimation of correlation for traits was calculated

by MSTAT-C computer software package.

3 Results and Discussion

3.1 The influence of irrigation regime on yield traits

For yield components, maize hybrids were significantly

influenced by irrigation treatments and, water stress lead to a

significant reduction in yield traits over control (Table 2).

Yield traits such as ear-up stem length, ear height, kernel

number per row, grain weight, grain yield, biomass yield and

harvest index were adversely affected by water deficit

condition except plant height, kernel row ear-1

and kernel

number m-2

. It was found that grain weight was significantly

affected by water stress and the highest grain weight (275 mg)

was observed under control and the lowest (253mg) under

water stress condition. Low grain weight due to drought stress,

as found in present experiments, may indicate that the plants

were unable to fully meet the demand of the growing grain.

Irrigation regimes effect was the most important source of

grain yield during grain growth stage. With respect to grain

yield, it was observed that water stress caused significant

reduction in grain yield (-16.36%) as shown in Table 2.

Table 2 Agronomic traits of maize hybrids under irrigation regime (Two years average).

PH

(cm)

E-SL

(cm)

EH

(cm)

KNR

(row-1

)

KRN

(ear-1

)

KNA

(m-2

)

GW

(mg)

HI

(%)

GY

(g m-2

)

BY

(g m-2

)

Water regimes

Irrigated 145 244 100 38.2 14.9 4764 275 53.2 1292 2435

Deficit irrigated 141 237 96 35.3 14.9 4332 253 50.5 1081 2154

P value ns * ** * ns ns * 0.052 *** **

Drought effect(%) -2.97 -2.86 -3.59 -7.47 0.11 -9.07 -7.96 -4.99 -16.36 -11.53

Hybrids

H1 140 224 84 36.2 15.5 4825 242 51.5 1149 2239

H2 146 244 98 37.2 14.6 4320 283 52.0 1219 2343

H3 151 238 86 34.9 16.3 4709 261 53.3 1217 2294

H4 140 241 101 36.5 14.2 4278 281 54.8 1191 2176

H5 142 241 102 37.7 15.6 5084 238 51.5 1199 2332

H6 145 245 99 39.7 13.3 4119 293 49.3 1198 2448

H7 138 251 113 35.1 14.5 4499 255 50.7 1132 2230

Mean 143 240 98 36.8 14.9 4548 264 51.9 1187 2295

LSD0.05 6.0 6.2 4.9 1.52 0.41 310.9 15.4 1.83 40.1 94.6

CV % 4.1 2.6 5.0 4.1 2.7 6.7 5.8 3.5 3.3 4.1

*,** and *** significant P<0.05, P<0.01 and P<0.001 levels respectively; ns, not significant; CV, coefficient of variation; PH, plant

height; E-SL, ear-up stem length; EH, ear height; KRN, kernel row number per ear; KNR, kernel number per row; KNA, kernel number

per area GW, grain weight; HI, harvest index; GY, grain yield; BY, biomass yield.H1,Sancia; H2, Indaco; H3, 71May69; H4,Aaccel;

H5, Calgari; H6, 70May82 and H7, 72May80.

Evaluation of maize hybrids to terminal drought stress tolerance by defining drought indices 612

_________________________________________________________



Figure 1 Pearson correlation coefficient between grain yield and agronomic traits of maize hybrids under irrigation regimes (Two years

average). †, significant P=0.057 level; PH, plant height; E-SL, ear-up stem length; EH, ear height; KRN, kernel row number per-ear;

KNR, kernel number per row; KNA, kernel number per area; GW, grain weight; HI, harvest index; GY, grain yield; BY, biomass yield.

The obtained results showed significant differences in kernel

number per row among irrigation regime with compare to

water stress that caused reductions in kernel number (-7.47%)

per row. In this study, a significant differences in harvest index

between the irrigation regimes and the lowest values for

harvest index (50.5%) were obtained in water stress condition

and the highest values (53.2%) were recorded when crop

grown under control condition (Table 2). Various

investigations have been recorded that grain yield and yield

attributes of maize were significantly influenced by irrigation

regime treatments (Moser et al., 2006; Abd El-wahed et al.,

2015; Barutcular et al., 2016; Rashwan et al., 2016). Further

researcher reported that drought stress conditions decreased

total productivity of maize due to reduction of kernel number

per row and total kernel number per ear (Shoa Hoseini et al.,

2007; Golbashy et al., 2010; ELSabagh et al., 2015). Further,

yield losses were associated with the reduction in kernel

number and kernel weight under deficiency of water at

vegetative and reproductive phases of growth (Pandey et al.

(2000).

3.2 Comparative evaluation of various hybrids of maize under

irrigation regiemes

Significant differences with respect to grain yield and yield

traits were observed among various genotypes, and highest

reduction in yield was observed in hybrid variety 72May80 and

Sancia (Table 2). Grain yield is the result of the expression and

association of several plant growths attributes. According to

grain weight, the hybrids Indaco, 70May82 and Aaccel were

showed more positive effect of grain weight. Achieved results

revealed that kernel number per area was significantly

influenced by water stress conditions and that maximum value

of kernel number per area was found in Calgary (5084 grains

m-2

) and minimum in 70May82 (4119 grains m-2

). The

obtained results in the same table revealed that maximum value

of kernel rows per ear was found in 71May69 (16.3 rows ear-1

)

and minimum rows in 70May82 (13.3 rows ear-1

), while, the

hybrid 70May82 produced higher values of kernel number per

row (Table 2). In this experiment, the hybrid Aaccel was

achieved the highest value of harvest index. The decrease in

harvest index under water deficit stress showed the fact that

both grain yield decreased under drought stress (Table 2). The

varietal differences were found by other investigators include

in which indicated actuality of high variety among hybrids

studied for drought tolerance (Golbashy et al., 2010).

Mostafavi et al. (2011) in a similar experiment observed that

drought stress adversely influenced on the yield attributes and

yield of maize hybrids. Perhaps, in addition to the reduction

that happens in dry matter, water deficit disrupts the

partitioning of carbohydrates to grains and hence, decreases

harvest index. When maize plants were exposed to drought

stress at tasseling stage, lead to substantial reduction in yield

and yield components such a kernel number per row, kernel

weight, kernels per cob, grain yield per plant, biological yield

per plant and harvest index (Anjum et al., 2011; Abd El-

Wahed et al., 2015).

3.3 Correlation analysis

Correlation coefficients between the studied variables and total

yield showed that only kernel row number and ear height were

negatively correlated with grain yield under drought condition.

While, the highest correlations were observed for grain yield

and grain weight (Figure. 1). It was observed, under control

conditions the kernel number per m2 was highly correlated

with grain yield therefore, the hybrids with larger kernel

number should be selected under irrigated condition to increase

grain yield. Therefore, kernels per row and grain weight could

be used as an important trait for prediction of grain yield under

drought stress at the grain growth stage (Figure. 1). This

finding is in agreement with the results of Shoa Hoseini et al.

(2007) and Golbashy et al. (2010).

-1.0

-0.6

-0.2

0.2

0.6

1.0

BY GW HI KRN KNR KNA EH E-SL PH

Irrigated Deficit Irrigated

†

Co

effic

ient o

f corr

ela

tion


_________________________________________________________



Figure 2 Pearson correlation coefficients between grain yield and drought indices(Two years average). **, significant P<0.01 level; SSI,

stres susceptibility index; TOL, tolerance index; YR, yield reduction ratio; DI, drought resistance index; GMP, geometric mean

productivity; HM, harmonic mean; MP, mean productivity; STI, stres tolerance index; YI, yield index; YSI, yield stability index.

3.4 Assessment, of maize hybrids by drought stress tolerant

indices

For determining suitable stress tolerance indices to identify the

hybrids for drought stress tolerance, grain yield of maize

hybrids under stress conditions were calculated todetermine the

various sensitivity and tolerance indices to provide the

appropriate criterion for drought stress tolerant (Table 3 and

Figure. 2). The high positive correlation was observed between

grain yield and DRI, GMP, HM, MP, STI, YI and YSI and

while, negative correlation was recorded between TOL, SSI

and YR and grain yield in drought condition (Figure. 2). It was

found that, genotypes, 70May82 and Indaco were recorded the

high values of stress tolerance index (STI), geometric mean

productivity (GMP) and mean productivity (MP), therefore, it

could be identified astolerant hybrids to water stress

conditions.Values of SSI lower than 1.0 denotes low drought

susceptibility (or high yield stability) and values higher than

1.0 indicate high drought susceptibility (or poor yield

stability). In the meantime the genotypes, 71May69, Aaccel

and Calgary showed the lowest value in yield reduction ratio

(YR) and therefore, would be more tolerant to water stress and

could were identified as drought resistant genotypes. Finally,

the genotypes with high values of yield stability index (YSI),

drought resistance index (DI) and harmonic mean (HM) can be

selected as tolerant genotypes to water stress such as

71May69, Aaccel and Calgarywere identified as drought

tolerant genotypes because, these genotypes had greater values

for DI, YSI and HM (Table 3). The genotypes with low value

DSI values are drought tolerance because they have lesser

reduction in grain yield under stress condition (Fayaz &

Arzani, 2011). SSI value more than 1.0 indicated above-

average sensitivity to water stress conditions (Guttieri et al.,

2001). Abdipour et al. (2008) reported that using MP, GMP,

and STI for screening drought stress tolerant as the most

suitable indices. Kargar et al. (2004) identified GMP and STI

as the best indices in separation superior genotypes in stress

and nonstress condition. Kharrazi & Rad (2011) reported that

MP and STI indices are benefits to classified the tolerant

genotypes.

Table 3 Calculated stress indices based on grain yield of maize hybrids.(Two years average).

Hybrids SSI(†)

TOL(†)

YR(†)

DI(§)

GMP(§)

HM(§)

MP(§)

STI(§)

YI(§)

YSI(§)

H1 1.075 222 0.176 0.662 1143 1138 1149 1.075 222 0.176

H2 1.138 250 0.186 0.689 1212 1206 1219 1.138 250 0.186

H3 0.995 216 0.163 0.719 1212 1208 1217 0.995 216 0.163

H4 0.880 185 0.144 0.728 1188 1184 1191 0.880 185 0.144

H5 0.763 160 0.125 0.758 1197 1194 1199 0.763 160 0.125

H6 1.035 222 0.169 0.699 1193 1188 1198 1.035 222 0.169

H7 1.109 226 0.181 0.646 1127 1121 1132 1.109 226 0.181

(†) and (§), low and high index values showed more tolerant cultivars for each indices, respectively. (SSI) Stress suscptibility index;

(TOL) Tolerance index; (YR)Yield reduction ratio; (DI) Drought Resistance Index; (GMP)Geometric Mean Productivity; (HM)

Harmonic Mean; (MP) Mean Productivity; (STI) Stress tolerance index; (YI )Yield Index; (YSI) Yield Stability Index.H1,Sancia; H2,

Indaco; H3, 71May69; H4,Aaccel; H5, Calgari; H6, 70May82 and H7, 72May80.

-1.0

-0.6

-0.2

0.2

0.6

1.0

SSI TOL YR DI GMP HM MP STI YI YSI

Co

eff

icie

nt o

f co

rre

latio

n ** ** ** ** ** **


_________________________________________________________



Conclusions

In the light of above results, water stress during grain filling

stage can lead to loss in grain yield and causing a reduction of

the productivity with compared to the full-irrigation condition

of maize hybrids. there were high variation among hybrids,

which could be befits for identifying drought-tolerant

genotypes, and the hybrids 71May69, Aaccel and Calgary were

more stable and appeared to more tolerant to drought stress

with respect to grain yield loss , and Accordingly, the

genotypes had high stress susceptibility index (SSI) and

tolerance index (TOL), thus they were susceptible to drought

and only suitable for irrigated conditions. Furthermore, GMP,

MP, YI, STI, SSI and TOL were appropriate indices to identify

maize hybrid tolerant to drought stress conditions. The results

from this study, drought indices are very useful for planning

future maize breeding programs especially, terminal drought

stressin Mediterranean conditions.

Conflict of interest

Authors would hereby like to declare that there is no conflict

ofinterests that could possibly arise.

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KEYWORDS

Nodule

Soybean

Bio-fertilizer

Nodule formation

Ultisol

ABSTRACT

Present study was aim to assess the effect of various concentrations of biological fertilizers on the

growth and nodules formation in soybean crop (Glicine max (L.) Merrill) in Ultisol under net house

condition. Study was conducted under net house condition in the month of July to October 2014. Study

was conducted in randomized block design (RBD) with six treatments and each treatment was replicated

with 3 replications. The variables studied were plant height, stem diameter, root length, nodule diameter,

nodule number, effective nodules number, fresh nodule weight, wet effective nodules weight, dry

nodule weight and dry effective nodule weight. Results of study revealed that treatments of M-bio

fertilizer at concentration of 12 ml per liter of water provide the highest growth such plant height as

44.65 cm, stem diameter as 0.86 cm and roots length as 24.49 cm. The treated soybean plant have 21.27

nodules and among these number of effective nodules are 16.63 pieces.

Sarawa1,*

, Halim2

and Makmur Jaya Arma2

1Specifications Agronomy, Department of Agrotechnology, Faculty of Agriculture, Halu Oleo University, Southeast Sulawesi, Indonesia

2Specifications Weed Science, Department of Agrotechnology, Faculty of Agriculture, Halu Oleo University, Southeast Sulawesi, Indonesia

Received – June 06, 2016; Revision – August 13, 2016; Accepted – October 19, 2016



EFFECT OF BIOLOGICAL FERTILIZER ON THE GROWTH AND NODULES

FORMATION TO SOYA BEAN (Glicine max (L.) MERRILL) IN ULTISOL UNDER

NET HOUSE CONDITIONS

E-mail: [email protected] (Sarawa Mamma)







ISSN No. 2320 – 8694















_________________________________________________________



1 Introduction

Soybean is leguminous crops which have ability to fix

atmospheric nitrogen. According to Sanginga et al. (2010) this

plant can fix annually 11-120 kg atmospheric nitrogen in the

form of Nitrate. Further, Handarson et al. (1989) reported that

nitrogen fixation ability of the soybean crop is influenced by

several factors such as plant varieties, soil types, climatic

conditions, crop management practices, availability of organic

matter (Kundu et al., 1996) and the availability of Phosphorus

and Sulfur (Kris Joko, 2001; Singh, 2004).

According to Sarawa (2014) response of soybean crop toward

biological nitrogen fixation (soybean– rhizobium symbiosis)

are more prominent than the other nitrogen fertilizers. The

possible explanation of this may be quick absorption and rapid

utilization of biologically fix nitrogen as compare to other

nitrogen fertilizers. Further it was reported that the excess use

of inorganic nitrogen fertilizers not only inhibit the rate of

biological nitrogen fixation but also inhibit the formation of

nodules. Further it was reported that this excess use of

chemical fertilizers also lead to increase soil acidity by

releasing hydrogen ions in applied soil.

On the other hand use of rhizobium based bio-fertilizer would

be hampered because it is very sensitive to soil acidity (Anetor

& Akinrinde, 2006). Further, addition of small amounts of

inorganic N fertilizer is not only spurs the growth of soybean

plant but also increase the legumes nitrogen fixation (Keyser &

Li, 1992; Bakere & Hailemariam, 2012). Integrated

management of land, crop, organic and inorganic fertilizer

along with microbial community is very important for

sustainable agriculture under acidic soil conditions. These

practices not only increase soil health and biodiversity but also

has positive influence crop yield (Bejiga, 2004; Ellafi et al.,

2011).

Soil acidity caused shortage of nitrogen, which lead to cause

deficiency of calcium (Ca) and phosphorus (P), which in turn

inhibits the growth and rhizobium infection on plant roots

(Barbara & Ndakidemi, 2010). Some bio-activators such as

biological fertilizer can enhance the activity of rhizobium in

the soil. These biological fertilizers not only help in nitrogen-

fixation but also help in aggregate phosphate stabilizing. In

Sulawesi, Indonesian farmer used commercial bio-fertilizer M-

bio fertilizers for increasing the biological nitrogen fixation by

rhizobium and increasing crop production. M-bio is a

microbial fertilizer that is mixed cultures of beneficial

microbes which consist of nitrogen-fixing microbes, produce

enzymes and hormones (Nurmayulis et al., 2014). Present

study was conducted to explore the effect of M-bio fertilizers

on rhizobium nitrogen fixation and nodule formation under

Ultisol soil condition.


2.1 Study Area and Experimental Setup

Present study was conducted (from July to October 2014)

under net house conditions. Experimental area is located in

Anduonohu Village, District Poasia, Kendari City and

Province of Southeast Sulawesi, Indonesia. Plant were grown

in polybag (30 cm x 40 cm) containing 10 kg Ultisol soil and

study was conducted in randomized block design (RBD) with

six treatments i.e. without M-bio (control), M-bio 3 ml per liter

of water, M-bio 6 ml per liter of water, M-bio 9 ml per liter of

water, M-bio 12 ml per liter and 15 ml M-bio per liter of water,

each treatment was replicated with 3 replications.

2.2 Preparation of Planting Media

The soil has been collected from the study area at layer of top

soil (0-30 cm); collected soil samples were taken into the

laboratory and debris such as twigs, roots, leaves and small

rocks particles were removed from these samples. Cleared soil

was shifted into a polybag with a weight of 10 kg soil. These

soil containing polybags were filled with water until it reached

the capacity of field and it was followed by the incubation, for

2 days under net house. The soybean seed soak for 10 minutes

in various concentrations of M-Bio solution were used as a

treatment. Seeds were planted @ 3 seeds per polybag and after

the age of 10 days, only two plants per poly bag were

maintained till the completion of study 50 days after plantation

(DAP), randomly three poly bags for each treatment were

selected and plants of these poly bags were harvested and used

to study various selected attributes.

Various attributes which were studied after harvesting (50

DAP) are plant height, stem diameter, root length, number of

nodules, nodule diameter, number of effective nodules, fresh

weight nodules, dry weight of nodules, fresh weight of

effective nodules and dry weight of effective nodules.

Effective nodules are those nodules which gave pink color

after cutting through razor. Among these stem and nodule

diameter was measured with the help of sigma (calipers) while

the dry weight of nodules was calculated after drying nodules

at 80οC for 48 hrs. Various nodule weights were calculated by

using following formula.

Nodules Weight = Weight of effective nodules + weight of non

effective nodules

2 Data Analysis

Data of each variable were analyzed by variance of analysis. If

the F count is greater than F table, than continued with

Honestly Significant Difference Test (HSDT) at 95%

confidence level. To determine the effect of a dose of M-bio to

the growth and formation of pimples, and then be made curve

and display the regression equation and the value of R.

618 Sarawa et al

_________________________________________________________



Table1. Effect of M-bio fertilizer to average of height plant, stem diameter and root length at the age of 50 DAP.

Treatments Plant height (cm) Stem diameter (cm) Roots length (cm)

With out M-bio (control)

M-bio 3 ml per liter of water





Average SEM Value

23.41±0.247e

28.53±0.302d

32.04±0.560c

38.21±0.303b

44.65±0.488a

37.59±0.072b

0.42

0.33±0.006e

0.46±0.006d

0.63±0.009c

0.82±0.015a

0.86±0.012a

0.74±0.019b

0.54

11.01±0.580e

15.52±0.490d

17.34±0.360c

21.79±0.338b

24.49±0.302a

17.90±0.401c

0.43

HSDT 95% 3.23 0.08 1.39

Here, DAP = day after planting, SEM = standard error mean, the number followed by the same superscript letters in the same column are

not significantly differ on HSDT 95%.

Figure 1 The concentration M-Bio relationship with plant

height

Figure 2 Theconcentration M-Bio relationship with stem

diameter

3 Results

3.1 Effect of M-bio on various growth characteristics

Effect of M-bio fertilizer on average plant height, stem

diameter and roots length are represented in table 1. Results of

study revealed that application M-bio fertilizer had significant

effect on plant of height, stem diameter and roots length of

soybean plant.

Among the various tested treatments, the treatment containing

12 ml per liter of M-bio provides a higher impact on plant

height, stem diameter and root length. It is significantly

different from other treatments, except in stem diameter where

this treatment was not significantly differ than the 9 ml per liter

of M-bio. Further, the lowest effect was obtained in the control

treatment for all the tested attributes.

Figure 3 The concentration M-Bio relationship with length of

roots

Effect of Biological Fertilizer on the Growth and Nodules Formation to Soya bean (Glicine max (L.) Merrill) in Ultisol under Net House conditions 619

_________________________________________________________



Table 2 Effect of M-biofertilizer on average of nodule diameter, the number of nodules and the number of effective nodules.

Treatments Nodule diameter

(mm)

Number of nodules

(pieces)

Number of effective nodules

(pieces)

Without M-bio (control)






Average SEM Value

1.03 ± 0.012f

1.93 ± 0.026e

2.86 ± 0.045d

3.94 ± 0.036c

5.14 ± 0.025a

4.80 ± 0.044b

0.003

6.63 ± 0.406d

10.27 ± 0.338c

12.43 ± 0.536c

17.00± 0.200b

21.27 ± 0.484a

17.40 ± 0.439b

0.54

2.57 ± 0.145e

5.80 ± 0.153d

10.17 ± 0.410c

13.63 ± 0.338b

16.63 ± 0.384a

14.23 ± 0.524b

0.37

HSDT 95% 0.17 2.15 1.77

Here, SEM = standard error mean, the number followed by he same superscript letters in the same column are not significantly differ on

HSDT 95%.

Figure 4 The concentration of M-Bio relationship with nodules

diameter

Figure 5 The concentration of M-Bio relationship with number

of nodules

Effect of the various concentrations of M-bio fertilizer on plant

height, stem diameter and root length are correlated with

highly significant R value in consecutive i.e. plant height R =

0.959 (Figure 1), stem diameter R = 0.997 (Figure 2), and

length root R = 0.945 (Figure 3).

3.2 Effect of M-bio biofertilizer on various nodules and

effective nodules characteristics

Effect of M-bio fertilizer on average nodule diameter, number

of nodule and number effective nodules are represented in

table 2. Like growth factors, the treatment containing 12 ml per

liter of water M-bio provides the highest influence on these

parameters while the lowest nodule number, nodule diameter

and number of effective nodule was reported from the control.

The treatment containing 15 ml per liter of M-bio did not show

any significant difference from 9 ml per liter of M-bio and

these two treatments are at par to each other. Highest nodule

diameter (5.14 mm), nodule number (21.47 pieces) and active

nodule (16.63 pieces) was reported from the 12 ml per liter of

M-bio.

Figure 6 The concentration of M-Bio relationship with number

of effective nodules

620 Sarawa et al

_________________________________________________________



Table 3 Effect of M-biofertilizer on the wet and dry weight of nodules,wet and dry weight of effective nodules (g).

Treatments

Wet weight of

nodules (g)

Dry weight of

nodules (g)

Wet weight of

effective

nodules (g)

Dry weight of effective

nodules (g)

Without M-bio (control)






SEM Value

0.97± 0.067d

1.57±0.186d

2.63±0.145c

4.17±0.088b

6.80±0.058a

4.23±0.176b

0.05

0.31±0.012d

0.48±0.056d

0.80±0.053c

1.25±0.026b

2.04±0.017a

1.27±0.053b

0.004

0.80±0.058e

1.37±0.067d

2.27±0.088c

3.73±0.120b

4.30±0.058a

3.80±0.115b

0.02

0.28±0.015e

0.41±0.022d

0.68±0.026c

1.12±0.037b

1.29±0.017a

1.14±0.032b

0.002

HSDT 95% 0.69 0.21 0.46 0.13

Here, SEM = standard error mean, the number followed by the same superscript letters in the same column are not significantly differ on

HSDT 95%.

Treatment of various concentrations of M-bio fertilizer on

nodule diameter, number of nodules and number of effective

nodules correlated highly significant with the R value in

consecutive i.e.nodule diameter R = 0.990 (Figure 4), number

of nodules R = 0.965 (Figure 5), and number of effective

nodules R = 0.996 (Figure 6).

3.3 Effect of M-bio fertilizer on fresh and dry weight of

nodules and effective nodules

A significant effect M-bio fertilizer on average fresh and dry

weight of nodules and effective nodules was reported in

present study (Table 3).

Here also the treatment 12 ml of M-bio shows superiority over

the other treatments and found effective in improving fresh

weight of nodules (6.80 g) and effective nodules (4.30 g).

Further, same treatments found effective in increasing dry

weight of nodules (2.04 pieces) and effective nodules (1.29

pieces). This growth pattern was immediately followed by the

treatment 15 ml M-Bio and 9 ml M-Bio and these two

treatments are at par to each other and are not significantly

different to each others. Among various treatments lowest dose

of M-bio (3 ml) is least effective and it is showing similar

weight to control.

Various concentrations of M-bio fertilizer are effective in

increasing fresh and dry weight of nodules and effective

nodules and it is correlated with highly significant R value in

consecutive i.e.wet weight of nodules R = 0.927 (Figure 7), dry

weight of nodules R = 0.926 (Figure 8), wet weight of

effective nodules R = 0.996 (Figure 9) and dry weight of

effective nodules R =0.996 (Figure 10).

Figure 7 The concentration of M-Bio relationship with heavy

wet nodules

Figure 8 The concentration of M-Bio relationship with dry

weight of nodules


_________________________________________________________



Figure 9 The concentration of M-Bio relationship with plant

heavy wet nodules effectively

Figure 10 The concentration of M-Bio relationship with plant

dry wet nodules effectively

4 Discussions

Application of biological fertilizer (M-bio) on soybean crops is

primarily aim to maximize and streamline the process of

nitrogen fixation in soybean plants grown in Ultisol conditions.

Generally Ultisol contain low organic matter and high bulk

density, these conditions are not favorable for the growth of

bradyrhizobium. Application of external rhizobium may cause

improvement in nitrogen fixation. Therefore, application of M-

bio fertilizers containing rhizobium increased the formation of

nodules. Use of biological fertilizers as a source of rhizobium

in order to improve nitrogen fixation in soybean crop should be

more careful, especially on marginal soils (Sarawa, 2014).

According to Remans et al. (2008) plants provides various

responses in terms of nitrogen fixation when combination of

rhizobium an azospirillum applied to the soybean crops.

Results of present study revealed that application of M-bio

caused significant improvement in the plant growth characters

such as plant height, stem diameter and root length. This

improvement was continuous till the concentration reached to

12 ml, further improvement in the concentration of M-bio

exerts a negative effect on the plant growth characteristics.

This improvement in the growth characteristics is because of

the presence of nitrogen fixing bacteria in M-bio. Further, the

higher nitrogen content spurs the growth of plant height.

Results of this study are in agreement with the findings of

Misran (2013) those who reported higher plant growth on the

application of biological fertilizer. Further, Andrews et al.

(2006) reported 6-12% improvement in soybean plant growth

as compared to the control on the application of compost

(manure).

The treatment of M-bio 12 ml per liter of water provides a

higher impact on stem diameter. The growth of plants stem

diameter usually concurrent with plants growth. It can be

understood because of their dominance of the high growth of

plants usually cause a drag on growth aside, including a stem

diameter (Sarawa, 2009a; Sarawa, 2009b). Further, Sarawa

(2009b) reported a negative correlation between plant height

and stem diameter.

The root growth in plants is strongly influenced by genetic and

environmental factors. In present study, the root growth pattern

is showing similarities with the plant height and stem diameter

pattern. At the time of the vegetative phase before the main

stream fotosintat used leaves, roots, and nodules. According to

Egli (1985) it can be assimilate in the form of starch, mainly in

the leaves and other organs. Further, Mayaki (1976) reported

that 25% - 40% root dry weight reached at the time of

vegetative growth. Root growth is generally parallel with the

development of shoot and reached maximum shortly after seed

filling (Hicks, 1978).

The growth of nodules in response bacterium rhizobium and

phosphate stimulates the formation of nodules and it may be

because of the synergetic effect of these two. Similar type of

findings was reported by Sarawa (2014) when he tried

combination of biological fertilizers contain rhizobium

bacteria, along with phosphate dissolution bacteria and some

microbial decomposers. Generally, biological fertilizer is a

mixture of specific microorganisms which are active involved

in nitrogen-fixate phosphate dissolution and decomposition of

organic material. According to Sarawa (2009b) plants nitrogen,

metabolism depended on various factors such as plant species,

availability of nitrogen, temperature and some other

environmental factors.

Formation of effective nodules those participated in the

atmospheric nitrogen fixation is strongly influenced by the

622 Sarawa et al

_________________________________________________________



availability of Molybdenum (Mo). The Mo element is a

cofactor for the enzyme nitrogenase (Sarawa, 2014).The

nodules are effective in addition to obtaining sufficient supply

assimilate of host plants, are also capable of forming a

nitrogenase enzyme conversion process fixation of N2 and

NH4NO3. Nitrogen fixation efficiency of a rhizobium strain

affect the number and size of the nodule (Provost et al., 2010).

Furthermore it is said that increased plant growth generally

correlates with the size of the nodule, nodule weight, number

and activity of nitrogenase.

M-bio has positive influence on fresh and dry weight of

nodules, and effective nodules. Weight of plant is the result of

partitioning fotosintat nodules to the roots of soybean plants. If

symbiotic rhizobium with soybean plants are effective, optimal

plant growth rate and, formation of nodules occurred.

Conversely if the soybean plant growth is inhibited and the

formation of nodules will also be hampered. Provision of M-

bio is able to increase the formation and growth of plants,

which in turn increases the weight of nodules, These findings

are in agreement with the revelation Sarawa (2011) that

reported the effect of nutriflora (liquid fertilizer) to dry weight

of plants, tends to increase with increasing concentration of

nutriflora given. From the findings of this study it can be

conclude that M-bio can enhanced plant growth and nodule

formation. A positive correlation was reported between growth

characters and M-bio does and highest growth was reported in

12 ml of M-bio.

Acknowledgements

The author would like to thank to the Ministry of National

Education, Republic of Indonesia for the financial assistance

through the scheme of Competitive Grants Research. The

author also thank to the Rector of Halu Oleo University and the

Chairman of the Research Intitute of Halu Oleo University for

providing us moral support and space carry out this study.


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

interests that could possibly arise.

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624 Sarawa et al

_________________________________________________________



KEYWORDS

Sorghum

Collection

Conservation

Genetic erosion

Chad

ABSTRACT

The objective of this study was to understand the farmer management practices in order to conserve the

genetic diversity of sorghum and to determine the level of genetic diversity and local taxonomy in two

regions (Logone Oriental and Moyen Chari) of the South of Chad. Total eight villages were visited and

from these 53 accessions were collected from 116 inventoried accessions. The number of collected

accessions varies from 3 to 9 per village and a loss of diversity was reported between 47 to 71%

(average rate of 54.31%) in all villages and this rate varies from village to village. Results of study

revealed that the farmer nomenclature is based on the criteria of accession using, origin, color of seeds,

type of panicle, crop cycle and the size of plants. This study suggested significant losses in the sorghum

diversity of Chad. Therefore, there is a strong need to run a national program to collect, validate and

protect the genetic resources of sorghum. This will be helpful in the reducing genetic erosion and to

improve the varieties of sorghum cultivated in Chad.

GAPILI Naoura* and DJINODJI Reoungal

Institut Tchadien de Recherche Agronomique pour le Développement (ITRAD), B.P. 5400, N’Djaména, Tchad

Received – June 08, 2016; Revision – August 26, 2016; Accepted – October 26, 2016



FARMER’S MANAGEMENT PRACTICES TO MAINTAIN THE GENETIC

DIVERSITY OF SORGHUM (Sorghum bicolor L. MOENCH) IN SOUTH OF CHAD

E-mail: [email protected] (GAPILI Naoura)







ISSN No. 2320 – 8694
















_________________________________________________________



1 Introduction

Plant genetic resources related to food and agriculture are the

biological basis of world food security (FAO, 2009). In Chad

sorghum [Sorghum bicolor (L.) Moench] is the main cereal

and it contributing approximately 38.6 % of overall cereal

production with a whole production of 360000 tons per year

(CNC, 2001). Chad along with other West and Central African

countries is considered as a secondary center of diversity of

cultivated sorghums (Chantereau et al., 1997). Local cultivated

varieties are adapted to the native environmental conditions

and fulfill the various objectives of farmers. Despite the

existence of research programs on genetic improvement of

sorghum from the sixties through the Chadian Institute of

Agricultural Research for Development (ITRAD), the adoption

rate of selected varieties remained very low and it was reported

between 2 to 5% of cultivated areas (Trouche et al., 2001).

Producers are very closer to their accessions and they

deliberately maintained millenniums, diversity and

systematically mixed crops in their fields to get natural hybrids

(Chambers et al., 1994). Prospecting allows to show the

evidence then to exploit the diversity already existing but not

revealed (Chantereau et al., 1997). This approach can

significantly contribute in the creation of variability. The

essential purpose of prospecting was to collect genetic material

with the most possible variability, which can contribute in the

actual improvement of sorghum crops. Prospecting is a way

which helps in the protecting endangered species (Pernes,

1984). This study was conducted to determine the genetic

diversity of sorghum's accessions in two regions of the

Sudanian area of Chad. The accessions was collected and

stored with a target of plant breeding and to know the mode of

management of this diversity and the farmer's taxonomy of

accessions.


2.1 Study area

Prospecting was conducted in the two regions viz Logone

Oriental and Moyen Chari, these two are located in the

Sudanian area of Chad. Study was conducted between 12 and

21 February 2016. The climate is tropical with alternating

seasons, a wet season characterized by a rainfall running from

May to November and a dry season from November to May.

The vegetation is characterized by Sudano-Sahelian savanna,

slightly wooded in the north part but more planted with trees in

the center, in the south, the savanna becomes as Sudano-

Guinean characterized by gallery forests. The Logone Oriental

Region is divided into 6 districts and the Moyen Chari in 3

districts. Eight locations are selected on the basis of distance

and geographic location so as to cover the maximum of

different geographical areas concerned and get a representative

sample.

2.2 Method and collected data

From each village, data were collected by using methods of

participative research (inquiries of group and field visits)

described by Orobiyi et al. (2013). In all surveyed villages,

administrative and local authorities are involved in facilitating

meetings in which information of general order (name of the

village and ethnic group) was collected. After a brief display of

the objectives of the research program to producers, they were

asked to make a list of all local accessions (common names)

still cultivated or not yet in the village. The samples were

collected to ensure the effective presence of different varieties

still cultivated and to set local synonym names difficulties.

Through group discussion, retailed information’s about

morphological, agronomic and culinary descriptions

(according to farmer perception) are also documented.

Information on the vegetative cycle, origin of raw seeds, uses

of each variety and the factors which determine the

maintaining or disappearance of each of the local varieties was

recorded on the collection sheet.

3 Results

3.1. Surveyed villages and identified ethnic groups

Sixteen villages were selected for the prospecting and among

these only eight villages (table 1) were canvassed. In Logone

Oriental, five villages were surveyed and Kouh Ouest

department was cannot be explored because of unavailability

of time during the study. While, in Moyen Chari provenance,

the prospection covered the three districts and the department

Bahr Kôh only two villages were surveyed. The study allows

raising eight ethnic groups.

Table 1 Villages and ethnic group statements.

Region Department Sub-prefecture Villages prospected Groupethnic

Logone Oriental

Monts de Lam Bessao Kamkoutou Laka

NyaPendé Goré Timbéri Kaba

Nya Beboni Mbanguirati 2 Ngambaye

Pendé Kara Maïbombaye Mongoh

Kouh Est Bédjo Békodo Gor

Moyen Chari

Bahr Kôh Djoli Doguigui, Doboro Sarah Madjingaï

Grande Sido Maro, Kobdogué Ngama

Lac Iro Kyabé Guilagondéré Sarah Kaba

626 Naoura and Reoungal

_________________________________________________________



3.2. Varietal diversity of sorghum and farmers management

practices

Fifty three accessions of sorghum grain were collected, among

these thirty four (34) were collected from Logone Oriental

while rest nineteen (19) were collected from Moyen Chari. In

Logone Oriental (Table 2), most of the accessions were

collected from the village Timbéri and Mbanguirati 2 (9

accessions from each village), these were followed by

Maïmbombaye (8 accessions) then by Kamkoutou (5

accessions) and finally by Bekodo 2 (3 accessions). An

average of 6.8 accessions per village was collected in Logone

Oriental, which determines an important genetic diversity. In

Moyen Chari (Table 3) total 19 accessions were collected,

among these 8 were collected from the village Djoli, 7 from

the Guilagonderé and rest 4 from the village of Kobdogué.

This represents collection of 6.3 accessions per village from

this region, characterizing an important genetic diversity.

Surveyed, producers manage accessions according to the

vegetative cycle in early and late raining season. Starting from

the first rains of the month of May, farmers firstly sow

accessions of delayed cycle followed by some accessions of

early cycle which will be reaped in the month of August which

is considered as lean period. The extra-early accessions

harvested during the lean season, are planted around huts and

other accessions are planted in the fields of bush in most of the

cases in association of culture.

Table 1 Sorghum accessions collected from the Logone Oriental region.

S. N Village Local name of the accessions Meaning Characteristics

1 Kamkoutou MougayeBonwing - Semi-compact, early

2 Ngoumhkass Ngoumh red Compact, red grains

3 Djingandoule Glumes black Red grain

4 Wakass Sorghum with red grain Red grain

5 Wanda Sorghum with black grain White grain

6 Timberi DôMbaïmeldjé - Grain and flour red

7 Madamkass Madam red Extra-early, white

8 BéléNda Bélé white Extra-late, old

9 MadamNda Madame white Extra-early, white

10 DjeMba On djingal Visitor does not eat broken White grain

11 Djakadji We are saved White grain

12 Garidjéjaune - Yellow grain

13 Garidjeé blanc - Grain and flour white

14 Bindocodo Braided hair of the rebels -

15 Mbanguirati 2 TelBaou On el easy to become a great producer -

16 KouranMbao - Early, white grain

17 Mir - Ancestral, red grain

18 Mainmbororo flee the cattle-breeders Early

19 Am-Timan From Am-Timan Red grain

20 Madam Madam Early

21 Godard Brought by Godard White, grain entirely covered

of glumes

22 Moyo Seedeath Red grain

23 Ingadombandje Braided hair of the "peules" -

24 Maïmbombaye MbatNang-Al Do not refuse the ground Yellow grain

25 Ngagetdjé - White grain, early

26 Yingté - Early, loose panicle, red grain

27 Malcamion-Al I do not take up the truck -

28 Godji Short Sweet stem, early, red

29 Kouranngang - -

30 Kolmonnda Kolmon white White grain

31 Galidjé -

32 Bekodo 2 Tamadekass - Considered, grain red.

33 Bére - Big and white grain

34 Godji Short -

Farmer’s management practices to maintain the genetic diversity of Sorghum (Sorghum bicolor L. Moench) in South of Chad 627

_________________________________________________________



3.3 Sorghum diversity and farmer ethnobotanic knowledge of

taxonomy

The study showed that farmers used variable nomenclature of

accessions and it varies between various ethnic groups. Several

criteria are used to assign various name to the available

accessions and basically it depends on the shape of the panicle,

colors of seeds, use reserved for each accession and its origin.

For example in Kamkoutou there is an accession named

"Djingandoule", which means black husk in Laka and

"WaKass" mean ingred sorghum grain. In Timberi accession

"DjeMba Ondjingal" means, in Kaba, "visitor does not eat

broken" because the grinding is difficult and seeds are very

glassy and very tough. Further, in Mbanguirati 2 the accession

"Tel Baou On el" meaning Ngambaye, it is easy to become a

great producer because of its high production. In Maïbombaye

the accession "Mbat Nang Al" means in Mongoh, something

suitable for any type of soil. In Bekodo 2 the accession

"Godji", also met with Maïbombaye, means in both villages

short sorghum. The accession "Moyo" of Mbanguirati 2, which

means seeing the death, is very old in the village and is used to

take the oath in a dispute between two parts.

The accessions collected from the Moyen Chari region were

also showed diversified nomenclature like the previous region

and it varies between ethnic groups to ethnic group and within

each group. The characteristics of panicles, color of grains and

special use types are determining in the farmer taxonomy.

There is "Godji" in Guilagonderé and in Djoli meaning short

sorghum. An accession named "Fall" met in Kobdogué is

extra-late and white grain, but its culture is increasingly rare

because of the threat of cattle breeders.

3.4 Genetic erosion of sorghum diversity

Table 4 shows the contribution of villages in the conservation

of genetic diversity and presents the rate of loss of sorghum

genetic diversity. In Kamkoutou (Logone Oriental region),

over 70% of accessions identified by producers during the

investigation, and could not be collected. In the region of

Moyen Chari, the village Maro showed the biggest loss with

more than 71% of not collected accessions.

Several reasons including reducing rainfall cycle are

mentioned by producers; these are resulted in the loss of

traditional accessions which are usually late-cycle. Also the

method of conservation which consists to beat the bulk seed to

keep them in bags, because the method collect of this present

study requires taking a whole panicle to avoid mixtures of

accessions. For some producers the period of collection and the

unpredictable of the prospecting mission did not allow gather

all accessions still cultivated in the village.

Table 2 The sorghum accessions collected from the Moyen Chari region.

S.N. Village Local name of the accessions Meaning Characteristics

35 Guilagonderé Bambara

36 Absolue Product absolutely Dwarfish and productive

37 GodjiKoh Short sorghum Red grain

38 Gad Panicle compact, white grain

39 Godjiprécoce Sorghum short and early Red grain

40 Toundou Red grain

41 Kelmani Brought by Kelmani

42 Kobdogué Fall Extra-late, white grain

43 Fall précoce Fall early Early, yellow grain

44 Gad Stem big and sweet, red grain

45 Am Timam From Amtiman Early, red grain, cultivated on all

type of soil

46 Djoli Lakemdar Red grain, promising

47 NgaguetDje White grain, tall height

48 GuidGodji Harvests after Godji Red grain

49 Kamsa Red grain

50 Air Djimra Introduce by Djimra Red grain

51 Bouroum Ostrich White grain

52 Gali On the level of thigh White grain

53 Godji Short Red grain


_________________________________________________________



Table 3 Accessions identified and loss rate of sorghum diversity per village.

Regions Villages Accessions identified Accessions collected % of loss

Logone Oriental Kamkoutou 17 5 70.6

Timberi 18 9 50

Mbanguirati 2 17 9 47

Maimbomaye 17 8 53

Bekodo 2 8 3 62.5

Total 1 77 34 55.84

Moyen Chari Guilagondere 14 7 50

Maro 14 4 71.43

Djoli 11 8 26.27

Total 2 39 19 51.28

Total 1+2 116 53 54.31

4 Discussions

The study allowed collecting 53 accessions, with 3 to 9

accessions collected by village, which represents an average of

6.62 accessions by village. This inventory is not exhaustive;

because it is likely that minor varieties have gotten away, and

the cultivated variety is constantly evolving. This average

accession number is higher than the Gapili et al. (2016), those

who collected 2.84 accessions per village on sweet sorghum

from Chad. This established the existence of an important

genetic diversity in this study which was managed by farmers.

This value is higher than the obtained by Missihoun et al.

(2012) and Sawadogo et al. (2015) those who obtained 5.54

and 1.24 values in sorghum of Benin and Burkina Faso

respectively. This important genetic diversity of Chad sorghum

represents a major advantage for the programs of genetic

improvement of this crop.

Farmers managed a varied cycle of diversity ranging from

extra-early to late using by the way intermediaries which they

sow depending on the rain. This spatial organization

significantly enhances the flow of genes between favorable

accessions to the increasing of genetic diversity within and

between accessions. It confirms the work of Barnaud et al.

(2007) at Cameroonian farmers who cultivate sorghum in poly-

varietal blend, but it is contrary to the Beninese producer

management practices which have predominantly separate

culture technique for the different varieties (Missihoun et al.,

2012).

The farmer taxonomy is based on the shape of the panicle, seed

color, cycle, plant size and the type of using. According to

Sawadogo et al. (2015), a perfect knowledge of the names

given to the varieties and the traditional classification system is

important to the extent that the local name is the basic unit

used by producers in the management and selection of genetic

resources. This expertise has consequences both on the level of

genetic diversity and on the evolution of the plant (Brocke et

al., 2003; Barry et al., 2007). The accession "Djingandoule"

met in Kamtoukou indicates the black color of the hull. In

Timberi, the accession "DjeMba Ondjingal" which means, "the

visitor does not eat broken" evokes the very glassy character of

the seed. The accession "Mainmbororo" means flees cattle-

breeders indicates here the character of precocity allowing

harvesting these accessions before the arrival of nomadic

herdsman. In Djoli, the accession "Gali" meaning as high as

the thigh, relates to the character of small size of the plant.

Thus, a good knowledge of the farmer naming allows

understanding their diversity management mode and integrate

it in a breeding program to create varieties adapted to their

objectives.

The study shows a significant loss of diversity in sorghum

accessions ranging from 47 to 71% by village. According to

FAO (2010), three quarters of the genetic diversity of

cultivated crops have been lost over the twentieth century.

Several reasons are reported by producers to explain this

genetic diversity loss. Among these, reduction in the rainfall

cycle is most important one and it is responsible for the

abandonment of several long cycle varieties, the most

appreciated by producers. According to Lambert (1983), still

there are farmers those who attached to their traditional longer

cycles varieties than the modern varieties. In Timberi accession

"BéléNda" of extra delayed cycle is by far the most liked by

farmers with the most expensive selling value but it is less and

less grown because of the reduction in the seasonal rain due to

climate change. Secondly the presence of transhumant’s cattle-

breeders in the growing area is a real threat, which leads to the

abandonment of late cycle accessions. Indeed, some accessions

of late cycle can still be grown despite the reduction in the

rainfall cycle, because of their resistance to drought and

pouring. However, their culture is abandoned because of the

presence of cattle-breeders. This is the case of accession "Fall"

met to Kobdogué, extra-late cycle whose culture is

increasingly rare because of the threat of cattle-breeders.

This study clearly shows that sorghum production in the study

area is increasingly oriented towards short cycle accessions.

This supports the work of Missihoun et al. (2012) who found

that sorghum producers in Benin are moving towards short

growing season accessions because of irregular periods of rain

Farmer’s management practices to maintain the genetic diversity of Sorghum (Sorghum bicolor L. Moench) in South of Chad 629

_________________________________________________________



and drought marked by the significant reduction in time rain

and the market value of short vegetative cycle accessions.

Conclusion

In present study total 53 accessions of sorghum which were

cultivated in the regions of Logone Oriental and Moyen Chari

was collected, with an average of 6.62 accessions per village,

characterizing the important genetic diversity of sorghum in

Chad. Producers use a local taxonomy to name accessions,

adding the using reserved for accessions, the type of panicle,

the cycle, the seed color and plant size. A good handling of this

nomenclature is an asset for the management of the genetic

diversity of these accessions and creating a “core collections”.

The study reveals a significant loss of genetic diversity,

characterized by abandonment of accessions extra-late

vegetative cycle by producers. The rate of this loss is around

54.31%, ranging from 47 to 71% by village, constituting a real

threat for the biodiversity of this culture. Prospection

perspective should be considered for the recovery and

conservation of the diversity of sorghum accessions in Chad.

Acknowledgement

We thank the Project “Opérationnalisation de la filière

semencière au Tchad”, of the GIZ financed by the Suisse co-

operation which provided financial support for this work.




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_________________________________________________________



KEYWORDS

Tomato

Fertilization

Physical

Chemical and biochemical

characterizations

ABSTRACT

The concentration of secondary metabolites can be influenced qualitatively and quantitatively by

ecological factors and farming practices. The purpose of this study was to determine the impact of

organic and mineral fertilization on physical characteristics and the content of chemical and biochemical

compounds of the fruits of the tomato var. Mongal F1. Physical, chemical and biochemical

characterizations of tomato samples were carried out from samples collected from control, organic and

mineral fertilized plants, to assess the nutritional potential according to fertilization. Samples collected

from the organic fertilizer had pH values of 4.21 ± 0.01 corresponding to measurable acidity of 8.47 ±

0.06g malic acid /100g Fresh Tomato (FT), dry matter of 4.18±0.02/100g FT and total ash content of

0.38±0.01/100g FT. The contents of fats, proteins are respectively 2.28 ± 0.01 and 0.70± 0.02 mg/100g

FT, totals sugar value of 2.83± 0.02 mg/100g FT. For mineral fertilization, the samples had pH values of

4.16 ± 0.01 corresponding to a measurable acidity of 8.10 ± 0.12g malic acid /100g FT, values of dry

matter 3.82 ± 0.02/100g FT and totals ash content of 0.37 ± 0.01. The contents of fats and proteins are

respectively 0.27 ± 0.01 and 0.64 ± 0.01 mg/100g FT with totals sugar value of 2.56 ± 0.01 mg/100g

FT. Result of study revealed that organic fertilization can increase the concentration of secondary

metabolites production in tomato var. Mongal F1 than mineral fertilization. This increase may be

probably due to the availability of various major and minor elements in organic fertilizer contrary to

mineral fertilizer which has only three major elements, Nitrogen (N), Phosphorus (P) and Potassium

(K). Globally fruit ripping has shown a positive effect on the accumulation of fats, proteins and total

sugar.

Christophe DABIRE1, Abdoulaye SEREME

1,*, Charles PARKOUDA

2, Marius K. SOMDA

3 and

Alfred S. TRAORE3

1Département Substances Naturelles/IRSAT/CNRST; 03 BP 7047 Ouagadougou 03; Burkina Faso

2Département Technologie Alimentaire/IRSAT/CNRST; 03 BP 7047 Ouagadougou 03; Burkina Faso

3Département de Biochimie-Microbiologie; Université Ouaga I Pr Joseph KI-ZERBO; 03 BP 7021 Burkina Faso

Received – July 19, 2016; Revision – August 11, 2016; Accepted – November 02, 2016



INFLUENCE OF ORGANIC AND MINERAL FERTILIZERS ON CHEMICAL AND

BIOCHEMICAL COMPOUNDS CONTENT IN TOMATO (Solanum lycopersicum)

VAR. MONGAL F1

E-mail: [email protected] (Abdoulaye SEREME)







ISSN No. 2320 – 8694
















_________________________________________________________



1 Introduction

Many epidemiological studies revealed the beneficial effects of

fruit and vegetables on human health and avoiding chronic

diseases such as cardiovascular disease including risk factors

such as hypertension, diabetes, obesity and prevent also

esophageal, stomach, pancreatic bladder and cervical cancers

(Van Duyn & Pivonka, 2000; Crawford et al., 1994;

Oguntibeju et al., 2013). These studies demonstrated that -

fruits and vegetables have various nutrients such as

carotenoids, phenolic compounds, vitamins, minerals, sulfur

compounds which are associated with beneficial outcomes

related to diseases cures (Wargovich, 2000; Chanforan, 2011).

High consumption of tomatoes and tomato products have been

associated with the reduction of carcinogenesis, especially

prostate cancer and it may be due to the presence of lycopene,

which give red color to the tomato (Giovannucci et al., 2002;

Boileau 2003). Fruit contributes to a healthy and balanced diet

rich in minerals (iron, phosphorus), vitamins (C, B), essential

amino acids, sugars as well as dietary fiber (Chanforan, 2011).

Further, Tohill et al. (2004) suggested that fruits and vegetable

intake have positive effect on weight management and obesity

prevention. The biochemical composition of tomato fruit varies

according to the variety, environmental factors such as light,

temperature, fertilization and farming practices (Dorai et al.,

2001).

Yanar et al. (2011) evaluated the effects of different organic

fertilizers on yield and fruit qualities of indeterminate tomato

and reported satisfactory increases in the tomato yield and

quality in plant treated by organic fertilizers. Similar results

were reported by Suge et al. (2011) when they studied the

effect of organic or inorganic fertilizers on the egg plant.

Finding of Tonfack et al. (2009) are contradictory to the

findings of Yanar et al. (2011) and Suge et al. (2011). Tonfack

et al. (2009) studied the effect of individual or combined

application of organic or mineral fertilizer on tomato plant

growth and fruit P, K, Ca and Na contents and reported no

major difference between the organic and mineral fertilizer.

Further these researchers were not reported any significant

effect of fertilizer application on the tomato fruit P, K, Ca and

Na content.

Çolpan et al. (2013) determined the effects of potassium on the

yield and yield components of tomato grown in greenhouse

conditions and reported a significant effect of potassium

application on the final yield of tomato crops. They also

reported dose depended effect on the plant stem diameter, plant

length, fruit diameter, fruit number, fruit weight, penetration

resistance and sugar content. In addition, the leaf N/K ratio

also affected the tomato yield. Contrary observation was

reported by Makinde et al. (2016), these researcher reported

lower potassium content in the plot treated by NPK plots as

compare to control but not different statistically from each

other. Combined applications of mineral and organic fertilizer

have higher sodium content as compared to individual

application. Information regarding the effect of organic or

mineral fertilizers on the Burkina Faso local verities of tomato

Mongal F1 is in scarcity. The purpose of the present study is to

determine the influence of organic and mineral fertilizers on

the physical, chemical and biochemical characteristics of the

variety of tomato Mongal F1 at different fruit ripping stages.


This experiment was conducted in the greenhouse of the

National Research Center (12°25′N, 1°29′W) in Burkina Faso.

The site was flat, with an elevation of 435m above sea level

(IGB, 2014).

2.1 Plant Materials

Plant materials used in this study was the local variety of

tomato var., Mongal F1 which was obtained from INRA

(France). This variety was adapted for the dry and hot weather

in Burkina Faso. It is also resistant to nematodes and some

bacterial and fungal diseases (Aïssa et al., 2014). The organic

fertilizer used in this study was well-decomposed livestock

manure and the mineral fertilizers were N-P-K (23-14-23).

2.2 Experimental design and studied factors

Study was conducted in factorial randomized block design and

each treatment was replicated four times. Farming operations

were carried out by following the user manual instructions

proposed for the variety. Two factors studied in this study were

type of fertilizer and harvesting period. Effect of three sources

of fertilizers viz. Organic Fertilization (OF); Mineral

Fertilization (MF) and Control (C) in combination with three

harvesting periods i.e. R1 (79 DAP- Days After Planting), R2

(85 DAP) R3 (89 DAP) were studied. Nine factorial

combinations which were formulated in this study are C/R1,

C/R2, C/R3, OF/R1, OF/R2, OF/R3, MF/R1, MF/R2, and

MF/R3. Spacing between blocks was 2.5 m and elementary

plots were 1.5 m apart. Each plot consisted of 4 rows of 3 m in

length. Spacing between adjacent rows was 0.8 m and plants

within each row were 0.5 m apart. Two border lines were

planted on both sides in each plot to reduce border effects.

Effect of these factors was studied on the physiological

maturity, chemical and biochemical characteristics of the

tomato fruits.

2.3 Physical and Chemical analysis

2.3.1 Determination of dry matter, pH, total ash content and

titratable acidity

The dry matter is determined by differential weighing before

and after heating at 70οC in the oven by following the method

of NF VO3-707 (2000). Measurements of tomato purees pH

were carried out by pH-meter (HI 8520, Hanna Instruments,

France). The ashes were obtained according to the

International Standard IS0 2171 (2007), by differential

weighing of samples before and after drying. The ash content

632 Christophe et al

_________________________________________________________



(mineral) is estimated by incineration in an oven at 550°C so

as to obtain all of the cations in the form of carbonate or other

anhydrous inorganic salts. The titratable acidity is the content

of organic and inorganic acids, determined by titration

according the European Standard EN 12147 of December

(1996) and NF V 05-101, January (1974). The principle of the

method is based on potentiometric titration of an aqueous

solution of tomato purees with sodium.

2.3.2. Biochemical analysis

2.3.2.1 Determination of total lipids

Total fat content was determined by Soxhlet extraction method

by using hexane as extraction solvent by following the guide

line proposed by International Standard ISO 659 (1998).

2.3.2.2 Determination of total sugars

Total sugars were assayed by sulfuric orcinol according to the

method described by Montreuil & Spik (1969).

2.3.2.3 Determination of total protein

Total proteins of tomato purees were measured by the

differential method according to the formula below.

𝑃𝑟𝑜𝑡𝑒𝑖𝑛 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 = 100 − (% 𝑕𝑢𝑚𝑖𝑑𝑖𝑡𝑦 + % 𝑎𝑠𝑕 + % 𝑓𝑎𝑡𝑠

+ % 𝑠𝑢𝑔𝑎𝑟)

2.3.2.4 Determination of energy value

The theoretical energy value is calculated using the

coefficients of Merrill, adopted by the Southgate & Durnin

(1970). With P, C, L, the respective percentages of dry weight

of protein, carbohydrates and lipids. The calorific value of the

sample is obtained as follows:

Energy value (kcal/100 g FT) = P x 4 kcal + C x 4 kcal + L x 9

kcal


The data were analyzed by factor analysis of variance

(ANOVA) with repetitions and the means were separated using

Fischer’s test at P = 0.05. The statistical analysis was

performed using XLSTAT software version 7.5.2.


3.1 Effect of fertilizers application on various physical

parameters of tomato var. Mongal F1

3.1.1 pH

All the studied samples are showing pH between 4.15 and 4.30

(Figure 1). These values are similar to those reported by Aoun

et al. (2013) who reported pH value between 4.19 and 4.45 in

16 tomato varieties. Further, it was reported that pH values of

organic fertilizer are higher than those of mineral fertilization.

Acidic pH of tomatoes samples doesn’t promote the

development of some bacteria but is appropriate for the

development of fungal flora (Reynes et al., 1994). The

potential of hydrogen is one of the variables used to

characterize the middle properties. Its value is correlated to

kinetic laws of reactions, the organoleptic qualities of products

or enzymatic activities (Boukhiar, 2009). Indeed, this pH level

significantly reduces the rate and range of micro-organisms

which can promote on the product. Only acidophil micro-

organisms, acetic bacteria and lactobacilli can grow, but not

coliformas Escherichia coli (Messaouda, 2013).

Figure 1 Effect of organic and mineral fertilizer on pH, dry matter, total ash and Titrable acidity of tomato var. Mongal F1

Influence of organic and mineral fertilizers on chemical and biochemical compounds content in tomato (Solanum lycopersicum) var. Mongal F1 633

_________________________________________________________



3.1.2 Dry matter content

Dry matter content was reported between 3.63% and 4.54% of

the fresh one and this value was reported higher in the plant

fertilized by organic manure as compared to the plant fertilized

by mineral fertilizer and the control (Fig 1). Dry matter values

reported in this study are lower than the findings of Messaouda

(2013) those have reported 5.74% of fresh material. This slight

difference could be due to the difference in the analysis

method or in tomato varieties.

3.1.3 Ash content

No significant difference was reported in the ash contents of

various treatments and this parameter varies from 0.33% to

0.39% fresh weight treatment (Fig 1). Like other two

parameters, ash contents of samples fertilized by organic

fertilizer are higher to those which are fertilized by mineral

fertilizer for all the three harvests. This result could be

explained by a greater variety of nutrients of organic manure

available for plant than mineral fertilizer. The values of the ash

content of the present study are lower than those reported by

the USDA (2007), which is 0.5% FT. This difference is

probably related to factors such as the variety of tomato used

and farming practices.

3.1.4 Titratable acidity

Value of titratable acidity varies from 7.72 to 8.81g/100g fresh

material and it was reported higher in the plant treated by

organic fertilizer and this value was higher for all the plant

treated by organic compounds as compared to the plant treated

by mineral fertilizers and control for all the 3 harvests (Fig 1).

The values of the titratable acidity of this study are higher than

the value reported by Messaouda (2013) who reported

5.74g/100g found in the dried tomato. This difference could be

explained by the difference in tomato variety used, but also the

fact that the author used powder processed tomatoes.

3.2 Biochemical characteristics

3.2.1 Fats

Lipids value for plant treated by organic and mineral fertilizer

are varies between 0.24 and 0.31% FT and the plant fertilized

by organic manure have higher fats values than the plant

fertilized with mineral fertilizer and control (Fig 2). Ripping

has a positive effect on the accumulation of fats for both

mineral and organic fertilizers. The values of the lipid content

are higher than the value found in the literature, USDA (2007)

which is 0.2% FT. This variability may be justified by the

difference in various parameters such as the geographical

origin of samples and the variety.

3.2.2 Total protein content

Like fat content, value of total proteins content also varies

between 0.62 and 0.73% FT (Fig 2). Further, like fat content

ripping has a positive impact on the accumulation for both

mineral and organic fertilizers. Protein content with mineral

fertilizer is lower than the organic manure. These values are

lower than those found by USDA (2007) which is 0.88 % FT.

This difference could be explained by the difference in variety

of present study and it’s origin.

Figure 2 Effect of organic and mineral fertilizers on the levels of total lipids, total protein, total sugar and energy value


_________________________________________________________



3.2.3 Total sugar content

Value of total sugars content also varies from 2.33 to 3.14%

FT and sample fertilized with mineral fertilizer shows lower

total sugars content as compared to organic manure for all the

three harvests. Result of study revealed that the level of total

sugar increase during fruit maturation. The sugar contents of

the present study are similar to those found by Messaouda

(2013) and USDA (2007) which is respectively 1.38 and 3.

92%. Indeed, the total sugar content is variable, and this

variability may be related to the reaction of the non-enzymatic

browning (Georgelis et al., 2006; Davoodia et al., 2006).

3.2.4 Energetic value

The energy value of tomato samples varies between 14.45 and

18.02 kcal/100g of fresh material. Samples fertilized with

mineral fertilizer presents lower energy values than those

fertilized with organic manure. These results are similar to

those found by USDA (2007) which is 18 kcal/100g of fresh

tomato.

Conclusion

Result of present study revealed that organic fertilizers have

positive and stimulating effect on the physical, chemical and

biochemical characteristics content of tomato var. Mongal F1.

Moreover it was reported that mineral fertilizer does not have

any significant effect on various studied parameter and it was

not significantly different than the control. For parameters such

as pH, fats, acidity, protein and total sugar contents, tomatoes

grown with organic manure showed higher values than those of

mineral (NPK) fertilizer and fruit ripping has a positive effect

on the accumulation of these compounds for both mineral and

organic fertilizers.

Acknowledgements

This research project was supported by a grant from the West

Africa Agricultural Productivity Program/National Center of

Specialization - Fruits and Vegetables (WAAPP / NCS-FV).




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KEYWORDS

Rosemary

Hydroponics

Population density

Growth curve

Biomass

ABSTRACT

This research was conducted to establish the relationship between population density to total dry

biomass production and estimate the nutrient absorption curve in a hydroponics system for Rosmarinus

officinalis L. Study was carried out at the Center for Research and Development of Hydroponics in

Faculty of Agronomy at the Autonomous University of Nuevo Leon. Three population densities viz., 8,

16 and 24 plants per square meter were evaluated in a hydroponic system, using volcanic rock with

grain diameter of 20-40 mm as an inert substrate and a standard hydroponic nutrient solution. Among

tested three plant densities, population density of 8 plants m-2

, total dry biomass production produced

highest, total dry biomass and it shows superiority over the plant density with 16 to 24 plants m-2

populations. There were no significant differences in plant height. The data obtained were fitted to linear

growth models, which were used to estimate nutrient absorption curves.

Alejandro Isabel Luna-Maldonado, Humberto Rodríguez-Fuentes*, Juan Carlos Rodríguez-Ortiz,

Juan Antonio Vidales-Contreras, Julia Mariana Márquez-Reyes and Héctor Flores-Breceda

Department of Agricultural and Food Engineering, Faculty of Agriculture, Autonomous University of Nuevo Leon, México

Received – August 19, 2016; Revision – September 19, 2016; Accepted – October 26, 2016



CULTIVATION OF Rosmarinus officinalis IN HYDROPONIC

SYSTEM

E-mail: [email protected] (Humberto Rodríguez-Fuentes)







ISSN No. 2320 – 8694
















_________________________________________________________



1 Introduction

Crop population density is a determining factor in biomass

production; it is related with the number of individuals or

plants per unit of surface area. Nutrient absorption curves is a

possible way to determine preliminary nutrient requirement

and to design a suitable fertilization programs for a crop which

can allowing the efficient use of chemical fertilizers with a

consequent reduction in pollution and avoidance of

unnecessary expenditure of crop production (Molina et al.,

1993; Jiménez-García et al., 2009). Nutrient absorption curve

estimations reflect the changes in the plant phenology which

can be associated with maximum nutrient absorption points at

key development stages of plant such as flowering and fruiting.

In perennial species for maintaining their ability to survive and

higher yield, proper management and optimal conditions

should provide to maintain a defined yield level. In relation to

traditional soil culture production systems, hydroponics

methods applied in greenhouses offer a greater level of control

over plants. Hydroponics is a technique which normally used

to estimate growth curves and nutrient absorption for any plant

species (Rodríguez & Leihner, 2006; Almaguer-Sierra et al.,

2009; Jiménez-García et al., 2009; Rodríguez-Fuentes et al.,

2009b). This is comparable to modern industrial production

systems in which automation and control concepts are applied

on a tactical and operational basis that supports decision

making at the level of management.

Mathematical crop models can also be generated to predict

nutritional and environmental requirements for better crop

production. Assessing nutrient extraction by the plant during

its life-cycle over time intervals can build an absorption curve

and in some cases allow for mathematical modeling (Roose et

al., 2001). Nutrient removal depends on both internal and

external factors, in this former consisting the genetic potential

of the plant and its phenological development stage, while

external factors relate to the environment where the plant is

grown such as soil texture, pH, electrical conductivity air

temperature, light and relative humidity (Prusinkiewicz, 1998;

Gary et al., 1998).

The population density of the culture refers to the number of

individuals or plants on a unit of surface area. Cruz-Huerta et

al. (2009) pointed out that population density is one of the

factor which influences the amount of biomass generated;

additionally, there is a relationship between the number of

individuals in a defined area and biomass produced. They

determined that in sweet pepper (Capsicum frutescens) fruit

per plant load were decreased with the increasing population

density, but overall fruit production per unit area increased.

Similar type of findings was also reported in banana (Rodrigo

et al., 1997) and potato (Flores-Lopez et al., 2009) and these

researchers suggested that higher population density decreased

the amount of total biomass per individual but overall

production per unit area increased (Flores-Lopez et al., 2009).

Martinez-Fernandez et al. (1996) mentioned that the wild

rosemary is found in average population density of 1-2 plants

m-2

and produces an aerial biomass from 266.4 to 836 g m-2

,

depending on water compensation mechanisms. Contradictory

observation was reported by Mishra et al. (2009) when they

conducted a two-year on rosemary cultivation under dry

conditions with three densities (6, 8 and 16 plants m-2

). These

researchers reported that higher population density led to

increased production of dry biomass and essential oils as

compared to lower densities in which yield per individual was

higher. In Spain this species is found naturally in population

densities of 1.0 to 2.0 plants m-2

and producing on average 551

g m-2

of dry biomass (Martinez-Fernandez et al., 1996).

Besides, Sardans & Peñuelas (2005) reported that rosemary

production was reported 200-300 g m-2

at population densities

of 1.5 to 2.0 plants m-2

. They also reported that addition of

nitrogen and phosphorus to the hydroponics solution increased

biomass production and the concentration of these elements in

leaves of in rosemary (Rosmarinus officinalis L.). SAGARPA

(2012) reported that in 2011 total 50.75 ha were under

rosemary cultivation in Mexico and the states of Baja

California Sur (11.75 ha) and Estado de Mexico (39 ha) are the

major rosemary cultivated area with mean annual biomass

production (not indicated whether it was wet or dry) 7 and 6 t

ha-1

respectively. However, there is little information regarding

crop management, nutritional needs and hydroponics

production for R. officinalis.

In this study, effect of population density on biomass

production and absorption of N, P, K, Fe and Mn was

evaluated in the hydroponic cultivation of rosemary (R.

officinalis). Further, growth curves and absorption of N, P, K,

Fe and Mn for hydroponically grown.

2 Material and Methods

2.1 Cultural conditions and setup of study

This study was conducted from the 30th October 2011 to 30

May 2012 at the Center for Research and Development for

Hydroponics of Marin Campus Faculty of Agriculture,

Autonomous University, Nuevo Leon, which is located in the

municipality of Marin, Mexico, at the geographical

coordinates: L 25º 23” N and L 100º 12” W with 393 m

altitude. Maximum rainfall was reported in the month of

October 2011 (110 mm) and January 2012 (334.6 mm)

(INIFAP, 2011; INIFAP 2012). The wind direction from north

to south with an average annual temperature of 24 °C;

maximum temperature of 38 °C and minimum of 7°C; the

warmer months are June, July and August (INIFAP, 2012).

A closed hydroponic system was developed on the terraces

which built up by of concrete blocks with dimensions of 14m

long and 1.10m wide (inside), 0.20 m in height and with a

polished concrete floor and sealed finish, was used in this

study. The terrace consists of two parts, the body and head

638 Humberto et al

_________________________________________________________



allowing the nutrient solution to be drained through a collector

below the floor connected below the floor to a 2.5 m 3 tanks.

Lava rock substrate (20-40 mm in diameter) was used to

anchor the plants and to help provide nutrients to the plant

roots. The substrate was previously cleaned and disinfected

with a solution of industrial-grade sulfuric acid buffered at pH

= 3.0, with this solution the terrace was flooded for a period of

three hours and subsequently washed twice by tap water.

The volume of HNS was prepared 2000 L and completely

renewed every 10 day intervals. The pH of HNS solution was

adjusted to 5.0-5.5. Irrigation with HNS was performed every

after third day. A 0.373 kW centrifugal pump of 3.81 cm in

diameter, located in the outlet, was used to saturate the

substrate contained in the terrace. The excess HNS solution

was drained (recycled) immediately into the tank by gravity.

To estimate the amount of water retained in the substrate, the

moisture holding capacity was determined by the known

volume method (Ramachandra et al., 2016). For transplanting,

about 30 cm of a rosemary landrace plant was used. To prevent

fungal attack, the plant roots and root source substrate (leaf

mulch) were immersed in a fungicide solution (30%

cymoxanil, 72% chlorothalonil) by dosing 90 g L-1

. The plants

were subsequently inserted into volcanic rock to a depth of 15

cm.

2.2 Experimental design

A randomized complete block design was used with 3

treatments (T1 - 8 plants m-2

; T2 – 16 plants m-2

and T3 -24

plants m-2

) and 4 replications.

2.3 Sampling before transplanting

Before transplanting, 10 plants were randomly selected to be

used in determination of initial dry biomass of plant aerial

parts (DBPA), root dry biomass (RDB), total dry biomass

(TDB= DBPA + RDB).

2.4 Assay I

Once the crop established and acclimatized, four plants for

each treatment were harvested every 30 days from 30 October

2011 until 30 May 2012. These harvested plants consisted of a

whole plant (aerial part + root). Once the plants were removed

from each treatment, they were identified and labeled. These

plants were placed in a container with clean water to remove

substrate residues and then washed under a water jet prior to

transfer to the laboratory. For each replication, estimation of

TDB, DBPA, RDB, plant height and concentrations of N, P, K,

Fe and Mn (Paech & Tracey, 2013). To estimate the moisture

content, the samples were placed in identified brown paper

bags and then dried in a forced convection oven (Brand Riossa,

Model H-62, Mexico), maintained at a temperature of 70 to 80

°C to constant weight. Information regarding TDB, DBPA and

RDB were determined for all three trials using the formula

described in Equation 1.

H = Pf – Ps (1)

whereas, H= moisture, g; Pf= Fresh weight (g); Ps= Dry weight

(g)

Total dry biomass (TDB) for all trials were estimated by

ground the samples in a Willey stainless steel mill, sieved with

a mesh of 20 microns, and then placed in a muffle furnace at

450-550ºC for 4 h. The Kjeldahl method (Labconco, 2016) was

used to determine total nitrogen content while the total P was

determined by optical spectroscopy (Spectronic 21D, Milton

Roy) according to the Vanadate/molybdate or yellow method.

Further level of K, Fe and Mn were determined by atomic

absorption spectroscopy (Rodriguez-Fuentes & Rodriguez-

Absi, 2015).

Table 1 Micro and macro nutrient concentration of hydroponic

nutrient solution (SNH) used in this study.

Element Concentration (mg/L) Source

N 200 ---

P 60 KH2PO4

K 250 KNO3

Ca 200 Ca(NO3)2.4H2O

Mg 50 Mg(NO3)2

S 100 H2SO4

Fe 0.50 FeSO4.7H2O

Mn 0.25 MNSO4.H2O

B 0.25 H3BO3

Cu 0.02 CuSO4.5H2O

Zn 0.25 ZnSO4.H2O

Mo 0.01 Na2MoO4.2H2O

Source: Rodríguez-Fuentes et al. (2011).

2.5Assay II

For II assay 10 plants were established in field under natural

condition, from 30 October 2011 to the completion of the

study which is May 30, 2012 in order to measure plants height

per treatment every 10 days. On the other hand, a total 10

whole plants (aerial and root) were harvested per treatment on

every 10 days intervals; these were identified and washed with

water. From these plants, 500 g samples of fresh material per

treatment were collected to determine the TDB of each

treatment.


To run variance analysis and mean comparisons, a software on

Design of Experiments (Olivares, 2012) and SPSS 17.0 (2008)

were used. To estimate the growth curves and nutrient

absorption, Sigma Plot software 10TM (Systat Inc., 2010) was

used.

Cultivation of Rosmarinus officinalis in hydroponic system 639

_________________________________________________________



Table 2 Monthly average of TDB (g/plant) reported in assay I.

Treatments Dec. Jan. Feb. Mar. Apr. May.

T1 85.68±3.53a 123.76±3.02

a 117.00±6.56

a 209.50±5.87

a 229.50±7.98

a 267.25±8.99

a

T2 55.60±2.36ab

67.01±1.89b 117.00±5.66

a 133.50±4.87

b 174.25±7.06

b 159.25±7.89

b

T3 46.79±2.03b 68.45±1.91

b 67.50±3.65

b 76.00±4.65

c 144.50±6.15

b 176.25±7.01

b

Different letters in same column show significant difference (p≤0.05)

Figure 1 Model of linear adjust for production of TDB in

Treatment1. TDB= Total dry biomass (g). Vertical bars in each

point represent standard deviation of the mean.


Treatment 2. TDB= Total dry biomass (g). Vertical bars in

each point represent standard deviation of the mean.


3.1 Assay I

Effect of plant density on TDB was reported in Table 1 and a

significant difference was reported between various treatments

T1, T2 and T3 (g plant-1

) during the trial period (December

2011 to May 2012) (P ≤ 0.05). Among various treatments, T1

showed the highest TDB production for each month (Table 2).

Scattering data of TDB (g plant-1

) were obtained for treatments

1, 2 and 3, with their respective standard deviations. It was

reported that all three treatments followed a similar growth

pattern (Figure 1, 2 & 3).


Treatment 3. TDB= Total dry biomass (g). Vertical bars in

each point represent standard deviation of the mean.

Based on the concentration of N, P, K, Fe and Mn in the TDB,

the extraction over time was estimated. For this, the linear

model was used. Table 3 showed the extraction curves for each

nutrient per treatment and the estimated model. Results of this

assay are coincided with results reported by Mishra et al.

(2009), who used more space between rows and plants (0.60 m

x 0.30 m). Treatment with 6 plants m-2

produced the greatest

amount of aerial parts of plants compared with the other

treatment dimensions evaluated: 0.30 x 0.20 m (17 plants m-2

)

and 0.40 x 0.30 m (8 plants m-2

) between rows and plants,

respectively. Experiment was conducted in India, using

rosemary plants under dry conditions. Moreover, Escalante-

Estrada & Linzaga-Elizalde (2008) evaluated the total dry

weight of sunflower plants that were set to 7.5, 10, 12.5 and 15

plants m-2

and concluded that lowest density (7.5 plants m-2

)

was the one with the highest dried biomass production.

The relationship between rosemary TDB production and

sampling time was adjusted to linear models (P ≤ 0.05); the

determination coefficients (R2) were 0.9380, 0.9405, 0.8503

for treatment 1, 2 and 3 respectively. It is considered that linear

equations adequately estimate growth (Rodas-Gaitan et al.,

2012).

640 Humberto et al

_________________________________________________________



Table 3 The extraction curves for each nutrient per treatment and the estimated model. Where: x= elapsed time (in days); y= g of

absorbed nutriment per plant (TDB) for Assay I.

Treatment Model Determination coefficient (R2)

Nitrogen T1 y = 0.2607+0.0188x 0.9813

T2 y = 0.0364+0.0149x 0.9596

T3 y = 0.0109+0.0116x 0.8996

Phosphorus T1 y = 0.0196+0.0029x 0.7699

T2 y = 0.0350+0.0022x 0.7581

T3 y = 0.0351+0.0019x 0.8540

Potassium T1 y = 0.0665+0.0190x 0.9415

T2 y = 0.1043+0.0125x 0.8848

T3 y = 0.0493+0.0109x 0.9100

Iron T1 y = 15.1179+0.3537x 0.7744

T2 y = 6.9924+0.2539x 0.8047

T3 y = 5.4824+0.2161x 0.8558

Manganese T1 y = 1.6682+0.0726x 0.9462

T2 y = 1.1717+0.0530x 0.8650

T3 y = 10.5213+0.0457x 0.8561

The linear fit may be due to the critical period of the trial and

due to the perennial nature of the species; this can be explained

by Rodriguez & Leihner (2006) study those who point out that

plants generally have a growth pattern which is represented by

a sigmoidal model, however through segmenting the model can

be separated into linear models. The sigmoidal model and the

determination coefficient values were also similar to the

estimated linear models; so it was decided to use a linear

relationship in order to make an easier calculation of the

nutrient extraction and to estimate the hydroponic nutrient

solution to be used as a first approximation in the nutritional

management in future studies.

Similar types of results were also reported by Mishra et al.

(2009) those who attributed these results to plants having an

improved ability to spread and grow better because the level of

competition for light, water and nutrients is lower when plants

have wider spacing. Also, Pakrasa et al. (1999) reported

increased TDB production in rosemary, when it grown under

irrigation and nitrogen fertilizer at a density of 3 plants m-2

with spacing of 60 x 60 cm. Both authors are agreeing that the

total production per unit area (and not per plant) is lower with

these densities.

3.2 Assay II

Comparison of means (p≤0.05) for production TDB plant-1

is

shown in Table 4, and it was observed that treatment 1 was

statistically superior to 2 and 3. Figure 4 represent that plant

height did not vary significantly between treatments (p≤ 0.05).

Figure 4 Trend in plant height (cm) between the three

treatments (Assay II).

Figure 5 Trend between treatments and linear model to predict

crop height.


_________________________________________________________



Table 4 Comparison of means for production of TDB plant-1

(Assay II).

Treatment Mean (p≤0.05)

T1 253.090±11.05 a

T2 167.556±8.55 b

T3 154.134±7.77 b

Different letters in the same column indicate significant

difference (Tukey P≤0.05)

In present study, production of TDB (g plant-1

) in treatment 1

was superior to treatment 2 and 3 (Tukey p≤0.05); these results

were similar to the assay 1and also are agreement with Misrah

et al. (2009) and Pakrasa et al. (1999). Further, similar results

in the cultivation of rosemary was reported by Escalante-

Estrada & Linzaga-Elizalde (2008), Cruz-Huerta et al. (2005)

and Vega et al. (2001) in studies of population density in other

plant species have concluded that increasing the population

density per individual production decreases but increases per

unit area.

About the plant height there were not significant differences

between treatments. Figure 5 shows trend between treatments

and linear model to predict crop height under these conditions.

Therefore, plant height sometimes has not relation to plant

growth (accumulated TDB) (Bidwell, 2002; Saldívar, 2010).

Conclusions

The highest dry biomass production per plant at the end of

assay II occurred in treatment 1 (8 plants m-2

) and

corresponded to 253.09g (P≤0.05). Total dry biomass

production per plant was fitted to linear models for treatments

1, 2 and 3 with R2

values of 0.9380, 0.9405 and 0.8503

respectively. Plant height was not significant (P ≤ 0.05) for

different population densities, after 240 days after

transplanting and plant heights ranged between 65.30 cm and

67.80 cm. For the last month of crop sampling, the

concentration (mg kg-1

) in all plant nutrients was not

significant (P ≤ 0.05).

Acknowledgement

Authors express their sincere gratitude to PAYCIT UANL and

National Council of Science and Technology for the financial

support. Authors also wish to give sincere thanks to Ph.D.

Alejandro S. Del Bosque for their comments on the manuscript

and support during this research and publication.

Abbreviations

HNS Hydroponic nutrient solution

DBPA Dry biomass of plant aerial parts

RDB Root dry biomass

TDB Total dry biomass




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_________________________________________________________



KEYWORDS

Deficit irrigation

Grain filling stage

Starch

Oil and protein content

Zea mays

ABSTRACT

Maize is considered one of the most essential dietary components in human food and animal feeding.

The objectives of the present study were to quantify the effects of drought stress on qualitative traits of

maize at grain-filling stages. Hybrids maize seeds were grown by applying full and water stress

conditions during the grain filling stage. Various nutritional properties (crude oil, starch, grain protein

content) were determined in 2014 and 2015 at the second crop growing season in Adana, Turkey. Based

on the results of this study, genotype and environment were found to influence all quality traits

significantly. Further, result of study suggest that water stress caused a significant reduction in major

quality traits. Grain weight and grain quality yield as well crude oil, protein and ash yield were

significantly decreased due to water deficit condition in the both growing seasons. Significant

differences were observed among hybrids in respect of all measurements due to irrigation regimes. The

genotypes, Sancia and Calgary were tolerant by producing higher grain weight. Accordingly, grain

qualities of 71May69, Aaccel and Calgary maize hybrids were less affected under drought stress.

Celaleddin Barutçular1,*

, Halef Dizlek2, Ayman EL-Sabagh

3, Tulin Sahin

2, Mabrouk Elsabagh

4 and

Mohammad Shohidul Islam5

1Department of Field Crops, Faculty of Agriculture, University of Cukurova, 01330 Adana,Turkey

2Department of Food Engineering, Faculty of Engineering, University of Osmaniye Korkut Ata, Turkey

3Department of Agronomy, Faculty of Agriculture, Kafrelsheikh University, 33516 Kafr El-Sheikh, Egypt

4Department of Nutrition and Clinical Nutrition, Faculty of Veterinary Medicine, Kafr El-sheikh University, 33516 Kafr El-Sheikh, Egypt

5Department of Agronomy, University of Hajee Mohammad Danesh Science and Technology, Bangladesh

Received – September 24, 2016; Revision – October 19, 2016; Accepted – October 16, 2016



NUTRITIONAL QUALITY OF MAIZE IN RESPONSE TO DROUGHT STRESS

DURING GRAIN-FILLING STAGES IN MEDITERRANEAN CLIMATE CONDITION

E-mail: [email protected] (Celaleddin Barutçular)







ISSN No. 2320 – 8694
















_________________________________________________________



1 Introduction

Maize (Zea mays) is an important food and feed crop for

human and livestock, and the demand of maize production is

increasing day by day due to its multipurpose uses which

include medicine and textile industies as well as biofuel

production (White & Johnson 2003; Ali et al., 2010). As a

temperate zone country, Turkey produces about 5.9 million

tons of maize per year and it is cultivated in approximately

0.66 million hectares (FAOSTAT, 2014). About 35% of

Turkish maize production is used for human consumption,

65% for animal feed (Kusaksiz, 2010).

Water shortage was extended in the crop production area.

Hence, Food, feed and industrial demant of quality properties

have gradually increasing in maize grain. Maize is a sensitive

crop to water stress and it’s growth is negatively affected by

unavailability of water at its growing stages (Byrne et al.,

1995). Drought might be severely reduced the various

qualitative trait such as grain starch, granule size and

increased relative protein content (Balla et al., 2011). Drought

stress increased is shortened the grain filling period and

reduced grain yield, grain weight and specific weight (Gooding

et al., 2003). According to Zhao et al. (2009) maize protein

components are very sensitive to drought stress during grain

filling stage. The degradation in the dough quality could be due

to the decline in the glutenin to gliadin ratio and in the

percentage of very large glutenin polymers in response to a

biotic stress (Balla & Veisz, 2007).

Maize production program has been primarily aimed for

increasing yield, quality and stability under different

environments (Ignjatovic-Micic et al., 2014). Therefore, grain

yield is the most commonly studied parameters, but grain

quality parameters had less attention for hybrids. Rehman et al.

(2011) reported grain protein, oil and starch content of maize

are generally stable in different environments. Storage

components of mature kernel of maize as a quality traits,

starch, protein and oil content are determinators of the final

grain weight (Boyer & Hannah, 2001). Based on the above

context, the objectives of the currentstudy was to elucidate the

effect of drought stress on maize grain weight and, as well as

nutritional properties (grain starch, protein, crude oil, ash and

yield) for seven maize hybrids under deficit irrigation in

Mediterranean climate condition.


2.1 Experimental design and cultural practices

Field trials were conducted in growing season of 2014 and

2015 as second crop maize at research field of Cukurova

University, Adana, Turkey. A summary of climatic data are

given in figure 1.The methodologies have been followed as

described previously by EL Sabagh et al. (2015). The

experimental design used in this study was strip-split plot in

four replications. The materials were consist of (1) 7 hybrids

variety of maize (Sancia, Indaco, 71May69, Aaccel, Calgary,

70May82 and 72May80) and, (2) two moisture levels (Full

irrigation and water stress ) and amount of irrigation are given

in Figure 1 and treatments were applied at grain growth stages.

Hybrids were sown during first and the second year on 28

June, 2014 and 12 June, 2015, respectively. The regular

agronomic practices of growing maize were similar to farmers’

practice and were followed as necessary. During experiments,

nitrogenous fertilizer was utilized within two times of planting,

100 kg N and P2O5 ha-1 (20-20-0) and V6-growth stage 200

kg N ha-1 (Urea).

2.2 Measurements

Proximate composition of grain including protein, starch, oil

and ash were analyzed based on the method prescribed by

AACC (2000).

Table 1 Effects of analysis of variance of maize hybrids under irrigation regimes in both seasons

Source of

variation

GW(mg) TW(kg/hl) SC(%) PC(%) OC(%) AC(%) SY

(kg/ ha)

PY

(kg/ha)

OY

(kg/ha)

AY

(kg/ha)

2014

Irrigation ns ns ns ns ns * *** * ** **

Hybrids ** * ns ns *** ** ** *** ns **

Interaction ns ns ns * ** * ns *** ** **

CV % 10.7 2.9 5.0 6.0 6.7 6.6 8.1 8.8 7.7 9.8

2015

Irrigation * ns ns ns ns ** ** * * ns

Hybrids ** ** *** ns * ** ns ns ** ns

Interaction ** ns *** ns ns ns * ns ** ns

CV % 5.2 1.8 3.8 4.6 11.6 6.5 8.5 7.6 14.0 9.0

ns: Indicates nonsignificant; *, ** and ***, significant P<0.05, P<0.01 and P<0.001probability respectively; GW: grain

weight,TW:testweight (kg/hl),SC:starch content (%), PC:protein content (%), OC:oil content (%),AC:ash content (%), SY:starch yield

(kg/ha), PY:protein yield (kg/ha), OY:oil yield (kg/ha) and AY:ash yield (kg/ha).

645 Barutçular et al

_________________________________________________________



Figure 1 Temperature and amount of water during 2014 and 2015 growing season (Arrow indicates pollination) (Source: Meteorological

Service of Turkish State, 2016).

Table 2 Irrigation regimes effects on grain quality parameters of maize hybrids in 2014 and 2015.

Traitments GW(mg) TW

(kg/hl)

S

C(%)

PC(%) OC(%) AC(%) SY(kg/

ha)

PY

(kg ha)

OY

(kg/ha)

AY

(kg/ha)

2014 growing season

Irrigated (Control) 258 77.9 62.8 7.66 2.77 1.08 8060 983 355 138

Deficit irrigated 232 77.9 62.5 8.01 2.79 1.02 6596 849 294 108

Drought reduction -0.10 0.00 0.00 0.05 0.01 -0.06 -0.18 -0.14 -0.17 -0.22

Probability ns ns ns ns ns * *** * ** ***

2015 growing season

Irrigated(Control) 292 72.2 64.0 8.05 2.63 1.07 9159 1150 376 152

Deficit irrigated 275 72.0 64.1 8.09 2.60 1.05 8069 1020 328 133

Drought reduction -0.06 0.00 0.00 0.01 -0.01 -0.01 -0.12 -0.11 -0.13 -0.13

Probability * ns ns ns ns ns ** ** * *

ns: Indicates nonsignificant; *, ** and ***, significant P<0.05, P<0.01 and P<0.001probability respectively; GW: grain

weight,TW:testweight (kg/hl),SC:starch content (%),PC:protein content (%),OC:oil content (%),AC:ash content (%),SY:starch

yield(kg/ha),PY:protein yield(kg/ha),OY:oil yield(kg/ha) and AY:ash yield(kg/ha).

Nutritional quality of maize in response to drought stress during grain-filling stages in mediterranean climate condition 646

_________________________________________________________



Table 3 Maize hybrids grain quality traits and quality yield parameters in 2014 and 2015 growing season.

Hybrids GW

(mg)

TW

(kg/hl)

SC

(%)

PC

(%)

OC

(%)

AC

(%)

SY

( kg/ha)

PY

(kg/ha)

OY

(kg/ha)

AY

(kg/ha)

2014 growing season

H1 236 76.3 64.5 7.99 2.56 0.96 8219 1020 326 123

H2 271 78.6 63.0 7.85 2.85 1.07 7379 925 332 126

H3 232 78.5 64.2 8.11 2.80 1.09 7912 1000 345 134

H4 263 76.7 60.6 7.59 2.83 1.06 6973 869 324 122

H5 223 76.6 62.3 7.63 2.60 1.09 7401 901 307 129

H6 257 78.6 61.0 8.05 3.01 1.06 6658 879 327 117

H7 234 80.1 62.9 7.62 2.80 1.01 6757 820 309 109

Mean 245 77.9 62.6 7.84 2.78 1.05 7328 916 324 123

LSD0.05 26.7 2.3 ns ns 0.187 0.072 601.6 81.2 ns 12.3

2015 growing season

H1 247 70.1 63.8 8.47 2.59 1.08 8715 1155 353 147

H2 294 73.6 63.6 7.93 2.67 1.04 7632 957 321 126

H3 290 72.7 65.2 8.54 2.52 1.03 9386 1232 365 152

H4 298 71.9 64.9 7.78 2.64 1.05 8965 1073 366 145

H5 253 69.5 62.4 7.86 2.62 1.14 7988 1005 336 147

H6 327 73.1 65.2 7.94 2.68 1.02 9484 1155 391 147

H7 275 73.7 63.2 7.98 2.58 1.05 8132 1019 332 135

Mean 283 72.1 64.0 8.07 2.61 1.06 8614 1085 352 143

LSD0.05 15.0 1.32 ns 0.379 ns 0.072 746.1 83.4 ns 13.0

ns: Indicates nonsignificant; GW: grain weight,TW:testweight (kg/hl),SC:starch content (%), PC:protein content (%), OC:oil content

(%),AC:ash content (%), SY:starch yield (kg/ha), PY:protein yield (kg/ha), OY:oil yield (kg/ha) and AY:ash yield (kg/ha); H1:Sancia

,H2:Indaco,H3:71May69, H4:Aaccel,H5:Calgari, H6:70May82 and H7:72May80

For grain weight determination randomly 10 ears were selected

and shelling, it was followed by weighed of grain to calculate

the percentage of shelling through (grain weight/grain

numbers) at 12.5%moisture level of grain.


The obtained results subjected to analyses of variance

according to Gomez & Gomez (1984). Significant means were

separated by the Least Significant Difference (LSD) at the 0.05

significance level (P≤0.05).The estimation of correlation for

traits was calculated by MSTAT-C computer software

package.


3.1 Effects of irrigation regimes on quality traits of maize

Effect of irrigation regimes was the most prominent source

which affects the grain quality during various growth stages

(Table 1 & 2). It was reported that grain quality of maize

hybrids were significantly influenced by irrigation treatments

and, water stress lead to a significant reduction in yield quality

traits over control (Table 1 & 2). Amount of maize oil, starch,

protein and ash yield were significantly reduced by the deficit

irrigation, starch showed higher sensitivity to drought

(P<0.001 and P<0.01 of first and second year, respectively)

than other traits (Table 2).

It was observed that grain weight was significantly affected by

water stress and the highest grain weight (258 and 292 mg)

was observed under control while the lowest grain weight (232

and 275 mg) under water stress condition for the 1st and 2nd

year respectively (Table 2). Low grain weight due to drought

stress, as found in present experiments, may indicate that the

plants were unable to fully meet the demand of the growing

grain. In present research, the differences between the water

regimes were statistically significant for ash content in first

season (Table 2). Protein content is also significantly

influenced by water stress conditions and, it was slightly

increased (non-significant) under limited irrigations (Table 2).

High starch, protein and crude oil yielding genotypes under

water shortage condition could be evaluated for the drought

tolerance genotypes. The obtained results, oil yield per unit

area considerably decreased under water stress (Table 2). Grain

qualities are governed by a number of factors particularly the

duration and rate of grain filling (Brdar et al., 2008) and

availability of assimilates that are negatively influenced under

water deficit conditions (Ali & Ashraf, 2011; Barutçular et al.,

2016a).


_________________________________________________________



Table 4 Grain quality traits of maize hybrids as influenced by water regimes in 2014 and 2015.

Hybrids GW

(mg)

TW

(kg/hl)

SC

(%)

PC

(%)

OC

(%)

AC

(%)

SY

( kg/ ha)

PY

(kg/ha)

OY

(kg/ha)

AY

(kg/ha)

Irrigated (Control) in 2014

H1 255 76.8 66.7 7.65 2.66 0.97 8481 972 338 123

H2 282 78.9 63.0 8.18 2.66 1.09 8177 1061 344 142

H3 237 77.6 62.3 7.92 2.77 1.06 8525 1084 378 146

H4 282 76.6 60.2 7.18 2.71 1.11 7833 935 352 144

H5 229 76.8 63.9 7.15 2.54 1.10 8306 929 330 142

H6 266 77.7 61.6 7.96 2.92 1.16 7198 930 340 136

H7 257 80.8 61.8 7.57 3.15 1.06 7903 969 402 135

Deficit irrigated (Water stress) in 2014

H1 217 75.7 62.2 8.33 2.45 0.96 7957 1067 314 124

H2 260 78.3 63.0 7.52 3.05 1.05 6582 789 320 110

H3 227 79.4 66.2 8.31 2.83 1.11 7299 915 311 122

H4 244 76.7 61.0 8.00 2.95 1.00 6113 803 296 101

H5 217 76.5 60.8 8.11 2.66 1.08 6496 872 284 117

H6 249 79.4 60.4 8.14 3.11 0.96 6117 829 315 98

H7 211 79.5 64.1 7.67 2.45 0.96 5610 672 215 84

LSD0.05 ns ns ns 0.676 0.264 0.101 ns 114.9 36.0 17.3


H1 240 70.0 64.1 8.70 2.60 1.05 8978 1216 363 147

H2 302 73.2 64.3 8.50 2.74 1.07 8254 1094 351 138

H3 303 72.3 63.2 8.39 2.56 1.09 10056 1337 409 174

H4 305 71.9 64.9 7.66 2.70 1.03 9543 1126 397 151

H5 260 70.7 61.1 7.55 2.56 1.14 8338 1027 349 155

H6 357 73.4 65.6 7.76 2.66 1.01 9699 1149 393 149

H7 279 73.8 65.0 7.80 2.59 1.08 9248 1104 368 154


H1 253 70.1 63.5 8.25 2.59 1.11 8452 1094 343 148

H2 286 74.0 62.9 7.36 2.60 1.00 7009 821 292 114

H3 277 73.1 67.2 8.69 2.48 0.98 8716 1128 322 130

H4 291 71.9 64.9 7.90 2.58 1.08 8386 1020 334 139

H5 247 68.3 63.8 8.17 2.68 1.15 7638 984 324 138

H6 298 72.9 64.9 8.12 2.70 1.03 9269 1161 389 145

H7 271 73.7 61.4 8.17 2.58 1.03 7015 934 296 116

LSD0.05 21.2 ns ns 0.54 ns ns ns 117.9 ns 18.4

ns: Indicates nonsignificant; GW: grain weight,TW:testweight (kg/hl),SC:starch content (%), PC:protein content (%), OC:oil content

(%),AC:ash content (%), SY:starch yield (kg/ha), PY:protein yield (kg/ha), OY:oil yield (kg/ha) and AY:ash yield (kg/ha).H1:Sancia

,H2:Indaco,H3:71May69, H4:Aaccel,H5:Calgari, H6:70May82 and H7:72May80

Water stress imposed during the grain filling period of wheat,

especially at the early filling stage, usually results in a

reduction in grain weight (Zhao et al., 2009). Further, Pierre et

al. (2008) and EL Sabagh et al. (2015) reported that water

deficit stress has a negative effect on grain weight. Protein

content is more strongly influenced by environment

(Mikhaylenko et al., 2000).Variations in flour quality in a

hard-grained were related to changes in protein composition

from drought stress during grain filling (Gooding et al., 2003).

It was also reported that water limitation significantly

decreases seed and oil yields of maize (Ghassemi-Golezani &

Dalil, 2011).The reduction in protein and oil yields under water

stress could be due to sharp decline in grain yield under

stressful condition (Ghassemi-Golezani & Lotfi, 2013) and the

results are also the results are also in agreement with the

findings of Rashwan et al. (2016) and Barutçular et al. (2016b).

3.2 Comparative evaluation of various hybrids of maize

Significant differences among various genotypes with respect

to grain quality traits were observed which indicates existence

of genetic variation and possibility of selection for favorable

genotypes in both environments. In this research, a greater

reduction in test weight was observed in genotype of Calgari

and Sancia (Table 1, 3). The hybrids genotype Indaco,

70May82 and Aaccel showed more positive effect of grain

weight.


_________________________________________________________



Table 5 Pearson correlation coefficient between grain quality traits in the irrigation regime (2014 and 2015 growing season).

Traits GW

(mg)

TW

(kg/hl)

SC

(%)

PC

(%)

OC

(%)

AC

(%)

SY

( kg/ha)

PY

(kg/ha)

OY

(kg/ha)

AY

(kg/ha)


GW (mg) 1.000

TW (kg/hl) 0.161 1.000

SC (%) -0.389 -0.236 1.000

PC (%) 0.266 0.370 0.072 1.000

OC (%) 0.150 0.769* -0.437 0.211 1.000

AC (%) 0.249 0.003 -0.750 0.017 0.126 1.000

SY (kg/ha) -0.485 -0.178 0.604 -0.076 -0.516 -0.749 1.000

PY (kg ha) -0.027 0.261 0.082 0.681 -0.068 -0.288 0.545 1.000

OY(kg/ha) -0.066 0.733 -0.399 0.097 0.782* -0.157 0.032 0.330 1.000

AY (kg/ha) -0.013 -0.033 -0.690 -0.051 -0.158 0.612 -0.014 0.308 0.137 1.000


GW (mg) 1.000

TW (kg/hl) 0.194 1.000

SC (%) -0.262 0.497 1.000

PC (%) -0.351 -0.311 -0.052 1.000

OC (%) 0.929** 0.320 -0.250 -0.161 1.000

AC (%) 0.055 0.055 0.412 0.102 0.213 1.000

SY ( kg/ ha) -0.179 -0.460 0.252 0.624 -0.247 0.242 1.000

PY (kg ha) -0.202 -0.590 -0.041 0.789* -0.218 0.112 0.939** 1.000

OY(kg/ha) 0.608 -0.257 -0.187 0.399 0.595 0.283 0.607 0.634 1.000

AY (kg/ha) -0.085 -0.508 0.142 0.600 -0.071 0.563 0.904** 0.870* 0.684 1.000

Irrigated (Control) 2015

GW (mg) 1.000

TW (kg/hl) 0.704 1.000

SC (%) 0.544 0.592 1.000

PC (%) -0.289 -0.209 0.075 1.000

OC (%) 0.468 0.371 0.564 0.077 1.000

AC (%) -0.572 -0.283 -0.890** -0.090 -0.589 1.000

SY ( kg/ ha) 0.491 0.256 0.436 -0.073 -0.207 -0.475 1.000

PY (kg ha) 0.098 -0.076 0.141 0.588 -0.293 -0.219 0.718 1.000

OY(kg/ha) 0.582 0.239 0.379 -0.092 -0.047 -0.478 0.959** 0.690 1.000

AY (kg/ha) 0.012 -0.057 -0.352 -0.092 -0.722 0.355 0.646 0.625 0.619 1.000

Deficit irrigated (Water stress) 2015

GW (mg) 1.000

TW (kg/hl) 0.758* 1.000

SC (%) 0.270 -0.030 1.000

PC (%) -0.331 -0.242 0.516 1.000

OC (%) -0.024 -0.385 -0.341 -0.354 1.000

AC (%) -0.668 -.931** -0.239 0.028 0.507 1.000

SY ( kg/ha) 0.303 -0.118 0.759* 0.516 0.050 -0.018 1.000

PY (kg/ha) 0.073 -0.205 0.676 0.753 -0.015 0.048 0.941** 1.000

OY(kg/ha) 0.268 -0.226 0.439 0.292 0.484 0.192 0.888** 0.821* 1.000

AY (kg/ha) -0.159 -0.633 0.428 0.392 0.346 0.574 0.798* 0.782* 0.844* 1.000

*, **, significant P<0.05and P<0.01 probability respectively; GW: grain weight,TW:testweight (kg/hl),SC:starch content (%),

PC:protein content (%), OC:oil content (%),AC:ash content (%), SY:starch yield (kg/ha), PY:protein yield (kg/ha), OY:oil yield (kg/ha)

and AY:ash yield (kg/ha).


_________________________________________________________



Achieved results revealed that oil content was significantly

influenced by water regime and that maximum value was

found for genotype 70May82 (3.01%) and minimum for Sancia

(2.56%) in the first season. The obtained results also revealed

that the highest value of protein content (8.54%) was found in

71May69 and the lowest (7.78%) in Aaccel. In this

experiment, genotype Calgari produced the highest value of

grain ash content in both seasons. The hybrids Indaco,

71May69 and Sancia showed more positive effects on starch

and protein yield/ha. Results of study revealed that ash yield

was significantly influenced by water stress conditions and that

the maximum value of ash (134 kg/ha) was found in 71May69

and the minimum (109 kg/ha) in 72May80. Drought stress

reduced grain weight but increased protein content in wheat as

reported by Rharrabti et al. (2003). Availability of assimilates

are negatively influenced under water deficit conditions (Ali &

Ashraf, 2011). Water deficient at flowering stage greatly

decreased starch content due to the decrease of photosynthesis

and thus makes an increase of grain protein ratio (Mousavi et

al., 2013). Environment is the major source of variation for

grain quality as reported by Mikhaylenko et al. (2000);

Gulluoglu et al. (2016) and Kurt et al.(2016).

3.3 Effects of maize hybrids and irrigation regimes on quality

traits

Genotypes and irrigation regimes were found to influence all

quality traits significantly. The treatments interactions showed

significant differences on the quality parameters (Table 4).

Under drought stress, the hybrids Sancia and Calgary were

more stable in grain weight (less reduction) and 71may69,

Aaccel and Calgary were less sensitive to grain quality (less

grain quality losses), maize quality properties are usually

influenced by genotypes, environmental factors and their

interactions and final growth stage of maize is dramatically

influenced by the water stress and, this adverse effects are

mainly reduced grain weight, and this resulted in low starch,

crude oil and protein content (Cirilo et al., 2011). Grain filling

process is sensitive to environmental conditions, this strongly

influencing the final grain development quantitatively and

qualitatively as well (Yang & Zhang, 2006). Mansouri et al.

(2010) found that, grain weight were decreased under water

stress condition. Farhad et al. (2013) observed that grain

protein and oil contents were significantly influenced by

irrigation regimes among maize hybrids.

3.4 Correlation analysis

Correlation coefficients among the major studied variables

were found positive association in both seasons (Table 5). It

was found that the grain weight was negatively affected by the

starch content in the first season, and protein content and ash

content in the second season. The highest correlation was

observed in starch, protein yield in both seasons, while,

negative correlation between oil content and starch content as

well as protein content was found in the first season. It was

also observed a negative correlation among grain weight, test

weight and ash in the second season. Oury & Godin (2007)

reported that, protein contents were negatively correlated with

grain weight under normal and stress conditions. The negative

correlation between grain yield and grain gluten content had

been established under genotype-by-environment interactions

in different studies of wheat (Tayyar, 2010). The positive

correlation between grain protein percentage and grain filling

rate and which results from irrigation-off at flowering stage,

has a significant effect on grain protein percentage increasing.

Furthermore, it was observed that, grain protein percentage

reduces in relation to starch under water deficient (Mousavi et

al., 2013).

Conclusion

In summary, it can be stated that the imposition of water stress

significantly influenced the nutritional quality traits of maize.

However, high starch, protein and crude oil yielding genotypes

could be evaluated for the drought tolerance in the drought

environment. In respect to the hybrids, Sancia and Calgary

were drought tolerant genotypes for their less sensitivity (more

stable) to grain weight under drought stress. The 71May69,

Aaccel and Calgary were less sensitive in grain quality traits

under drought stress.




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KEYWORDS

Off-season cucumber

Technical

Allocative

Economic

Efficiency

DEA Approach

Tobit Model

ABSTRACT

The current research was designed to estimate technical, allocative and economic efficiency and

determinants of inefficiency in the cultivation of off-season cucumber in Punjab, Pakistan. Simple

random sampling was selected for the collection of primary data from 70 off-season cucumber growers

in 2014. Data Envelopment Analysis Procedure revealed that average value of technical efficiency was

higher (87.4%) followed by allocative (42.0%) and economic efficiency (37.2%). It shows the potential

of 12.6% reduction in the level of input use and 58.0% reduction in total cost for obtaining same output

level with same technology. The lowest value of technical (60.7%), allocative (13.7%) and economic

(9.9%) efficiency was also calculated. Medium farmer shows high value of technical (96.7%) and

economic (46.5%) efficiency while allocative (49.0%) efficiency was higher in case of small farmer.

Inefficiency determinants shows that the education, experience in off-season cucumber production and

number of meetings with extension staff had significant and negative effect on inefficiency score. The

effect of family size, off-season cucumber area and distance of vegetable market from vegetable farm

was significant and positive on inefficiency score. Government should take steps for the improvement in

education, technical knowledge, meetings with extension staff and quality of inputs. Government should

provide subsidy to small farmers in the purchase of tunnel material.

Qamar Ali1,2,*

, Muhammad Ashfaq3 and Muhammad Tariq Iqbal Khan

4

1Institute of Agricultural and Resource Economics, University of Agriculture, Faisalabad

2Instructor, Department of Economics, Virtual University of Pakistan, Faisalabad Campus

3Professor and Doctor, Institute of Agricultural and Resource Economics, University of Agriculture, Faisalabad

4Lecturer, Department of Economics, Government Postgraduate College, Jaranwala

Received – September 12, 2016; Revision – October 25, 2016; Accepted – November 06, 2016



ANALYSIS OF OFF-SEASON CUCUMBER PRODUCTION EFFICIENCY IN

PUNJAB: A DEA APPROACH

E-mail: [email protected] (Qamar Ali)







ISSN No. 2320 – 8694
















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1 Introduction

Government promoted new technologies for the improvement

in agriculture sector. The share of agriculture sector was 19.8%

in gross domestic product with the involvement of 42.3 %

labor force (Government of Pakistan, 2016a). There is a strong

association exists between agriculture and various climate

factors like precipitation, temperature, floods which ultimately

influence on the economy of a country. Increase in the

production as well as yield of agricultural crops is a need of

time for successful achievement of food security (Government

of Pakistan, 2015; Government of Pakistan, 2016a).

Vegetables are considered as an essential part of agriculture

because these are a source of livelihood and foreign exchange.

These are useful for health, maintenance of nutrition level and

resistance against diseases (Ogunniyi & Oladejo, 2011;

Ibrahim & Omotesho, 2013). There exists 120% expansion in

the production of vegetables on the globe (Bozoglu & Ceyhan,

2007). Major problems faced by developing countries were

unemployment, poverty and malnutrition. The sector of

vegetables can tackle these problems in short period of time.

Their short growing period was also helpful in the cultivation

of many crops in a particular season (Akter et al., 2011).

The value of vegetables and fruits export was 47895.6 million

rupees in 2010-11 but the amount becomes 66531.3 million

rupees in 2015-16 (Government of Pakistan, 2016b). Per capita

recommended use of vegetables was 73 kg on annual basis but

per capita annual vegetable consumption was 27.4 kg less in

Pakistan (Shaheen et al., 2011).

Cucumber (Cucumis sativus L.) is a popular vegetable of

Cucurbitaceae family having 118 genera and 825 species

(Khan et al., 2015; Maurya et al., 2015). It is growing in

western Asia since last 3,000 years but India is marked as their

homeland (Maurya et al., 2015). Its local name is Khira and is

an essential ingredient of salad. It is real versatile vegetable

because of variety in their use from salad to pickles as well as

from digestive aids to beauty products. It was found useful

against human constipation and improvement in digestion

(Maurya et al., 2015). It is used as a cooling food in summer

(Maurya et al., 2015). A fresh cucumber provides vitamin C,

niacin, iron, calcium, thiamine, fibers and phosphorus (Khan et

al., 2015; Sanjeev et al., 2015). More than 50% production of

cucumber comes from Asia. Turkey, Iran, Uzbekistan, Japan

and Iraq were considered as leading cucumber producing

countries in Asia (Khan et al., 2015).

In Pakistan, the cultivation area under cucumber and gherkins

was 3,528 ha in 2013 while it was 3,499 ha in 2012. Total

production of cucumber and gherkins was 50,164 tonnes in

2013 while it was 49,947 tonnes in 2012 (FAO, 2016). Yield

of cucumber and gherkins was 14,218.8 kg ha-1

in 2013 while

it was 14,274.6 kg ha-1

in 2012. So, the area and total

production showed 0.83% and 0.43% increase, respectively.

However, there is 0.39% decrease in per hectare yield (FAO,

2016).

In Punjab, the cultivation area under cucumber was 1,795

hectares in 2012-13 while it was 1,742 hectares in 2011-12.

Total production of cucumber was 40,439 tonnes in 2012-13

while it was 38,952 tonnes in 2011-12. Punjab contributes

80.96% in the total production of cucumber in 2012-13 while

its area under cucumber cultivation was 51.30% of total area

under cucumber cultivation in Pakistan in 2012-13. It shows

that the average yield was higher in Punjab as compared to

other provinces (Government of Pakistan, 2014).

Off-season vegetable production was useful for the reduction

in high prices at start and end of vegetable season.

Temperature and moisture level were under the control of

farmers in off-season or tunnel farming (Government of

Pakistan, 2013). Extension in the season and yield of a

particular vegetable is observed in case of off-season

cultivation (Iqbal et al., 2009).

The yield difference was observed in case of different farmers

due to difference in the use of inputs. It indicates the existence

of inefficiency in input usage (Khan & Ghafar, 2013).

Production function, mathematical programming and frontier

function techniques were used for the measurement of

technical efficiency of agricultural farms (Bozoglu & Ceyhan,

2007). Therefore, it is required to uplift the living standard of

vegetable farmers by improving their technical efficiency

(Ibrahim & Omotesho, 2013).

Alboghdady & Shata (2014) explored the technical efficiency

in the production of cucumber under greenhouses, plastic

tunnels and open field system. Results confirmed the

difference in efficiency among various cultivation systems.

They pointed out toward the improvement of efficiency and

productivity. Education, extension services and agricultural

knowledge were found beneficial for the improvement of

efficiency.

Similarly, Shrestha et al. (2014) demonstrated the efficiency in

the production of vegetables in Nepal. Average technical

efficiency was 0.77 and pointed out 23% expansion in the

production of vegetables. They recommended improvement in

land, seed quality, pesticide and fertilizer availability, labour

skills, women participation, extensions services and credit

availability.

The current research was designed for the estimation of

production efficiency in off-season cucumber production and

checked the opportunity of input reduction keeping output

level as constant or opportunity of obtaining more output

keeping the input use level constant. The study also designed

to give policy implications in the light of results. The

production efficiency of off-season cucumber production was

further decomposed into technical, allocative and economic

654 Qamar et al

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efficiency with the help of Data Envelopment Analysis

Procedure.

2 Material and Methods

2.1 Data and study area

The present study used a comprehensive questionnaire for

primary data collection from off-season cucumber growers in

Toba Tek Singh and Faisalabad districts of Punjab, Pakistan in

2014. Simple random technique was adapted to interview off-

season cucumber growers about socio-economic variables like

education, size of family, off-season cucumber growing

experience, contacts with extension agents, distance of

vegetable market. They were also asked about the prices and

quantity of inputs and output. A sample size of 60 respondents

was suitable for the purpose of decision in the presence of

large population as mentioned by Poate & Daplyn (1993), cited

in Mari (2009). Therefore, the current study used a sample size

of 70 off-season cucumber growers. Farmers were divided

according to farm size in three groups which are small,

medium and large. According to Hassan et al. (2005), a farmer

having less than 12.5 acres was considered as small farmer; a

farmer with greater than 12.5 acres and less than 25 acres was

considered as medium; and a farmer having greater than 25

acres was considered as large. Software like Microsoft Excel,

SPSS-15, DEAP-2.1 and Eviews 7 were used for empirical

analysis.

2.2 Efficiency Background

A comparison between existing and maximum productivity of

a firm is called as efficiency (Farrell, 1957). Maximum

productivity of a firm was determined by using production

frontier. Production frontier was developed by using two

different techniques such as stochastic frontier analysis (SFA)

and data envelopment analysis (DEA). The technique of linear

programming was used in DEA model. The increasing

difference among actual data and frontier explored the

presence of increasing inefficiency of a firm (Javed, 2009).

Coelli et al. (1998) mentioned both output and input oriented

nature of DEA model but a farmer has more control on inputs.

Therefore, input oriented DEA model was used in this study.

According to Javed (2009), technical efficiency is the

achievement of maximum output by utilizing given input

resources on the basis of production model. DEA model based

on constant as well as variable return to scale was used for the

estimation of technical efficiency. According to Coelli et al.

(1998), constant returns to scale DEA model was feasible

when all firms were working at an optimal scale otherwise it

gives technical efficiency confounded by scale efficiency.

Banker et al. (1984) incorporated convexity constraint in

proposed variable returns to scale DEA model. DEA model

based on constant and variable return to scale were used in this

study.

2.2.1 Empirical Models

Present study calculated total technical and pure technical

efficiency by using DEA model based on constant and variable

return to scale, respectively. Total revenue (Y) was considered

as output variable in the calculation of efficiency scores. Land

(X1), tractor (X2), seed (X3), fertilizer (X4), pesticide (X5),

irrigation (X6), labour (X7), polythene sheet (X8) and mulch

sheet (X9) were used as input variables in the analysis.

(a) DEA Model for technical efficiency estimation

Input oriented constant return to scale DEA model was applied

for technical efficiency estimation as mentioned by Javed

(2009) like:

min θ,λ θ,

subject to:

-yi + Yλ ≥ 0

θxi -Xλ ≥ 0

λ ≥ 0

Where:

Y represents the output matrix for N off-season

cucumber farmers.

θ represents the total technical efficiency.

λ represents Nx1 constants.

X represents input matrix for N off-season cucumber

farmers.

yi represents the total revenue (Rs.)

xi represents the vector of inputs x1i,x2i,……x9i

X 1i represents the area under off-seasonal cucumber

(acres)

X2i represents the total tractor used (hours) in farm

operations

X3i represents the total quantity of seed (kg)

x4i represents weight of NPK (kg)

x5i represents the chemical applications (No.)

X6i represents the total irrigation (hours)

X7i represents the total labour man days required for

all farm operations

x8i represents the polythene sheet weight (kg)

x9i represents the mulch sheet weight (kg)

(b) DEA Model for Pure Technical Efficiency Estimation

An input oriented variable return to scale DEA model was used

by Coelli et al. (1998), cited in Javed (2009) for pure technical

efficiency estimation. It is expressed as:

min θ,λ θ,

subject to

-yi+ Yλ ≥ 0

θxi - Xλ ≥ 0

N1/ λ= 1

λ ≥ 0

Analysis of off-season cucumber production efficiency in Punjab: a dea approach 655

_________________________________________________________



Where:

θ represents the pure technical efficiency for ith off-

season cucumber farmer.

N1/λ= 1 represents a convexity constraint to ensure

that an inefficient farmer was benchmarked against

same size farmers.

(c) Scale Efficiency Estimation

Scale efficiency was obtained by dividing total technical

efficiency with pure technical efficiency and expressed as:

SE = TECRS/TEVRS

The firm was scale efficient or working at constant return to

scale when it shows a value of 1. A firm whose value of scale

efficiency was less than 1 represents scale inefficiency. A

firm’s working either at increasing or decreasing return to scale

causes scale inefficiency.

(d) Economic Efficiency Estimation

Cost minimization DEA model is considered as first step for

the estimation of economic efficiency and it is simply a ratio

between minimum to observed cost as mentioned by (Javed,

2009). Cost minimization DEA model was expressed as:

min λ, xiE wi xi

E

subject to

–yi +Yλ ≥ 0

xiE–Xλ ≥ 0

N1/λ = 1

λ ≥ 0

Where:

wi represents input price vector w1i, w2i

,………,w9i

xiE represents the vector of cost minimizing input

quantities

N represents the total off-season cucumber farmers

w1i represents land rent in Rs.

w2i represents total money spent on tractor use in Rs.

w3i represents total cost of seed in Rs.

w4i represents total cost of NPK in Rs.

w5i represents total cost of pesticide in Rs.

w 6i represents total cost of irrigation in Rs.

w7i represents total cost of labour in Rs.

w8i represents total cost of polythene sheet in Rs.

w9i represents total cost of mulch sheet in Rs.

Economic efficiency is simply a ratio between

minimum cost and observed cost.

Economic Efficiency = minimum cost/observed cost

EE = wi xiE/ wi xi

(e) Estimation of Allocative Efficiency

Allocative efficiency is obtained by dividing economic

efficiency with technical efficiency.

AE = EE/TE

Allocative Efficiency = Economic Efficiency /

Technical Efficiency

(f) Tobit Regression Model

Efficiency improvement studies also explored the causes of

efficiency variations between different farmers (Ibrahim &

Omotesho, 2013). The score of inefficiency for each farmer

was obtained by subtracting their efficiency score from 1. The

technical, allocative, and economic inefficiency score were

separately regressed on selected variables. The range of

efficiency score by using DEA model was from 0 to 1. It

shows that the dependent variable in the model was not

normally distributed. Biasness in results becomes a hurdle for

the use of ordinary least square technique (Javed, 2009). So,

the current study used Tobit regression model proposed by

Tobin (1958).

Socio-economic and farm related variables were education of

farmer, family size, contact with extension agents, off-season

cucumber growing experience and area, and distance of

vegetable market. Tobit regression model used by Javed (2009)

for the determinants of inefficiency was expressed as:

Ei = Ei*= β0 + β1Z1i + β2Z2i + β3Z3i + β4Z4i + β5Z5i + β6Z6i +µi

If E* > 0

E = 0 if If E* ≤ 0

Where

i represents ith farmer in the sample

Ei represents the technical, allocative, and economic

inefficiency

Ei* represents the latent variable.

Z1i represents the education (years)

Z2i represents the total family size (no.)

Z3i represents the off-season cucumber experience

(years)

Z4i represents the contact with extension agents (no.)

Z5i represents the area under off-season cucumber

(acres)

Z6i represents the vegetable market distance (km) from

ith farm

ß’s represents unknown parameters.

µi represents the error term.

3 Results

3.1 Summary statistics

Table 1 reveals the summary statistics of socio-economic

variables. Average age of off-season cucumber growers was

40.81 years with minimum (15 years) and maximum (80

years). Mean value of education was 9 years. Average family

size was 9.17 members with minimum (6) and maximum (24).

656 Qamar et al

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Table 1 Summary statistics of socio-economic variables.

Variables Unit Mean Maximum Minimum Standard Deviation

Age Year 40.81 80 15 13.80

Education Year 9.00 18 0 4.97

Size of family No. 9.17 24 4 3.31

Off-season cucumber experience Year 6.85 20 1 4.52

Contact with extension agent No. 4.54 10 1 1.44

Off-season cucumber area Acre 4.61 40 0.5 5.79

Vegetable market distance Km 74.64 105 15 27.79

Off-season cucumber growers had 6.85 years experience about

this activity while some farmers were new entrants in this

business. Extension services are also important for this

business and off-season cucumber growers had 4.54 contacts

with extension staff. On average, the cultivation area under

cucumber in off-season was 4.61 acres. On average, the

distance of vegetable market from cucumber farm was 74.64

km.

Table 2 shows the summary statistics of variables incorporated

in DEA model. It shows the mean value of a particular variable

as well as their range. There exists variation in the use of input

level because it depends on financial power of farmers and

small farmers used fewer resources due to financial constraints.

Credit availability was an alternative option but many farmers

considered high interest as a hurdle to avail this opportunity.

On average, per acre total output of off-season cucumber was

124.12 tonnes with minimum (26.78 tonnes) and maximum

(220 tonnes). The wide range of output supported the concept

of production inefficiency among the farmers. Per acre average

revenue was Rs. 1,328,569.64 or Rs. 1.329 million (USD

12733.06). Total variable cost was Rs. 555,531.05 and total

cost was Rs. 671,935.96 calculated on per acre basis. On

average, tunnel material cost was Rs. 61,205.14 in this activity.

Tunnel material cost do not includes the cost of long life tunnel

material. The cost of long life tunnel material was a part of

fixed cost in the form of depreciation. A farmer paid Rs.

29,270.83 in the form of land rent calculated for seven months

in off-season cucumber production. Tractor cost was Rs.

14,391.07 on average. On average, a farmer allocated Rs.

55,653.93 as seed cost. Average expenditure on fertilizer was

Rs. 100,559.64. Chemical cost in off-season cucumber

production was Rs. 38,607.14 per acre. Cultivation of

cucumber is a water intensive activity and a farmer spends Rs.

15,528.82 on irrigation. Labor was used in various farm

practices. On average, the share of labour in total variable cost

was Rs. 102,711.07 with minimum (Rs. 32,000.00) and

maximum (Rs. 198,750.00).

Table 2 Summary statistics of variables used in DEA model.

Variables Unit Mean Minimum Maximum Standard Deviation

Yield Kg/acre 124123.21 26775.00 220000.00 31955.61

Revenue Rs./acre 1328569.64 630000.00 2200000.00 294432.92

Variable cost1 Rs./acre 555531.05 183205.00 825570.00 123328.34

Total cost2 Rs./acre 671935.96 242988.05 975329.37 147565.27

Tunnel material cost3 Rs. 61205.14 18140.00 103950.00 19594.85

Land rent Rs./acre 29270.83 17500.00 40833.33 6073.16

Tractor use cost Rs./acre 14391.07 7250.00 25500.00 3077.37

Seed cost Rs./acre 55653.93 25000.00 162000.00 17799.66

NPK cost Rs./acre 100559.64 13800.00 251375.00 45270.00

Chemical cost Rs./acre 38607.14 5000.00 65000.00 11880.72

Irrigation cost Rs./acre 15528.82 4180.00 68000.00 9485.92

Labor cost Rs./acre 102711.07 32000.00 198750.00 26314.77

On per acre basis - 1Variable cost consists of tunnel preparation cost, land preparation cost, seed cost, pesticide cost, irrigation cost, fertilization cost,

picking cost and marketing cost; 2Fixed cost includes depreciation, interest on initial investment, interest on variable cost,

administration charges, rent of land and water charges by Govt. (abyana); 3Tunnel material cost includes cost of string, nut bolt,

polythene sheet, mulch sheet, labour charges


_________________________________________________________



Table 3 Frequency distribution of efficiencies.

Efficiency range Technical efficiency Allocative efficiency Economic efficiency

N % N % N %

0.01-0.30 0 0 33 47.15 35 50

0.31-0.40 0 0 4 5.71 8 11.43

0.41-0.50 0 0 11 15.71 8 11.43

0.51-0.60 0 0 4 5.71 8 11.43

0.61-0.70 5 7.14 9 12.86 5 7.14

0.71-0.80 21 30 5 7.14 2 2.86

0.81-0.90 14 20 3 4.29 3 4.29

0.91-1.00 30 42.86 1 1.43 1 1.43

Total 70 100 70 100 70 100

Mean 0.874 0.420 0.372

Maximum 1 1 1

Minimum 0.607 0.137 0.099

3.2 Efficiency score estimation

Table 3 reveals that the mean total technical efficiency in the

production of off-season cucumber was 87.4% with minimum

(60.7%) and maximum (100%). It depicts the possibility of

12.6% reduction in inputs for working at technical efficient

level while output and technology remains unchanged. Results

showed that 42.86% off-season cucumber growers had more

than 90% value of technical efficiency and 57.14% remaining

falls between 60% and 90%. Average value of allocative

efficiency was 42% with lowest (13.7%) and highest (100%).

It depicts the possibility of 58.0% reduction in total cost for an

allocatively efficient farmer keeping the level of output and

technology constant. Score of allocative efficiency was more

than 70% for only 12.86% farmers. Average pure technical

efficiency was 96.4% with lowest (78.3%) and highest (100%).

It is more due to the absence of production scale. Average

scale efficiency was 90.4% with lowest (62.7%) and highest

(100%). Economic efficiency was 37.2% on average with

minimum (9.9%) and maximum (100%).

Table 4 explores the impact of farm size efficiency scores. All

production efficiency scores were found for small, medium and

large off-season cucumber farmers. The mean of total technical

efficiency was 96.7% for medium farmers followed by large

(95.0%) and small (92.1%) farmers. The average allocative

efficiency was higher for small farmers (49.0%) followed by

medium (48.0%) and large (43.1%) farmers. Economic

efficiency was more for medium farmers and it was 46.5% on

average while its value was 45.7% and 40.8% for small and

large farmers, respectively. Small farmers were more in

Pakistan and their prosperity was also important for the uplift

of Pakistani society (Adil et al., 2004).

3.3 Inefficiency determinants

3.3.1 Education

Education was included to test the hypothesis that a farmer

with more schooling is more efficient in off-season cucumber

production. The results revealed a negative and significant

education coefficient for economic and allocative inefficiency.

Therefore, it confirmed the hypothesis and showed a decrease

in allocative and economic inefficiency with increase in

education.

3.3.2 Family Size

Family size was included to test the hypothesis that a farmer

with increasing size of family had high value of inefficiency

score. There exists a significant and positive coefficient for the

size of family in off-season cucumber production for all

production inefficiencies. So, it confirmed the direct

relationship of inefficiency score with family size. Generally a

farmer spends more financial resource in case of large family

and has fewer resources to invest in a business that involve

new technology. Off-season cucumber cultivation is profitable

but requires higher initial investment.

Table 4 Estimation of production efficiencies with respect to farm size.

Farm Size

Efficiency estimates

TE(CRS) TE(VRS) SE AE EE

Small 0.921 0.995 0.925 0.490 0.457

Medium 0.967 0.994 0.972 0.480 0.465

Large 0.950 0.982 0.968 0.431 0.408

658 Qamar et al

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Table 5 Determinants of inefficiency.

Variables

Unit

Technical inefficiency Allocative inefficiency Economic inefficiency

β Sig. β Sig. β Sig.

Education year 0.007 0.381 -0.009 0.015 -0.007 0.002

Size of family no. 0.081 0.000 0.094 0.000 0.072 0.000

Off-season cucumber experience year -0.050 0.001 -0.022 0.000 -0.002 0.522

Extension agent contacts no. -0.171 0.000 -0.008 0.074 -0.014 0.000

Off-season cucumber area acre 0.037 0.019 0.022 0.000 0.001 0.854

Vegetable market distance km 0.010 0.000 -0.001 0.163 0.000 0.129

3.3.3 Off-season cucumber experience

Experience in off-season cucumber cultivation was included to

test the hypothesis that the inefficiency decreases with increase

in experience. The coefficient of experience was significant

and negative for technical as well as allocative inefficiency. It

revealed that the decrease in the level of inefficiency was

associated with the increase in the value of off-season

cucumber growing experience.

3.3.4 Contact with extension agent

Extension services are important for a new technique and it

was included to test the hypothesis that there is a negative

impact on production inefficiency in the presence of extension

services. The coefficient of contacts with extension agents was

significant and negative for all kind of production inefficiency.

It showed that the value of inefficiency decreases when a

farmers increases the contact with extension staff.

3.3.5 Off-season cucumber area

The coefficient of off-season cucumber area was positive and

significant for allocative and technical inefficiency. It showed

an increase in the value of inefficiency due to more area under

control. Generally small farmers were recognized as more

efficient because they utilize the scarce resources more

efficiently.

3.3.6 Distance of vegetable market

Distance between vegetable market and vegetable farm was

included to test the hypothesis that a distant farm had more

value of inefficiency. The coefficient of distance from

vegetable market was significant and positive for technical

inefficiency. A distant vegetable farm bears more labour cost

and transportation cost.

4 Discussion and Conclusions

The present research explored the technical, allocative and

economic efficiency in cucumber production in off-season with

the help of primary data collected from 70 respondents in

Punjab, Pakistan. Data Envelopment Analysis showed a higher

mean value for technical efficiency (87.4%) followed by

allocative (42.0%) and economic (37.2%) efficiency. It

explored the possibility of 12.6% reduction in inputs and

58.0% reduction in production cost for a technical and

allocative efficient farmer while output and technology

remains unchanged. The mean value of technical efficiency

was 77% in cucumber production as found by Shrestha et al.

(2014). Tobit regression was applied to explore the sources of

technical, allocative and economic inefficiency. Results

showed that the education, experience of cucumber cultivation

in off-season, contacts with extension agents had significant

and negative effect on production inefficiency. The negative

effect of education on inefficiency was also explored by

Bozoglu & Ceyhan (2007); Ogunniyi & Oladejo (2011);

Shaheen et al. (2011); Khan (2012); Adenuga et al. (2013);

Khan & Ali (2013) and Shrestha et al. (2014). The effect of

family size on production inefficiency was matched with

Bozoglu & Ceyhan (2007).

The impact of extension service was in line with the findings

of Bozoglu & Ceyhan (2007), Khan (2012), Khan & Ali

(2013) and Shrestha et al. (2014). The impact of family size,

area under off-season cucumber and vegetable market distance

was significant and positive on the score of technical,

allocative and economic inefficiency. Result confirmed a

significant potential for the improvement of technical,

allocative and economic efficiency in off-season cucumber

production.

Government should improve the technical education of farmers

for the decrease in inefficiency score. Extension department

should improve their contact with farmers and create

awareness about this profitable business. Government should

control the prices of various inputs like fertilizers, hybrid seed,

electricity and chemicals. Government should also improve the

quality of inputs like seed, sprays and fertilizers. High Initial

investment on tunnel material is a problem for small farmers.

Government should provide subsidy to small farmers in the

construction of tunnel structure.





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KEYWORDS

Fiber strength

Micronaire

Nitrogen-Sulfur interaction

ABSTRACT

Agronomic practices significantly influence the productivity and quality of cotton plant. Present study

was undertaken to evaluate the effect of nitrogen and sulfur fertilizer application on the fiber quality of

cotton, during the year 2011/2012 and 2012/2013 under Mediterranean environmental conditions. All

the treatments were laid in randomized complete block design in factorial arrangement each treatment

were replicated thrice. Five rates of nitrogen (0, 60, 120, 180 and 240 kg ha-1

) and five rates of sulfur (0,

15, 30, 45 and 60 kg ha-1

) were involved in the experiments. Results of study indicated that increases in

the rate of sulfur have negative impact on the quality of the cotton fiber and the highest rate of sulfur

fertilizer gave the lowest fiber length compared with the other sulfur rates. On the other hand, the lowest

uniformity ratio was observed by applications of sulfur at 30, 45 or 60 kg ha-1

. It was observed that

application of sulfur had no significant effect on micronaire and fiber strength. Further, application of 60

to 120 kg N ha-1

have positive effect on the fiber length and caused 2.7 to 3.4% improvement in fiber

lengths in 2012 compared to the treatment without N, while applications of nitrogen at 180 and 240 kg

ha-1

did not provide an additional increase in fiber lengths. Further, it was reported that application of N

significantly improved fiber strength, but these differences were not statistically different from the

Gormus O1,*

and EL Sabagh A2

1Department of Field Crops, Faculty of Agriculture, Cukurova University, Turkey

2Department of Agronomy, Faculty of Agriculture, University of Kafrelsheikh, Egypt




EFFECT OF NITROGEN AND SULFUR ON THE QUALITY OF THE COTTON

FIBER UNDER MEDITERRANEAN CONDITIONS

E-mail: [email protected] (Gormus O)







ISSN No. 2320 – 8694
















_________________________________________________________



1 Introduction

Inadequate and unbalanced nutrient supply can affect the yield

and quality of the cotton. So, proper nutrient management is

the primary needs of the sustainable crop production, higher

yield and improved fiber quality. This quality of cotton is

gradually changing with introducing new and improved

varieties for cultivation. Further, these introduced new high

yielding varieties change the concept of nutrient requirement

of cotton (Khader & Prakash, 2007; Rochester et al., 2012).

Fiber properties can be a strong yield components and the

quality of cotton lint is an important consideration since it is a

major determinant of its price in the international markets

(LaFerney, 1969; MacDonald et al., 2010).

It stands to reason that if a plant has more, longer or heavier

fibers then it have a higher yield. The availability of fertilizers

are the major constraints in cotton production in most of cotton

producing area (Morrow & Krieg, 1990). Proper fertilization

practices in cotton crop ensure improved economics of

production, efficiency of nutrient use, and environmental

protection. Most of the researcher worked on the application of

primary fertilizers N, P & K and reported a pronounced effect

of these fertilizers on the cotton production (Mullins &

Burmester, 1990; Nawaz et al., 1996; Gill et al., 2000; Seagull

et al., 2000; Reddy et al., 2004). According to Hutmacher et al.

(2004) nitrogen is a limiting factor in both dryland and

irrigated cotton production systems. Further, Gerik et al.

(1994) reported that cotton deficiency caused reduction in the

vegetative and reproductive growth in cotton crop. Moreover,

Tewolde & Fernandez, (1997) and Howard et al. (2001)

reported the significant effect of nitrogen fertilizers on the

reproductive development especially at bloom or at early boll

fill. Another important aspect of N nutrition is its effect on

fiber quality as well as on yield.

However, studies show different results. Boquet (2005)

reported that fiber quality characteristics did not improve by N

rates unless severe N deficiency conditions occur. Varying

rates of the N fertilizer did not affect fiber length, strength and

micronaire (Rashidi & Gholami, 2011; Saleem et al., 2010;

Seilsepour & Rashidi, 2011). On the contrary, there are many

reports which are showing the significant effects of N fertilizer

applications on cotton fiber quality (Fritschi et al., 2003; Read

et al., 2006). Rochester et al. (2001) indicated that fiber length

and fiber strength generally increased with an increase in N

application rate, whereas a decline in micronaire was detected

with increasing rates of applied N. Similarly, Bauer & Roof

(2004) observed lower fiber length and strength when no

nitrogen was applied. Further, Tewolde & Fernandez (2003)

indicated that fiber length and micronaire was significantly

affected with increasing rate of applied nitrogen. Girma et al.

(2007) reported significantly reduced fiber length, strength and

micronaire with application of N rates greater than 90 kg ha-1

.

Ali & Hameed (2011) also reported increased fiber length with

increase in N fertilizer rate. Like nitrogen, potassium and

phosphorus fertilizers also affect the vegetative and

reproductive quality of the cotton crop (Nawaz et al., 1996;

Gill et al., 2000). Although most of the researches are based on

N, P & K but very limited information are available regarding

the use of sulfur and its effect on the quality and yield of the

cotton crop. Because both nitrogen and sulfur is required to

promote the components of seeds and lint, it is essential to

keep these two companion nutrients in balance with each other

and to meet adequately balanced supply of both nutrients to

plant. Sulfur (S) deficiencies in crops have increasingly

occurred because of the less concern of the researchers toward

the sulfur.

Excess use of sulfur free fertilizers, greater removal of sulfur

from soil by crops, less sulfur deposition to soil from the

atmosphere and declined use of sulfur containing pesticides are

the some causes of less availability of the fertilizer of cotton

crop (Scherer, 2001). Tucker (1999) reported that addition of

sulfur into the soil not only increases yield and protein quality

of forage and grain crops but also increases the production and

quality of fiber crops. Very little knowledge has been available

regarding the influence of sulfur fertilizer on cotton. The

application of 30 kg S ha-1

resulted in increased span length

and uniformity ratio (Sharma et al., 2000). Quality of lint (fiber

length, uniformity, fiber strength) increased with increase in

gypsum level from 0 to 200 kg ha-1

, compared to the untreated

control (Makhdum et al., 2001). Cotton literature contains little

information on fiber quality response to sulfur fertilization.

Further, information regarding the interaction of S and N on

fiber quality of high-yielding cotton cultivars is also available

in scarcity. So, the objectives of the current research were to

determine the optimum rate of N and S applications to cotton

and to evaluate the effect of N and S nutrition and their

interactions on fiber properties of cotton under Mediterranean

conditions.

663 Gormus and EL Sabagh

lowest rate of application and the control treatments in both years and averaged across years. On

the other hand, the highest values for uniformity ratio was recorded by using 60 to 180 kg N ha-1

in

2011.On the basis of these observations, it can be recommend that the use of 120 to 180 kg ha-1

N

in terms of fiber length and fiber strength and 30 to 45 kg ha-1

S, particularly in terms of fiber

length and gin turnout in other areas with similar ecologies. Interestingly, the combination of 60 kg

ha-1

N and 15 kg ha-1

S were the optimal and could be the most beneficial application for achieving

the maximum fiber strength in similar ecologies.

_________________________________________________________




2.1 Experimental site and Initial Soil Characteristics

The experiments were conducted in clay soil in the

Mediterranean type climate at the experimental area of

Cukurova University, Adana, Turkey (37°N 35°E and altitude

161 m), during 2011/2012 and 2012/ 2013. The soil of the

experimental plots is classified as slightly alkaline and had

low levels of nitrogen N (37 ppm), organic matter (0.67%),

sulfate- sulfur (10 ppm) and the content of pH was 7.5

(Gormus, 2015).

2.2 Experimental materials, Design and agronomic practices

Randomized complete block design in factorial arrangement

with three replicates was used in the experiment. Treatments

comprised five levels of Nitrogen (0, 60, 120, 180, and 240 kg

ha-1

) as ammonium nitrate (33.5% N), corresponding to N0,

N60, N120, N180 and N240 kg N ha-1

and five S levels (0, 15,

30, 45, and 60 kg S ha-1

) with gypsum source (18% S),

corresponding to S0, S15, S30, S45 and S60 kg S ha-1

for this

study. Treatment N0 and S0 represent to the fertilizers control.

Nitrogen was provided in broadcast as a top dressing in three

doses among these first one third applied at the time of

planting, while the second one third was applied at first

blooming and the remaining one third at peak bloom stages.

Whole amount of sulfur fertilizer was broadcasted and

incorporated in the soil at the time of final land preparation.

The crop also received a basal application of 70 kg P ha-1

as

triple superphosphate at the time of final land preparation.

Cotton, variety SG 125, was planted on April 25, 2011 and on

May 5, 2012. Plots consisted of six rows, 10m long with 0.70

m row spacing, and a buffer zone of 1.4 m unfertilized area

between each plot. All plots were maintained throughout the

season with standard herbicide, insecticide, and irrigation

production practices as recommended for the region.

2.3 Measurements and Instruments

Defoliation was performed when 60 to 70 percent of the bolls

were open. All plots were hand-harvested by picking seed

cotton from the center four rows of each plot on October and

the seed cotton was weighed. Subsamples were collected from

each plot to determine gin turnout and fiber characteristics.

Seed cotton samples were ginned in small roller gin and lint

samples were sent to Commodity exchange in Adana, Turkey

for HVI (high volume instruments) fiber measurements. The

fiber quality parameters analyzed were fiber length, uniformity

ratio, micronaire and strength.


All collected data were subjected to analysis of variance

according to Gomez & Gomez (1984). Analysis of variance

was performed using the MSTATC statistical package and the

grouping of means was determined using the LSD test at the

5% probability level.


In this research, efforts were made to improve quality traits of

cotton lint through nitrogen and sulfur managing in

Mediterranean ecologies.

3.1 Gin turnout

Based on the results of this study, it was observed some

variation in the gin turnout but no significant interaction was

reported among the combination of year X N-rate X S-rate for

any studied traits. The interactions between N and S-rate have

significant effect on the fiber length and fiber strength. Further,

main effects of individual applications of N and S were

significant for gin turnout, fiber length and uniformity ratio.

Interaction between Year X N-rate was also reported

significant for gin turnout and uniformity ratio. While the

interaction between the Year X S-rate was not significant

results for any traits studied (Table 1). In 2011, application of

N (60, 120, 180, and 240 kg ha-1

) at all four rates increased gin

turnout compared with the control treatment. Maximum gin

turnout was achieved at 120 kg N ha-1

treatment. While in

2012, highest gin turnout was reported from the plant treated

by 60 or 120 kg N ha-1

and these two treatments were almost at

par to each other (Table 2).

Table 1 Mean squares from analysis of variance ofgin turnout and fiber properties.

Source df Gin turnout Micro naire Fiber length Unif. ratio Fiber strength

Replicate 2 3.023 0.708 3.24 12.00 32.4

Year (Yr) 1 67.872** 0.000 2.52 0.00 0.88

Nitrogen(N) 4 100.22** 0.029 3.6** 33.1** 20.0**

Yr x N 4 16.45** 0.012 0.02 18.9** 1.50

Sulfur (S) 4 4.218** 0.130 2.36* 6.8** 3.16

Yr x S 4 1.032 0.054 0.04 0.76 1.18

N x S 16 1.821 0.133 1.8** 1.86 9.43**

Yr x N x S 16 1.526 0.033 0.03 0.88 0.86

Error 98 1.006 0.099 0.68 1.68 2.18

Note: * and ** are significant at 0.05 and 0.01 probability levels, respectively

Effect of nitrogen and sulfur on the quality of the cotton fiber under mediterranean conditions 664

_________________________________________________________



Table 2 Effect of N and S rates on gin turnout and micronaire.

Gin turnout (%) Micronaire

N rate (kg ha-1

) 2011 2012 Mean 2011 2012 Mean

0 37.6e 37.6

c 37.6

d 5.4 5.4 5.4

60 40.8c 40.7

a 40.8

b 5.5 5.5 5.5

120 43.4a 40.6

ab 42.0

a 5.4 5.5 5.5

180 42.8b 39.7

b 41.3

b 5.5 5.5 5.5

240 39.2d 38.4

c 38.8

c 5.5 5.5 5.5

LSD(0.05) 0.52 0.89 0.66 ns ns ns

S rate (kg ha-1

)

0 40.5 38.7c 39.6

b 5.6 5.6 5.6

15 40.7 39.3ac

40.0ab

5.5 5.5 5.5

30 40.9 40.0a 40.5

a 5.5 5.4 5.4

45 41.0 39.9ab

40.5a 5.4 5.5 5.4

60 40.7 39.1bc

39.9b 5.5 5.6 5.5

LSD(0.05) ns 0.89 0.48 ns ns ns

Means followed by the same letter are not significantly different at P=0.05 level

Averaged across years, 120 kg ha-1

N application significantly

increased gin turnout throughout the year while the higher rate

than this caused significantly decrease in gin turnout.

Application of sulfur fertilizers at all rate did not show any

effect on the gin turnout in 2011, however, in 2012 some

improvement in gin turnout was reported on the application of

30 to 45 kg ha-1

. Averaged across years, maximum response of

gin turnout to S applications occurred with application of 15 to

45 kg ha-1

. The increase in gin turnout might be due to the

effect of N accumulation of photosynthates, which would

directly influence boll weight and seed cotton weight per boll

and increase in gin turnout. Similar type of results was reported

by Phipps et al. (1996), these researchers suggested that higher

concentration of nitrogen fertilizers minimum or non-

significantly effect on plant growth. Similarly Hussain et al.

(2000) reported that gin turnout did not respond to N

fertilization.

3.2 Micronaire

Both N and S treatments did not have any significant effects on

micronaire quality for both the years and averaged across years

(Table 1, 2). In general, N application had negative effects on

micronaire. Tewolde & Fernandez (2003) reported that the

increase in nitrogen rate have small but highly significant

linear improvement in micronaire quality. Bauer &Roof (2004)

found that micronaire was affected by N fertilizer rate where

cotton with control treatments produced lower micronaire than

the cotton grown at 78.4-112.0 kg N ha-1

.

Table 3 Effect of N and S rates on fiber length, uniformity ratio and fiber strength.

Fiber length (mm) Uniformity ratio (%) Fiber strength (g tex-1

)

N rate (kg ha-1

) 2011 2012 Mean 2011 2012 Mean 2011 2012 Mean

0 29.6 29.3b 29.5

b 80.9

d 83.7 82.3

d 29.9

b 30.2

b 30.1

b

60 30.2 30.1a 30.2

a 85.2

ab 84.1 84.7

ab 30.5

b 30.8

b 30.1

b

120 30.6 30.3a 30.4

a 83.8

c 83.4 83.6

c 31.7

a 31.7

a 31.7

a

180 30.2 29.9ab

30.1a 85.5

a 84.5 85.0

a 32.3

a 31.7

a 32.0

a

240 30.1 29.8ab

30.0ab

84.3bc

84.1 84.2bc

31.9a 31.4

a 31.7

a

LSD(0.05) ns 0.61 0.54 0.97 ns 0.68 1.15 1.03 0.84

S rate (kg ha-1

)

0 30.4 30.1 30.3a 84.7 84.5 84.6

a 30.8 31.4 31.1

15 30.2 29.9 30.1a 84.4 84.2 84.3

ab 31.9 31.6 31.7

30 30.3 30.0 30.2a 83.9 83.8 83.9

bc 31.3 30.9 31.2

45 30.2 29.9 30.1a 83.0 83.6 83.3

c 31.5 31.2 31.3

60 29.6 29.5 29.6b 83.9 83.8 83.9

bc 31.0 30.8 30.9

LSD(0.05) ns ns 0.40 ns ns 0.67 ns ns ns

Means followed by the same letter are not significantly different at P=0.05 level


_________________________________________________________



Table 4 Effects of interaction between N rate (kgha-1

) and S rate (kgha-1

) on fiber length and fiber strength (averaged over two years).

N0 N60 N120 N180 N240

Fiber length (mm)

S0 29.8 30.0 30.6 31.2 29.8

S15 29.3 30.0 30.0 30.0 31.0

S30 29.6 31.0 30.5 29.3 30.6

S45 29.4 29.6 31.0 30.3 29.9

S60 29.4 29.3 30.2 29.4 29.5

LSD0.05 1.54

CV (%) 2.76

Fiber strength (gtex-1

)

S0 30.0 30.1 31.5 33.1 30.8

S15 28.5 33.9 30.6 32.9 32.9

S30 30.7 30.3 31.3 31.3 32.2

S45 30.5 30.0 33.4 31.4 31.4

S60 30.7 29.2 32.0 31.3 31.3

LSD0.05 3.52

CV (%) 4.73

3.3 Fiber lengths

In 2011, neither N nor S treatments have significant effect on

the fiber lengths (Table 3). In year 2012, application of 60 to

120 kg N ha-1

increased fiber lengths by 2.7 to 3.4% compared

to the control (without N) while the applications of nitrogen

fertilizer at 180 and 240 kg ha-1

did not provide an additional

increase in fiber lengths. In this manner, findings of Tewolde

& Fernandez (2003) are contradictory to the findings of this

study; these researchers reported that nitrogen had a significant

quadratic effect on fiber length, while the results of the present

study are similar to the findings of Gormus et al. (2016). The

significant N X S interaction revealed that mean maximum

fiber length (31.2 mm) was recorded in treatment containing

180 kg N ha-1

and 0 kg S ha-1

treatment that was followed by

(45 kg S ha-1

+ 120 kg N ha-1

) with fiber length (31.0 mm)

(Table 4). It was observed that S treatments did not have any

significant effects on fiber length for both the years. Averaged

across years, application of nitrogen at all four rates improved

fiber length compared with control treatment. By contrast to N

applications, fiber length decreased from 30.3 mm with no S to

29.6 mm with 60 kg S ha-1

. The shortest fibers were attained

when S was applied at rate of 60 kg ha-1

(Table 3).

3.4 Uniformity ratio

N application significantly affected uniformity ratio in 2011,

but it was not reported for the year 2012.The optimum

responses of uniformity ratio to N fertilizer was achieved by

adding 60 to180 N kg ha-1

. Nitrogen at 120 and 240 kg ha-1

resulted in similar uniformity ratios in 2011. Averaged across

years, the maximum response of mean uniformity ratio for

both years to S application occurred in the control treatment

which was followed by S application of 15 kg ha-1

. Uniformity

ratio tended to decrease by the use of higher S rates (30, 45,

and 60 kg ha-1

S), but the effects were small on quality (Table

3). Yin et al (2011) reported that application of 22 or 34 kg S

ha-1

increased micronaire by 4 to 5% compared to the

treatments without S, although other fiber quality

characteristics including length, uniformity and strength were

found not to be affected by S applications.

3.5 Fiber strength

Application of N significantly increased fiber strength,

although differences were not statistically significant between

the lowest rate and the control treatments in both years and

averaged across years. Fiber strength did not change with S

rate in both years. N application gave the greatest increase in

fiber strength when N was applied at rates of 120, 180 or 240

kg ha-1

, while applying N at rate of 60 kg ha-1

gave the same

mean strength values as the control treatment (Table3).The

significant N X S interaction revealed that maximum fiber

strength (33.9 gtex-1

) was observed with the treatment

consisting of 15 kg S ha-1

and 60 kg N ha-1

and it was followed

by the combined application of 45 kg S ha-1

+ 120 kg N ha-1

)

with fiber strength (33.4 g tex-1

) (Table.4). Either the addition

of more S, i.e. 45 instead of 30 kg S ha-1

along with the same

rate of N, i.e. 60 kg N ha-1

, or a reduction in the rate of N, i.e.

from 180 to 120 kg N ha-1

with the same amount of S, i.e. 30

kg S ha-1

resulted in decreased the fiber strength. Plots that

have not received S (control) but have 120 kg N ha-1

produced

an increase in fiber strength by 1.5 g tex-1

, when S was added

at the rate of 15 kg ha-1

the decrease in fiber strength with 180

kg ha-1

over lowest N rate was 1.0 g tex-1

(Table 4).

In a longer period and more tempered cellulose accumulation

process benefited to higher strength fiber formation. According

to Bradow & Davidonis (2000) during the fiber development

process, the stage at which the cotton plant is under N stress is

crucial for fiber quality. The reduction in fiber length and

strength (Read et al., 2006) and improved micronaire value

(Reddy et al., 2004) were reported due to the nitrogen

deficiencies. Under both N deficiency and excess N conditions,


_________________________________________________________



nitrogen accumulation is reduced and this results in decreased

fiber length. N and S treatments did not result in significant

differences in micronaire. Findings of present study confirm

that nitrogen is excess than certain rate does not necessarily

result in the longest fibers. Just as in yield, there seems to be an

optimum nitrogen rate that results in the longest fibers. In the

present study, the nitrogen deficient plants (0 kg N ha-1

)

produced the weakest fibers but this strength value was not

significantly different from the value of 60 kg N ha-1

treatment.

The quality of lint was maximized with the increase in gypsum

level from 0 to 200 kg ha-1

, fiber length, uniformity ratio and

fiber strength over control (Makhdum et al., 2001). Mangal

(2000) reported that application of sulfur significantly

influenced the fiber strength. Sulfur applications produced 4 to

5% increases in micronaire compared to zero S treatment;

however, length, uniformity and strength were not significantly

affected by S applications (Stewart et al., 2011). Yin et al.

(2011) observed 4 to 5% increases in micronaire and no

differences in fiber length, uniformity ratio and strength with S

applications compared to zero S treatment.

Conclusions

Under the conditions of this study, the results from the two

years support evidence that N deficiency decreased fiber

length, strength and uniformity ratio. Application of nitrogen

gave the higher fiber lengths compared with the untreated

control treatment, while applying 180 kg N ha-1

produced more

uniform fibers. Trends toward higher strength values were

observed with the higher rates of N fertilizer applied. The

highest rate of S fertilizer gave the lowest fiber length

compared with the other sulfur and the untreated control

treatments. On the other hand, the lowest uniformity ratio

values were obtained when plant was treated with S at 30, 45

or 60 kg ha-1

. Micronaire revealed no significant differences

due to treatment effects. On the basis of these observations, we

recommend use of 120 to 180 kg ha-1

N in terms of fiber length

and fiber strength and 30 to 45 kg ha-1

S, particularly in terms

of fiber length and gin turnout in other areas with similar

ecologies. Interestingly, the combination of 60 kg ha-1

N and

15 kg ha-1

S were the optimal treatment and could be the most

beneficial application for achieving the maximum fiber

strength in similar ecologies.




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_________________________________________________________



KEYWORDS

Azolla

Proximate Analysis

Chemical composition

Livestock

Fee

Evaluation

Dry matter

ABSTRACT

Present study was undertaken to explore the nutritive potential of Azolla pinnata as an animal feed. For

this Azolla was cultivated in water trough, harvested and sundried. Sundried Azolla sample was

analysed for proximate principles. The dry matter content of azolla was 4.7 percent. Analysis of dry

matter revealed the presence of total 82.66 percent organic matter. Among these includes 22.48 percent

crude protein, 4.5 percent ether extract, 14.7 percent crude fiber, and 40.98 percent nitrogen free extract.

The total Ash content was17.34 percent.The chemical analysis proves that azolla is a rich source of

crude protein, trace minerals and vitamins. The mineral profile of Azolla indicates 1.64% Calcium,

2.71% Potassium and 0.34% Phosphorus and other minerals in trace levels. Thus Azolla can be

considered as potential unconventional feed for livestock.

Anitha KC*, Rajeshwari YB, Prasanna SB and Shilpa Shree J

Department of Livestock Production and Management, Veterinary College, Bengaluru- 560 024




NUTRITIVE EVALUATION OF AZOLLA AS LIVESTOCK

FEED

E-mail: [email protected](Anitha KC)







ISSN No. 2320 – 8694
















_________________________________________________________



1 Introduction

Azolla is an aquatic free floating fern belonging to the family

Salviniaceae. Nutritive value of Azolla is well documented

which shows that it is a good source of protein with almost all

essential amino acid required for animal nutrition (notably

lysine). Furthermore, it also provides macronutrients like

calcium, magnesium, potassium and vitamins like vitamin A

(precursor beta-carotene) and B12. All these facts suggested

that Azolla can be used as unconventional feed with protein

supplement for many species including ruminants, poultry,

pigs and fish (Hossiny et al., 2008). Due to ease of cultivation,

high productivity and good nutritive value it is used as a

beneficial fodder supplement by various researchers (Singh &

Subudhi, 1978; Prabina & Kumar, 2010).

Azolla pinnata tried as a feed for broiler chicken (Alalade &

Iyayi, 2006; Balaji et al., 2009; Dhumal et al., 2009; Bolka,

2011), goats (Samanta & Tamang, 1993) and buffalo calves

(Indira et al., 2009). Azolla filiculoides was also used in diets

for sows (Leterme et al., 2010) and as partial replacement of

protein source for growing fattening pigs (Duran, 1994;

Becerra et al., 1995). Furthermore, it was also tried as a protein

supplement for Rabbits (Gualtieri et al., 1988; Wittouk et al.,

1992, Sreemannaryana et al., 1993; Abdella et al.,1998; Sadek

et al., 2010). In view of the above facts, the present

experiment, the nutritional value of Azolla pinnata was

undertaken.

Plate 1: Sun drying of azolla.


Present study has been carried out at the department of

Livestock Production and Management, Bangalore Veterinary

College, Karnataka

2.1 Cultivation of Azolla in Water Troughs

Three water troughs with even bottom and 10 sft. capacity

were taken for the study. All the roots and other unwanted

particles were removed from the floor and sealed the bottom

with cement and the same level in order to maintain a uniform

water level. Any thin layer of 10-15 cm made up of fine soil

were spread and then, the water tank filled with water and

maintain the constant level of the water. About 1.5 kg of cow

dung dissolves in 3.5 liters of water and spread evenly in the

water trough. Preparation once completed, the water tank

injected with fresh azolla culture of 300 g / m 2 on it. Once in

every 15 days, application of 1.5 kg dung, 0.2g super

phosphate and 0.2g of mineral mixture was done to obtain

continuous growth of azolla and to avoid nutrient deficiency

and also check the pH. In the case of pits contaminate with

insects and contaminates, a fresh pure culture was added.

2.2 Collections and storage of azolla

Azolla multiplied rapidly and covered the complete pits within

7 days. Fully grown azolla (Plate 2) was harvested every week

from the water trough. Harvesting azolla was cleaned and

thoroughly washed and sundried for 2-3 days and dried till

crispy dried and stored in air tight aluminium foils.

Table 1 Chemical composition of azolla.

Nutrients Azolla

Dry matter 4.70

Organic matter 82.66

Crude Protein 22.48

Ether extract 4.50

Crude fibre 14.70

Total ash 17.34

NFE 40.97

NDF 54.85

ADF 36.57

ADL 24.05

2.3 Chemical evaluation of azolla

The DM content of collected azolla samples were analysed by

drying to a constant weight in a forced hot air oven at 105oC.

The ash content in the samples was estimated as residue after

incineration of samples at 600oC for 3 hours. Crude protein (N

X 6.25) was analysed using Gerhardt digestion and distillation

unit (AOAC, 2005). The Ether extract (EE) content in the

sample was analysed after extraction with petroleum ether

using the procedure of AOAC (2005). The fiber fractions were

determined according to the methods described by Van Soest

et al. (1991). Mineral profile of Azolla was analysed by

inductively coupled plasma-atomic emission

spectrophotometer and amount was calculated by below given

formula.

µg / g =

Concentration of mineral in sample solution (mg / L) x

Volume made (ml)/ Weight of sample (g)

671 Anitha et al

_________________________________________________________



Plate 2 Azolla (Azollapinnata) grown in water trough

Parameters were analyzed by analysis of variance with using

GraphPad Prism version 5.1. Individual differences between

means were tested using Tukey’s Multiple Comparison Test

when treatment effect was significant.


The results of proximate analysis of sun dried azolla (Plate 1)

sample are presented in the Table 1.The values were Total dry

matter 4.7 per cent, 82.66 per cent of the organic matter, 22.48

per cent crude protein, 4.5 percent of the ether extract, 14.7 per

cent of crude fiber, 17.34 percent of total ash and 40.98 per

cent of nitrogen free extract.

The chemical composition of sun dried azolla as presented in

the table 2 revealed that dry matter content was 4.7 which are

in agreement with the findings of Giridhar et al. (2012) and

Kavya (2014) whereas, Parashuramulu et al. (2013) reported

almost double (8.9%) of DM content. Though the DM content

in the fresh azolla was slightly less, but can be used as a

supplement to meet the DM requirements in livestock feeds.

The result of crude protein were in agreement with the findings

of Basak et al. (2002), Lukiwati et al.(2008), Prasanna et al.

(2011), Bolka (2011), Chatterjee et al. (2013) and Kavya

(2014), those have reported crude protein values ranged from

21.0 (Kavya, 2014) to 25.8 (Basak et al., 2002). High protein

content azolla suggests that it’s a potential natural protein

source.

The crude fibre content was close agreement with the values

obtained by Balaji et al. (2009) and Cheryl et al. (2014),

respectively. On the contrary Singh & Subudhi (1978) reported

less value and it ranged between 9.1 to 13.07 percent while

Alalade & Iyayi (2006) was reported 12.7 per cent CF. Further

the higher range of CF values from 15.17 to 19.85 was

recorded by Bolka (2011) and Kavya (2014). Slight variations

in the contents of CF in azolla was observed in the present

study, when compared to other research workers which might

be due to changes in the dry matter content of azolla used for

CF estimation.

Nitrogen-free extract obtained was comparable to the findings

of Kavya (2014).The higher values 47 and 47.4 percent were

observed by Samanta & Tamang (1993) and Alalade & Iyayi

(2006) respectively.

Table 2 Mineral profile of Azollapinnata (on per cent DMB).

Minerals Percentage Ppm

Calcium 1.64

Phosphorus 0.34

Potassium 2.71

Copper 9.1

Manganese 2418

Zinc 325

Iron 1569

Cobalt 8.11

Chromium 5.06

Boron 31

Nickel 5.33

Lead 8.1

Cadmium 1.2

Total Ash in this study were similar with values of Balaji et

al.(2009), Prasanna et al.(2011), Bolka (2011), Chattereji et al.

(2013) Parashuramulu et al. (2013) and Kavya (2014) whose

values were in the range from 16.21 percent was reported by

Prasanna et al.(2011) and 19.47 percent by Chattereji et al.

(2013). Whereas Subudhi & Singh (1978) reported 10.50-

15.82 percent of TA in dried azolla. The higher value (24.26)

of TA was reported by Cheeryl et al. (2014) while this value

was 28.7 percent were also reported by Lukiwati et al.

(2008).The large variation in the values of TA in azolla might

be due to mineral inputs in the ingredients added for

cultivation of azolla.

From the study it was revealed that the ether extract was 4.5

percent, the results are in agreement with findings (3.38-

4.41%) of other researchers Basak et al. (2002), Balaji et al.

(2009), Bolka (2011) and Kavya (2014). The lower values of

2.73 and 3.27 percent were reported by Tamang et al. (1993)

and Chatterjee et al. (2013) respectively. Slight variation was

observed in the content of EE can be attributed to the nutrient

inputs used to cultivate the azolla.

The NDF content of azolla is in close agreement with the value

reported by Parnerkar et al. (1986), Kavya (2014) but higher

than the values reported by Buckingham et al. (1978), Taklimi

(1990); Ali & Leeson (1995) and Alalade & Iyayi (2006).

Nutritive evaluation of azolla as livestock feed 672

_________________________________________________________



The ADF content of azolla is almost similar to the value

reported by Khatun et al. (1996). The ADL content of azolla

obtained in present study is almost similar to the value reported

by Ramesh (2008) Kavya (2014) but higher than the value

reported by Tamang et al. (1993).

The mineral profile of azolla obtained in the present study is

almost similar to the values reported by Anand & Geetha

(2007), Kavya (2014). Calcium content of azolla is similar to

the reports of Tamang et al. (1993). Magnesium content of

azolla obtained in the present study is similar to the value

reported by Alalade & Iyayi (2006). Higher level of heavy

metals like nickel, lead, cadmium was also obtained in the

sample of azolla used for the present study indicating

bioaccumilation of heavy metals by azolla. Padmavathiamma

& Li (2007) studied the absorption of iron, copper, cadmium,

nickel, lead, zinc, manganese, and cobalt by Azolla pinnata

indicating bioaccumilation of heavy metals by azolla.

Yield of Azolla was reported around 120 g/m2/day fresh

weight per water trough which is similar to Duran (1994) those

who reported 120-200 g/m2/day of fresh azolla production can

be harvested and Gerek (2001) reported that 1122 g/m2 of fresh

azolla can be harvested after15 days from the inoculation of

fresh weight of 300 g/m2azolla.

Azollapinnata differences in nutrient composition may be due

chemical composition of soil nutrients and also may be due to

differences in environmental conditions such as respond to

heat, light intensity and its resulting impact on their growth,

morphology. Moreover, epiphytic algal contamination resulted

in affect the chemical composition (Sanginga & VanHove,

1989).

Conclusion

Sun dried azolla on chemical analysis showed that rich in

crude protein, trace minerals and vitamins and hence it can be

used as livestock feed as a unconventional feed




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KEYWORDS

Water stress

PEG

Antioxidant enzymes

Oryza sativa

Growth traits

Physiological aspects

SSR markers

ABSTRACT

The aim of the current investigation was to study the influence of drought-stressed by using PEG on

some rice genotypes at seedling stage. The performance was judged by growth, physiological,

biochemical and molecular constituents at seedling stage. The results of study suggested that growth

attributes were reduced under different drought stress (70 and 140 g/L PEG) in most of the cases as

compared with control. Among various tested genotypes IRAT 259, Line 7 and Line 8 exhibited the

lowest reduction values of relative water content, chlorophyll content and membrane stability index at

70 and 140 drought levels. The Line 8 produced the highest amount of proline under stress conditions

which is indicating its highest tolerance to drought stress. The antioxidant enzymes such as catalase,

peroxidase and polyphenol oxidase were induced by the drought levels. The growing expressions of

antioxidant enzymes assist the plant for adaptation of plant under environmental conditions and tolerate

stress. The IRAT 259 has highest increase percentage in antioxidant enzymes under stress. Total sixteen

SSR primers examine for characterizing the power of each SSR primer by calculating polymorphic

information contents and a total of 41 alleles were amplified using 16 SSR primers. The variation in

number of amplified alleles per primer ranged from one allele as for wmc27 to five alleles for wmc179

and wmc 215, with an average of 2.56 alleles. The highest value was 100% polymorphism belonged to

Al-Ashkar IM1, Zaazaa EI

1, EL Sabagh A

2,*, Barutçular C

3

1Department of Agronomy, Faculty of Agriculture, University of Al-Azhar, Cairo, Egypt

2Department of Agronomy, Faculty of Agriculture, University of Kafrelsheikh, Egypt

3Department of Field Crops, Faculty of Agriculture, University of Cukurova, Turkey




PHYSIO-BIOCHEMICAL AND MOLECULAR CHARACTERIZATION FOR

DROUGHT TOLERANCE IN RICE GENOTYPES AT EARLY SEEDLING STAGE

E-mail: [email protected] (EL Sabagh A)







ISSN No. 2320 – 8694
















_________________________________________________________



1 Introduction

Rice (Oryza sativa L.) is considered the most essential food

crops and it needs huge amount of water as compare to other

crops during growth life cycle (Wang et al., 2012). Rice plays

a major role as a staple food which providing nutrition to more

than three billion people and comprising 50-80% of their daily

calorie intake (Khush, 2005). Rice crop plays an important role

in Egypt for strengthen self-sufficiency of food and for

maximizing the export of rice as strategic crop. Furthermore,

the average yield of rice has to be increased by 25 – 30 % to

face the demands of the increase of population growth rate

(RRTC, 2013). Identifying rice genotypes and breeding lines

with high levels of tolerance to drought to use as donors in

breeding and gene discovery is one of the most challenges for

rice research (Serraj & Atlin, 2008).

Drought is one of the most important environmental stresses

that influence the growth and development of plants and it is

also an important challenge to agricultural researchers and

plant breeders.Water stress causes severe threat in production

of rice and it affects morphological, physiological, biochemical

and molecular characteristics of rice crops along with its

productivity. Hence, with decline worldwide water availability

for agriculture there is a need for improving drought adaptation

in rice and screening of drought tolerance genotypes becoming

necessary. Drought tolerance is complex phenomenon which

depends on the combined function of different morphological,

physiological, biochemical and molecular properties. The

mechanisms associated with the tolerance to water-stress and

the systems that regulate adaptation of plant to water stress in

rice have been completed studied (Pandey & Shukla, 2015).

Drought stress influence the expressions of antioxidant in

plants, osmotic modification, chlorophyll and transpiration

reduction and inhibition of growth (Gupta & Huang, 2014).

The different stages of growth exhibited catalase and

peroxidase activity and with the growing drought intensity

accumulation of proline also increases with high levels of

stress (Mahdi et al., 2007). Biotic and abiotic stresses

conditions also have negative impact on cell due to

accumulation reactive oxygen species (Vaidyanathan et al.,

2003).

According to Pandey & Shukla (2015) development and

selection of drought tolerant rice varieties depend on the

understanding of the different mechanisms that manage the

productivity of rice under water stress condition. Molecular

characterization of the available genotypes is beneficial for the

evaluation of the genetic potential of the rice crops and help in

stop erosion which can justified here as a reduction of genetic

diversity in time (Manifesto et al., 2001). Therefore, the

objectives of the present study were: (1) to evaluate and screen

the available rice genotypes for the drought tolerance and to

develop comprehensive understanding of the mechanism of

plants response against drought stress with the help of

integrated approach of combining mechanisms based on

growth, morphological, physiological and biochemical related

to drought tolerance and (2) estimate genetic diversity of six

rice genotypes using molecular markers technique.


The present investigation was carried out at the Cell and Tissue

Culture Laboratory of the Agronomy Department, Faculty of

Agriculture, Al-Azhar University, Nasr City, Cairo, Egypt. Six

rice genotypes including the three varieties viz., IRAT 170

(check), IRAT 259 (drought-tolerant) and Giza 182 (drought-

sensitive) as well as the lines (Line 7, Line 8 and Line 9) were

tested for drought tolerance. Mature rice seeds, were husked

manually, and washed for 2-5 min with sterile distilled water.

Seeds were then cultured on; a modified MS medium

(Murashige & Skoog, 1962) supplemented with various levels

of polyethylene glycol 6000 (0, 70 and 140 g/L PEG). The

basal medium was supplemented with 30 g/L sucrose and 6

g/L agar. The cultures were incubated at 28 ± 2°C with 16 h

photoperiod for 20 days. All the treatments were laid in a

complete randomized design and replicated five times and the

replicate was included 10 seeds. An analysis of variance was

predestined for all traits according to Steel et al. (1996) to

define the significant differences between genotypes.

2.1 Growth, physiological and biochemical traits measured

The performance of rice genotypes was studied under drought

stress conditions at the early seedling stage; Root and shoot

lengths (cm) and fresh and dry weights (g) were measured.

Relative water content was measured by the method described

by Schonfeld et al. (1988) with some modification. Proline was

measured according to Bates et al. (1973). Chlorophyll content

(SPAD unit), was measured on three leaf seedlings taken from

each replicate by chlorophyll meter (SPAD-502, Soil- Plant

Analysis Department (SPAD) section, Minolta camera Co.,

Osaka, Japan) by Minolta (1989).

676 Al-ashkar et al

13 out of the 16 primers. Phylogenetic analyses per primer were ranged from 0.00 to 0.794 with an

average of 0.427. Average observed heterozygosity ranged from 0.00 to 0.670 with an average of

0.45. It was found the value of heterozygosity was 0.00 to 0.670 and the mean value of

heterozygosity was 0.45. On the basis of phenotypic and genetypic (reaction with markers)

performances under drought stress conditions, the Line 8 and the Line 7 can be recommended as a

drought tolerant and a drought sensitive, respectively. This result can be acclaimed the important

source for genetic diversity of rice in future breeding programs.

_________________________________________________________



Membrane stability index (%) is a measure phenomenon of

drought resistance and the level of MSI was calculated by

modifying Sairam et al. (2002), leaves of control and drought-

stressed plants were collected and thoroughly washed with

distilled water. 100 mg of leaf sample was placed in 10 ml of

double distilled water at 40ºC for 30 min and thereafter,

electric conductivity (C1) was measured with conductivity

meter. It was followed by calculation the electric conductivity

(C2) the same samples were settled on boiling water bath (100

ºC) for 15 min. The MSI was calculated using following

formula:

MSI = [1 – (C1/ C2)] x100

For antioxidant enzymes analysis, fresh leaf leaves samples

(0.2 g) were ground in liquid nitrogen and homogenized in an

ice-bath in 4 ml homogenizing solution containing 50 mM

potassium phosphate buffer and 1% (w/v

polyvinylpyrrolidone) (pH 7.8). The homogenate was

centrifuged at 14000 rpm at 4°C for 10 min and the resulting

supernatant was used as enzyme source for catalase,

peroxidase and polyphenol oxidase assays.

Assay of catalase, the assay mixture in total volume of 3 mL

contained 1.5 mL of 100 mM phosphate buffer (pH 7.2), 0.5

mL of (v/v) H2O2 and 0.03 ml of enzyme. The final volume

was made 3 ml by adding distilled water. The reaction was

started by adding enzyme and change in optical density was

measured at 240 nm at 60s. The enzyme activity was showed

by calculating the magnitude of decomposed H2O2 according to

Aebi (1984).

For assay of peroxidase (POD), determined by

spectrophotometer at 420 nm, according to Chance & Maehly

(1955) and Assay of polyphenol oxidase (PPO), determined

according to Duckworth & Coleman (1970) through

spectrophotometrically at wave length 420 nm at 25°C.

2.2 Molecular characterization

2.2.1 DNA extraction and PCR amplification

A 500 mg sample of frozen young leaves of the six rice

genotypes were ground to powder in a mortar and a pestle in

the presence of liquid nitrogen. The DNA extraction was done

using the cetyltrimethyl ammonium bromide (CTAB) method

(Saghai-Maroof et al., 1984). Twenty SSR primers were used

in the present investigation. The PCR products were

electrophoreses in 2% agarose gels stained with ethidium

bromide and visualized under UV light or were separated via

capillary electrophoresis using a QI Axcel Advanced system

device.

2.2.2 Data handling of SSR marker

SSR data was registered based on the existence of the

amplified Products for each primer To investigate the

discriminatory power of each SSR primer, the polymorphic

information content (PIC) was measured according to Smith et

al. (2000). As well as, Heterozygosity (Ho) was measured

according to (Hormaza, 2002).


3.1 In vitro screening of rice genotypes for drought tolerance

As screening technique, the survival ability of the six rice

genotypes was evaluated by culturing mature seeds on MS

medium supplemented by three doses of PEG-6000 (0, 70 and

140 g/L)during germination stage. Highly significant

differences were recorded among two-way interaction

(genotypes and drought levels) (P≤ 0.01) for all studied traits,

revealing the presence of genetic diversity in the material used

(Figure 1 and 2).

3.2 Growth traits as affected by drought stress

In all genotypes, the seedlings growth decreased with

increasing the levels of stress (Figure 1). Among various tested

genotypes, lowest reduction of shoot length (0.00%) was

observed in IRAT 259 while the highest reduction (36.62%)

was reported from the genotype Giza 182 at 70 g/L PEG

induced stress. On the other hand, the reduction percentages

were 19.44 and 67.83 for IRAT 259 and Line7 genotypes

under 140 g/L PEG stress treatments respectively. In the

present study, IRAT 259 was followed by the Line 9 genotypes

and registered as the lowest means reduction than other

genotypes under level 70 g/L PEG this thing is indicating that

these genotypes are more tolerant and relatively showing small

decreases in shoot length under drought stress (Figure 1).The

present findings are in line with Lum et al. (2014) and in

agreement with these findings, a previous study (EL Sabagh et

al., 2015a).

Concerning shoot fresh weight, reduction percentages of all

genotypes ranged from 23.38 to 41.67% (average 29.58%) and

43.18 to 75.86% (average 57.14%) under 70 and 140 g/L PEG

drought stress, respectively (Figure 1). Shoot fresh weight of

the genotypes Giza 182 and Line 7 were more adversely

affected than other genotypes by the 70 and 140 g/L PEG

stress treatments, respectively. The reduction range of shoot

dry weight was 5.42 to 43.99% at 70 g/L drought stress while

the maximum reduction of shoot dry weight (63.77%) was

recorded in genotype IRAT 170 and it was followed by IRAT

259 (60.75%) at 140 g/L PEG induced drought stress (Figure

1). The lowest reduction of mean shoot dry weight (less than

15%) was recorded at the Line 8 genotype under 70 g/L PEG

followed Giza 182 and Line 9 genotypes.

Roots play great role in plant existence under stress conditions.

It was observed that under stress conditions a significant

reduction was reported in root length. The lowest reduction in

root length (8.29 and 14.85%) was recorded at the genotypes

of Line 8 and IRAT 259 while the highest reduction (30.73 and

57.14%) was observed at IRTAT 259 and Line 8 under 70 and

140 g/L PEG induced stresses, respectively (Figure1).

Physio-biochemical and molecular characterization for drought tolerance in rice genotypes at early seedling stage 677

_________________________________________________________



Figure 1 Mean of six rice genotypes of seven growth traits under (0, 70 and 140 g/L) polyethylene glycol. NB: Numerical values above

bars showed the percentage reduction in the trait relative to the control; R%= reduction percentage; +, ++

Grand mean of reduction

percentage at 70 and 140g/L PEG, respectively.

678 Al-ashkar et al

_________________________________________________________



Table 1 Size of DNA fragments generated from SSR analysis of six rice genotypes.

Markers Product size

(bp)

Genotypes

Giza 182 IRAT 170 IRAT 259 Line 7 Line 8 Line 9

Xwmc27 400 - - + - + +

Xwmc147 110 - + - - - -

170 - - + - + +

Xwmc149 190 - + + + + -

205 - - - - - +

240 - - - - + -

270 - + - - - -

Xcfd1 225 + - + + + +

250 - - + - + -

275 - - - - + -

Xwmc179 100 - + - - + -

110 + - + + - -

150 - + - - - +

170 - - + - + -

270 - - + - + +

Xwmc215 100 + - - + - +

120 - - + - - -

170 - - + + - -

240 + - + + + +

290 - - + - - -

Xwmc233 270 - - + - + +

300 - - - - + -

Xgwm249 70 + - + - - -

90 - + - - - -

150 - - + - + -

Xwmc387 160 - - - - + -

Xwmc169 145 - + - - + +

155 - - + - + -

170 - - + - + +

Xwmc44 220 + + + - + -

Xwmc14 90 - - - - + -

250 - - - - + -

Xwmc18 70 + + + + + +

270 - - - - + +

Xwmc31 60 + + + + + +

150 - - - - + -

Xwmc327 120 - - - - + +

130 - - - - + -

Xwmc8 70 + + + + + +

110 - - + - + +

140 - - - - + +

(+) means present, (-) means absent

The genotype IRAT 259 maintained lower mean reduction

(10.28 and 14.85%) under 70 and 140 g/L PEG levels,

respectively. As shown in (Figure 1), root fresh weight was

reduced significantly by media moisture deficit. Reduction

percentages of genotypes ranged from 0.00 to 53.33% and

25.93 to 66.67% was recorded under 70 and 140 g/L PEG

drought stress, respectively. Further, higher reduction in root

fresh weight was reported, which averaged 23.41 and 48.87%

relative to the control under 70 and 140 g/L PEG stress levels,

respectively. Three genotypes (IRAT170, Line 7 and Line 8)

showed low means reduction (less than 15%) under 70 g/L

PEG level, this thing is suggesting that these genotypes are less

sensitive to drought stress as compared to the three other

genotypes. Similar type of findings was reported by Fraser et

al. (1990).


_________________________________________________________



Regarding root dry weight, the reduction mean values of

genotypes ranged from 5.06 for Line 8 to 39.29 % for Line 7,

and 20.00 for Giza 182 to 59.00 % for IRAT 259 under 70 and

140 g/L PEG stress, respectively with an averages of 21.09 and

38.06% overall genotypes under treatments, respectively

(Figure 1). The Line 8 registered the lowest means reduction

than other genotypes under level 70 g/L PEG which is

indicating that this genotype is tolerant to drought. With

respect to the number of root, all genotypes exhibited means

reduction in number of root under both treatments relative to

the control treatment with same genotypes, except the Giza

182 registered increased under 70 and 140 g/L PEG levels,

which gave 2.86 and 15.00%, respectively (Figure 1). Two

genotypes registered low means reduction (less than 15%)

under 70 g/L PEG in number of root were IRAT 170 (9.09%)

and IRAT 259 (8.33), indicating that these genotypes are more

tolerant under this level of drought stress, but most genotypes

recorded moderate means reduction under water stress

treatment (140 g/L PEG).

It is evident from the results that PEG treatments had

inhibitory effect on all the growth attributes of rice accessions.

The genotypes which sensitive to drought stress are exhibited

more decline in biomass as compared to the resistant

genotypes. Similar observations were reported by Jiang &

Lafitte (2007) and Lum et al. (2014). Based on the results, a

variation in drought tolerance was reported among various

genotypes during seedling growth stage. So, results of study

suggested that most drought-tolerant variety is IRAT 259 while

the highest drought sensitive variety is Giza 182 (Figure 1).

The major rice genotypes showed a significant decrease in

shoot length at the various drought levels as compared with

control in major traits. These results were in compliance with

those of Mohammadkhani & Heidari (2008). A significant

reduction in root length for all genotypes was reported higher

at high levels of drought as compare to control (Figure 1).

These results were in compliance with those of Fraser et

al.(1990) and Ahmad et al. (2013).

3.3 Physiological and biochemical traits as influenced by

drought stress

Effect of drought on certain physiological and biochemical

traits related to crop productivity in rice included relative water

content, chlorophyll content, proline content, membrane

stability index and antioxidant enzymes activities measured for

three stress levels of 0, 70 and 140 g/L PEG (Figure 2). Result

of study suggest that the relative water content, chlorophyll

content and membrane stability index are depressed by drought

stress, while the level of proline content and antioxidant

enzymes activities (polyphenol oxidase,peroxidase and

catalase) increased when plants subjected to drought stress.

The differences among the genotypes in response to drought

stress for final germination were highly significant (Figure 2).

A significant variation was found in terms of the relative water

content (RWC) in the leaves of plants due to drought stress in

major genotypes compared the control (Figure 2). The data

were agreement with those obtained by Halder & Burrage

(2003). Among the genotypes IRAT 170, Giza 182 and Line 9

were maintained the highest means reduction of RWC, which

gave 8.61, 12.40 and 9.81% under 70 g/L PEG and 22.57,

19.22 and 17.38% under 140 g/L PEG, respectively. These

genotypes lost high amount of leaf water when subjected to

drought, and consequently they are considered sensitive to

drought stress compared to the genotypes IRAT 259, Line 7

and Line 8, illustrating that these genotypes retained more

water in leaf tissue under same drought stress. The average

reduction was as much as 6.63 and 14.32% at 70 and 140 g/L

PEG, demonstrating that the genotypes are tolerant, similar

results was reported by earlier researchers Alizade (2002) and

Islam et al. (2011).

In this study, chlorophyll synthesis in plant was reduced due to

drought stress conditions. The reduction percentages of

chlorophyll content were 4.84 and 10.58% under 70 and 140

g/L PEG drought stress of all genotypes, respectively (Figure

2).The result suggests that this trait is more tolerant to drought

stress. Reduction percentages under 70 g/L PEG treatment

ranged from 0.09% for the Line 8 to 9.12% for the Line 9 and

from 1.80% for the Line 8 to 13.24% for the IRAT 170 under

140 g/L PEG, indicating that these genotypes are more tolerant

to drought stress. Evain et al. (2004) suggested that the lowest

values of stomatal conductance, photosynthesis and relative

water content cloud be due to stress conditions. Similar types

of results were reported by Seemann & Sharkey (1986) and

Barutçular et al. (2016).

Membrane stability index decreased with increasing moisture

stress and there were significant differences among the

genotypes (Figure 2). The mean reduction of this trait was 3.65

and 7.51% under 70 and 140 g/L PEG treatments respectively;

this thing is suggesting that this trait is more tolerant to

drought stress. The reduction mean values of genotypes ranged

from 1.96 for Line 8 to 6.44 % for IRAT 170 and 3.38 for Line

7 to 14.64 % for IRAT 170 under 70 and 140 g/L PEG stress,

respectively. Hence, the approach of tolerance of these

genotypes to drought stress were reflected by its higher value

of membrane stability index. These results are correspond with

Munns (2002); Al-Ashkar& El-Kafafi (2014) and EL Sabagh

et al.(2015b).

Proline was accumulated in leaf of rice plants subjected to

water stress and the highest accumulation was recorded in the

severe stress treatment (Figure 2). Average proline content

increased 23.90 and 45.93% under drought stress treatments of

70 and 140 g/L PEG, respectively (Figure 2). The genotype

IRAT 259 accumulated the highest proline content of 44.08

and 69.21% while Line 8 recorded 37.15 and 74.72% under 70

and 140 g/L PEG, respectively, these results are in agreements

with Pireivatloum et al. (2010); Lum et al. (2014) and EL

Sabagh et al. (2016). Besides, acts as an excellent osmolyte;

proline plays three major roles during drought stress, i.e., as a

metal chelator, an antioxidative defence molecule and a

signaling molecule (Hayat et al., 2012).

680 Al-ashkar et al

_________________________________________________________



Figure 2 Mean of six rice genotypes of seven physiological and biochemical traits under (0, 70 and 140 g/L) polyethylene glycol; NB:

Numerical values above bars showed the percentage reduction or increase in the trait relative to the control; R%= reduction percentage;

In%= increase percentage; +, ++

Grand mean of percentage reduction or increase at 70 and 140 g/L PEG, respectively.


_________________________________________________________



Proline accumulation might promote plant damage repair

ability by increasing antioxidant activity during drought stress.

In plants under water stress, proline content increases more

than other amino acids, and this effect has been used as a

biochemical marker to select varieties aiming to resist such

conditions (Fahramand et al., 2014). Thus, proline content can

be used as criterion for screening drought tolerant rice

varieties.

The results of this study clearly showed that drought stress

treatments of 70 and 140 g/L PEG increased polyphenol

content of 10.42 and 26.26%, respectively (Figure 2). The

genotypes IRAT 259 and Line 9 recorded highest increase

percentage in phenol accumulation reached 27.27 and 12.31%

under 70 g/L PEG while the genotypes IRAT 259 and Line 7

recorded highest increase percentage in phenol accumulation

under 140 g/L PEG, which gave 73.33 and 46.15%

respectively. High values of phenol indicated high stress

tolerance, IRAT 259 and Line 9 genotypes showed more

tolerance to drought stress than other genotypes. Agastian et al.

(2000) observed that, polyphenol content was increased in

various tissues of plants undera biotic stress conditions. A

common effect of drought stress is the disturbance between the

generation and quenching of reactive oxygen species (ROS)

(Subhashini & Reddy, 1990; Zheng & Wang, 2001; Faize et

al., 2011).

Peroxidase activity (POD) was significantly increased when

plants of different genotypes were subjected to water stress.

The average POD was 17.27 and 23.86% in all genotypes

under 70 and 140 g/L PEG, respectively (Figure 2). Catalase

activity (CAT) also significantly increased under drought stress

as compared with control for all the genotypes. Catalase

activity increased 31.08 and 25.87% for all genotypes under

drought stress treatments (70 and 140 g/L PEG), respectively

(Figure 2). The genotypes IRAT 259 recorded highest increase

percentage in catalase activity (63.55 and 66.76%) under 70

and 140 g/L PEG respectively, indicating its tolerance to

drought stress. The genotype Giza 182 recorded the catalase

activity (11.74 and 6.45%) under 70 and 140 g/L PEG

respectively, indicating it’s sensitive to drought stress. The

plants are tolerance to drought-stress has higher levels of

antioxidant systems and substrates (Athar et al., 2008).

In this research increased value of CAT and POD activities

was reported in drought tolerant varieties, but, this value

decreased in the sensitive varieties. These results are best way

to protect the plants against H2O2. So, the growing of POD

action could effectively withstand the oxidative stress that

caused by stress (Mandhania et al., 2006). The tolerance

genotypes against environmental stresses has been associated

with higher activities of antioxidant enzymes and produced as

protective defence system to counteract the oxidative injury

that caused by drought stress in rice. The activities of

antioxidants can effectively diminish the ROS, and cloud

decrease passive effect of drought stress (Lum et al., 2014).

The results of reduction percentages for studied traits can be

classified into three categories according to Farag (2005) these

are (i) Drought tolerant traits included relative water content,

chlorophyll content and membrane stability index reduced less

than 15% under 70 and 140 g/L PEG suggesting that these

traits could be used as selection criteria for screening to

drought resistant genotypes in rice; (ii) Moderately tolerant

traits included growth traits, i.e. shoots and roots under 70 g/L

PEG reduced more than 15% ; and (iii) Drought susceptible

traits like shoots and roots under 140 g/L PEG reduced more

than 30%. It is worthy to note that the breeder should be taken

into consideration to increase positive percentages in proline

content and antioxidant enzymes activities (polyphenol

oxidase, peroxidase and catalase) occurred under drought

stress, which related to tolerance plant under these conditions

(Farag, 2005).

3.4 Genetic diversity analysis based on SSR markers technique

3.4.1 SSR analysis

Twenty SSR primers were screened for their ability to amplify

the genomic DNA from six rice genotypes. The number of

amplification bands per primer varied from 1.0 as for primer

Xwmc 27 to 5.0 for primer Xwmc 215 depending on the

primer and the DNA sample. Among these 41 amplified

fragments, 8.38% were not polymorphic while 91.62% was

polymorphic among the six rice genotypes (Table 2). The

highest value was 100% polymorphism belonged to 13 out of

the 16 primers. An example of polymorphism with wmc147,

wmc149, wmc179 and wmc215 are shown in Figure 3.The

highest levels of polymorphism were recorded for wmc179 and

wmc215 while the lowest levels of polymorphism were

recorded for wmc27, wmc387, wmc18 and wmc31 (Table 1).

Using potential markers generated in the present research to

improve drought tolerance–associated DNA markers are

showed in Table 1. Specific DNA bands generated from

analysis SSR primers here some rice genotypes reported to be

drought tolerant/sensitive (on the basis of data

performance/pedigree) were used. The results from Figure 3

and Table 1 revealed that DNA band at 120, 170 and 290 bp

are present in IRAT259 as drought tolerant, but not in Giza

182 as drought sensitive, when primer wmc215 is used. On the

other hand, specific DNA bands at about 100 bp is present in

Giza 182 as drought sensitive but not in the IRAT259 and

IRAT170 as drought tolerant, when primer Xwmc215 was

used. Moreover, specific DNA bands, generated from SSR

primers (Table 1), could be used to characterize between

IRAT259 (drought tolerance) and Giza 182 (drought sensitive).

682 Al-ashkar et al

_________________________________________________________



Table 2 Levels of genetic information generated by sixteen SSR primers on six rice genotypes.

Primer Sequence of primer (5' -3') No. of

amplification

products

No. of

polymorphic

products

Polymorphism

(%)

PIC Ho

Xwmc27 F= AATAGAAACAGGTCACCATCCG

R= AGAGCTGGAGTAGGGCCAAAG

1 1 100 0.00 0.00

Xwmc147 F= AGAACGAAAGAAGCGCGCTGAG

R= ATGTGTTTCTTATCCTGCGGGC

2 2 100 0.320 0.00

Xwmc149 F= ACAGACTTGGTTGGTGCCGAGC

R= ATGGGCGGGGGTGTAGAGTTTG

4 4 100 0.390 0.33

Xcfd1 F=ACCAAAGAACTTGCCTGGTG

R= AAGCCTGACCTAGCCCAAAT

3 3 100 0.532 0.33

Xwmc179 F= CATGGTGGCCATGAGTGGAGGT

R= CATGATCTTGCGTGTGCGTAGG

5 5 100 0.794 0.67

Xwmc215 F= CATGCATGGTTGCAAGCAAAAG

R= CATCCCGGTGCAACATCTGAAA

5 5 100 0.720 0.50

Xwmc233 F= GACGTCAAGAATCTTCGTCGGA

R= ATCTGCTGAGCAGATCGTGGTT

2 2 100 0.375 0.16

Xgwm249 F= CAAATGGATCGAGAAAGGGA

R= CTGCCATTTTTCTGGATCTACC

3 3 100 0.640 0.16

Xwmc387 F= CATTTTGACACCCACACTCG

R= CTGGATCCCCTCTTCGCTAT

1 1 100 0.00 0.00

Xwmc169 F= TACCCGAATCTGGAAAATCAAT

R= TGGAAGCTTGCTAACTTTGGAG

3 3 100 0.658 0.50

Xwmc44 F= GGTCTTCTGGGCTTTGATCCTG

R= TGTTGCTAGGGACCCGTAGTGG

1 1 100 0.00 0.00

Xwmc14 F= ACCCGTCACCGGTTTATGGATG

R= TCCACTTCAAGATGGAGGGCAG

2 2 100 0.500 0.16

Xwmc18 F= CTGGGGCTTGGATCACGTCATT

R= AGCCATGGACATGGTGTCCTTC

2 1 50 0.625 0.33

Xwmc31 F= GTTCACACGGTGATGACTCCCA

R= CTGTTGCTTGCTCTGCACCCTT

2 1 50 0.245 0.16

Xwmc327 F= TGCGGTACAGGCAAGGCT

R= TAGAACGCCCTCGTCGGA

2 2 100 0.445 0.16

Xwmc8 F= CACGCGCACATCTCGCCAACTAA

R= CGTGGTCTAGTCCGCGTTGGGTC

3 2 66 0.595 0.50

Total 41 39

Mean 2.56 91.62 0.427 0.25

PIC = Polymorphic information content; Ho = Observed heterozygosity.

There reducibility of these variety-specific markers was

confirmed in SSR analyses for which DNA isolation, PCR

amplification, and gel electrophoresis were carried out

separately. The technique of molecular marker has provided

and helping in identification and genetic characterization of

QTLs with positive effects on stress tolerance during various

plant stages (Foolad, 2005). Comparatively, however, a limited

research has been conducted to identify genetic markers

associated with drought tolerance in different plant species. In

the current research, genotypes analyzed were mainly

identified according to various traits under water stress

conditions. In this regard, the molecular characterization was

more efficient in the generation of an unbiased picture of

diversity than an agronomic approach.

3. 4 .2 Levels of genetic information generated by SSR primers

A total of 41 alleles were amplified of sixteen SSR among rice

genotypes. The variation in number of amplified alleles per

primer ranged from one allele as for wmc27 to five alleles for

wmc179 and wmc215, with an average of 2.56 alleles (Table

2). The amplified alleles varied from 70 to 400 bp in sizes

(Table 2). Polymorphism information content (PIC) provides

information on allele diversity and frequency among

genotypes. PIC differed greatly for sixteen SSR markers

studied in six rice genotypes used in this study. Overall, the

mean value of the PIC was (0.000 to 0.794) and the average

was (0.427) (Table 2). The lowest value was recorded for three

SSR viz. wmc27, wmc387 and wmc44.While the maximum

PIC value was 0.794 for primer wmc179.


_________________________________________________________



Figure 3 Examples of the SSR fingerprinting produced by Wmc147, Wmc149, Wmc179 and Wmc215 primers in the six rice genotypes.

It was observed, a significant and positive correlation (0.797)

between PIC values and number of amplified alleles per

primer. The value of heterozygosity were (0.00 to 0.67) and the

mean value of heterozygosity were (0.25) (Table 2). The

lowest Ho value was recorded (0.00) for four SSR such as

wmc147 primer, while the highest value recorded (0.67) for

primers wmc179 (Table 2).

The results indicated that the total number of amplified alleles

(41) as well as the average number of amplified alleles per

primer (2.56) was relatively lower compared to previous

results. These results agree with Babu et al. (2014) and

Choudhary et al. (2013). These differences could be attributed

to differences in genotypes as well as SSR primers. While, in

this study, the mean value of (Ho) was higher (0.25) compared

to the 0.01 recorded by Kyung et al. (2015) using 49 SSR

primers. Only previously reported polymorphic SSR primers

were employed in the present study. The low mean value of

PIC (0.427) showed the presence of high genetic homogeneity

among genotypes as well as additional polymorphic SSR

primers to achieve successful characterization for entails

improvement of rice genotypes. The mean value of PIC was

(0.58) among 76 Korean rice varieties (Kyung et al., 2015)

Conclusion

Considering the results of this study, Significant differences

were observed among rice genotypes in respect of all

measurements (morphological, physiological and biochemical

traits) and molecular analysis. Based on phenotypic and

genetypic (reaction with markers) performances under drought

stress conditions, the Line 8 and the Line 7 can be

recommended as a drought tolerant and a drought sensitive,

respectively. This result can be acclaimed the important source

for genetic diversity of rice in future breeding programs.

Acknowledgments

We would like to express our sincere thanks and appreciation

to Dr. E. I. Zaazaa, Agronomy Dept., Fac. of Agric., Al-Azhar

Univ., Egypt, who provided us with rice genotypes. We also

extend our thanks and appreciation to Prof. Abdullah

AbduIaziz AL-Doss, Plant Production Department,College of

Food and Agriculture Sciences, King Saud University, for

allowing us to use biotechnology Laboratory.




684 Al-ashkar et al

_________________________________________________________



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