Determinants of Productivity and Technical Efficiency in Soybean Production among Small-holder Farmers

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Published on International Journal of Agriculture & Agribusiness
Publication Date: May 17, 2019

Regasa Dibaba Wake, Mesay Yami & Adam Bekele
Ethiopian Institute of Agricultural Research, Assosa Agricultural Research Center, Assosa, Ethiopia
Ethiopian Institute of Agricultural Research, Sebeta Fishery Research Center, Sebeta, Ethiopia
Ethiopian Institute of Agricultural Research, Addis Ababa, Ethiopia

Journal Full Text PDF: Determinants of Productivity and Technical Efficiency in Soybean Production among Small-holder Farmers (Study in Benishangul-Gumuz Region, Western Ethiopia).

The study estimated the level and principal determinants of productivity and technical efficiency soybean among small-holder farm households using 2016/17 rural farm survey data. The aim of this study was to determine the level of technical efficiency of smallholder soybean producers and identify factors affecting technical efficiency among smallholder farmers of soybean production in Benishangul-Gumuz Regional State, Ethiopia. Multi-stage sampling technique was employed to select 266 sample farmers. Cobb-Douglas stochastic frontier production function was used, in order to estimate the level of technical efficiency in a way consistent with the theory of production and the inefficiency effect model was used to estimate level of technical efficiency and identify inefficiency among soybean producing farmers. The technical inefficiency effects function was estimated simultaneously with the stochastic production function using a one-stage procedure using frontier approach. The results reveal the existence of technical inefficiencies in soybean production among farmers in the study area. The mean technical efficiency of soybean producer farmers was found to be 72.81%. On the determinants of inefficiency, the study found that; educational level, farming experience, distance to urban and input centers significantly reduce the technical inefficiencies among soybean producer farmers in the study area. The study suggested the need for building rural infrastructure and access to social groups and input centers to encourage and improvements of farmers in the study area.

Keywords: Soybean, Small-holder farmers, Efficiency, Stochastic frontier, Benishangul-Gumuz region.

Soybean has a great potential in terms of food, income, nutrition and human health and soil fertility improvements through biological nitrogen fixation in the farming systems of small-holder farmers. As a legume crop, soybean improves soil fertility by fixing atmospheric nitrogen. Soybean also presents the farmers with the much needed alternative cash income source, employment creation, and poverty reduction objectives.
Measuring technical efficiency of soybean producing farmers and identifying the factors that affect it, may provide useful information for the formulation of economic policies likely to improve soybean producer technical efficiency (Nchare, 2007). Farm efficiency is one of the important issues of production economics and production function analysis (Biekelile, 2011). Technical efficiency is a way to measure the level and extent of inefficiencies in production system. Technical efficiency describes the relationship between output and input by considering different combinations of input for output.
Assosa agricultural research center made unlimited effort to generate and adaptation of new improved soybean varieties and further promote and disseminated this technology in potential production areas of western Ethiopia, particularly in the Benishangul-Gumuz Region for more than ten years. Assosa zone is among the areas where this technology was introduced and disseminated to improve food security and income of small holder farmers. In the study area, soybean is widely produced by the majority of small-holder farmers and playing a crucial and diverse role in the diets of community, cash generation and enhancing soil fertility. This study is mainly concerned with productivity variances regarding resources targeting optimum production and identifying inefficiency in soybean production that helps small-holder farmers producing soybean to use their inputs efficiently the already scarce resources. Moreover, the study is designed to help find solutions which would promote increases in soybean productivity as well as overall output and determinants of inefficiency among soybean producing small-holder farmers.

1.1 General Objective
The main objective of the study was to explore ways that would increase technical efficiency of soybean producer farmers through a better use of the factors employed in soybean production in the study area. In order to achieve this, the following specific objectives were pursued;
a. To determine the level of technical efficiency of soybean production among small-holders in the study area
b. To examine factors that influence efficiency of soybean producers among small-holders in the area.

1.2 Theoretical framework and Concept of Efficiency
Efficiency is generally defined as the use of resources in such a way as to maximize the production of goods and services; or comparison of what is actually produced or performed with what can be achieved with the same level of resources (land, capital, labour, time, etc.). In fact, the concept of efficiency is relative and differs from productivity. Productivity is the ratio of what was produced and what was spent to produce while efficiency compares what has been produced, given the resources available, with what could have been produced with the same resources (Fellipe et al., 2012). If the production unit of this parameter is far away, it is considered to be inefficient. As a component of productive efficiency, technical efficiency is derived from the production function. Productive efficiency consists of technical efficiency and allocative or factor price efficiency. Productive efficiency represents the efficient resource input mix for any given output that minimizes the cost of producing that level of output or, equivalently, the combination of inputs that for a given monetary outlay maximizes the level of production (Forsund et al., 1980). Technical efficiency reflects the ability of a firm to maximize output for a given set of resource inputs, while allocative (factor price) efficiency reflects the ability of the firm to use the inputs in optimal proportions given their respective prices and the production technology.
The level of technical efficiency of a particular farmer is characterized by the relationship between observed production and some ideal or potential production (Greene, 1980). The measurement of the firm specific technical efficiency is based upon deviations of observed output from the best production or efficient production frontier. If a farmer’s actual production point lies on the frontier, it is perfectly efficient. If it lies below the frontier, then it is technically inefficient with the ratio of actual to the potential production defining the level of efficiency of the individual farmer. Thus, the evaluation of a firm’s technical efficiency level results from the estimation of a frontier production function.

2.1 Location of the study area
The study area covers one of the main soybean production potential of the country. It is located in western part of Ethiopia that extends to the Sudanese border. Benishangul-Gumuz region is located 661 km West of Addis Ababa. The study area is located at 9◦ 30′- 11◦ 30′ latitude in the North and 34◦ 20′- 36◦ 30′ longitudes in the East. It is bordered with the Sudan in the West, Amhara regional state in the North, Oromia regional state in the East and South East and Gambella regional state in the South. The region have three administrative zones, and one special district.
The altitude of the region ranges mainly between 580-2731meters above sea level. The region is highly characterized by its ethnic diversity. It is endowed with various resources that if properly utilized can significantly contribute to the economic development of the country. Hence the study has been conducted at Assosa and Bambasi districts of Assosa zone which have the best practice and concentration areas for soybean production in the region.

2.2 Sample Size Determination
As specified by (Kothari, 2004) determination of the sample size followed a proportionate to size sampling methodology is calculated as:
Where; n= required sample size
= confidence level at 95% (standard value of 1.96)
= estimate of small-holder soybean producer farmers which is at 0.78. This was an assumption that 78% of household participates in soybean production in the study area.
= this is the weighting variable given by 1-
= margin of error at 5% (standard value of 0.05)
2.3 Sampling Techniques
The study was conducted in Benishangul-Gumuz region of the country with considering population of all soybean producers in the region. A multi-stage sampling technique was employed for the purpose of this study. The first stage of the sampling involved selection of districts from the region where the survey was conducted. They included Assosa and Bambasi districts which were selected based on their soybean production potentials. The second stage involved random selection of 9 rural villages (4 from Assosa and 5 from Bambasi districts) that were sampled for the study. Finally, the third stage involved random selection of soybean producers from each village, giving a total sample size of 266 soybean producers (106 for Assosa and 120 for Bambasi) small-holder farmers.

2.4 Methods of Data Collection
This study involved the use of both primary and secondary data sources. The primary data was collected through interview with soybean producing farmers in the study area for 2016/2017 cropping season. Secondary data which acted as supplementary data was collected from different sources. Information was also obtained from journals, books, and the internet. The socio-economic data collected included sex of respondent, age, marital status and educational levels and other demographic and institutional factors. Production information collected included size of farm land owned, land tenure system, size of land under soybean production, labour used in production, varieties of seed planted, amount of seed used, prices of input used(seeds and fertilizer)and seasonal yields. Access to credit and extension services were also among production information (number of visits), amount of fertilizers used. Data about constraints faced by soybean farmers was also collected.

Descriptive statistics was used to analyze the socio economic characteristics of soybean producers in the study area. The descriptive statistics such as frequencies, percentage, means, range and standard deviations was employed to summarize the collected data. Econometric model; stochastic production frontier model, is used to estimate the production function, determine the determinants of inefficiency and estimate the level of efficiency. Given that we are considering a developing country setting where by the main concern is output shortfall rather than input over use, preference has been given to primal or output oriented approach of measuring efficiency.

3.1 Econometric Analysis
3.1.1 Model specification of stochastic frontier function
Stochastic production frontier approach requires a prior specification of the functional form. Cobb-Douglas production function is selected for this study for several reasons. Foremost it was selected due to its simplicity and the logarithmic nature of the production function that makes econometric estimation of the parameters a simple matter. It is also very parsimonious with respect to degrees of freedom and it is convenient in interpreting elasticity of production. The linear functional form of Cobb Douglas production function used for this study is given by:
The existence of inefficiency can be tested using γ parameter and can be interpreted as the percentage of the variation in output that is due to technical inefficiency. Likewise the significance of indicate whether the conventional average production function adequately represent the data or not.
Production function: The production of each farm was assumed to be characterized by a Cobb– Douglas function. Cobb Douglas function is one of the most popular ways of functional form to estimate the relationship between inputs and outputs. The dependent variable is given by the following equation.
Where; represents the total soybean output in quintal/ha, area denotes soybean area cultivated (ha), fert denotes quantity of fertilizer (kg/ha) used, Seed denotes quantity of seed (kg/ha) used, labour denotes labour (man-day/ha), chem denotes quantity/volume of agrochemical (kg/ha) used, oxen denotes oxen (oxen-day/ha),  are unknown parameters of the production function, vi are two sided normally distributed random error and ui is a one sided efficiency component with a half normal distribution.

3.2 Definition, Measurement and Expectation of Variables
Variables used in the analysis include: production, fertilizer, seed, labour and farm size/area under soybean production and they are also the inputs which are used in this study for soybean production.
Production/output is the quantity of soybean produced by each household in the 2016/17 cropping season measured in quintals. Output which is the dependent variable in the estimation of production functions, is measured in quintals and inputs refers to explanatory variables used in the estimation of production functions.
Fertilizer was assumed to be the quantity of inorganic fertilizers that was purchased and applied per hectare of land by soybean producers during the period under considered and was measured in kilograms. Fertilizer refers to the quantity of chemical fertilizer applied on soybean plot in kg per ha during the 2016/17 cropping season. Fertilizer is expected to have a positive effect on yield, but when overdose happens it can lead to low yield or total crop failure.
Seed is the quantity of soybean seed planted by each soybean producer farmer per hectare of land under soybean cultivation. Seed was a measure of the quantity of soybean seeds in kilograms and used in 2016/17 cropping season. Seed are the backbone of agricultural production. Moreover, determinants of inefficiency refers those socioeconomic, institutional, production, and biological variables, chosen in reference to former studies and logical reasoning, are used in identifying the determinants of inefficiency.
Labour is measured as man-day used in soybean production by the farmers in the study area and in this case it was considering family labour and casual labour used during the stated cropping season. Labour is measured as the man-days spent on the farm from land preparation to harvesting and transporting on a hectare of land.
Farm size is the area which was cultivated for soybean production during the period defined by sample farmers and it is measured in hectares.
The following tables show the definition, measurement and effected expectation of variables used in this efficiency study.
Efficiency Indices was the dependent variable and show the efficiency level of an individual farm/farmer in the study area. Several socio-economic independent variables are known to have influenced it; a positive sign of an estimated parameter implies that the associated variable has a positive effect on efficiency but negative effect on inefficiency and vice versa.

The results and discussion of the study would be begins with description of the characteristics of the sample respondents (i.e. the farmers). It also presents area of land cultivated by household, average farm size in the study area and average yield of soybean. It also presents socio-economic factors affecting farmer’s efficiency.

4.1 Descriptive Results
Farm level efficiency has been discussed widely in literature. It has been influenced by several farm and household characteristics (Kumbhaker and Lovell, 2000). The age, sex, education level, household size, access to credit and extension services, membership of cooperative, farming experience for agricultural cultivation and soybean production and institutional access to farmers, frequency of extension contacts with development agents in their localities and soil fertility condition are the characteristics that were analyzed for the purpose of this study. With respect to sample distribution of farmers that collected from 266 soybean producers of the study area were analyzed.
Credit is important variable that influences farm level efficiency that has been considered in this study. The percentage of sample respondents’ have access to different sources of credit and received services was 69.55% reported having access (table 3). Farmers who reported having access and control to farm tools were about 90.23 % the right to use by all the family members in the house and male have only 8.57 percents and the majority of the farm households of about 240, has access to farm tools. About 84.59 percent of the farmers reported to have been the right to use and control oxen equipment’s by male household/husband members, and very few of the sampled farmers (7.52 percents) reported to have been access to use by children in their household members.