Modeling Agricultural Soil Nitrous Oxide Emissions Reductions and Uncertainty at the Regional Level
Keywords
soil nitrous oxide, agriculture, DayCent, Uncertainty, Mitigation
Start Date
25-6-2018 3:40 PM
End Date
25-6-2018 5:20 PM
Abstract
Managers of greenhouse gas (GHG) mitigation projects in the agriculture sector frequently struggle with verifying GHG emissions reductions and quantifying uncertainty. Soil nitrous oxide emissions in agricultural systems represent about 4% of the U.S. GHG inventory, however they may be the most difficult emissions to quantify in the agriculture sector. In collaboration with the Climate Action Reserve (CAR), we modeled crop rotations at more than 15,000 randomly-selected points throughout the U.S. that were predicted by the cropland data layer (CDL) to be in crop rotations. We constructed crop rotations using CDL predictions about past crops at those points, and used historical regional data on tillage, planting dates, harvest dates, and fertilizer use as inputs to the DayCent ecosystem model for each of those points. We predicted soil nitrous oxide emissions for eleven major crops in twelve regions throughout the country, and used a linear, mixed effect model to correct for model bias as well as estimating structural uncertainty in DayCent model. We estimated model uncertainty through Monte Carlo simulation, modeling 3 classes of potential emissions mitigation scenarios: reducing amounts of synthetic fertilizer, using enhanced efficiency products (nitrification inhibitors, slow-release fertilizers), and combinations of fertilizer reductions and enhanced efficiency products. Model results demonstrate that the GHG mitigation benefits of reducing applied fertilizer amounts, combined with nitrification inhibitors and slow-release fertilizers, can be predicted effectively, and that uncertainty can be estimated for regionally-based greenhouse gas mitigation projects.
Modeling Agricultural Soil Nitrous Oxide Emissions Reductions and Uncertainty at the Regional Level
Managers of greenhouse gas (GHG) mitigation projects in the agriculture sector frequently struggle with verifying GHG emissions reductions and quantifying uncertainty. Soil nitrous oxide emissions in agricultural systems represent about 4% of the U.S. GHG inventory, however they may be the most difficult emissions to quantify in the agriculture sector. In collaboration with the Climate Action Reserve (CAR), we modeled crop rotations at more than 15,000 randomly-selected points throughout the U.S. that were predicted by the cropland data layer (CDL) to be in crop rotations. We constructed crop rotations using CDL predictions about past crops at those points, and used historical regional data on tillage, planting dates, harvest dates, and fertilizer use as inputs to the DayCent ecosystem model for each of those points. We predicted soil nitrous oxide emissions for eleven major crops in twelve regions throughout the country, and used a linear, mixed effect model to correct for model bias as well as estimating structural uncertainty in DayCent model. We estimated model uncertainty through Monte Carlo simulation, modeling 3 classes of potential emissions mitigation scenarios: reducing amounts of synthetic fertilizer, using enhanced efficiency products (nitrification inhibitors, slow-release fertilizers), and combinations of fertilizer reductions and enhanced efficiency products. Model results demonstrate that the GHG mitigation benefits of reducing applied fertilizer amounts, combined with nitrification inhibitors and slow-release fertilizers, can be predicted effectively, and that uncertainty can be estimated for regionally-based greenhouse gas mitigation projects.
Stream and Session
In Stream F: System Identification Approaches for Complex Environmental Systems
Session: F5: New and Improved Methods in Agricultural Systems Modelling