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.

本项研究旨在探讨如何在不增加农民损失的前提下，寻找最佳农业肥料管理策略。利用一种名为“赌博算法”的模型，通过风险感知的度量标准和新颖的农学指标来比较农业肥料的效果，提高农户抵抗风险的能力。

引导农民识别最佳作物管理的高效风险意识策略