Technical Efficiency of Small-Holder Cocoyam Farmers in Anambra State, Nigeria: Implications for Agricultural Extension Policy
This study employed the Cobb-Douglas stochastic frontier production function to measure the level of technical efficiency in small-holder cocoyam production in Anambra state, Nigeria. A multi-stage random sampling technique was used to select 120 cocoyam farmers in the state in 2005 and from them input-output data were obtained using the cost-route approach. The parameters of the stochastic frontier production function were estimated using the maximum likelihood method. The result of the analysis shows that individual farm level technical efficiency was about 95%. The study found education and farming experience to be positively and significantly related to technical efficiency at 1% while practice index, fertilizer use and membership of cooperative societies also had a direct relationship with technical efficiency and were significant at 5% level. Age and farm size had an indirect relationship with technical efficiency and was significant at 1% and 5% level respectively. There were no significant relationship between technical efficiency and knowledge index, credit access and family size. Expected increases in agriculture require increase in agricultural productivity. In other words, agricultural productivity very much depends on the efficiency of the production process. Hence, policies designed to educate people through proper agricultural extension services will have a great impact in increasing the level of efficiency and hence agricultural productivity of these farmers.
|Date of creation:||Sep 2006|
|Date of revision:|
|Publication status:||Published in Journal of Agricultural Extension 1.12(2008): pp. 107-116|
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- Lau, Lawrence J & Yotopoulos, Pan A, 1971. "A Test for Relative Efficiency and Application to Indian Agriculture," American Economic Review, American Economic Association, vol. 61(1), pages 94-109, March.
- Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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