Artificial intelligence in agricultural management: A simulation model for efficiency and sustainability

Abstract

Agriculture faces critical challenges—climate change, food security, and resource degradation—that demand innovative solutions. This study analyzes, through a systematic literature review (46 articles, 2020–2025), the impact of artificial intelligence (AI) on agricultural systems, evaluating technologies such as machine learning, the Internet of Things (IoT), and precision agriculture. The results demonstrate that AI significantly optimizes productivity, with increases of up to 30% in fertilizer efficiency [1] and reductions of up to 40% in labor costs [2] and environmental sustainability. However, its adoption is still limited by the weight of economic barriers (high initial costs) and social barriers (mistrust, lack of training) [3], [4]. In order to analyze these dynamics, a simulation model based on Forrester's system dynamics is developed, which highlights three critical factors in the adoption of this technology: Public investment in new digital infrastructure; Training programs for rural communities; Ethical strategies that prioritize transparency and community participation [5],[6]. The study concludes that the effective integration of AI into agriculture requires a sustainable and adaptive approach to public policies that reduce technological gaps and promote resilient agricultural systems