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

TL;DR

This paper introduces a bandit algorithm-based strategy for identifying optimal crop management practices that minimizes farmers' losses and accounts for risk, outperforming traditional multi-year trial methods.

Contribution

The study presents a novel risk-aware identification strategy using a bandit algorithm combined with a modified crop model, improving upon intuitive trial approaches.

Findings

Bandit algorithm outperforms intuitive strategies in risk mitigation.

The approach increases farmers' protection against worst outcomes.

It introduces the Yield Excess (YE) metric combining yield and nitrogen efficiency.

Abstract

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.