Russian Journal of Agricultural and Socio-Economic Sciences, 2013, №8 (20) Август


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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