A stochastic dynamic programming framework for weed control decision making: an application to Avena fatua L.
构建了一个随机多期决策模型，用于分析澳大利亚南部受野燕麦侵扰的连续小麦种植系统，通过动态规划与生物经济模拟结合，优化除草剂用量，发现考虑未来利润的决策能带来显著经济收益。
Abstract This paper develops a stochastic multi‐period decision model to analyse a continuous wheat cropping system infested by wild oats ( Avena fatua L.), in southern Australia. The multi‐period solutions is obtained by employing a dynamic programming model in conjunction with a bioeconomic simulation model. An empirically estimated dose response function is used to derive the optimal herbicide rate. Uncertainties due to environmental effects on the performance of herbicide and crop yields are modelled and optimal decision rules derived. The results indicate that substantial economic gains can be realised if herbicide dose decisions are taken by considering future profit effects of current decisions, as opposed to the more common approach of only considering the current‐period effect.