Relationships between methane emissions and soil microorganisms in a double-rice field in southern subtropical China

Abstract

Methane is produced in anaerobic environments by obligate anaerobic microorganisms through the decomposition of organic matters. To investigate the regression relationships between methane emissions and soil microbes in a double-rice paddy under various field practices, a two-year study was conducted to investigate the seasonal variation of methane emissions and the total activities of soil microbes (TASM) as well as the populations of methanogens (PMET) using the static closed-chamber-GC (gas chromatography) and the most probable number methods. Seven management practices were included in this study to look at the average effect of field treatments on methane emissions and TASM as well as PMET, viz. CWS (conventional tillage + without straw residues + urea), NWS (no tillage + without straw residues + urea), SCU (conventional tillage + without straw residues + controlled-release urea), HN (high stubbles + no tillage + urea), HC (high stubbles + conventional tillage + urea), SN (straw cover + no tillage + urea) and SNF (straw cover + no tillage + urea + continuous flooding). The daily average values of methane emissions and TASM as well as PMET from seven treatments were used for the analysis. Regression analysis was conducted using the R statistical software. Different field practices have significant effect on methane flux and TASM and PMET and similar seasonal variations of methane flux and TASM as well as PMET were found during the rice-growing seasons. Pronounced positive correlations between methane flux and TASM, and PMET were observed. Such relationships can be well described by the exponential or quadratic polynomial models, respectively. Regression analysis indicated that PMET could explain individually at least 97% of variance of methane flux (R2 = 0.97, P < 0.001), while the fitting precision of multiple nonlinear regression model for methane flux with two predictors of TASM and PMET was slightly higher than the univariate regression analysis (R2 = 0.98, P < 0.001). However, as we know, methane emissions from paddy soils are affected by many factors, of which TASM and PMET are the most direct influential variants. In order to reasonably reveal the interactions between methane emissions and environmental factors, the multivariate nonlinear regression analysis should be carried out based on data derived from the extensive field experiments rather than few laboratory trials.