|Malone, Robert - Rob|
|FANG, Q - Qingdao Agricultural University|
Submitted to: Soil & Tillage Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/30/2016
Publication Date: 9/15/2016
Publication URL: http://handle.nal.usda.gov/10113/5495790
Citation: Gillette, K.L., Ma, L., Malone, R.W., Fang, Q.X., Halvorson, A.D., Hatfield, J.L., Ahuja, L.R. 2016. Simulating N20 emissions in different tillage systems of irrigated corn using RZ-SHAW model. Soil & Tillage Research. 165:268-278. doi: 10.1016/j.still.2016.08.023.
Interpretive Summary: Nitrous oxide is a potent greenhouse gas (GHG) and through fertilizer ammendments agricutlure is a leading source of nitrous oxide (N2O) emissions. Ecosytem models are important tools to enhance field studies and may offer insight to complex biological processes. We used Root zone water quality (RZWQM) model to predict N2O emisisons from a conventional and no-till (NT) irrigated corn field study conducted in Colorado from 2003 to 2006. RZWQM simulated crop growth was typically within 5% of observed yeild and biomass for all N and tillage treatments. The model correctly predicted cooler spring soil tempertures and lower emissions in the NT system by simulating higher emissions of N2 gas derived from dentirification. This is the first test of the newly added GHG component in RZ-SHAW under no-till management, and results suggest with some improvements the model has potential in testing N2O emissions from different management strategies. Continuously testing the N2O emission kinetics in ecosystem models will increase the undstanding of biological process that regulate N2O emissions and improve model estimates of environemntal impacts from agricultural GHG emissions. Increased understanding from this research benefits efforts to reduce GHG by producers and managers of agriculture systems.
Technical Abstract: Nitrous oxide (N2O) is potent greenhouse gas (GHG) and agriculture is a global source of N2O emissions from soil fertility management. Yet emissions vary by agronomic practices and environmental factors that govern soil moisture and temperature. Ecosystem models are important tools to estimate N2O emissions by accounting for such variables, and models can strengthen field research. The objective of this study was to test RZ-SHAW accuracy in predicting crop production and N2O emissions from conventional till (CT) and no-till (NT) systems at high (HN) rate and low nitrogen input (LN) N treatments in an irrigated corn (Zea mays L.) field in Colorado from 2003 to 2006 growing seasons. Simulated crop yield were within 0.7 and 0.9% of measured yield for HN-NT and CT treatments, and 32 and 3% of measured yield for LN-NT and CT treatments, respectively. Spring soil temperatures were cooler by 2oC in NT compared to CT, and were correctly simulated using RZ-SHAW coupled model. RZ-SHAW simulated N2O emissions were slightly under predicted by 0.10 (1.5%) and 0.56 (7.1%) kg N ha-1 for HN-NT and HN-CT treatments, respectively. Results for LN treatments showed larger differences in simulated N2O emissions and were over predicted by 0.11 (16%) kg N ha-1 in NT and under predicted by 0.29 (29%) g N ha-1 day-1 and CT. Annual emissions were in close agreement, with observed and simulated showing 12 and 10% lower N2O emissions from HN-NT than HN-CT, respectively. Cooler surface soil temperature and higher soil water content caused slower breakdown of crop residue and slightly more denitrification than HN-CT, resulting in lower N2O emissions in HN-NT. In 2005 and 2006 N2O fluxes were low as enhanced efficiency fertilizer was applied to HN treatments. However the model overestimated flux rates for these periods, especially from the NT system, where the cooler soil temperatures significantly lowered emissions compared with CT. Future work should consider adding N2O mitigation strategies to the model, such as using enhanced efficiency fertilizers. This is the first test of the newly added GHG component in RZ-SHAW under no-till management, and results suggest with some improvements the model has potential in testing N2O emissions from different management strategies.