Abstract
The physical environment of farming systems is rarely considered when conducting farm level efficiency analysis, which is likely to lead to bias of performance measurements based on benchmarking methods such as Data Envelopment Analysis (DEA). We incorporate variations of the physical environment (rainfall and length of growing season) through the specifications of the linear programming in DEA to investigate performance measurement bias. The derived technical efficiency estimates are obtained using a sub-vector DEA which ensures farms are compared in a homogenous environment (i.e. accounting for differences in rainfall levels amongst distinct farm units). We use the Farm Business Survey to analyse a representative sample of 245 cereal farms in the East Anglia region between 2009 and 2010. Efficiency rankings obtained from a standard DEA model and a non-discretionary DEA model that incorporates the variations in the physical environment. We show that incorporating rainfall and the length of the growing season as non-discretionary inputs into the production function had significantly altered the farm efficiency ranking between the two models. Hence, to improve extension services to farmers and to reduce biased estimates of farm technical efficiency, variations in environmental conditions need to be integral to the analysis of efficiency.
Highlights
Climate change and the increased demand for food are two of the most important future global challenges for agricultural systems
In all cases the SBV model is skewed towards unity. This means that incorporating the additional restrictions in Technical efficiency distribution Conventional Data Envelopment Analysis (DEA) model
A different approach that accounts for both spatial dependence and spatial heterogeneity is presented in the recent works of Andreano et al (2017), Billé et al (2017) and Billé et al (2018)
Summary
Climate change (expressed in the short term as extreme weather phenomena) and the increased demand for food are two of the most important future global challenges for agricultural systems. According to the Department for the Environment, Food and Rural Affairs (Defra, UK), agricultural Total Factor Productivity (TFP) has realised a significant drop during the period 2007-2013 mainly due to the frequent appearance of extreme weather phenomena such as floods (2007, 2012, 2013) and persistent drought periods (2010, 2011, 2012). According to Defra (2013), these variations in the observed agricultural TFP are due to phenomena and disease outbreaks which are usually out of the sphere of control of farm managers. We argue that these variations in the physical environment should be isolated in cases where we are interested in measuring farm performance only (e.g. policies aiming to identify farmers that could potentially perform better regardless of the weather conditions)
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