Enhancing Predictive Capabilities of Phytobiome Decision Tools through Predictive Weather and Fine-scale Soil Moisture

More than 30% of all irrigated US agricultural output comes from the lands sustained by the Ogallala Aquifer in the western Great Plains. The agricultural production practices in six states (CO, KS, NE, NM, OK, and TX) affect water usage and the interactive multi-scale phytobiome processes of the individual crop types. Tested methods to optimize water use and crop production at the field-scale are needed as the Ogallala water resources undergo change. This work presents methods used to link predictive weather and downscaled soil moisture at 10-30 m scales for use in crop and irrigation applications, and other decision tools. Our focus is on the CSU Water Irrigation Scheduler for Efficient (WISE) Application tool, crop yield models, and remote soil moisture characterization as a demonstration of how complex fine-scale phytobiome processes can be linked to weather and environmental remote sensing data sets in near real-time using scalable technologies.