An Agent-Based Model-Driven Decision Support System for Assessment of Agricultural Vulnerability of Sugarcane Facing Climatic Change

Abstract

In recent years, there have been significant changes in weather patterns, mainly caused by sharp increases in temperature, increases in carbon dioxide, and fluctuations in precipitation levels, negatively impacting agricultural production. Agricultural systems are characterized by being vulnerable to the variation of biophysical and socioeconomic factors involved in the development of agricultural activities. Agent-based models (ABMs) enable the study, analysis, and management of ecosystems through their ability to represent networks and their spatial nature. In this research, an ABM is developed to evaluate the behavior and determine the vulnerability in the sugarcane agricultural system; allowing the capitalization of knowledge through characteristics such as social ability and autonomy of the modeled agents through fuzzy logic and system dynamics. The methodology used includes information networks for a dynamic assessment of agricultural risk modeled by time series, system dynamics, uncertain parameters, and experience; which are developed in three stages: vulnerability indicators, crop vulnerability, and total system vulnerability. The development of ABM, a greater impact on the environmental contingency is noted due to the increase in greenhouse gas emissions and the exponential increase in extreme meteorological phenomena threatening the cultivation of sugarcane, making the agricultural sector more vulnerable and reducing the yield of the harvest.

Evaluation of Cropland System Resilience to Climate Change at Municipal Scale Through Robustness, Adaptability, and Transformability: A Case Study of Hubei Province, China

“…Although less attention has been paid to the evaluation of CSRCC, we can refer to research on similar socio-ecological system resilience, such as farming system resilience and agricultural system resilience. Some evaluated socio-ecological resilience through a single indicator (Zampieri et al, 2021), fuzzy assessment based on an index system (Mutabazi et al, 2015;Zampieri et al, 2020), and decision behavior modeling (Badillo-Márquez et al, 2021;Herrera et al, 2022). Other studies attempted to innovate the theoretical framework for the evaluation (Altieri et al, 2015).…”

The Application of Artificial Intelligence Models for Food Security: A Review

“…The impact of climate change on crop production, soil health, pest and disease patterns, and the vulnerability of crops [60,76,77].…”