Development of a proxy for technical efficiency for specialised grain farmers

• New integrative approach that links econometrics, crop simulation and remote sensing.
• Conceptual framework developed that allows for the inference of technical efficiency.
• Framework will help quantifying technical efficiency at non-surveyed locations.

Technical efficiency is the effectiveness with which a given set of inputs is used to produce one or more outputs. As such it is an important indicator that helps to differentiate between production entities that represent current best practice (or operate close to it) from those that do not. State of the art methods to estimate technical efficiency of a production entity are econometric frontier techniques such as data envelopment analysis (DEA) or stochastic frontier analysis (SFA) where observational data on inputs and outputs are analysed. While efficiency scores are then known for the observational units, efficiency scores cannot simply be interpolated to estimate efficiency scores for production units that have not been part of a survey. This paper focuses the development of a proxy measure of technical efficiency for specialised grain farms where no observational data are available for. The proxy measure is based upon crop simulation results and technical efficiencies that are estimated from available survey data. A moderate to strong correlation (0.5–0.7) is found between metrics inferred from crop simulation and technical efficiency scores. Fitted linear models with technical efficiency as the predictor variable returned an R2 lower equal to 0.42 (linear models have zero intercept). To apply the linear model beyond observed locations, the integration of a remote sensing component is discussed and put into a conceptual framework. The suggested method would be suitable to be applied on a regional level and will help improving the regionalisation of technical efficiency. The findings are based on the analysis of a 4 year plot level survey dataset from Australian grain specialists. Findings are to be considered preliminary at this stage and more research that involves a remote sensing component is needed to confirm the applicability of the suggested framework.