Carbon Dioxide and Nitrous Oxide Fluxes in Corn Grown under Two Tillage Systems in Southwestern Quebec

Abstract

Agriculture has an important potential role in mitigating greenhouse gas emissions (GHG). However, practices that reduce CO2 emissions from soils and increase the soil organic C level may stimulate N2O emissions. This is particularly critical in Quebec where heavy soils and a humid climate may limit the adoption of agricultural practices designed to mitigate GHG. The objective of this work was to study the effects of two tillage and N fertilization regimes on CO2 and N2O fluxes and the seasonal variability in emissions of these gases, associated with corn (Zea mays L.) grown in southwestern Quebec. Different seasonal emission patterns of CO2 and N2O were observed. Higher N2O fluxes occurred during the spring and were associated with precipitation events, while higher CO2 fluxes occurred in mid‐season and were related to temperature. Conventional tillage (CT) had greater peaks of CO2 emissions than no‐till (NT) only after disking in the spring. Once corn was established, differences between tillage systems were small. Peaks of N2O emission occurred in both systems (NT and CT) following N application. Plots receiving 180 kg N ha−1 in both tillage systems had large peak of N2O emission rates during the wettest parts of the season. The CT and NT systems generally had similar cumulative CO2 emissions but NT had higher cumulative N2O emissions than CT. Our findings suggests that changing from CT to NT under the heavy soil conditions of Quebec may increase GHG, mainly as result of the increase in N2O emission. This negative effect of NT could be reduced by avoiding fertilizing when precipitation is more intense.

Tillage and Fertilizer Management Effects on Soil-Atmospheric Exchanges of Methane and Nitrous Oxide in a Corn Production System

“…In the Midwest, Kessavalou et al [49] reported that an NT fallow system exhibited the least threat to cause the deterioration of atmospheric quality due to greater CH 4 uptake and decreased N 2 O emissions. Contrary to our findings, Almaraz et al [48] suggested that changing from CT to NT system under the heavy soil conditions of Quebec may increase greenhouse gas contributions mainly as result of the increase in N 2 O emission. However, the above authors indicated that this negative effect of NT could be reduced by avoiding fertilizer application during high precipitation periods, thus indicating that the results were mainly due to the effect of soil moisture conditions at fertilizer application time.…”

“…Jacinthe and Dick [20] also reported average daily N 2 O emissions ranging from 0.1 to 326 g N 2 O ha −1 day −1 in a corn/soybean (Glycine max L.)/wheat (Triticum aestivum L.)/hairy vetch (Vicia villosa Roth) rotation study, with seasonal N 2 O N losses which were highest in continuous corn plots and lowest in soybean plots. Hernandez-Ramirez et al [46] found significant seasonal variation in N 2 O emissions in an eastern Corn Belt soil, while Almaraz et al [48] found higher N 2 O fluxes during the spring which were associated with precipitation events in a study in Quebec. In our study, we did not find significant correlations between soil N 2 O fluxes and soil temperature and moisture conditions during measurements.…”

Nitrous oxide emissions from black soils under a continuous soybean cropping system in northeast China

“…Soybean growth did not significantly affect soil N 2 O emission during the growing season (Table 1), which is consistent with observations in other agricultural systems (e.g., Huang et al, 2002;Almaraz et al, 2006). Yang and Cai et al, 2005;Almara et al, 2009;Hu et al, 2010).…”

Modeling temporal variability of soil CO2 emissions from an apple orchard in the Harran Plain of southeastern Turkey

“…In early studies, soil CO 2 emissions were modeled using linear, exponential, or nonlinear equations (cubic, quadratic) (Almaraz et al, 2009;Mapanda et al, 2010;Wu et al, 2010) separately, but MARS application incorporates both linear and nonlinear relationships by fitting local regression curves at subregions and including higherorder interactions among predictors (Friedman, 1991). MARS has been used with other materials (Srivastava and Solanky, 2003;Put et al, 2004) and was reported to give better results as compared to other linear and nonparametric regression techniques like generalized linear models, artificial neutral networks, and classification and regression trees.…”