Profitable use of seasonal climate forecasts: farm management case studies in the Philippines and Australia

Rainfed agriculture in the Philippines and eastern Australia is greatly affected by an El Nino Southern Oscillation (ENSO) phenomenon. Advances in the science of seasonal climate forecast (SCF) give farmers greater ability to manage risk brought about by highly variable climatic conditions. This study aimed to assess the effects of perfect and currently available seasonal climate forecasts (SCFs) on farm profitability in the Philippines and Australia. Specifically, it attempted: 1) describe the dominant corn-based cropping patterns in the Philippines and opportunity cropping strategies in Australia; 2) present a valuation framework for estimating the economic benefits of SCFs information; 3) assess SCF skill in terms of forecast versus actual rainfall; 4) assess the economic value of SCFs to corn farmers in the Philippines and opportunity cropping in Australia; and 5) draw policy implications on how to use SCF profitably in agriculture for both countries. A stochastic decision tree analysis within the framework of expected monetary value of crop choice was applied for corn production decision in the Philippines. The RAINMAN International (ACIAR version) was used to provide the probability of a good, average and poor season based on the average SO1 system of forecast. The stochastic gross margin for the outcome of each season was calculated using the triangular distribution function of SIMETAR to generate the cumulative distribution of expected value of choice for both with and without climate forecast scenario. The value of SCF was derived as the difference between the expected value of choice with forecast and the expected value of action without forecast. For the Australian case study, the value of SCF was computed using stochastic dominance analysis. Yield distribution functions for each forecast type in the two forecasting systems, SO1 average and SO1 phase, were derived from APSIM simulations, which consequently converted to gross margins equivalent for different soil moisture regimes. Results showed that in Leyte, Philippines, SCFs were found to have an average value of around $AU 3/ha/season or P119/ha/season. Forecast was found to be valuable in deciding when to plant corn. In the Australian context, the average value of SCF information ranged from $0 to $8/ha depending on soil moisture regime of the soil. SCFs were found to be of some value to farmers in better managing their opportunity cropping systems. In both countries, the value of a whole forecasting system is often driven by just one or two forecast types. Some forecast types do not have a sufficient influence on expected yields and returns to change a decision. Defining the acceptable level of forecast skill has implications for forecast valuation. Growers need to be conscious of when to apply and when to disregard the information provided by SCFs.