Economic Value of Seasonal Climate Forecasts for Agriculture: Review of Ex-Ante Assessments and Recommendations for Future Research

Abstract

Abstract Advanced information in the form of seasonal climate forecasts has the potential to improve farmers’ decision making, leading to increases in farm profits. Interdisciplinary initiatives seeking to understand and exploit the potential benefits of seasonal forecasts for agriculture have produced a number of quantitative ex-ante assessments of the economic value of seasonal climate forecasts. The realism, robustness, and credibility of such assessments become increasingly important as efforts shift from basic research toward applied research and implementation. This paper surveys published evidence about the economic value of seasonal climate forecasts for agriculture, characterizing the agricultural systems, approaches followed, and scales of analysis. The climate forecast valuation literature has contributed insights into the influence of forecast characteristics, risk attitudes, insurance, policy, and the scale of adoption on the value of forecasts. Key innovations in the more recent literature include explicit treatment of the uncertainty of forecast value estimates, incorporation of elicited management responses into bioeconomic modeling, and treatment of environmental impacts, in addition to financial outcomes of forecast response. It is argued that the picture of the value of seasonal forecasts for agriculture is still incomplete and often biased, in part because of significant gaps in published valuation research. Key gaps include sampling of a narrow range of farming systems and locations, incorporation of an overly restricted set of potential management responses, failure to consider forecast responses that could lead to “regime shifts,” and failure to incorporate state-of-the-art developments in seasonal forecasting. This paper concludes with six recommendations to enhance the realism, robustness, and credibility of ex-ante valuation of seasonal climate forecasts.