Mathematical Modeling in Agricultural Sciences: Strategies for Reducing Water Consumption in Irrigation

Abstract

Water scarcity is one of the most significant issues worldwide because agricultural water usage constitutes the largest share of freshwater resources. Only water-efficient irrigation practices can ensure food security without wasting water. The present work aims at improving mathematical models to optimize irrigation water use without reducing crop yields. An analysis of a 500-entry dataset using regression modeling and optimization was conducted based on input variables such as soil moisture, rainfall, crop type, and water consumption. These models predicted water usage and developed sustainable irrigation schedules in accordance with environmental factors. The approach can reduce water consumption to up to 30% with no yield penalties. The sensitivity analysis revealed that the most important parameters for the selection of irrigation frequency were soil moisture and rainfall, whereas Monte Carlo simulations validated the model's robustness under varying conditions. This study provides actionable knowledge for farmers and policymakers with scalable and adaptable solutions for sustainable water management. The study's strength is that predictive regression models can be combined with the optimization frameworks developed, and each crop-specific strategy will be able to adapt, using a very flexible and scalable approach to irrigate and challenge water scarcity issues.