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Identifying performance benchmarks for Ghanian farm households through efficiency analysis and whole-farm modeling
This research has two primary objectives. The first is the analysis of efficiency in Ghanaian agriculture. This will use farm household data and fit production technology frontiers to measure technical efficiency and investigate the influences on a household’s efficiency. We will estimate the parameters of the production frontier using Bayesian econometrics. The Bayesian frontier generates the entire distributions rather than the point estimates of efficiency measures (using classical econometrics).
The second objective of the research is to build a whole farm model for agriculture using these estimated production frontiers with additional data on cropping and livestock production practices. This model would be used to optimize land use and the synergy between cropping and livestock enterprises. The model would provide a much needed research/policy evaluation tool in Ghana for assessing the impact of changes in technologies or the context within which land use decisions are made in a holistic manner.
Agriculture is the mainstay of the economy of Ghana, employing over 60% of the population and generating a significant part of the GDP. The bulk of the country’s output of agriculture is produced by smallholder farmers with limited resources (land, labour, capital, management) under challenging environmental conditions. Little is known about the efficiency performance of these farms. Studies that have tried to understand the performance of smallholder farms have used partial productivity measures such as crop yield. Those that have attempted to model the whole farm (Runge-Metzger, 1988) have not taken into consideration the contribution of livestock enterprises, although these form an important and integral part of the farming systems of smallholders. This study will use data collected from farm households for whole farm modelling that will fill a knowledge gap that currently exists in the literature on Ghana.