Scaling Impact of Tillage Practices On Soil Organic Carbon in Iowa: Towards a Robust Decision Support System.

Soil organic carbon is a potential source or sink of atmospheric carbon dioxide depending on agricultural management practices. While in situ measurements can accurately describe conditions in plots or small fields, remote sensing offers a practical method to account for the inherent spatial variability across agricultural landscapes. Many of the biophysical characteristics of vegetation and soil needed to parameterize process-based models can be derived from remotely sensed data, either directly or indirectly.

Our primary study area is the South Fork of the Iowa River watershed, a Conservation Effects Assessment Project (CEAP) watershed in central Iowa, covering 788 km² and is 85% cropland that is 99% corn and soybeans. Soil, weather, and topographic databases, farmer surveys, surface reference data, and remotely sensed data provided spatially-explicit input data for the hydrologic and soil carbon models. Management scenarios, including different crop rotations, tillage intensities, and crop residue removal rates, were evaluated using watershed and field-scale models. Removal of the corn residue may be sustainable for nearly level fields under no-till management. However, as slope increased, the amount of residue that can be harvested in a sustainable manner decreased rapidly. With the variation in weather, topography, soil type, and crop yield that occur across most agricultural fields, it is difficult to predict the amount of corn residue that can be removed in a sustainable manner at the field scale.  Different rates of crop residue harvest may be required for different areas of the same field. Currently, no single model can adequately address all agronomic and environmental issues.  Robust decision support systems will utilize suites of models to address the wide range of agronomic, environmental, and economic questions likely to be posed by producers, stakeholders, and policymakers.