Climate smart agriculture opportunities for mitigating soil greenhouse gas emissions across the U.S. Corn-Belt. (20th September 2020)

• Record Type: Journal Article
• Authors: McNunn, Gabriel; Karlen, Douglas L.; Salas, William; Rice, Charles W.; Mueller, Steffen; Muth, David; Seale, Jeffrey W.
• Is Part Of: Journal of cleaner production. Volume 268(2020)
• Publication Date: 2020-09-20
• Subjects: Climate smart agriculture (CSA) -- GHG mitigation -- Soil organic carbon -- Carbon sequestration -- Subfield scale; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.122240
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Widespread adoption of climate smart agriculture (CSA) has the potential to greatly mitigate agricultural greenhouse gas (GHG) emissions by increasing soil organic carbon (SOC) stocks and decreasing nitrous oxide (N2 O) emissions. However, quantifying the impact of land management practices on soil GHG fluxes including CO2 and N2 O is difficult due temporal and spatial variability of localized weather conditions and subfield soil properties. A process-based biogeochemistry modeling framework coupled with public soils, weather, and yield data layers was used to estimate regionally specific soil GHG reductions associated with the adoption of one or more CSA practices in maize ( Zea mays L.) and soybean ( Glycine max (L.) Merr. ) crop production fields. Site-specific, multi-year DeNintrification-DeComposition model simulations were performed on agricultural fields within 11 U.S. Corn-Belt states that were identified to be in maize or soybean production from 2013 to 2016. Differences between modeled changes in SOC stocks to a depth of 50 cm, N2 O emissions, and CH4 fluxes corresponding with alternative land management scenarios simulated across field sites were converted to a CO2 equivalent basis and spatially weighted to a county-scale to highlight regional variability. County-scale GHG reductions corresponding with a conversion from conventional tillage to no-tillage practices are estimated to be have a mean reduction potential of 1477 kg CO2 e ha −1 yr −1 (SOC, N2 O, and CH4 flux reductions of 945, 549, −17 kg CO2 e ha −1 yr −1, respectively, where a negative reduction indicates an increase in emissions.) with a standard deviation of 605 kg CO2 e ha −1 yr −1 . Additionally, the adoption of cover crops is predicted to provide a mean reduction of 678 kg CO2 e ha −1 yr −1 (SOC, N2 O, and CH4 flux reductions of 824, -173, 26.7 kg CO2 e ha −1 yr −1, respectively), and improved N-fertilizer timing is estimated to mitigate 413 kg CO2 e ha −1 yr −1 (SOC, N2 O, and CH4 flux reductions of 75, 337, 1 kg CO2 e ha −1 yr −1, respectively). The adoption of multiple CSA practices is estimated to have the greatest mean reduction potential of 2861 kg CO2 e ha −1 yr −1 (SOC, N2 O, and CH4 flux reductions of 2210, 611, 39 kg CO2 e ha −1 yr −1, respectively). Use of the spatially explicit subfield modeling approach based on public data provides a relatively low-cost approach for strategically targeting CSA practices to agricultural regions where adoption is most impactful. Highlights: Modeling spatially explicit soil GHG reduction potential of CSA practices. 11 U.S. state analyzed to quantify magnitude of GHG impacts at county-scale. Soil organic carbon increases may be offset by increased nitrous oxide emissions.