** AWARDS ISSUED PRIOR TO JANUARY 20, 2025, WERE FUNDED UNDER PREVIOUS ADMINISTRATIONS AND MAY NOT REFLECT THE PRIORITIES AND POLICIES OF THE CURRENT ADMINISTRATION.** ACCURATE PREDICTION OF NITROUS OXIDE (N2O) EMISSIONS FROM AGRICULTURAL SOILS IS A PERSISTENT CHALLENGE FOR EXISTING PREDICTION TOOLS, ESPECIALLY BIOGEOCHEMICAL MODELS. BIASED PREDICTIONS AND THE LARGE GLOBAL WARMING POTENTIAL OF N2O LEAD TO SUBSTANTIAL UNCERTAINTY IN ASSESSMENTS OF THE MITIGATION POTENTIAL OF CLIMATE-SMART CROPPING SYSTEMS. THE RECENT EMERGENCE OF HIGH-FREQUENCY N2O MEASUREMENTS OFFERS A NOVEL OPPORTUNITY TO ADDRESS THIS CHALLENGE: DATA FROM NEAR-CONTINUOUS AUTOMATED CHAMBER METHODOLOGIES ARE NOW SUFFICIENT TO INFORM A NEW CLASS OF MACHINE LEARNING (ML) APPROACHES FOR IMPROVED N2O FLUX PREDICTIONS. WE PROPOSE THE FIRST-EVER EFFORT TO COLLATE DENSE MEASUREMENT FLUXES FROM ALL AVAILABLE SITES WORLDWIDE IN ORDER TO 1) CREATE A PUBLIC DATABASE OF HIGH-FREQUENCY N2O FLUXES FROM INTENSIVELY CROPPED SYSTEMS THAT CAN BE USED BY US AND OTHERS TO IMPROVE QUANTITATIVE N2O MODELS; 2) DEVELOP ADVANCED DEEP LEARNING MODELS FOR TIME-SERIES N2O PREDICTIONS THAT CAN BE TESTED AGAINST AUTOMATED AND NON-AUTOMATED N2O FLUX DATA; AND 3) USE ML-PROCESS BASED HYBRID MODELING TO MEET PREDICTOR DATA NEEDS FOR ML AND INFORM DATA NEEDS FOR FURTHER PROCESS-LEVEL MODEL DEVELOPMENT. OUR RESEARCH ADDRESSES PRIORITIES IN THE AFRI BIOENERGY, NATURAL RESOURCES, AND ENVIRONMENT PRIORITY AREA AND CONTRIBUTES TO MANAGING A DOMINANT COMPONENT OF CLIMATE-SMART AGRICULTURE - SOIL-BASED N2O FLUXES. OUR PROJECT TEAM BRINGS TOGETHER EXPERTISE IN SOIL BIOGEOCHEMISTRY AND AGRICULTURAL GREENHOUSE GAS FLUXES TOGETHER WITH ADVANCED DATA AND PROCESS MODELING TO ADDRESS ONE OF THE MOST RECALCITRANT PROBLEMS FACING THE DESIGN OF CLIMATE SMART CROPPING SYSTEMS TODAY.