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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.稻田温室气体排放与土壤微生物菌群的多元回归分析.生态学报,2012,32(6):1811~1819 本文二维码信息
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稻田温室气体排放与土壤微生物菌群的多元回归分析
Multivariate regression analysis of greenhouse gas emissions associated with activities and populations of soil microbes in a double-rice paddy soil
投稿时间:2011-02-26  修订日期:2011-08-01
DOI: 10.5846/stxb201102260224
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
Key WordsCH4 and N2O emission  double rice field  most probable number method  methanogens  nitrifiers  denitrifiers  multivariate regression
基金项目2011年公益性行业(农业)科研专项经费资助(201103039);国家"973"计划课题(2012CB417106)
作者单位E-mail
秦晓波 中国农业科学院农业环境与可持续发展研究所, 北京 100081
农业部农业环境与气候变化重点开放实验室, 北京 100081
Semiarid Prairie Agricultural Research Centre, Agricultural and Agri-Food Canada, Swift current, Saskatchewan, S9H 3X2, Canada 
 
李玉娥 中国农业科学院农业环境与可持续发展研究所, 北京 100081
农业部农业环境与气候变化重点开放实验室, 北京 100081 
yueli@ami.ac.cn 
石生伟 中国农业科学院农业环境与可持续发展研究所, 北京 100081
农业部农业环境与气候变化重点开放实验室, 北京 100081 
 
万运帆 中国农业科学院农业环境与可持续发展研究所, 北京 100081
农业部农业环境与气候变化重点开放实验室, 北京 100081 
 
纪雄辉 湖南省土壤肥料研究所, 长沙 410125  
廖育林 湖南省土壤肥料研究所, 长沙 410125  
Hong Wang Semiarid Prairie Agricultural Research Centre, Agricultural and Agri-Food Canada, Swift current, Saskatchewan, S9H 3X2, Canada  
刘运通 中国农业科学院农业环境与可持续发展研究所, 北京 100081
农业部农业环境与气候变化重点开放实验室, 北京 100081 
 
李勇 中国科学院亚热带农业生态研究所, 长沙 410125  
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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.
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