Short Term Prediction of Agricultural Structural Change using Farm Accountancy Data Network and Farm Structure Survey Data

Abstract

The prediction of farm structural change is of large interest at EU policy level, but available methods are limited regarding the joint and consistent use of available data sources. This paper develops a Bayesian Markov framework for short-term prediction of farm numbers that allows combining two asynchronous data sources in a single estimation. Specifically, the approach allows combining aggregated Farm Structure Survey (FSS) macro data, available every two to three years, with individual farm level Farm Accountancy Data Network (FADN) micro data, available on a yearly basis. A Bayesian predictive distribution is derived from which point predictions such as mean and other moments are obtained. The proposed approach is evaluated in an out-of-sample prediction exercise of farm numbers in German regions and compared to linear and geometric predic-tion as well as a “no-change” prediction of farm numbers. Results show that the proposed approach outperforms the geometric prediction but does not significantly improve upon the linear prediction and a prediction of no change in this context.