A new adaptive identification strategy of best crop management with farmers
Gautron, Romain | Baudry, Dorian | Adam, Myriam | Falconnier, Gatien, N | Hoogenboom, Gerrit | King, Brian | Corbeels, Marc | Consultative Group on International Agricultural Research [CGIAR] (CGIAR) | The Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) [Cali] ; Alliance of Bioversity International and the International Center for Tropical Agriculture (CIAT) [Rome] (Alliance) ; Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR) | Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL) ; Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS) | Amélioration génétique et adaptation des plantes méditerranéennes et tropicales (UMR AGAP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier ; Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Montpellier (UM) | Département Systèmes Biologiques (Cirad-BIOS) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | Institut de l'Environnement et Recherches Agricoles [Ouagadougou] (INERA) ; Centre national de la recherche scientifique et technologique [Ouagadougou] (CNRST) | Agroécologie et intensification durables des cultures annuelles (UPR AIDA) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad) | International Maize and Wheat Improvement Center [Zimbabwe] (CIMMYT) ; International Maize and Wheat Improvement Center (CIMMYT) ; Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR) | University of Florida [Gainesville] (UF) | International Institute of Tropical Agriculture (IITA Kenya) ; International Institute of Tropical Agriculture [Nigeria] (IITA) ; Consultative Group on International Agricultural Research [CGIAR] (CGIAR)-Consultative Group on International Agricultural Research [CGIAR] (CGIAR) | The French Agricultural Research Center for International Development (CIRAD). | The Consultative Group for International Agricultural Research (CGIAR) Platform for Big Data in Agriculture. | The French Ministry of Higher Education and Research, | Hauts-de-France region | Inria within the Scool team project | MEL métropole de Lille
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Показать больше [+] Меньше [-]Английский. 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 "intuitive strategy" that was formulized as the ExploreThen-Commit strategy. Our case study was for maize production in southern Mali. Bandit framework is a machine learning approach in which an agent learns from the feedback over time and accordingly selects actions in order to maximize its cumulative reward in the long term. To mimic the maize responses to nitrogen fertilization, we used the Decision Support System for Agrotechnology Transfer (DSSAT) crop model. We compared nitrogen fertilizer practices using a risk-aware measure, the Conditional Value-at-Risk (CVaR), and a novel agronomic metric, the Yield Excess (YE). The YE accounts for both grain yield and agronomic nitrogen use efficiency. The bandit algorithm performed better than the intuitive strategy: it minimized farmers' yield losses during the identification process. This study is a methodological step which opens up new horizons for risk-aware identification of the performance of a range of crop management practices in real conditions.
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