Sources of technical efficiency among subsistence maize farmers in Uyo, Nigeria

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1 Discourse Journal of Agriculture and Food Sciences Vol. 1(4), pp , April 2013 Sources of technical efficiency among subsistence maize farmers in Uyo, Nigeria *Nsikak-Abasi A. Etim and Sunday Okon Department of Agricultural Economics and Extension, University of Uyo, P.M.B. 1017, Uyo, Akwa Ibom State, Nigeria * for correspondence: ABSTRACT: Farming requires the utilization of available inputs as efficiently as possible to optimize production, farmers being primary managers of land and other productive resources, need to manage problems arising from deteriorating natural resources. This study was conducted to identify sources of technical efficiency among subsistence maize farmers. This was investigated using the stochastic frontier production function which incorporates a model for the technical efficiency effect. Farm-level survey data from 110 traditional maize farmers were obtained using well structured questionnaire. The parameters were estimated simultaneously with those of the model of efficiency effects. Using the maximum likelihood estimation technique, asymptotic parameter estimates were evaluated to describe efficiency determinants. Results reveal a mean efficiency of 0.71 implying that output from maize production could be increased by 29 percent using available technology. Results further reveal land, labour, inorganic fertilizer and planting materials were found to have positive and significant impact on technical efficiency. Other variables which were identified as sources of technical efficiency and which have impact on technical efficiency include age, technical assistance, credit and market. These results underscores the need for appropriate policy intervention to stimulate maize production by encouraging farmers through timely provision of inputs, and availability of credit facilities, and road infrastructure development which will provide easy access to market and adequate technical assistance. Keywords: Efficiency, maize, farmers, Nigeria. INTRODUCTION The world food crisis is rising astronomically and various countries, international organizations and development stakeholders globally have responded with pragmatic approaches aimed at curbing the global food imbalance. According to CSIS (2008) and Aye and Mungatana (2010), the current food crisis is caused by a web of interconnected forces involving agriculture, energy, climate change, trade, and new market demands from emerging markets. These have threatening implications on economic growth and development, social security and cohesion. Despite Nigeria s physical and human resources, there had been progressively worsening welfare conditions of it nationals (Okunmadewa, 2001; Etim et al 2010). Majority of the poor in Nigeria live in rural areas as over 70% of them derive their livelihood from farming (Etim, 2007). Because most of the poor who engage in agriculture reside in rural communities, increase agricultural production is necessary for economic stability. One of such viable crops that would stimulate growth in the economy and redirect agricultural production for the rural poor is maize. Maize (Zea mays L.) is the most important cereal crops in sub-saharan Africa and is the world s most widely grown cereal crop as well as essential food source for millions of the world s poor. Farmers grow conventional maize on an estimated 100 million hectares (200 million acres) throughout the developing world (Ferris and Graver 2000). Maize is high yielding, easy to process, readily digested, and costs less than other cereals. It is also a versatile crop, allowing it to grow across a range of agro ecological zones. Every part of the maize plant has economic value: the grain, leaves, stalk, tassel, and cob can all be used to produce a large variety of food and non-food products (Bourdillon et al 2003). In sub-saharan Africa, maize is a staple food for an estimated 50% of the population and an important source of carbohydrate, protein, iron, vitamin B, and minerals. Current production of maize is about 8 million tones and its average yield is 1.5 tonnes per hectare. The average yield is lower compared to the world average of 4.3 tonnes/ha and to that

2 49 from other African countries such as South Africa with 2.5 tonnes/ha (FAO, 2009). There has been a wide gap between the demand for maize and its supply. Aye and Mungatana (2010) noted that the strong force of demand for maize relative to supply is evidenced in frequent rise in price of maize and therefore, greatly implied for the food security situation and development of the economy. To reduce the problem of the current food challenge, government at different levels have embarked on various programmes aimed at stimulating maize production particularly among the resource poor farmers. Despite these efforts and the involvement of farming families in maize production over the years, the generality of their income and productivity has remained low thus raising questions about the efficiency of resources utilization by subsistence maize farmers. Information on how efficient subsistence maize farmers utilize resource inputs is pauce. This study attempts to fill this knowledge gap by identifying the different sources of efficiency among subsistence maize farmers. Specifically, the study estimates the factors that influence technical efficiency among subsistence maize farmers. METHODOLOGY The Study Area, Sampling and Data Collection Procedure The study was conducted in Uyo Local Government Area, the capital of Akwa Ibom State, Nigeria. Uyo is situated 55 kilometers inland from the coastal plain of South-East Nigeria. It has an estimated population of 309,573 (NPC 2006). The area is located between latitude 5 o 17 and 5 o 27 North and longitude 7 o 27 and 7 o 58 East. Uyo covers an area approximately 35 square kilometers. The area lies within the humid tropical rainforest zone with two distinct seasons the rainy and short dry season. The annual precipitation ranges from mm per annum. According to Etim and Ofem (2005) and Etim and Edet (2009), this rainfall regime received in most parts of the state encourages farming throughout the year. The occupations of the inhabitants reflect the economic activity of the residents. The settlements are majorly Ibibios though there are settlers from other ethnic groups. The settlement pattern in Uyo is nucleated and being an administrative headquarters, majority of the residents are government employees and political office holders. These people engage in farming activities and other commercial ventures within and around their urban residences as a means of augmenting and supplementing family income and food supplies (Etim et al 2006). Most of the inhabitants of rural communities in the study area are farmers and the crops commonly cultivated include cassava, maize, oil palm, yam, cocoyam, fluted pumpkin, okra, waterleaf, bitter leaf. In addition, some micro-livestock are usually raised at backyards of most homesteads. Sampling and Data Collection Procedure Data used for this study are mainly primary and were obtained from the farmers using structured questionnaire during 2011 cropping season. Specifically 110 maize farmers were selected using two-stage sampling procedure. The first stage involved the random selection of two out of the four clans that make up Uyo viz: Etoi and Offot clans. The second stage involved the selection of 55 farmers from the two clans to make a total of 110 farmers. Baseline information on socio-economic characteristics, input and output levels were collected and analyzed. The empirical model In recent times, econometric modeling of stochastic frontier methodology associated with efficiency estimation has been important aspect of economics research. Both time varying and cross sectional data based on Cobb-Douglas and transcendental production function or cost functions have been used by Bagi and Hunag (1983); Bagi (1984); Apezteguia and Garate (1997), Yao and Liu (1998), Udoh and Akintola (2001a) (2001b), Etim et al (2005); Etim and Udoh (2006), Udoh and Etim (2009) to estimate individual firm efficiency. The study utilized stochastic production frontier, which builds hypothesized efficiency determinants into the inefficiency error component (Coelli and Battese 1996). Assuming we specified a Cobb-Douglas functional form as: Ln(Qty) = o + I Ln (Land) + 2 Ln (Labour) + 3 Ln (Inorganic fertilizer) + 4 Ln (seeds) + 5 Ln (Capital) + Vi Ui (1) Where Qty is the grain equivalent measured in kilogram; Land is the farm size measured in hectares; Labour employed in farm operations measured in man-day; Inorganic fertilizer is fertilizer applied on the soil measured in kilogram; value of maize seeds measured in Naira; capital is the depreciation value of farm tools measured in Naira.

3 50 Table 1. Summary of the analysis of explanatory variables Variables Unit Mean value Min value Max value Land Square metre Labour Man-day Inorganic fertilizer Kilogram Capital Naira 3,860 2,985 4,867 Age Years Seeds Naira With Vi N (O, v 2 ); and e -ui = o+ 1 (Age) + 2 (Technical) + 3 (Household size) + 4 (Credit) + 5 (Market) + Zi (2) Where Age is the age of the farmers in years; Technical is access to technical assistance from extension personnel (dummy), Household size is the number of person in the household, Credit is access to credit facilities (dummy); Market is access to market (dummy) and Zi is an error term assumed to be randomly and normally distributed. The values of the unknown coefficient in equation (1) and (2) are jointly estimated by maximizing the likelihood function (Yao and Liu 1998, Udoh and Akintola 2001b; Etim et al 2005, Udoh and Etim 2006). RESULTS AND DISCUSSION Results for the variables were summarized in Table 1. The average land used in the cultivation of maize was 0.38 square metres. This indicates that maize farming in the area is on subsistence level with intercropping of maize most prominent. This small farm size could either be due to the labour-intensive nature of the cultural practices involved or because the farmers cannot acquire large hectares. Farm practices include land clearing, making of bed, planting, fertilizer application, weeding and harvesting. All these practices require substantial amounts of labour man-day. The results of the analysis on age and educational level are indications that the producers are within an active and productive age group, and have acquired considerable level of formal education. Maximum likelihood estimates results The model specified was estimated by the maximum likelihood (ML) using a frontier 4.1 software developed by Coelii (1995). The ML estimates and inefficiency determinants of the specified frontier are presented in Table 1. The sigma square (0.4399) is statistically significant and different from zero at = This indicates a good fit and the correctness of the specified distribution assumption of the composite error term. The variance ratio defined as = u 2 ( u 2 + v 2 ) is estimated to be percent. The result suggests systematic influences that are unexplained by the production function as the dominant sources of random errors. Putting differently, the presence of technical inefficiency among subsistence maize farmers explains percent variation in the output level of the maize cultivated. The presence of one-sided error component in the specified model is thus confirmed implying that the ordinary least square estimation would be inadequate representation of the data. The generalized likelihood ratio ( 2 = ) is highly significant. This implies the presence of one-sided error component. The results of the diagnostic analysis therefore confirm the relevance of stochastic parametric production function and maximum likelihood estimation. The land variable is aimed at capturing the effect of scale production on the technical efficiency of the farm. A study by Lundvall and Battese (2000) established a varied relationship between farm size and technical inefficiency in developing countries using the frontier production function. Analysis of land as a variable used in this study was negatively significant in the model. This can be explained by the fact that increased farm size diminishes the timeliness of input use thus leading to decline in technical efficiency. The inverse relationship confirms the findings of Msuya (2008), Okoye et al (2006, 2009), Peterson (1997) and Aye and Mungatana (2010). Results underscore the need to formulate policies that encourage small holder farmers to continue in production as they are the backbone of agricultural production and growth in developing countries. Labour variable refers to the family labour provided for farming operations. In this study, labour appears to be the most important production resources with an elasticity of The relative large coefficient for labour is an indication that

4 51 cultivation of maize is labour intensive particularly during weeding and fertilizer application. Inorganic fertilizer variable with an elasticity of is positive and significant. This result is an indication that because of the moderate cost of inorganic fertilizer as indicated by the value of the elasticity, farmers were able to obtain more of inorganic fertilizer to apply to their crop which invariably resulted in increased maize production. This underscores the need for relevant agencies to make conscious efforts at availing local farmers with fertilizer at affordable prices for meaningful production. Seeds are the maize seeds used for planting. The variable is positively significant as expected. Result however stresses the need to encourage proper storage and preservation of seeds for use by local farmers. This will not only ensure timely availability of planting materials to farmers but will reduce the additional cost which would have been incurred in purchasing these seeds. The variable age could have either positive or negative effect on technical efficiency. Older farmers are more experienced and would be more technically efficient than younger farmers. However, with respect to new ideas and techniques of farming older farmers are less likely to adopt innovations and thus would be less technically efficient than younger farmers. In this study, age has a positive sign and significantly impacts on technical efficiency in the model thus, the variable, age indexes experience and serves as evidence for human capital revealing that farmers with more years of experience in farming will have more technical skills in management and thus higher efficiency than younger farmers as the study revealed. Increased experience in cultivation may also enhance critical evaluation of the relevance of better production decisions, including efficient utilization of productive resources. This result is in conformity with the findings of (Khai et al; 2008; Aye and Mungatana, 2010). The variable technical assistance refers to assistance from extension personnel. Farmer s access to the variable enhances their access to information and improved farming techniques. This variable was expected to be positive in the model, however, it was found to be negative but significant. Studies by (Okoye et al 2006; Haji 2006; Aye and Mungatana, 2010) reported similar negative sign for technical assistance. Result suggests that extension services delivery in Nigeria is lagging in effectiveness and efficiency, especially after the withdrawal of funding of the Agricultural Development Project (ADP) by the World Bank. It therefore becomes imperative for a more proactive and effective policy decisions aimed at improving the service delivery of extension agents in Nigeria. Frantic efforts geared at updating the knowledge base of extension personnel as well as timeliness in disseminating information on modern farming techniques should be topmost in priority. The variable CREDIT was positive as expected. Result implies that accessibility and availability of credit loosens the production constraints and hence makes it easier for timely purchase of resources thereby increasing productivity through efficiency. The result agrees with the finding of Muhammad (2009) and Aye and Mungatana (2010) but contrary to that of Haji (2006) who reported a negative impact of access to credit on technical, allocative and cost efficiency. Market variable captures farmers access to market. It serves as a proxy for development. The variable was correctly signed. Farms located farther to and from the market are believed to be less technically efficient than the farms closer to the market as this will not only increase the cost of production but also impacts on various operations on the farm particularly accurate prompting of resources use. Resource-use efficiency distribution An important feature of the stochastic production frontier is its ability to estimate individual, farm-specific technical, allocative and economic efficiencies. Table 2 shows farm specific resource use efficiency indices. The efficiency indices across maize farms on Table 3 show considerable variation, as the technical efficiencies of all the sampled maize farms are less than one. This implies that no farm reached the frontier of production and therefore has the potential to increase efficiency. With a mean technical efficiency index of 0.71, there is still scope for increasing farm output. The observed distribution suggests that little marketable product is wasted due to inefficient use of resource inputs. However, none of the farmers reached the frontier of production which according to Etym. et al (2005) such farms is confronted with multifaceted production challenges ranging from technical constraints, socio-economic factors to environmental factors. This further confirms the small-scale production, resources are mostly allocated to various uses on the basis of their marginal shadow prices thereby preventing the farmers from reading the efficiency frontier (Udoh and Akintola, 2001a; Etim et al 2005). CONCLUSION The study analyzed sources of technical efficiency among subsistence maize farmers in Uyo Nigeria using parametric estimation techniques. The parameters of the maximum likelihood and explainers of inefficiency estimates are asymptomatically efficient, unbiased and consistent and were obtained using Cobb-Douglas production function estimated by maximum likelihood estimation method. Farmers resource use efficiency indices reveals a mean technical

5 52 Table 2.Maximum Likelihood Estimates and Inefficiency Function z Variable Coefficients Asymptotic t-value Production Function Constant term ( o) Land ( 1) Labour ( 2) Capital ( 3) Inorganic fertilizer ( 4) Seeds ( 5) Explainers of Inefficiency Intercept ( o) Age ( 1) Technical Assistance ( 2) Housing size ( 3) Credit ( 4) Market ( 5) Diagnostic Statistics Sigma-square ( s 2 ) Gamma z All explanatory variables are in natural logarithms The value of sigma square is statistically significant at =0.01 Asterisk indicate significant ***1%, **5%, *10% *** ** 4.560*** *** ** *** * * *** Table 3. Farmers Resource Use Efficiency Indices Efficiency class Frequency Percentage Total Mean efficiency = 0.71 Minimum = 0.01 Maximum = 0.98 efficiency of 0.71 implying that production can still be increased by 29 percent using available technology. Results show that farm size, labour seeds, age, technical assistance, access to credit and market have significant impact on technical efficiency. Road infrastructure should be developed particularly in rural areas (where most of farming activities take place) to provide easy accessibility to market for farmers to readily dispose their farm products. This will not only increase the incomes of farmers but will raise their economic wellbeing. Findings emphasize the need for appropriate policy intervention that will curb farmers technical inefficiency in production and poverty among the farmers. REFERENCES Apezteguia BI, GarateMP (1997). Technical efficiency in the Spanish agro and food industry, Agric. Econs. 16:

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