Corn (Zea mays L.) residues and perennial C4 grasses are two Midwest bioenergy feedstock candidates due to their compatibility with agricultural infrastructure and potential for ecosystem service delivery. We validated the ecosystem process model AGRO-BGC by comparing model estimates with empirical observations from corn and perennial C4 grass systems across Wisconsin and Illinois under no-tillage, nitrogen fertilized, and unfertilized management. Validation parameters included soil organic carbon (SOC), total soil nitrogen (N) to 1.2m, aboveground net primary productivity (ANPP), net ecosystem productivity (NEP), and leaf area index (LAI). We parameterized AGRO-BGC to represent ecophysiological characteristics of corn and perennial prairie grasses, and constructed scenarios to represent corresponding edaphic, climate, and management conditions. Unfertilized annual model estimates had normalized mean average errors relative to field measurements of 0.3, 23, and 4tha-1 for ANPP, SOC, and N, respectively. Fertilized simulations erred from observations by 0.6, 29, 5tha-1 for ANPP, SOC, and N, respectively.We also estimated long-term implications of varying residue removal rates on SOC. Model estimates compared to field data tested the hypothesis that long-term increased residue removal decreases SOC. Field observations showed 0.17, 0.09, and a -0.17tCha-1yr-1 change for control, harvest, and bare grass residue removal treatments, respectively. Simulated SOC loss was greatest for the most intensive residue removal scenarios (-0.48 and -0.68tCha-1yr-1 for corn and grass, respectively), compared to no-harvest scenarios that increased SOC by 0.05tCha-1yr-1 for both corn and grass. AGRO-BGC estimated a 0.07tCha-1yr-1 loss under corn residue harvest, while estimating 0.09tCha-1yr-1 loss for grass. Results suggest long-term increased corn and grass residue harvest (beyond grain) for biofuel feedstock will decrease SOC and soil productivity by approximately 15% in corn and 21% in grass systems over 47 years.
Bibliographical noteFunding Information:
This work was funded by the DOE Great Lakes Bioenergy Research Center (DOE BER Office of Science DE-FC02-07ER64494). We thank Ryan Anderson and Alan V. Di Vittorio for their provision of the AGRO-BGC model assistance in this study, and Dr. John Norman and Dr. Larry Bundy for their involvement in generating the Arlington dataset. We also thank the anonymous reviewers whose comments greatly improved this manuscript.
© 2015 Elsevier B.V.
- Agroecosystem process model
- Corn stover
- Soil organic carbon