  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.
