Investigating Impacts of Multiple Parameters on CO2 Fluxes from a Continuous Corn Field in South Dakota.

It is valuable for mitigating CO₂ emissions to analyze various parameters’ impacts on CO₂ fluxes from soil seeded to crops. However, most of the previous studies focused on one or two parameters influencing CO₂ fluxes from croplands. Little is known about analysis of CO₂ fluxes using multiple parameters because it is practically difficult to measure many parameters simultaneously. In this study, DAYCENT model was used to simulate these variables. The improved methodology CPTE was used to calibrate and validate DAYCENT model. The Semi-log linear model was built using these variables for analyzing impacts of multiple parameters on CO₂ fluxes from continuous corn (Zea mays L.) field. High frequency soil surface CO₂ fluxes from soils were monitored for 3 years (2008, 2009 and 2011) at this study site, near Lennox, South Dakota, USA. The results showed that precipitation, soil temperature and moisture, net primary productivity, active carbon in soil organic matter, and water filled pore space significantly impacted the CO₂ fluxes. However, air temperature, aboveground live carbon, and ammonium did not impact CO₂ fluxes. The impact of simulated parameters on soil surface CO₂ fluxes is different from that of single parameter used in most of the published studies. The forecasting yearly average CO₂ fluxes have an increasing trend. All the parameters interact to emit high CO₂ fluxes in the corn land, growing larger areas of corn with increase of its sale price being a bioenergy source could result in increased CO₂ emissions. This study provides an understanding about how different parameters impact on CO₂ emissions from continuous corn field.