Prediction of Consumptive Use Under Different Soil Moisture Content and Soil Salinity Conditions Using Artificial Neural Network Models

The response of the water use of crops to soil moisture and salinity is complex to quantify using traditional field experiments. Based on field experimental data for 2 years, artificial neural network (ANN) models with five inputs including soil moisture content, total salt content, plant height, leaf area index and crop reference evapotranspiration (ET₀) were developed to estimate daily actual evapotranspiration (ET). The models were later used to simulate the response of crop water consumption to soil moisture and salinity stresses at different growth stages. The results showed that the ANN model has a high precision with root mean squared error of 0.41 and 0.52 mm day^-1, relative error of 19.6 and 25.6%, and coefficient of determination of 0.87 and 0.79 for training and testing samples, respectively. Furthermore, the simulation results showed that the seed corn ET is sensitive to soil salt stress at all growth stages, although the salinity threshold at which the impact becomes felt and the extent of the impact vary for the different growth stages, with the booting and tasseling stages being the most robust. The study offers a more direct approach of evaluating actual crop evapotranspiration by considering explicitly water and salinity stresses.