**AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** MAIZE IS ONE OF THE MOST ECONOMICALLY IMPORTANT FOOD CROPS IN THE WORLD, MAKING IT AN IDEAL STUDY SYSTEM FOR SUSTAINABLE AGRICULTURAL PRODUCTION. DESPITE BEING THE SUBJECT OF NUMEROUS STUDIES AND INNOVATIONS OVER THE PAST SEVERAL DECADES, MAIZE IS STILL PLAGUED BY THE WESTERN CORN ROOTWORM (WCR; DIABROTICA VIRGIFERA VIRGIFERA). ADVANCES IN PESTICIDE TECHNOLOGY AND GENETICALLY MODIFIED MAIZE VARIETIES HAVE BEEN DEVELOPED TO COMBAT THIS PEST, BUT MANY OF THESE TECHNOLOGIES HAVE HAD ADVERSE ENVIRONMENTAL EFFECTS AND OFTEN LEAD TO WCR DEVELOPING RESISTANCE. BELOWGROUND INTERACTIONS LIKE THAT OF MAIZE AND WCR ARE OFTEN DIFFICULT TO STUDY COMPARED TO ABOVEGROUND INTERACTIONS DUE TO THE OPACITY AND COMPLEXITY OF THE SOIL, WHERE SOIL TEXTURE, POROSITY, NUTRITIONAL CONTENT, AND MICROBIAL COMPOSITION CAN ALL AFFECT THESE INTERACTIONS. BECAUSE SOIL IS SO IMPORTANT TO PLANT YIELD, MANY AGRICULTURAL PRACTICES HAVE BEEN IMPLEMENTED TO IMPROVE SOIL QUALITY, LIKE PLANTING NON-COMMODITY COVER CROPS OVER THE WINTER. IN ADDITION TO IMPROVING SOIL FERTILITY AND WATER RETENTION, RECENT STUDIES HAVE SHOWN THAT COVER CROPPING CAN ALTER BELOWGROUND FUNGAL COMPOSITION, AFFECT NEMATODE POPULATION AND DYNAMICS, AND ALTER NUTRIENT UPTAKE IN SUBSEQUENT CROPS. MAIZE HAS BEEN USED IN COVER CROPPING ROTATIONS BOTH HISTORICALLY AND TODAY, AND UNDERSTANDING HOW COVER CROPS AFFECT WCR WILL BE IMPORTANT AS FOOD PRODUCTION STRUGGLES TO MEET DEMAND WHILE ALSO MITIGATING ENVIRONMENTAL DAMAGES.FOR THIS PROJECT I WILL BE USING GREENHOUSE EXPERIMENTS AND COMPUTATIONAL LEARNING TO UNDERSTAND WHETHER DIFFERENT SPECIES AND MIXTURES OF COVER CROPS PLANTED BEFORE CORN CAN AFFECT WESTERN CORN ROOTWORM FITNESS. IN THIS CONTEXT, FITNESS IS DEFINED NOT ONLY BY WCR SURVIVAL AND PERFORMANCE, BUT ALSO BY INDUCTION OF PLANT DEFENSES AND ATTRACTION OF NATURAL ENEMIES IN THE SOIL.THE ULTIMATE GOAL IS TO DEVELOP A MACHINE LEARNING TOOL THAT CAN HELP PREDICT COMPLEX BELOWGROUND INTERACTIONS INVOLVING A DEVASTING CORN PEST.IDEALLY, THIS TOOL CAN BE IMPROVED OVER TIME WITH MORE EXPERIMENTATION AND DATA COLLECTION. AS THE FARMING INDUSTRY SHIFTS TOWARD PRECISION AGRICULTURE, USING COMPUTATIONAL LEARNING TO HELP PREDICT WHAT HAPPENS BELOWGROUND WILL BE INVALUABLE FOR OVERALL PEST MANAGEMENT. IN ADDITION TO CONTRIBUTING TO THE SPARSE LITERATURE ON BELOWGROUND ECOLOGICAL INTERACTIONS, ANOTHER GOAL OF THIS RESEARCH IS TO AID IN DEVELOPING SPECIFICALLY TAILORED MANAGEMENT TOOLS FOR BELOWGROUND PESTS. PRECISION AGRICULTURE IN COMBINATION WITH SUSTAINABLE PRACTICES LIKE COVER CROPPING CAN LEAD TO IMPROVED YIELD WITH FEWER INPUTS BOTH CHEMICALLY AND ECONOMICALLY.