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

Aparicio-Montenegro P.; Velásquez-Castillo L.; Garcia-Alvarez M.Y.; De La Cruz Garcia A.; Rayu Vega J. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2026) · DOI: 10.18687/laccei2026.1.1.939

Resumen

Agriculture faces critical challenges-climate change, food security, and resource degradation-that demand innovative solutions. This study analyzes, through a systematic literature review (29 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 improves productivity, with increases of up to 30% in fertilizer efficiency [1] and reductions of up to 40% in labor costs [2], while enhancing environmental sustainability. However, its adoption remains limited by economic barriers (high initial costs) and social barriers (mistrust and lack of training) [3], [4]. To analyze these dynamics, a simulation model based on the system dynamics approach proposed by Forrester is developed, which identifies three critical factors for technology adoption: public investment in digital infrastructure, training programs for rural communities, and ethical strategies grounded in data governance frameworks and principles of responsible artificial intelligence, including data anonymization, informed consent mechanisms, and active participation of agricultural communities in technology adoption processes [5], [6]. The study concludes that the effective integration of AI into agriculture requires sustainable and adaptive public policies aimed at reducing technological gaps and promoting resilient agricultural systems.

Instituciones: Universidad Tecnológica del Perú; Universidad Continental; Universidad Nacional Federico Villarreal; Universidad César Vallejo