Abstract
Climate change and public policies restricting freshwater use for agricultural irrigation are compelling farmers to maintain production with limited water. Advanced irrigation scheduling tools that combine data and computer simulations are needed to optimize water use and maximize crop productivity. Limited studies have evaluated the performance of data assimilation and model-based simulation optimization of irrigation scheduling under field conditions. The objective of this study was to evaluate model-based irrigation scheduling with and without assimilation of LAI in processing tomatoes. The treatments included two DSSAT CropGro-Tomato models. The treatments were T1 (TM0005 with LAI data assimilation), T2 (TM0030 with LAI data assimilation), and T3 (Control: TM0005 without LAI data assimilation). This study was conducted near Davis, California. Model performance was evaluated using applied water, soil water content, growth, yield, and fruit quality. Results showed no significant yield differences between treatments that assimilated LAI and the control. All the models accurately predicted LAI and yield within one standard deviation of measured values, suggesting that model-based optimization was effective with or without data assimilation. The framework reduced applied water by 26% compared to current irrigation recommendations for processing tomatoes. The average applied irrigation was 391 mm, compared to the recommended (533 to 762) mm. No significant differences in fruit quality were observed between the treatments. Overall, the model-based simulation-optimization irrigation scheduling approach maintained the desired yield and fruit quality while reducing water use. A well-calibrated crop model did not benefit from LAI data assimilation, implying that model-based irrigation scheduling could be easily implemented without the need for monitoring and additional computation costs of assimilating LAI during the season which also include labor and instrumentation costs. The model-based irrigation scheduling framework proposed in this study could be applied to other crops to help growers cope with limited water supplies.