Russian Journal of Agricultural and Socio-Economic Sciences, 2013, №8 (20) Август
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Russian Journal of Agricultural and Socio-Economic Sciences, 8(20)
IMPACT OF IMPROVED SEEDS ON SMALL FARMERS’ PRODUCTIVITY, INCOME AND LIVELIHOOD OF BARA LOCALITY IN NORTH KORDOFAN STATE, SUDAN
Elkhalil Elnour Briema Ahmed, Researcher Elobeid Agricultural Research Corporation, Sudan Maryoud Elnow Maryoud, Associate Professor
Faculty of Natural Resources & Environmental Studies, University of Kordofan, Sudan Elrashied Elimam Elkhidir, Associate Professor
College of Agricultural Studies, Sudan University of Science & Technology, Sudan Tarig Elsheikh Mahmoud, Associate Professor
Gum Arabic Research Centre, University of Kordofan, Sudan E-mail: rashiedimam@sustech.edu
ABSTRACT
This study was designed to test and identify the impact of improved seeds on small farmers’ productivity, income and livelihood in Bara locality. Sixty households participants were randomly selected through a field survey during 2011 for 2008/2009, 2009/2010 and 2010/2011 cropping seasons. The study applied Multi-stage random sample technique. Based on existing farm situation and price level, the sampled farmers were obtained SDG 8604 as gross margin to cover all expenses. Results of this study also depicted that the required net income and off-farm income were 16293 and 11378 SDG, respectively. With respect to Linear Programming (LP) results, a total of SDG 8890 were obtained and all crops were entered and solved. The optimal plan and existing farm situation were changed by 3.3 and 5.6% for gross margin and cash income, respectively. Results of LP also indicated a positive change in production patterns of resource use; 3.3, 6.2, 3.5, 3.3 and 9.1% for land, cash income, labour, seeds supply and productivity, respectively under existing and optimal plan. Partial crop budgeting revealed that, all treatments were financially gave positive returns. Dominance analysis showed that cowpea ainelgazal, okra, roselle and sesame herhri crops were dominated by crops of millet ashana, watermelon, groundnut and guar, respectively. Marginal analysis exposed that, for every SDG 1.00 invested in improved seeds cultivation, farmer can expect to cover the SDG 1.00 and obtain an additional SDG 1.345; then, additional seed rate implies a further marginal rate of SDG 43.9. Sensitivity analysis for cost over run and benefit reduction by 10% indicated highly stability with MRR of 1.22, 3.991 and 1.21 and 3.951% for watermelon and guar, respectively. The productivity of improved seeds compared to local ones was increased in some varieties and decreased among others. This study reached to some recommendations for improving crop productivity, production and livelihood of small farmers in Bara locality.
KEYWORDS
Linear programming; Dominance; Marginal analysis; Roselle; Sesame; Millet; Watermelon; Groundnut; Guar.
North Kordofan state is located between latitudes 11-16oN and longitudes 27-32oE. Bara locality lies between latitudes 13-14oN and longitudes 28-31oE. The State faces a number of complex and interconnected problems such as environmental, socio-economical and political problems. The majority of small farmers in Bara locality experience a situation of food insecurity, which is mainly attributed to successive crop failures. The project area was selected by the proceeding IFAD mission in the consultation process with federal and state government for its concentration of deprived population, relative lack of development but reasonable potential (IFAD, 1999). Improved seeds can achieve its purpose only if it is transferred to and adopted by farmers. Effective technology of improved seeds can result in higher agricultural production and increased incomes of farming families, which has positive impact on rural poverty. Improved crop yields will reduce costly imports of agricultural commodities and the cost of production of basic raw materials for agro-industries. In the long run the adoption of improved seed technology by farmers can make agro-industries more
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Russian Journal of Agricultural and Socio-Economic Sciences, 8(20)
competitive in the international markets (Bauer, 2004). Hazell (1986) reported that linear programming model is a method of determining a profit maximization combination of farm enterprises that is feasible with respect to a set of farm constraints. Grover et al., (2004) applied linear programming (LP) model to test the impact of improved seeds and the model was specified in terms of its objective function, activities and constraints under normal conditions to determine the optimum resource allocation for specific activities for improving the income level at the household level. Partial crop budgeting is another tool to determine the costs and benefits of the various alternatives (Cymmit, 1988). Ultimate goal of this research was to determine the relationship between improved seeds and farmers’ productivity, income and livelihood. This study hypothesized that investors would get the benefit when grow improved seeds.
ECONOMETRIC METHODOLOGY
Households’ survey questionnaire regarding crop production activities was developed and tested in pre-survey to collect primary data through direct interviewing with IFAD farmers. A form of multistage random sampling of 60 respondents was selected covering ten villages of the two administrative units (Rural Bara and Tayba). Data were analyzed using descriptive analysis, linear programming model (LP), partial crop budgeting, dominance, and marginal and sensitivity analyses. Relevant secondary sources of data were used.
Linear programming model. Pomeroy et al., (2005) stated that linear programming requires the information of the farm and non-farm activities and options with their respective resource requirements and any constraints on their production, the fixed requirements and other maximum, minimum constraints that limit family or farm production, cash costs and returns of each activity and defined objective function. In this context, a linear programming model has been developed to determine the area to be used for different crops for maximum contribution and for improving farmers' income. The model expressed as follows:
Objective equation:
Maximise Z =
1:'x.
J=1
Subject to:
LajX>- " '*"
Xj > 0 all j = 1 to m non-negativity constraint activities
where:
Z = Gross margin
Cj = Price of production activities
Xj = level of jth production activity aij = the ith resource required for a unit of jth activity bi = the resource available with the sample farmers j = refers to number of activities from 1 to n i = refers to number of resources from 1 to m
Constraints:
(i) Land:
ZaijXjs OL and ZaijXjs RL, where:
OL and RL are the size of holding owned and rented land, respectively.
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Russian Journal of Agricultural and Socio-Economic Sciences, 8(20)
(ii) Family labour:
Zatj-htXj < Lt, htXj < At
where:
Lt and At = available family labour and hired labour in the tth period.
ht = is the amount of hired labour required in the tth period for jth activity.
Atj = is the amount of labour required in the tth period for jth activity.
(iii) Working capital:
ZkijXj < WK
where:
WK = is the amount of available working capital.
Kij = is the amount of working capital required for production and non production activities.
Working capital is the value of inputs (purchased or owned) allocated to an enterprise with the expectation of a return at a later point. The cost of working capital is the benefit given up by the farmer by trying up the working capital in the enterprise for a period of time (Cimmyt, 1988).
(v) Seed supply:
Z PiX< IMPS
where:
IMPS = is the amount of improved seeds supply available with the sample farmers.
Pij = is the amount of seed supply required for production activities.
(vi) Crop Productivity:
ZSij < PD
where:
PD = is the amount of seed productivity available with the sample farmer.
Sij = is the amount of seed productivity required for production activities.
General formula of objective function:
Maximize Z = aX1+bX2+cX3+dX4+eX5+fX6+gX7+hX8+iX9+jX10+kX11+ lX12
where:
a, b, c, d, e, f, g, h, I, j, k and l are coefficients of objective function.
General formula of the inequalities:
aX1+bX2+cX3+dX4+eX5+fX6+gX7+hX8+iX9+ jX10+kX11+ lX12 < RHS
where: a, b, c, d, e, f, g, h, i, j, k and l are the coefficient of the constraints inequalities and RHS is the right hand side.
The improved production activities and decision variables used in the study are: X1 = Millet ashana, X2 = Cowpea ainelgazal, X3 =Okra Khartoum-red, X4 = Roselle X5 = Watermelon cashair, X6 = Sesame hirhri, X7 = Groundnut sodri, X8= Guar improved.
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Russian Journal of Agricultural and Socio-Economic Sciences, 8(20)
Farm model. This model was conducted to identify and analyze the empirical crop-mix problem of farmer who has to allocate his fixed resources like land, labour and working capital for different crops. The link between the tableau and algebraic formulations of the model can be illustrated as: eight crops can be grown and each of which has specified per hectare requirements. Production of one hectare requires 3, 5, 5, 5, 5, 4, 4, 4, and 4.4, 33, 32, 9, 9, 26, 57, and 21, man hours and working capital for the above decision variables, respectively. A total of 60 man hours of labour is potentially available, being the amount provided by family workers during season. The activity gross margins in the objective function are differed for each unit hectare (Table 1).
Partial crop budgeting. Partial budgeting technique was used for the analysis of data. The technique involved selecting of those costs that vary with particular treatment being analyzed and the net benefits of each treatment (Mahmood et al., 2000).
Table 1 - Linear programming tableau
Row name i Xi x2 Хз i X4 Хз Xs x7 Xe RHS i
Objective function Max Z i 989 92 57 i 701 1648 361 2105 2653
Resources (constraints):
Land/ha 1 1 1 1 1 1 1 1 7 ;
Labour/MH 3 5 5 5 5 4 4 4 60 i
Working capital/SDG 4.4 33 32 9 9 26 57 21 2007 ;
Seed supply kg/ha 4 0.4 0.2 0.2 0.4 0.8 40 0.1 48 i
Productivity kg/ha 71 60 30 92 98 84 205 101 741 i
Average cultivated area/ha 1 0.26 0.11 0.67 2.73 1.1 0.18 0.80 з ;
Source: Field survey, 2011. SDG: Sudanese Gienh.
Dominance analysis. Dominance analysis is carried out in order to rank the treatments in order of increasing costs that vary. Any treatment has net benefits that are less than or equal to those of treatment with lower cost that vary i9s dominant (marked with D).
Marginal analysis. Marginal analysis is conducted to know returns to investment and thus the less benefited treatments were eliminated by making the use of dominance analysis. Marginal rate of return indicate what farmers can expect to gain, on average, in return for their investment when they decide to change from one practice to another (Cymmit, 1988). Marginal values were calculated as:
Incremental net benefits
Marginal rate of returns (MRR) = ------------------------X 100%
Incremental net costs
Maximizing TPP: when:
dTPP
—— = MPP = 0 dx
where:
TPP = Total physical productivity (output price per unit).
MPP = Marginal physical productivity. x = Input used (cost price per unit).
Sensitivity analysis. The sensitivity analysis was done to check risk factors which cause price variability. The analysis was done assuming costs over run by 10% keeping the benefits same, and then by assuming benefits reduction by 10% keeping costs same.
Crop productivity. Productivity is the amount of output per unit of input. It refers to the volume of output produced from a given volume of inputs or resources. Productivity used to know and explore the trend of improved seeds versus local.
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Russian Journal of Agricultural and Socio-Economic Sciences, 8(20)
RESULTS AND DISCUSSION
The existing farm situation of small holders was estimated in order to explore the potential for improvement in agricultural production, productivity, labour use efficiency and hence the gross margins per unit of land at household micro level. Farmers derived income from both farm and non-farm activities. Based on the existing farm situation and prevailing price levels, the sample farmers were obtaining SDG 8604 as gross margin to cover all expenses including subsistence and livelihood requirements and hired labour expenses. Results revealed that, farmers obtained net cash income and off-farm income of SDG 16293 and 11378, respectively (Table 2).
Table 2 - Sources of cash income and expenses of the sampled farmers (Averages taken from
2008/2009 to 2010/2011) in SDG
Particulars SDG* i
1. Gross margin: 8604 ;
1.1 Improved seed 8604 ;
2. Off-farm income 11378 i
3. Total income (1 + 2) 19982 i
4. Expenses: 3689 :
4.1 Subsistence 3498 i
4.2 Hired labour 191 i
Farm cash income (1 - 4.1) 5106 ;
Net cash income (3 - 4) 16293 ;
Source: Field survey, 2011.
*One US$ = 5.2 SDG.
Based model was solved and the algebraic versions depend on linear programming model. With respect to unit area hectare, the results of optimal solution or farm plan for crops indicated that all crops were optimally emerged with a total gross margin of SDG 8890. Watermelon and guar were the most profitable with gross margin of SDG 4496 and 2130, respectively (Table 3).
Table 3 - Optimal solution or farm plan for the base model in SDG/ha
Improved crop Unit Area/ha Objective coefficient Optimal solution
Millet ashana 1 989 989
Cowpea ainelgazal 0.26 92 24
Okra Khartoum-red 0.11 57 6
Roselle improved 0.67 701 469
Watermelon cashair 2.73 1647 4496
Sesame hirhri 1.1 360 396
Groundnut sodri 0.18 2105 379
Guar improved 0.803 2653 2130
Final value 8890
Source: Field survey, 2011.
Farm income upon the optimal plan under reallocation of resources indicated an improvement in gross margin and cash income per hectare by 3.3% and 5.6% or by 0.033 and 0.056 units, respectively (Table 4).
Resource productivity in terms of gross margin for land, cash income, labour, seed supply and productivity were increased in optimal plan by 3.3%, 6.2%, 3.5%, 3.3%, and 9.1%, respectively; over existing farm situation. The available labour productivity of gross margin per man hour (GM/MH) was the total of man equivalent for the representative farm (Table 5).
Partial crop budgeting showed that all improved crops gave positive returns, this is actually due to higher field prices and lower costs of production in such seasons (Table 6).
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Russian Journal of Agricultural and Socio-Economic Sciences, 8(20)
Table 4 - Change in farm income under optimal base model over existing plan (Sample holdings from 2008/2009 to 2010/2011 cropping seasons) in SDG
Particulars Existing Optimal value % increment
Gross margin 8604 ; 8890 3.3
Subsistence 3498 i 3498
Cash income 5106 ■ 5392 5.6
Source: Field survey, 2011.
Table 5 - Marginal value productivities of various resources under existing and optimal plan
(Sample holding from 2008/2009 to 2010/2011 cropping seasons) in SDG
Particular Existing Optimal value % increment
Land Gm/ha 1229 1270 3.3
Cl/ha 1.6 1.7 6.2
Labour Gm/MH 143 148 3.5
Seed supply Gm/ha 179 185 3.3
Productivity Gm/ha 11 12 9.1
Source: Field survey, 2011. GM: Gross margin, ha: hectare.
Table 6 - Partial crop budgeting for different improved crops in Bara Locality
(Averages taken from 2008/2009 to 2010/2011cropping seasons) in SDG
Yield Adjusted yield Gross field benefit Total costs Net :
Improved variety kg/ha kg/ha SDG/ha SDG/ha benefit j
SDG/ha ;
Millet ashana 71 57 789 19 770 ;
Okra Khartoum-red 30 24 90 26 64 i
Cowpea ainelgazal 60 48 68 24 44 i
Roselle improved 92 74 542 55 487 ;
Sesame hirhri 84 67 279 161 118 i
Watermelon cashair 98 78 1305 239 1066 i
Groundnut sodri 205 164 1673 541 1132 :
Guar improved 101 81 2122 551 1571 i
Source: Field survey, 2011. Total costs include: Costs of seeds, costs of seed dressing, costs of insecticide, cash labor and family labor, costs of by-product and rental costs in SDG/ha.
Dominance analysis revealed that okra khartoum-red, cowpea ainelgazal, roselle and sesame herhri were dominated and eliminated by millet ashana, watermelon cashair, groundnut sodri and guar, while the net field benefit were highest for T8 (Guar improved), followed by T7 (Groundnut Sodri), T6 (Watermelon Cashair) and T1 (Millet Ashana). Therefore, these treatments were accepted as the best (Table 7).
Bearing in mind the minimum acceptable rate of returns was assumed to be 100%. Analysis of marginal rate of returns revealed that, T6 was higher than minimum acceptable rate of returns. However T6 and T8 were emerged as the best among the alternatives, thus every SDG 1.00 invested in improved seeds cultivation, farmer can expect to recover the SDG 1.00 and obtained additional SDG 1.345. Hence, increasing seed rate implies a further marginal rate of SDG 43.9 (Table 8).
Table 7 - Dominance analysis of improved seeds in SDG hectare
Treatments Total costs Net field benefits
T1 Millet ashana 19 770
T2 Cowpea ainelgazal 24 44 D
T3 Okra Khartoum-red 26 64 D
T4 Roselle improved 55 487 D
T5 Sesame herhri 161 118 D
T6 Watermelon cashair 239 1066
T7 Groundnut sodri 541 1132
T8 Guar improved 551 1571
Source: Field survey, 2011.
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Russian Journal of Agricultural and Socio-Economic Sciences, 8(20)
Table 8 - Marginal analysis of improved seeds in SDG per hectare
Treatments Total costs Marginal costs Net field benefits Marginal net field benefit MRR = V/lll*100%
1 II III IV V
Ti 19 770
T6 239 220 1066 296 134.5
T7 541 302 1132 66 22.0
T8 551 10 1571 439 4390
Source: Field survey, 2011.
The sensitivity analysis of costs over run ensured that treatment six and eight significantly remain same (watermelon and guar) and thus T8 was considered as the best with MRR 3991% and T6 rank second with MRR 122.3% (Table 9).
Table 9 - Sensitivity of marginal analysis for costs over run in SDG per hectare
Treatments Total costs Marginal costs Net field benefits Incremental net benefits MRR = V/lll* 100%
I II III IV V
Ti 20.9 770
T6 262.9 242 1066 296 122.3
T7 595.1 329.2 1132 66 20.0
T8 606.1 11 1571 439 3991
Source: Field survey, 2011.
Sensitivity analysis that assumed benefits reduction; indicated that T6 and T8 were the best among alternative with MRR 121.1% and 3951%. Based on the analysis of partial budget T8 was highly stable (Table 10). In spite of low rainfall, pests and diseases damage, productivity of improved seeds trend versus local goes further in some varieties and declined in others.
Table 10 - Sensitivity of marginal analysis for benefits reduction in SDG per hectare
Treatments Total costs Marginal costs Net field benefits Incremental benefits MRR = V/lll* 100% i
I II III IV V
Ti 19 - 693 - - .
T6 239 220 959.4 266.4 121.1 :
T7 541 302 1018.8 59.4 19.7 :
T8 551 10 1413.9 395.1 3951 i
Source: Field survey, 2011.
CONCLUSION
Analysis of data showed that improved seeds were most economically for growers. The optimal base model showed improvement in gross margin, farm income, resource use, and production patterns. Partial crop budgeting revealed that, all improved crops financially gave positive returns. Marginal rate of returns revealed that farmer can benefit from improved seed. Sensitivity analysis founded that treatment five and seven were highly stable. Crop productivity trend goes up in some improved varieties compared to the local ones.
REFERENCES
[1] Bauer, S. and Karki, L. (2004). Rural poverty reduction through research for development and transformation. Technology adoption and household food security, Analyzing Factors Determining Technology Adoption and Sustainability of Impact- A Case of Smallholder Peasants in Nepal. Giessen University press, Germany. PP84.
[2] Cimmyt, (1988). From agronomic data to farmer recommendation: An economic training manual PP 8-37.
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Russian Journal of Agricultural and Socio-Economic Sciences, 8(20) [3] Grover, D. K. and Temesgen, A. (2004). Agricultural technology dissemination program. Alleviating rural poverty through efficient smallholders farming system in Ethiopia: Relevance of macro polices with ground relation. Punjab Agricultural University press, Ludhiana, India. PP 11. [4] Hazell, P. B. R. and Norton, R. D. (1986). Mathematical programming for economic analysis in agriculture. Macmillan publishing company, University of New Mexico press, New York, USA. PP 1-77. [5] IFAD, (1999). North Kordofan Rural Development Project (NKRDP) reappraisal report, volume 1: Main report. [6] Mahmood, K., Subhani, S., Chaudhry, M. and Ghafoor, A. (2000). Impact of various packages of herbicides use on yield of transplanted rice. Department of agricultural economics, University of agriculture, Fiasalabad-38040, Pakistan. J. Agri., vol. 2, no. 12. P 1. [7] Pomeroy, C., Gough, A., Baker, M. and Hildebrand, P. (2005). The influence of household composition upon a diversified tropical Hillside farming project. (file:// A //: / Huyam.htm). Accessed on 30 March 2005. The Dominican Republic. University of Florida press, PP. 4. 10
Russian Journal of Agricultural and Socio-Economic Sciences, 8(20)
RESPONSE OF RICE (ORYZA SATIVA L.) UNDER ELEVATED TEMPERATURE AT EARLY GROWTH STAGE: PHYSIOLOGICAL MARKERS
Muhammad Kazim Ali, Abid Azhar, Saddia Galani, Researchers Karachi Institute of Biotechnology and Genetic Engineering (KIBGE), University of Karachi, Karachi, Pakistan E-mail: ali.kazimm@gmail.com
ABSTRACT
A reliable and rapid assessment technique, for evaluation of cultivars having potential to combat harsh environmental conditions is imperative. This experiment was carried out to screen 8 local (Pakistan) accessions of rice at early growth stage (germination and seedling) at control (28±3°C) and heat shock (42±3°C) for different time periods (24, 48, 72 h). Heat stress indices, including promptness index (P.I.) and germination stress index (G.S.I.), were used to explore thermotolerance at germination stage. At seedling stage, relative membrane permeability (RMP) were assessed through measurement of electrolyte leakage (EC), melondialdehyde (MDA) and production of hydrogen peroxide (H2O2). It is observed that heat stress delayed germination and decreased germination percentage at germination stage. However cultivars showed significantly different response. Among all, “Kanwal-95” showed more thermotolerance in terms of maximum number of germination as well as in speediness to germination. Physiological indicators manifested, increased electrolyte leakage is associated with increased level of lipid peroxidation and hydrogen peroxide. It can be concluded that antioxidants enzymes could play major role in thermotolerance by scavenging free radicals to protect lipid peroxidation consequently improve cell membrane thermostability. Results analysis revealed that these indicators were simple and accurate selection criteria to assess heat stress effect and can be adopted to save resources and time of formers.
KEY WORDS
Heat stress; Germination percentage; Electrolyte leakage; Lipid peroxidation; Free radical.
Rice (Oryza sativa L.) is one of the most important food crop among cereals (Eckardt, 2009). It is consumed by 90% in tropical Asia (Parasad et al., 2006), and is being used as staple food by world’s half population (Muhammad et al., 2009). Rice current production rate should increase approximately by 1% per year not only for the world’s growing population food demands (Sass et al. 2002) but also due to adverse climatic conditions. Human development and some natural factors (Eitzinger et al., 2010), projected to uplift surface air temperature 2-4 °C by the end of 21st Century (IPCC, 2007). According to a study conducted at International Rice Research Institute, Manila, Philippines, annual mean maximum and minimum temperature have increased by 0.35 °C and 1.13 °C for the period of 1979-2003, respectively (Peng et al., 2004). For each 1°C increase in temperature during crop growing period, grain yield of rice declined by 10% (Peng et al., 2004), developing 4050% gape between attainable and the actual yield (Vanderauwera et al., 2007). Due to sharp decline in cultivable land avaiability, unsufficient supply of water and continous increase in food demand (Eldakak et al., 2013), farmers are forced to cultivate the rice in marginal environment with warmer temperature (Prasad et al., 2006) which ultimately leads to the crop vulnerability along with reduced yield (Nakagawa et al., 2003).
Certain abiotic stresses including extreme temperature have detrimental effect on plant growth and crop yield. Among all changing climatic factors, mainly increasing average temperature is well known factor causing reduction in growth and productivity (Southworth et al., 2000). Long exposure of high temperature during seed development triggers delaying in germination, seed vigor (Grass et al., 1995) and dry mass reduction (Wahid et al., 2007). While seed germination and seedling establishment stage play vital role for sustainable cropping, and are more sensitive to high temperatures (Spiertz et al., 2006; Dias et al., 2011) assessing varying degree of stress tolerance at different developmental stages. Such inter
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Russian Journal of Agricultural and Socio-Economic Sciences, 8(20)
specific and intra-specific variations at different growth stages (Takeda et al., 1995; Ashraf et al., 2004) may improve early germination and faster field emergence to cope with adverse germination conditions (McDonald et al., 2000).
Among the major impacts of heat stress is induction of oxidative stress (Wahid et al., 2007). First event of stress conditions is the generation of reactive oxygen species (ROS) i.e. hydrogen peroxide, hydroxyl radicals and singlet oxygen (Noctor and Foyer, 1998). These are highly reactive as the can interact with number of cellular molecules and essential plant metabolites hereby leading to destructive processes causing cell death (Ashraf, 2009). Reactive oxygen species causes the autocatalytic per oxidation of membrane lipids and pigments leading to loss of membrane semi permeability and modifying its function (Xu et al., 2006).
Due to complexity of heat stress, there is need to develop quick and fast screening tests for heat tolerance and plant breeders are still in quest for identifying such efficient screening tools for detecting heat tolerance potentials at early growth stages in crop plants. In this regard, there is entire dearth of information regarding potentials of germination ability difference and cell membrane thermostability among rice cultivars with respect to genotype screening of heat tolerance under heat stress. Keeping in view these facts, this experiment was carried out using eight cultivars of rice Sindh Pakistan to determine response of high temperature at germination and seedling stages.
MATERIALS AND METHODS
This experiment was conducted at Agricultural Biotechnology Division, Karachi Institute of Biotechnology and Genetic Engineering (KIBGE) University of Karachi Pakistan. Rice cultivars were collected from Rice Research Institute, Dokri, Sindh Pakistan (Table 1).
Table 1 - Rice cultivars used in the study
S no Genotype 100 grains weight (g) Seed Size
1 IR-6 1.40 Small
2 IR-8 1.45 Large
3 DR-82 1.08 Medium
4 DR-83 0.97 Small
5 DR-92 1.20 Small
6 Kawai 95 1.12 Large
7 Sada Hayat 1.23 Medium
8 Shahkar 1.18 Medium
Fresh seeds were washed with distilled water and treated with 70% ethanol for 20 second followed by sterilizing with 10% Sodium Hypochlorite (commercial bleach) for 30 minutes. Seeds were rinsed five times with autoclaved distilled water and then soaked in water for 24 hrs. Seeds were divided into two groups one for germination at normal (28±3°C) and one at high temperature (42±3°C). Later, seeds kept at high temperature for 24 hrs, 48 hrs and 72 hrs. Both control and stressed seeds allowed to germination in Petri plates with filter paper wetted with distilled water at 28±3°C in controlled growth chamber. When the radical emerged through the seed coat it was considered as germinated. From the start of the test, data were documented for two weeks. Data collected after every second day. Then, Promptness index (P.I), germination stress index (GSI) were calculated according to Bouslama and Schapaugh, 1984.
i. P.I= nd2 (1.0) +nd4 (0.75) +nd6 (0.50) +nd8 (0.25) n number of seeds germinated at day d. In which nd2, nd4, nd6, nd8, and nd10 represent the percentage of germinated seeds after 2, 4, 6, 8 and 10 days after sowing, respectively.
ii. G.S.I.(%) = [P.I of stressed seeds / P.I of control seeds] x 100.
Seedling estalishment and Stress treatment. Seeds were primed in water for 2 days. Seedling allowed to grow in natural sunlight with 700 flux light intensity, daily mean temperature was 28 °C. After 20 days plants transferred to growth chamber for stress treatments. Plants maintained at 28 °C and leaf samples collected 24 hours before heat
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