Optimization of Irrigation Scheduling Based on Soil Moisture Depletion in Potato under Sandy Loam Soils

Abstract

The efficient management of irrigation water is critical for maximizing crop productivity, particularly in sandy loam soils where rapid drainage and low water retention can lead to significant moisture losses. This study focuses on the optimization of irrigation scheduling based on soil moisture depletion in potato cultivation under sandy loam soil conditions. The proposed methodology involves field experimentation combined with data-driven analysis to evaluate different irrigation regimes triggered at varying levels of soil moisture depletion. Key parameters such as soil moisture content, crop growth stages, evapotranspiration, and yield attributes are monitored using soil sensors and standard agronomic measurements. The collected data are preprocessed through cleaning, normalization, and statistical validation to ensure accuracy and consistency. Analytical and machine learning models, including regression techniques and Random Forest algorithms, are employed to establish relationships between soil moisture depletion levels, irrigation frequency, and crop yield. Experimental results indicate that optimizing irrigation scheduling at moderate depletion levels significantly enhances water use efficiency and crop yield compared to both excessive and deficit irrigation practices. The study also demonstrates that precise moisture-based scheduling reduces water wastage while maintaining optimal soil moisture conditions for tuber development. Furthermore, the findings highlight the importance of aligning irrigation timing with critical growth stages to maximize productivity. The study concludes that soil moisture depletion-based irrigation scheduling provides a reliable and efficient approach for improving water management, enhancing potato yield, and promoting sustainable agricultural practices in sandy loam soils.