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Journal of
Experimental
Biology and
Agricultural
Sciences
Volume 4 || Issue VI
November 2016
Production and Hosting by Horizon Publisher India [HPI] (http://www.horizonpublisherindia.in/).
All rights reserved.
ISSN No. 2320 – 8694
Peer Reviewed - open access journal Common Creative Licence - NC 4.0 Volume No – 4 Issue No – VI November, 2016 Journal of Experimental Biology and Agricultural Sciences
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JEBAS
Welcome Message - Managing Editor (Dr Kamal Kishore Chaudhary, M.Sc, Ph.D)
_____________________________________________________________________________
Dear Authors,
It is with much joy and anticipation that we celebrate the launch of special
issue VI (Volume 4) of Journal of Experimental Biology and Agricultural
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November 2016
JEBAS
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
http://dx.doi.org/10.18006/2016.4(Issue6).617.624
Farmer’s management practices to maintain the genetic diversity of sorghum (Sorghum
bicolor L. moench) in south of Chad
http://dx.doi.org/10.18006/2016.4(Issue6).625.630
Influence of organic and mineral fertilizers on chemical and biochemical compounds content
in tomato (Solanum lycopersicum) var. Mongal F1
http://dx.doi.org/10.18006/2016.4(Issue6).631.636
Cultivation of Rosmarinus officinalis in hydroponic system
http://dx.doi.org/10.18006/2016.4(Issue6).637.643
Nutritional quality of maize in response to drought stress during grain-filling stages in
mediterranean climate condition
http://dx.doi.org/10.18006/2016.4(Issue6).644.652
Analysis of off-season cucumber production efficiency in Punjab: a DEA approach
http://dx.doi.org/10.18006/2016.4(Issue6).653.661
Effect of nitrogen and sulfur on the quality of the cotton fiber under mediterranean
conditions
http://dx.doi.org/10.18006/2016.4(Issue6).662.669
Nutritive evaluation of azolla as livestock feed
http://dx.doi.org/10.18006/2016.4(Issue6).670.674
Physio-biochemical and molecular characterization for drought tolerance in rice genotypes at
early seedling stage
http://dx.doi.org/10.18006/2016.4(Issue6).675.687
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: ayman.elsabagh@agr.kfs.edu.eg (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)
Journal of Experimental Biology and Agricultural Sciences
http://www.jebas.org
ISSN No. 2320 – 8694
Production and Hosting by Horizon Publisher India [HPI]
(http://www.horizonpublisherindia.in/).
All rights reserved.
All the article published by Journal of Experimental
Biology and Agricultural Sciences is licensed under a
Creative Commons Attribution-NonCommercial 4.0
International License Based on a work at www.jebas.org.
_________________________________________________________
Journal of Experimental Biology and Agricultural Sciences
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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
_________________________________________________________
Journal of Experimental Biology and Agricultural Sciences
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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
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Journal of Experimental Biology and Agricultural Sciences
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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
613 Barutcular et al
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Journal of Experimental Biology and Agricultural Sciences
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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 ** ** ** ** ** **
Evaluation of maize hybrids to terminal drought stress tolerance by defining drought indices 614
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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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Evaluation of maize hybrids to terminal drought stress tolerance by defining drought indices 616
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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
Available Online – November 13, 2016
DOI: http://dx.doi.org/10.18006/2016.4(Issue6).617.624
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: sarawa60@yahoo.com (Sarawa Mamma)
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)
Journal of Experimental Biology and Agricultural Sciences
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ISSN No. 2320 – 8694
Production and Hosting by Horizon Publisher India [HPI]
(http://www.horizonpublisherindia.in/).
All rights reserved.
All the article published by Journal of Experimental
Biology and Agricultural Sciences is licensed under a
Creative Commons Attribution-NonCommercial 4.0
International License Based on a work at www.jebas.org.
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1 Introduction
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 Materials and Methods
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
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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
M-bio 6 ml per liter of water
M-bio 9 ml per liter of water
M-bio 12 ml per liter of water
M-bio 15 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
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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)
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 of water
M-bio 15 ml per liter of water
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
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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)
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 of water
M-bio 15 ml per liter of water
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
Effect of Biological Fertilizer on the Growth and Nodules Formation to Soya bean (Glicine max (L.) Merrill) in Ultisol under Net House conditions 621
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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
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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.
Conflict of interest
Authors would hereby like to declare that there is no conflict of
interests that could possibly arise.
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KEYWORDS
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
Available Online – November 13, 2016
DOI: http://dx.doi.org/10.18006/2016.4(Issue6).625.630
FARMER’S MANAGEMENT PRACTICES TO MAINTAIN THE GENETIC
DIVERSITY OF SORGHUM (Sorghum bicolor L. MOENCH) IN SOUTH OF CHAD
E-mail: gap_pablo@yahoo.fr (GAPILI Naoura)
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)
Journal of Experimental Biology and Agricultural Sciences
http://www.jebas.org
ISSN No. 2320 – 8694
Production and Hosting by Horizon Publisher India [HPI]
(http://www.horizonpublisherindia.in/).
All rights reserved.
All the article published by Journal of Experimental
Biology and Agricultural Sciences is licensed under a
Creative Commons Attribution-NonCommercial 4.0
International License Based on a work at www.jebas.org.
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1 Introduction
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 Materials and Methods
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
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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
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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
628 Naoura and Reoungal
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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
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Journal of Experimental Biology and Agricultural Sciences
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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.
Conflict of interest
Authors would hereby like to declare that there is no conflict of
interests that could possibly arise.
References
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630 Naoura and Reoungal
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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
Available Online – November 13, 2016
DOI: http://dx.doi.org/10.18006/2016.4(Issue6).631.636
INFLUENCE OF ORGANIC AND MINERAL FERTILIZERS ON CHEMICAL AND
BIOCHEMICAL COMPOUNDS CONTENT IN TOMATO (Solanum lycopersicum)
VAR. MONGAL F1
E-mail: asereme@yahoo.fr (Abdoulaye SEREME)
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)
Journal of Experimental Biology and Agricultural Sciences
http://www.jebas.org
ISSN No. 2320 – 8694
Production and Hosting by Horizon Publisher India [HPI]
(http://www.horizonpublisherindia.in/).
All rights reserved.
All the article published by Journal of Experimental
Biology and Agricultural Sciences is licensed under a
Creative Commons Attribution-NonCommercial 4.0
International License Based on a work at www.jebas.org.
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1 Introduction
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.
2 Materials and Methods
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
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(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
2.4 Statistical analysis
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 Results and Discussion
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
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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
634 Christophe et al
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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).
Conflict of interest
Authors would hereby like to declare that there is no conflict of
interests that could possibly arise.
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KEYWORDS
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
Available Online – November 13, 2016
DOI: http://dx.doi.org/10.18006/2016.4(Issue6).637.643
CULTIVATION OF Rosmarinus officinalis IN HYDROPONIC
SYSTEM
E-mail: humberto.rodriguezfn@uanl.edu.mx (Humberto Rodríguez-Fuentes)
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)
Journal of Experimental Biology and Agricultural Sciences
http://www.jebas.org
ISSN No. 2320 – 8694
Production and Hosting by Horizon Publisher India [HPI]
(http://www.horizonpublisherindia.in/).
All rights reserved.
All the article published by Journal of Experimental
Biology and Agricultural Sciences is licensed under a
Creative Commons Attribution-NonCommercial 4.0
International License Based on a work at www.jebas.org.
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1 Introduction
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
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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.
2.6 Statistical analysis
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.
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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.
Figure 2 Model of linear adjust for production of TDB in
Treatment 2. TDB= Total dry biomass (g). Vertical bars in
each point represent standard deviation of the mean.
3 Results and Discussion
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).
Figure 3 Model of linear adjust for production of TDB in
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).
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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.
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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
Conflict of interest
Authors would hereby like to declare that there is no conflict of
interests that could possibly arise.
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Cultivation of Rosmarinus officinalis in hydroponic system 643
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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
Available Online – November 13, 2016
DOI: http://dx.doi.org/10.18006/2016.4(Issue6).644.652
NUTRITIONAL QUALITY OF MAIZE IN RESPONSE TO DROUGHT STRESS
DURING GRAIN-FILLING STAGES IN MEDITERRANEAN CLIMATE CONDITION
E-mail: cebar@cu.edu.tr (Celaleddin Barutçular)
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)
Journal of Experimental Biology and Agricultural Sciences
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ISSN No. 2320 – 8694
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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 Materials and Methods
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
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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
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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.
2.3 Statistical analysis
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 Results and Discussion
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).
647 Barutçular et al
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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
Irrigated (Control) in 2015
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
Deficit irrigated (Water stress) in 2015
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.
Nutritional quality of maize in response to drought stress during grain-filling stages in mediterranean climate condition 648
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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)
Irrigated (Control) in 2014
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
Deficit irrigated (Water stress) in 2014
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).
649 Barutçular et al
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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.
Conflict of interest
Authors would hereby like to declare that there is no conflict of
interests that could possibly arise.
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KEYWORDS
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
Available Online – November 13, 2016
DOI: http://dx.doi.org/10.18006/2016.4(Issue6).653.661
ANALYSIS OF OFF-SEASON CUCUMBER PRODUCTION EFFICIENCY IN
PUNJAB: A DEA APPROACH
E-mail: qamarali2402@gmail.com (Qamar Ali)
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)
Journal of Experimental Biology and Agricultural Sciences
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ISSN No. 2320 – 8694
Production and Hosting by Horizon Publisher India [HPI]
(http://www.horizonpublisherindia.in/).
All rights reserved.
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Creative Commons Attribution-NonCommercial 4.0
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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
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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
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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.
Conflict of interest
Authors would hereby like to declare that there is no conflict of
interests that could possibly arise.
Analysis of off-season cucumber production efficiency in Punjab: a dea approach 659
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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
Received – October 25, 2016; Revision – November 06, 2016; Accepted – November 11, 2016
Available Online – November 13, 2016
DOI: http://dx.doi.org/10.18006/2016.4(Issue6).662.669
EFFECT OF NITROGEN AND SULFUR ON THE QUALITY OF THE COTTON
FIBER UNDER MEDITERRANEAN CONDITIONS
E-mail: ogurmus@cu.edu.tr (Gormus O)
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)
Journal of Experimental Biology and Agricultural Sciences
http://www.jebas.org
ISSN No. 2320 – 8694
Production and Hosting by Horizon Publisher India [HPI]
(http://www.horizonpublisherindia.in/).
All rights reserved.
All the article published by Journal of Experimental
Biology and Agricultural Sciences is licensed under a
Creative Commons Attribution-NonCommercial 4.0
International License Based on a work at www.jebas.org.
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1 Introduction
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.
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2 Materials and Methods
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.
2.4 Statistical analysis
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.
3 Results and Discussion
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
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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
665 Gormus and EL Sabagh
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Journal of Experimental Biology and Agricultural Sciences
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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,
Effect of nitrogen and sulfur on the quality of the cotton fiber under mediterranean conditions 666
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Journal of Experimental Biology and Agricultural Sciences
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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.
Conflict of interest
Authors would hereby like to declare that there is no conflict of
interests that could possibly arise.
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KEYWORDS
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
Received – October 20, 2016; Revision – November 03, 2016; Accepted – November 06, 2016
Available Online – November 13, 2016
DOI: http://dx.doi.org/10.18006/2016.4(Issue6).670.674
NUTRITIVE EVALUATION OF AZOLLA AS LIVESTOCK
FEED
E-mail: anithakcmallik@gmail.com(Anitha KC)
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)
Journal of Experimental Biology and Agricultural Sciences
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ISSN No. 2320 – 8694
Production and Hosting by Horizon Publisher India [HPI]
(http://www.horizonpublisherindia.in/).
All rights reserved.
All the article published by Journal of Experimental
Biology and Agricultural Sciences is licensed under a
Creative Commons Attribution-NonCommercial 4.0
International License Based on a work at www.jebas.org.
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1 Introduction
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.
2 Materials and Methods
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
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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.
3 Results and Discussion
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
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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
Conflict of interest
Authors would hereby like to declare that there is no conflict of
interests that could possibly arise.
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KEYWORDS
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
Received – October 18, 2016; Revision – November 01, 2016; Accepted – November 06, 2016
Available Online – November 13, 2016
DOI: http://dx.doi.org/10.18006/2016.4(Issue6).675.687
PHYSIO-BIOCHEMICAL AND MOLECULAR CHARACTERIZATION FOR
DROUGHT TOLERANCE IN RICE GENOTYPES AT EARLY SEEDLING STAGE
E-mail: ayman.elsabagh@agr.kfs.edu.eg (EL Sabagh A)
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)
Journal of Experimental Biology and Agricultural Sciences
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ISSN No. 2320 – 8694
Production and Hosting by Horizon Publisher India [HPI]
(http://www.horizonpublisherindia.in/).
All rights reserved.
All the article published by Journal of Experimental
Biology and Agricultural Sciences is licensed under a
Creative Commons Attribution-NonCommercial 4.0
International License Based on a work at www.jebas.org.
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1 Introduction
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.
2 Materials and Methods
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.
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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 Results and Discussion
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
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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
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Journal of Experimental Biology and Agricultural Sciences
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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).
Physio-biochemical and molecular characterization for drought tolerance in rice genotypes at early seedling stage 679
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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).
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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.
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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).
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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.
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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.
Conflict of interest
Authors would hereby like to declare that there is no conflict of
interests that could possibly arise.
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