制定高效且具有风险意识的策略，指导农民确定最佳作物管理
Towards an efficient and risk aware strategy for guiding farmers in identifying best crop management
摘要：
在一系列与田间试验形成对比的施肥实践中确定最佳施肥实践具有挑战性，因为作物损失对农民来说代价高昂。为了确定最佳管理做法，一个“直观的策略”是设置多年的实地试验，每种做法的比例相等。我们的目标是提供一个识别策略，使用强盗算法，更好地减少农民的损失发生在识别过程中，相比“直观的策略”。我们使用了修改的农业技术转让决策支持系统（DSSAT）作物模型，以模仿田间试验的反应，在马里南部的案例研究。我们使用风险意识措施，条件风险价值（CVaR）和一种新的农艺指标，超额产量（YE）比较施肥措施。YE同时考虑了籽粒产量和氮素农学利用效率。强盗算法比直觉策略表现得更好：在大多数情况下，它增加了农民对最坏结果的保护。这项研究是一个方法的步骤，开辟了新的视野，风险意识合奏识别的性能对比作物管理措施在现实条件下。
英文摘要：
Identification of best performing fertilizer practices among a set of contrasting practices with field trials is challenging as crop losses are costly for farmers. To identify best management practices, an ''intuitive strategy'' would be to set multi-year field trials with equal proportion of each practice to test. Our objective was to provide an identification strategy using a bandit algorithm that was better at minimizing farmers' losses occurring during the identification, compared with the ''intuitive strategy''. We used a modification of the Decision Support Systems for Agro-Technological Transfer (DSSAT) crop model to mimic field trial responses, with a case-study in Southern Mali. We compared fertilizer practices using a risk-aware measure, the Conditional Value-at-Risk (CVaR), and a novel agronomic metric, the Yield Excess (YE). YE accounts for both grain yield and agronomic nitrogen use efficiency. The bandit-algorithm performed better than the intuitive strategy: it increased, in most cases, farmers' protection against worst outcomes. This study is a methodological step which opens up new horizons for risk-aware ensemble identification of the performance of contrasting crop management practices in real conditions.
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