Research Article
Technical Efficiency and Productivity of Yam in Kogi State Nigeria
Benson Idahosa University, Benin City, Edo State, Nigeria
Sylvester I. Orewa
Benson Idahosa University, Benin City, Edo State, Nigeria
Yam is a highly valued staple food crop in Nigeria with the bulk of it consumed as fresh tuber. Sub-Saharan Africa currently produce about 90% of the worlds total yam output while the rest is grown in the West Indies and parts of Asian, South and Central America. Over 600 yam species are currently grown all around the world but only three species are known to be grown in West Africa. The species are (Dioscorea rotundata) white yam (Dioscorea cayenesis) yellow yam and (Discorea alata) water yam, (IITA, 1998). In Nigeria the species cultivated are D. rotundata (Okaka et al., 1991), D. cayenesis (yellow or guinea yam) and D. alata (Okaka and Anajekwu, 1990). It is estimated also that 4 million hectares of land is cultivated with yam the world over of which about 69% of this cultivated land is in Nigeria (FAO, 2005). Yam production in Nigeria has more than tripled over the past forty years, from 6.7 million tons/year in 1961 to 27 million tons/year in 2001. This increase in output is attributed more to the large area planted to yam than with increased productivity. Though the area cultivated to yam is still being increased, production growth rate has declined tremendously from the average of 27.5% between 1986 and 1990 to 3.5% in the 1996-99 periods. Decline in average yield per hectare has been more drastic; it dropped from 14.9% in 1986-90 to -2.5% in 1996-99. The observed productivity decline in Nigeria represents a major challenge to increasing yam production and its availability as food in the country (CBN, 2002; Amegbeto et al., 2002). The objectives of this study are to examine the socioeconomic characteristics of yam farmers in Kogi State Nigeria, to determine the return to scale and technical efficiency of yam farmers in the study area.
The study was carried out in the Eastern part of Kogi State a major yam producing areas in Nigeria. Kogi State is made up of twenty (20) L.G.A. (namely; Adavi, Ajaokuta, Ijumu, Bassa, Dekina Idah, Ankpa East Yagba, West Yagba, and Kogi. Others are Ofu, Okehi, Okene, Olamaboro, Olale igalometa Ibaji Ogori/Mongongo and Mopamuro). The L.G.A selected for the study are; Omala, Dekiha, Idah, Ankpa and Ofu. Based on the 1991 Census the State has a Population of 2,099,046 people made up of 1,055964 males and 1,043,082 females. The State is blessed with suitable ecological and climatic conditions and this is attested to by the wide variety of crops grown in the area such as yam. Geographically the state lies between longitude 6 and 9° East and latitude 4 and 7° North of the Greenwich Meridian.
A multi stage sampling technique was adopted in selecting yam producers within the yam producing areas. The first stage was a purposive sampling of five Local Government Areas (LGA) out of nine in Kogi East. This was based on the population of yam producers and the availability of market for yam products. The second stage involves a simple random sampling of five villages from each LGA and 8 ADP contact farmers from each village. In all 200 farmers were interviewed using enumerators who administered structured questionnaires, however only 144 questionnaires were suitable for analysis.
Descriptive statistics (Mean, Standard deviation etc.) was used to analyse the socio-economic characteristics of the farmers in the study area. The stochastic frontier production function was used to analyse the efficiency of inputs used in the production of yam in the study area. A production frontier is defined in terms of the maximum output that can be achieved from a set of inputs given the technology available to the farm. The production technology of the farmers was specified by the Cobb-Douglas frontier production function defined by,
(1) |
Log | = | Natural logarithm. |
Y | = | Value of yam produced in Naira/ha |
X1 | = | Area cultivated with yam (Hectare) |
X2 | = | Cost of planting materials (Seed yam)/ha |
X3 | = | Labour cost in Naira/ha |
β0, β1, β2 and β3 | = | Regression Coefficients |
Vi | = | Are random variables which are assumed to be independent of μi, identical and normally distributed with zero mean and constant variance N (0,σV2). |
Ui | = | Which are non-negative random variables which are assumed to account for technical inefficiency in production and are often assumed to be independent of Vi such that U is the non-negative truncated (at zero) of half normal distribution with |N (0,σU2)|. (Coelli, 1994) and (Battese and Coelli, 1995). |
The inefficiency of production, Ui was model in terms of the factors that are assumed to affect the efficiency of production of the farmers. Such factors are related to the socioeconomic variables of the farmers. The determinant of technical inefficiency is defined by:
(2) |
Where:
Z1 | = | Gender |
Z2 | = | Age |
Z3 | = | Marital status |
Z4 | = | Family size |
Z5 | = | Educational level |
Z6 | = | Farming experience |
μ | = | Technical inefficiency |
δi | = | Inefficiency parameters |
These variables are assumed to influence technical efficiency of the farmers. Also the gamma (Y = δ2/δ2 s) and Sigma squared (δ2) which is a summation of U and V variance was determined. The Maximum Likelihood Estimate Method using the computer FRONTIER version 4.1 was used to estimate the parameters of the stochastic Frontier Production function (Coelli, 1994).
RESULTS AND DISCUSSION
Socio-economic characteristics of farmers: The results revealed that 98.6 and 2.4% of respondents are male and female, respectively. The mean age of farmers is 53 years and in terms of family size, an average of 9 persons per family was recorded. The average year of formal education of farmers in the study area was about 4 years (3.9 years). This mean statistics shows that farmers in the study area have less education. The average farming experience for yam farmers was 25 years. This shows that farmers from Kogi State have long been involved in the yam business (Table 1).
Efficiency: The results revealed that the all independent variables (Farm size, planting materials and Labour) have positive signs. The OLS functions for farm size and planting materials were statistically significant at 5 and 1% levels of significance, respectively. Also, the MLS functions for farm size and planting materials were statistically significant at 1% level of significance. The results obtained from the stochastic production function as shown by the Ordinary Least Squares (OLS) and the Maximum Likelihood Estimate (MLE) for yam production in Kogi State is presented in Table 2. The sign of the coefficient of the variable in the inefficiency model is very important in explaining the observed level of technical efficiency of the farmers. A negative coefficient implies that the variable has the effect of reducing technical inefficiency. While a positive coefficient has the effect of increasing technical inefficiency.
Table 1: | Mean statistics of socioeconomic variables of yam farmers in kogi state |
Figures in parenthesis are percentages |
Table 2: | Stochastic estimation of production function for kogi states |
Figures in parenthesis are t-ratio; *Significant at 5% level; **Significant at 1% level |
Table 2 reveals that age and farming experience contributed negatively to farmers inefficiency. This means that farming experience and age will lead to decline in technical inefficiency. This result has also confirmed a priori expectation. More experienced farmers are expected to have higher level of technical efficiency than less experienced farmers. While gender, family size, marital status and education contributed positively to farmers inefficiency. Only Gender and Age were statistically significant at 5% level of significant.
The sigma square (σ 2) is statistically different from zero at 1% level thus gives credibility to the goodness of fit of the model from the MLE as well as the correctness of the specific distributional assumption of the composite error term (V-U). The variance ratio (γ = σ 2/σ 2 s) estimated was 0.98 at 1% level. This infers that 98% of the variation in yam output among the farmers in Kogi State was due to differences in their technical efficiencies.
Range of technical efficiency: Looking at the distribution minority of the farmers in Kogi State can be said to be more technically efficient (Table 3). The range shows that 23.5% of the farmers in Kogi State fall within the 81-90% range of technical efficiency. The technical efficiency of farmers in Kogi State shows that yam farmers were not technically efficient.
Table 3: | Range of technical efficiency (%) in Kogi states |
Figures in parenthesis are percentages |
Table 4: | Elasticity and return to scale |
Technical efficiency of yam producers in Kogi States was computer as shown in Table 2.
Return to scale and productivity: The return-to-scale parameter indicates what happen to yam output as inputs are increased simultaneously. The results of the data analysis from the stochastic estimate showed that yam production is in the stage II of the production process with return to scale of 0.30. This indicates a positive decreasing return to scale. Table 4 shows the elasticity and return to scale of yam in the study area.