Improving Farm Yield through Agent Based Modeling

Abstract

This paper explores the potential of Agent-Based Modeling (ABM) using NetLogo to enhance farm yield. Traditional approaches to agricultural management often overlook the complexity and interdependencies within farming systems. ABM offers a dynamic and flexible framework to simulate the behaviours of individual agents within a farming ecosystem, enabling a more nuanced understanding of the factors influencing yield. By modeling the interactions between agents such as farmers, crops, pests, weather conditions, and market dynamics, this study aims to identify optimal strategies for improving farm yield while minimizing input costs and environmental impacts. Through experimentation and scenario analysis, various farming practices and policies can be simulated and evaluated in silico, providing valuable insights for real-world decision-making. This interdisciplinary approach integrates concepts from computer science, economics, ecology, and agronomy to develop a holistic understanding of agricultural systems. The findings of this research contribute to the development of sustainable farming practices and policy interventions to address food security challenges in a rapidly changing world.