The use of intelligent irrigation decision support systems (DSS) has become important for optimizing water consumption and enhancing productivity in agriculture. This study evaluates the "HooshAb" DSS in a 20-hectare silage maize field in Qom Province, Iran, under water scarcity. Two adjacent fields managed by the same farmer were selected as pilot sites. In the control field, irrigation followed traditional farmer management, while in the treatment field, irrigation followed DSS schedules.

Results showed that by optimizing irrigation scheduling and simulating soil moisture, the DSS reduced water consumption by approximately 6% compared to traditional methods. Furthermore, the system increased irrigation water productivity by 6.3%. Field monitoring indicated that the DSS had high accuracy in predicting soil moisture (R² = 0.75) and was effective in optimizing on-farm water management. Soil salinity monitoring revealed a relative increase during the growing season due to reduced water availability and forced deficit irrigation. Comparison of estimated versus measured salinity showed decreased accuracy toward the end of the season, highlighting the need for updating irrigation water quality data during the growing season.

Overall, the findings suggest that irrigation management DSS can contribute to soil and water sustainability and increased water productivity under climate change. Such systems enable precise irrigation scheduling, reduce unnecessary water use, and support long-term soil health. Regular updating of key input data, especially water quality parameters, is essential for maintaining system accuracy and reliability.