KEYWORDS:
Resource-use efficiency, Stochastic Frontier Analysis, adoption, tobit regression, probit regression
DOI NUMBER:
10.15414/raae.2016.19.02.29-38
ABSTRACT:
This study determined the technical efficiency (TE) of production of Quality Protein Maize (QPM) and the effect on the adoption of QPM in Oyo State, Nigeria. QPM is an improved maize variety developed to reduce protein deficiency problems. A total of 100 maize farmers were sampled through a two-stage sampling procedure. Stochastic frontier approach using maximum likelihood estimation (MLE) was used to analyse the TE in the production of QPM, while probit regression was used to determine the effect of TE and other socioeconomic characteristics of the respondents on the adoption of QPM. The results revealed a mean TE of 0.89 and 0.78 for adopters and non-adopters of QPM respectively. This implied that adopters of QPM are more technically efficient than the non-adopters. Quantity of seed planted and fertilizer directly and significantly affected the TE of QPM while gross margin of maize farmers and income from other sources (at P<0.05), the level of education of farmers and QPM farm size (P<0.01) have significant and a negative effects on technical inefficiency from the results of the Tobit regression. The age (P<0.05) of the farmer has direct effect on technical inefficiency. In conclusion, TE, level of output, information availability on QPM and early maturity were significant determinants of QPM rather than the gross margin of production. Farmers decide to adopt QPM technology because of the high level of technical efficiency in the production of this variety. Their output from QPM can be increased by 11 percent, while the non-adopters can increase their maize output by 22 percent using the available technology.
Please Cite this Article as:
Abiodun Elijah OBAYELU, Cocou Muriel Dorian MONCHO, Chukwunoso Christopher DIAI (2016) Technical Efficiency Of Production Of Quality Protein Maize Between Adopters And Non-adopters, And The Determinants In Oyo State, Nigeria. Review of Agricultural and Applied Economics. XIX (Number 2, 2016): 29-38. doi: 10.15414/raae.2016.19.02.29-38
URL for sharing:
https://roaae.org/1336-9261/doi/abs/10.15414/raae.2016.19.02.29-38
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