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Article Dans Une Revue European Journal of Agronomy Année : 2022

Maximize crop production and environmental sustainability: Insights from an ecophysiological model of plant-pest interactions and multi-criteria decision analysis

Résumé

Satisfying the demand for agricultural products while also protecting the environment from negative impacts of agriculture is a major challenge for crop management. We used an ecophysiological model of plant-pest interaction and multi-criteria decision analysis to optimize crop management when considering two contrasting objectives: (1) maximizing crop production and (2) minimizing environmental impact related to fertilization, irrigation and pesticide deployment. The model provides an indicator of crop production for 27 management scenarios, obtained combining three levels of fertilization, irrigation and pesticide use, respectively. We computed the environmental impact relevant to each management scenario by means of a weighted sum of costs assigned to fertilization, irrigation and pesticide use. We identified the optimal scenarios with respect to the considered objectives analysing the Pareto front. These scenarios were mostly characterized by high fertilization and no pesticide use. We evaluated the multi-functionality of the optimal scenarios by mean of the Gini coefficient: the scenario better assuring the equality between the two objectives was characterized by high fertilization, intermediate irrigation and no pesticide. Although our results remain qualitative and not immediately transferable to agronomic practices, our analytical framework provides a useful tool to evidence trade-offs among two contrasting objectives and provide solutions to act in an efficient way by leaving a certain degree of freedom to the decision maker.
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hal-03741954 , version 1 (31-01-2024)

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Marta Zaffaroni, Danièle Bevacqua. Maximize crop production and environmental sustainability: Insights from an ecophysiological model of plant-pest interactions and multi-criteria decision analysis. European Journal of Agronomy, 2022, 139, ⟨10.1016/j.eja.2022.126571⟩. ⟨hal-03741954⟩

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