Modeling Sustainable Agricultural Residue Removal at the Subfield Scale

Abstract

This study developed a computational strategy that utilizes data inputs from multiple spatial scales to investigate how variability within individual fields can impact sustainable residue removal for bioenergy production. Sustainable use of agricultural residues for bioenergy production requires consideration of the important role that residues play in limiting soil erosion and maintaining soil C, health, and productivity. Increased availability of subfield‐scale data sets such as grain yield data, high‐fidelity digital elevation models, and soil characteristic data provides an opportunity to investigate the impacts of subfield‐scale variability on sustainable agricultural residue removal. Using three representative fields in Iowa, this study contrasted the results of current NRCS conservation management planning analysis with subfield‐scale analysis for rake‐and‐bale removal of agricultural residue. The results of the comparison show that the field‐average assumptions used in NRCS conservation management planning may lead to unsustainable residue removal decisions for significant portions of some fields. This highlights the need for additional research on subfield‐scale sustainable agricultural residue removal including the development of real‐time variable removal technologies for agricultural residue.