作 者 ：秦晓波,李玉娥,石生伟,万运帆,纪雄辉,廖育林,Hong Wang,刘运通,李勇
期 刊 ：生态学报 2012年 32卷 6期 页码：1811~1819
关键词：甲烷和氧化亚氮排放；双季稻田；稀释培养计数法；产甲烷菌；硝化细菌；反硝化细菌；多元回归；
Keywords:CH4 and N2O emission, double rice field, most probable number method, methanogens, nitrifiers, denitrifiers, multivariate regression,
摘 要 ：为揭示多种田间管理措施综合影响下双季稻田温室气体平均排放通量与土壤微生物菌群的多元回归关系,利用静态箱-气相色谱法和稀释培养计数法进行了温室气体排放通量和土壤产气微生物菌群数量的连续观测。2a研究结果显示,稻田甲烷排放通量与土壤微生物总活性和产甲烷菌数量关系密切,甲烷排放通量与二者的关系可分别由指数和二次多项式模型拟合。一元回归分析表明,仅产甲烷菌数量就能单独解释96.9%的稻田甲烷排放通量变异(R2=0.969,P<0.001),但考虑两种因素的二元回归拟合优度高于一元回归(R2=0.975,P<0.001)。氧化亚氮排放通量与土壤硝化细菌和反硝化细菌数量也密切相关(P <0.05),氧化亚氮排放通量与二者的二元非线性混合回归模型可以解释至少70.4%的稻田氧化亚氮排放通量(R2≥0.704, P <0.001),其拟合优度也高于一元回归。稻田温室气体排放通量受多种影响因素控制,土壤产气微生物活性和数量是多种因素影响的直接响应,因此二者与温室气体排放存在显著相关,基于田间试验的多元非线性回归分析客观的揭示了温室气体排放通量与环境因子的相关关系。
Abstract:To investigate the regression relationships between greenhouse gas (GHG) emissions and soil microbes in a double-rice paddy soil under various management practices, a two-year study was conducted to observe the seasonal variation of GHG emissions and activities of soil microbes (SMA ) as well as their populations (SMP) using the closed static chamber-GC (gas chromatography) and the most probable number methods. There were seven management practices (or treatments), including 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 average values of seven treatments‘ daily fluxes of GHGs and SMA as well SMP were used for the analysis in this study. Regression analysis was conducted using the R statistical software. Similar seasonal variations of methane flux and SMA as well as the amount of soil methanogens (MET) were found in the rice growing season of 2008-2009; and same regularity occurred in the temporal distribution of nitrous oxide flux and the amount of soil nitrifiers and denitrifiers. Furthermore, there was a strong correlation between methane flux and SMA as well as the population of MET. The relationships of methane flux vs. SMA and methane flux vs. MET can be represented by using the exponential and quadratic polynomial models, respectively. Simple regression indexed that the quantity of MET could explain individually at least 96.96% of variance of methane flux (R2=0.969, P<0.001), but the fitting precision of multiple nonlinear regression of methane flux with two factors of SMA and MET (R2=0.975, P<0.001) was higher than the univariate regression analysis. Besides, the pronounced positive dependency of nitrous oxide flux with soil nitrifiers and denitrifiers has also been found (P<0.05). The mixed binary nonlinear regression of nitrous oxide flux with the SMP of the two types of microbes can explain at least 70.4% of variance of nitrous oxide flux (R2≥0.704, P<0.001), and of course the fitting precision of multiple nonlinear regression was higher than the simple regression using the SMP of either nitrifiers or denitrifiers. However, as we know, GHG emissions from paddy soils are affected by many factors, of which SMA and SMP are the most direct influential variants. In order to reasonably reveal the interactions between GHG emissions and environmental variables, the multivariate nonlinear regression analysis should be carried out based on data derived from the extensive field experiments rather than few laboratory trials.