Mathematical Modeling in Agricultural Economics: Predictive Tools for Sustainable Development

Abstract

The agricultural sector is fundamental to global food security, economic stability, and sustainable development. However, it faces significant challenges such as resource insufficiency, climate change, fluctuating markets, and increasing demand for food. Mathematical modeling has emerged as a powerful tool to address these challenges by providing predictive insights and optimizing decision-making processes. This paper explores the role of mathematical models in agricultural economics with a focus on their application as predictive tools for sustainable development. This study highlights various modeling techniques, including Linear Programming (LP) Model, Nonlinear Programming (NLP) Model, Dynamic Optimization Model, Multi-Objective Optimization Model, Linear Regression Model, Time Series Model (ARIMA) and Panel Data Regression Model within agricultural systems. These models provide valuable insights into critical areas such as crop yield optimization, resource allocation, supply chain management, and climate impact assessments.