Awards issued prior to january 20, 2025, were funded under previous administrations and may not reflect the priorities and policies of the current administration.** the project will assist farms…

Awards issued prior to january 20, 2025, were funded under previous administrations and may not reflect the priorities and policies of the current administration.** the project will assist farms dealing with increasing competition for freshwater resources and intensifying regulatory pressure for sustainable water resource management (groundwater management in particular), which is increasing the cost of water and reducing availability in many areas. Increasing energy costs and electrical grid congestion further complicate irrigation management for energy-intensive irrigation, but also creates financial incentives for farms that effectively align irrigation schedules with energy load shifting or local renewable energy production. The project will involve an advanced decision support system (DSS) designed to facilitate planning and management for economically optimal and sustainable irrigation, particularly where water supplies are insufficient for full irrigation and/or water or energy costs are high.economically optimal irrigation management will often involve deliberate under-irrigation at some times during the season (i.e. deficit irrigation). Optimal management of limited irrigation requires modeling biophysical and economic relationships to determine water allocation strategies that maximize net income. Effective modeling of those relationships depends on locally specific calibration of the parameters that define the models, but few farms have the technical resources needed to effectively calibrate those models. The sbir phase i project developed and demonstrated the technical feasibility of a prototype auto-calibration system using an adaptive feedback procedure to auto-calibrate the modeling algorithms in the irrigation decision support system mentioned above. This sbir II project will significantly upgrade that auto-calibration system by including data from a remote sensing platform (either satellite or drone based data) to calibrate for the spatial variability of field conditions. The project will also test the autocalibration system under diverse applications involving additional crops, fields and farming practices. Andthe project will develop a program of on-farm technical support to enable farms with limited technical expertise to utilize the auto-calibration system. The expected impact l of the project will be to facilitate use of a decision support system designed to enable optimal irrigation management by farms with limited relevant technical training.