HISTORICAL YIELD GAINS HAVE INCREASED CROP RESIDUE PRODUCTION TO UNPRECEDENTED LEVELS. CONTEMPORARY RESIDUE INPUTS HAVE BECOME SO LARGE THAT THEY CAN REDUCE THE YIELD AND NITROGEN USE EFFICIENCY OF SUBSEQUENT CROPS. THIS PROJECT WILL DEVELOP INNOVATIVE SOLUTIONS TO THE INCREASING CHALLENGE OF SUSTAINABLE CROP RESIDUE MANAGEMENT. OUR OVERARCHING HYPOTHESIS IS THAT A SYSTEMS LEVEL UNDERSTANDING OF CROP RESIDUE DECOMPOSITION DYNAMICS CAN LEAD TO RESIDUE MANAGEMENT STRATEGIES THAT: 1) INCREASE YIELD, 2) REDUCE N FERTILIZER INPUTS, AND 3) IMPROVE SOIL HEALTH. BECAUSE YIELD CAN BE INCREASED WHILE REDUCING N FERTILIZER INPUTS, BOTH FARMERS AND THE ENVIRONMENT WILL BENEFIT.AS CROP RESIDUE PRODUCTION CONTINUES TO GROW, THERE IS A CRITICAL NEED FOR IMPROVED CROP RESIDUE MANAGEMENT STRATEGIES. NEVERTHELESS, WE LACK AN UNDERSTANDING OF HOW MULTIPLE SYSTEMS COMPONENTS INTERACT TO CONTROL RESIDUE DECOMPOSITION DYNAMICS AND CASCADING EFFECTS ON YIELD, NITROGEN USE EFFICIENCY, AND ENVIRONMENTAL NITROGEN LOSSES. MOREOVER, RESIDUE MANAGEMENT HAS INCONSISTENT EFFECTS ON YIELD AND NITROGEN DYNAMICS ACROSS SPACE AND TIME. PROCESS-BASED SIMULATION MODELS CAN IMPROVE THE EFFICIENCY OF CROPPING SYSTEMS BY EXPLAINING THESE INCONSISTENCIES, BUT CURRENT MODELS RELY ON OUTDATED RESIDUE DECOMPOSITION ALGORITHMS THAT RESULT IN LOW PREDICTION ACCURACY. HERE, WE PROPOSE A COUPLED EXPERIMENTAL-MODELING APPROACH THAT WILL: 1) USE EXPERIMENTS TO DEVELOP NEW FUNDAMENTAL KNOWLEDGE ON CORN AND SOYBEAN RESIDUE DECOMPOSITION DYNAMICS; 2) INCORPORATE EXPERIMENTAL RESULTS INTO A DYNAMIC SIMULATION MODEL TO IMPROVE PREDICTION AND EXPLANATORY POWER; AND 3) DEPLOY THE IMPROVED MODEL TO EXTRAPOLATE KNOWLEDGE ACROSS ENVIRONMENTS, IDENTIFYING RESIDUE MANAGEMENT STRATEGIES THAT ARE PROFITABLE IN THE NEAR-TERM AND SUSTAINABLE IN THE LONG-TERM.