Abstract
The study applies descriptive analysis, Slacks-Based Measure (SBM) of efficiency model and fractional regression model to data collected in 2016 using cross-sectional survey of maize producers in Nigeria. The purpose was to determine the impact of microfinance on the technical efficiency of maize producers and evaluates factors that influence inefficiency among credit beneficiaries and non-credit beneficiaries. Results show that the respective mean technical efficiency of credit beneficiaries and non-credit beneficiaries were 79 and 69%, which is far from the frontier technology. This means that technical efficiency can be improve by 21 and 31% respectively, with the same set of inputs. Slacks analysis shows that in order to attain optimum efficiency, credit beneficiaries should reduce fertilizer usage by 32.34%, seeds by 6.03%, labour by 7.79% and agrochemicals by 2.44% per hectare. Similarly, non-credit beneficiaries should reduce the usage of fertilizer slacks by about 19.48%, seeds by 2.73%, labour by 2.54% and agrochemicals slacks by 1.76% per hectare. Microfinance credit, household size, years of farming experience and education increases efficiency, while drought and age declines efficiency. Findings are useful to the farmers as appropriate input reduction for inefficient farms can be set to enable them attain optimum efficiency level. Maize producers should be encouraged to collect microfinance loan in order to increase their scale of operations and government in collaboration with research institutes should educate farmers on the actual input quantities to apply. This could help to reduce production costs, increase the farmers’ efficiency and provide maize to consumers at an affordable rate.
Highlights
Maize (Zea mays L.) is cultivated extensively all over the world in a series of agro-ecological environments occupying over 160 million hectares
Alene and Hassan (2006) compared the efficiency of traditional and hybrid maize production in eastern Ethiopia using DEA. These findings show that farmers are technically inefficient and as such they were able to attain a mean technical efficiency levels of 0.68 and 0.78, while Olarinde (2011) analyzed technical efficiency differentials and their determinants among maize farmers in Nigeria using SFA
Data defined on the interval [0, 1] such as DEA scores requires the use of regression models that are appropriate in dealing with fractional data in the second stage DEA analysis. the DEA results in this study reveals that all farms have scores above zero and since the second stage DEA analysis is been carried out to estimate the determinants of technical inefficiency, this implies that all farms that are operating at one are already efficient so there is no need to conduct further analysis
Summary
Maize (Zea mays L.) is cultivated extensively all over the world in a series of agro-ecological environments occupying over 160 million hectares. America produced about 52% of the total world maize production in the year 2014. This is followed by Asia (29.76%), Europe (11.03%), Africa (7.57%) and others (0.13%) respectively (FAOSTAT, 2015). East Africa happens to be the largest producer (32 million tons) which accounted for about 41% of the total maize produced in the year 2014, followed by West Africa (20 million tons) which is equivalent to 25.15%. According to FAOSTAT (2016), Nigeria is the leading maize producer in West Africa with about 7.2 million tons in 2016 (Table 1)
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