This paper presents a modeling framework for simulating the decision-making processes of artificial farms populating an agent-based model for the Italian wheat production system. The decision process is based on a mathematical programming model with which farms (i.e., agents) decide the target yield (production per hectare) and the mix of inputs needed to obtain such production, namely 1) fertilizers, 2) herbicides, and 3) insecticides. The environmental impacts of conventional production practices are assessed through a Life Cycle Assessment (LCA), using the ReCiPe 2016 methodology at the Endpoint level. Agents are made aware of the environmental consequences of their choices through two indicators: Disability-Adjusted Life Years (DALYs), which capture human health impacts, and the number of species lost per year, reflecting impacts on ecosystems. By internalizing this information, agents can make more balanced and sustainable production decisions.

Accounting for Environmental Awareness in Wheat Production Through Life Cycle Assessment

Giulioni, Gianfranco
Primo
;
2026-01-01

Abstract

This paper presents a modeling framework for simulating the decision-making processes of artificial farms populating an agent-based model for the Italian wheat production system. The decision process is based on a mathematical programming model with which farms (i.e., agents) decide the target yield (production per hectare) and the mix of inputs needed to obtain such production, namely 1) fertilizers, 2) herbicides, and 3) insecticides. The environmental impacts of conventional production practices are assessed through a Life Cycle Assessment (LCA), using the ReCiPe 2016 methodology at the Endpoint level. Agents are made aware of the environmental consequences of their choices through two indicators: Disability-Adjusted Life Years (DALYs), which capture human health impacts, and the number of species lost per year, reflecting impacts on ecosystems. By internalizing this information, agents can make more balanced and sustainable production decisions.
2026
9783032317117
9783032317124
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11564/892793
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