Robustness of Recommended Farm Plans in England under Climate Change: A Monte Carlo Simulation
- Cite this article as:
- Gibbons, J.M. & Ramsden, S.J. Climatic Change (2005) 68: 113. doi:10.1007/s10584-005-1585-3
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A methodology is described for estimating robustness of recommended farm plans under climate change while maintaining a meaningful representation of the underlying farm system. Monte Carlo Simulation (MCS) of crop yield data is used in conjunction with a fully specified farm-level model and output from a field worktime model. Estimates of farm net margin, enterprise mix (choice and area of enterprises), labour, machinery, storage and animal housing under mean crop yields and field worktimes for current (2000s) and 2050s conditions are generated. MCS is used to estimate the effect of crop yield variation on farm profitability and enterprise mix for the same periods by running the farm-level model with no constraints and running it constrained to the mean data plan. Estimates of robustness, measured as the percentage difference and the probability of exceeding the mean farm net-margin, were calculated from the outputs from these runs. For three representative farm types, mean farm net margin increased; however changes in robustness as shown by percentage difference in farm net margin depended on farm type while the probability of exceeding the mean plan net-margin decreased by 2050 indicating an increase in robustness. The most robust farm type had a diversified mix of enterprises and required no additional fixed resources by the 2050s. The least robust farm type was in a marginal location and mean plan recommendations for the 2050s required additional investment in fixed resources, particularly irrigation. It is concluded that the information provided by the methodology would be particularly useful to farmers: where mean data plans are not robust, MCS results could be used with financial planning techniques to minimise the impact of variability, rather than using high cost inputs to reduce variability per se.