Bayesian technical efficiency analysis of groundnut production in Ghana

Abstract

This paper considered Bayesian Stochastic Frontier Model to analyse technical efficiency and their determinants of groundnut farmers in Ghana. The paper used a cross-sectional data of three-hundred (300) observations to obtain posterior distributions of the farmers’ technical efficiency levels. All computations were done using Markov Chain Monte Carlo methods (MCMC). Results revealed that the groundnut farmers produce at an increasing return to scale of 1.10. Average technical efficiency of the farmers was found to be 70.5%, ranging from a minimum of 13.0% to a maximum of 95.1%. Frequency of extension visit, educational level and gender of the farmers were identified to significantly explain inefficiency of the farmers. The paper concluded that groundnut farmers in the northern part of Ghana are operating in the first stage of the production function and could increase their frontier output by 29.5%.

Keywords:

• Bayesian stochastic frontier model
• posterior distributions
• Markov Chain Monte Carlo Methods
• return to scale

PUBLIC INTEREST STATEMENT

Groundnut is an important leguminous crop in Ghana in terms of its role in enhancing food security of the rural farmers. Evidence suggests that achieving optimum productivity among groundnut farmers in Sub-Sahara Africa remains a bottleneck. This study sought to estimate the productivity and efficiency levels of the groundnut farmers in Ghana as well as the factors that determines groundnut output of farmers in Ghana. The result of the study indicates that groundnut farmers are producing at an increasing return to scale that demands expansion of operation to achieve optimum output. The estimated mean technical efficiency value revealed that given the technology adopted and the level of inputs, groundnut farmers obtained a yield of 70.5% of the achievable output. Extension visit, education and gender were the factors that explains the differences in the efficiency levels of farmers. We therefore recommend increase in scale of operation by farmers, regular extension visit to farmers and facilitation of information to farmers who did not attain formal education.

Acknowledgement

The authors declare that this research has not receive any funding for the executing of the work nor has it receive any funding for the publication cost.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Dominic Chakuri

Dominic Chakuri is a PhD student at the Department of Agricultural Economics and Agribusiness, University of Ghana, Legon, Accra. His research interest includes productivity and efficiency analysis, poverty analysis, and environmental sustainability.

Freda Elikplim Asem

Freda Elikplim Asem is a lecturer at the Department of Agricultural Economics and Agribusiness, University of Ghana. She holds a PhD in Development Studies, from the University of Ghana. She is passionate about agriculture development in Ghana and Africa at large, particularly in relation to small holder farmer. Her research interests include agricultural marketing, agribusiness, agrifood systems and agricultural value chains analysis.

Edward Ebo Onumah

Edward Ebo Onumah is a Senior lecturer at the Department of Agricultural Economics and Agribusiness, University of Ghana. He holds a PhD in Agricultural Sciences with a specialty in Agricultural Economics from Georg-August University of Göttingen, Germany. His research interest includes productivity and efficiency analysis, agricultural production risk and uncertainty, food security investigation, microfinance and poverty analysis, economics of aquaculture and fisheries investigation, and agricultural trade and market analysis.