Towards an efficient and risk aware strategy for guiding farmers in identifying best crop management

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.

Thompson Sampling for CVaR Bandits

Risk awareness is an important feature to formulate a variety of real world problems. In this paper we study a multi-arm bandit problem in which the quality of each arm is measured by the Conditional Value at Risk (CVaR) at some level {\alpha} of the reward distribution. While existing works in this setting mainly focus on Upper Confidence Bound algorithms, we introduce the first Thompson Sampling approaches for CVaR bandits. Building on a recent work by Riou and Honda (2020), we propose {\alpha}-NPTS for bounded rewards and {\alpha}-Multinomial-TS for multinomial distributions. We provide a novel lower bound on the CVaR regret which extends the concept of asymptotic optimality to CVaR bandits and prove that {\alpha}-Multinomial-TS is the first algorithm to achieve this lower bound. Finally, we demonstrate empirically the benefit of Thompson Sampling approaches over their UCB counterparts.