**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** CROP IMPROVEMENT EFFORTS ARE CRITICAL TO SUSTAIN FOOD PRODUCTION TO FEED GLOBAL POPULATION AND COMBAT ADVERSE EFFECTS OF CLIMATE CHANGE. IN A TYPICAL CROP IMPROVEMENT/BREEDING PROGRAM, GERMPLASMS ARE EVALUATED AT MULTIPLE LOCATIONS FOR MULTIPLE YEARS TO CAPTURE CROP RESPONSES ACROSS MULTIPLE G X E SCENARIOS PRIOR TO A RELEASE OF A VARIETY FOR COMMERCIAL PRODUCTION. WITH VERY LOW SELECTION EFFICIENCY (<1% OVERALL), THE CROP IMPROVEMENT PROCESS REMAINS RESOURCE INTENSIVE AND INEFFICIENT. WITH PHENOMICS FACILITATING OBJECTIVE ASSESSMENT AND MODELING OFFERING OPPORTUNITIES TO EVALUATE PERFORMANCE ACROSS A VARIETY OF ENVIRONMENTS, INTEGRATING PHENOMICS AND MODELING INTO THE BREEDING CYCLE HAS THE POTENTIAL TO INCREASE THE GENETIC GAIN VIA DATA-INFORMED IMPROVED SELECTION. IN THIS PROJECT, WE PROPOSE TO DEVELOP AND DEMONSTRATE A DECISION SUPPORT SYSTEM INVOLVING THE INTEGRATION OF PHENOMICS AND MODELING FOR INFORMED CLIMATE-ADAPTED CROP GERMPLASM SELECTION IN BREEDING PROGRAMS. IN THIS PROJECT, HETEROGENOUS DATA WILL BE ACQUIRED FROM INTERNET OF THINGS (IOT) BASED REMOTE SENSING NETWORK, UNMANNED AERIAL SYSTEM (UAS), AND LOW-ORBITING SATELLITE (LOS) IMAGING SYSTEMS, ALONGSIDE MICROCLIMATE DATA, WHICH WILL BE INTEGRATED WITH THE DATA MINING APPROACHES. THE PROJECT WILL UTILIZE WHEAT (TRITICUM AESTIVUM, SPRING AND WINTER WHEAT) AS A MODEL CROP. THE PHENOMICS-DATA DRIVEN MODELS WILL THEN BE DEVELOPED TO PREDICT THE PERFORMANCE OF CROP GERMPLASMS OVER CURRENT AND LONG-TERM PROJECTIONS OF CLIMATIC CONDITIONS FOR THE WHEAT-PRODUCING AREA OF THE US PACIFIC NORTHWEST.

Washington State University, Pullman WA

Investigators