Economic forecasting in agriculture

Forecasts of agricultural production and prices are intended to be useful for farmers, governments, and agribusiness industries. Because of the special position of food production in a nation’s security, governments have become both principal suppliers and main users of agricultural forecasts. They need internal forecasts to execute policies that provide technical and market support for the agricultural sector. Government publications routinely provide private decision makers with commodity price and output forecasts at regional and national levels and at various horizons. Routine forecasts are not found in the agricultural economics journals that are the sources for most of this review. The review emphasizes methodological contributions and changes. Short-term output or ‘outlook’ forecasting uses a unique form of leading indicator. Because the production process has long been well understood, production forecasts are based on the quantifiable features of livestock or a growing crop. Price forecasts are largely made by conventional econometric methods, with time series approaches occupying minor roles. Because of the dominance of agricultural economists, there has been an overemphasis on explanation, and little interest in the predictive power of models. In recent years, some agricultural economists have begun to compare forecasts from different methods. Findings generally conform to widely held beliefs. For short-term forecasting, combining leads to more accurate forecasts, better than those produced by vector autoregression, which surprisingly is the best single method. Also surprising is that econometric models and univariate methods both do badly compared with naive models. (This paper is followed by discussion by: David A. Bessler (Texas A&M University) pp. 137-138, John Freebairn (Monash University) pp. 139-142 and William G. Tomek (Cornell University) pp. 143-145.)