Application of Fuzzy Logic to Predict Instantaneous Water Use Efficiency in a Forage Grass Under Organic and Mineral Fertilization and Water Deficit Conditions

Pastures are the primary food source for cattle, yet their productivity is often limited by management practices and water scarcity. In this context, approaches capable of representing nonlinear relationships and handling uncertainties can support sustainable water management. The objective of this study was to develop and compare fuzzy inference systems (FISs) to predict the instantaneous water use efficiency (iWUE) in a forage species subjected to organic and mineral fertilization under different levels of water deficit. The models were built in MATLAB R2024a using Mamdani and Sugeno inference methods. Input variables (fertilization and water deficit) were represented by triangular, trapezoidal, and Gaussian membership functions, while the output variable (iWUE) was modeled using triangular, trapezoidal, and Gaussian membership functions in the Mamdani system and singleton functions in the Sugeno system. Different defuzzification strategies were evaluated, resulting in 21 fuzzy systems. The results showed satisfactory model performance, with coefficients of determination above 0.90 and strong agreement between observed and simulated values. The Mamdani system with trapezoidal membership functions and centroid defuzzification achieved the best predictive performance (R2 = 0.9846, NSE = 0.9887, RMSE = 0.0923). The response surface generated by the best-performing fuzzy system indicated a smaller reduction in iWUE under organic fertilization compared to mineral fertilization as water deficit intensified. The developed fuzzy systems demonstrated potential to represent the interaction between nutritional management and water availability, supporting decision-making in forage production systems.