Enhancing Drip Irrigation Efficiency through Novel Statistical Modeling of Wetting Patterns in Water-Scarce Agriculture

Abstract

Drip irrigation is a critical solution for agricultural water scarcity, especially in water-scarce regions. This study introduces innovative statistical models to estimate essential wetting patterns in clay loam soil for optimizing drip irrigation systems. Using a drip system with a 45 cm emitter spacing and a 4 litre per hour discharge rate, operational durations from 10 to 140 minutes were examined. Statistical models (Exponential, Linear, Logarithmic, Polynomial, and Power) were developed to predict vertical and horizontal water movement. The results highlight the Logarithmic model's excellence in predicting vertical water movement, with a remarkable Coefficient of Correlation (CC) 0.98 cm and Root Mean Square Error (RMSE) 0.76 cm, while the Polynomial model is best for horizontal movement with a CC 0.98 cm, and RMSE 1.25 cm respectively. Importantly, these models can be applied to generate artificial data for both horizontal and vertical water movements under unchanged conditions when no observable wetting pattern is present.