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Identification of best performing fertilizer practices with on-farm trials is challenging, in particular in rainfed farming due to weather uncertainty. However, it remains crucial to test a range of viable practices to ascertain their performances, given that they are not known beforehand. This process also involves the testing of practices that could potentially yield inferior results in comparison to the best available practice(s). To identify a best management practice, an “intuitive strategy” typically sets up multi-year, multi-location field trials, wherein each practice is tested in a proportionally equal manner over a set number of years. Our objective was to provide an identification strategy for nitrogen fertilizer management designing a bandit learning algorithm. We aimed for the bandit algorithm to be better at minimizing farmers’ losses occurring from the testing of management practices that do not perform best, compared with the …