Effects of Different Types of Straw Application on CH4 Emissions and Functional Microbial Quantities in Single-cropping Paddy Fields
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摘要:
目的 阐明稻田土壤CH4排放及其相关功能微生物对不同种类秸秆施用的响应机制，为稻田生态系统CH4排放预估和减排措施的选择提供理论依据。 方法 以太湖地区典型单季稻田的原柱状土为研究对象，通过设置温室栽培试验，同步监测水稻秸秆（RS）、小麦秸秆（WS）、玉米秸秆（MS）施用模式下水稻各生长期CH4排放通量、水稻产量、土壤微生物量碳氮含量等因子，定量化研究CH4排放相关菌群及功能基因的群落丰度。 结果 与对照相比，RS、WS和MS处理下水稻生长期CH4排放量分别增加289.65%、263.30%和344.43%，单位水稻产量CH4排放量分别增加210.40%、182.35%和282.80%。水稻生育期中，土壤产CH4菌（mcrA）群落丰度呈现上升趋势而CH4氧化菌（pmoA）呈先上升后下降趋势。与对照相比，拔节期RS处理显著增加细菌16S rRNA和pmoA基因拷贝数，成熟期WS处理显著增加mcrA拷贝数，而MS处理对上述基因拷贝数均无显著影响。 结论 相关分析和结构方程模型表明，细菌丰度、产CH4菌丰度、CH4氧化菌丰度和微生物碳氮比（MBC/MBN）是直接影响稻田土壤CH4排放的主要因素。 Abstract:Objective The response mechanism of CH4 emissions and related functional microorganisms in paddy fields to different types of straw were clarified, which will provide a theoretical basis for CH4 emission prediction and mitigation in paddy soils. Method A typical single-season paddy soil in Taihu Lake area was used as the research object, and the greenhouse cultivation experiment was set up to simultaneously monitor the CH4 emission flux, rice yield, soil organic carbon, soil microbial biomass carbon (MBC), soil microbial biomass nitrogen (MBN) and other factors under different straw application treatments (rice straw, RS; wheat straw, WS; maize straw, MS) . The above factors were coupled with the microbial processes of CH4 generation and oxidation, in which the community abundances of CH4 emission-related microbes were quantitatively by molecular biology technology. Then the microbial response mechanism of CH4 emissions to different types of straw application was elucidated. Results The results showed that CH4 emissions during rice growing period were significantly increased by 289.65%, 263.30% and 344.43% by RS, MS and WS treatments, respectively, and CH4 emissions per unit yield increased by 210.40%, 182.35% and 282.80%, respectively. The seasonal dynamics of soil methanogenes (mcrA) abundance showed an upward trend along the rice growth, while the methanotrophs (pmoA) showed an upward and then downward trend. The RS treatment significantly increased the copy numbers of bacterial 16S rRNA and pmoA genes at the rice jointing stage, and the WS treatment significantly increased the copy number of mcrA gene during the mature stage, but the MS treatment had no significant effect on the above copy numbers. Conclusion Correlation analysis and structural equation models showed that the abundances of bacterial, methanogens and methanotrophs, and microbial carbon-nitrogen ratio (MBC/MBN) were the main factors directly affecting CH4 emission from paddy fields. -
Key words:
- Straw types /
- Paddy field /
- CH4 emission /
- Soil microorganism /
- Structural equation model
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图 1 水稻生长期CH4排放通量的季节变化
BF：基肥，TF：分蘖肥，PIF：穗肥，MSA：烤田期
Figure 1. Seasonal variation in CH4 emission flux during rice growth season
图 2 水稻生长期土壤细菌（16S rRNA）、产甲烷菌（mcrA）和甲烷氧化菌（pmoA）的群落丰度变化
Figure 2. Dynamics of abundance of bacteria (16S rRNA), methanogens (mcrA), and methanotrophs (pmoA) during rice growth season
图 3 水稻不同生育期土壤微生物量碳（MBC）、微生物量氮（MBN）含量及其比值变化
不同大写字母表示同一处理不同时期间差异显著，不同小写字母表示同一时期不同处理差异显著（P < 0.05）
Figure 3. Dynamics of TOC, MBC, MBN and the ratio of MBC/MBN during rice growth season
图 4 土壤理化因子和微生物丰度对水稻生长期CH4排放影响的结构方程模型
路径旁边显示标准化的路径系数。实箭头和虚箭头分别表示P < 0.05和P > 0.05。箭头的宽度与路径系数的程度成正比。绿色和红色箭头分别表示正相关关系和负相关, 模型拟合系数：gfi:0.949 rmsea:0.000 srmr:0.0014
Figure 4. Structural equation model for the effects of soil physical and chemical factors and microbial abundance on CH4 emissions during rice growth season
表 1 供试秸秆基本性质
Table 1. Basic properties of tested straw
供试秸秆
Straw总氮（%）
Total nitrogen总磷（%）
Total phosphorus总钾（%）
Total potassium总碳（%）
Total carbon碳氮比
C/N水稻秸秆（RS） 1.49 ± 0.08 a 0.21 ± 0.02 b 1.54 ± 0.12 b 39.30 ± 0.05 a 26.35 ± 1.12 b 小麦秸秆（WS） 1.22 ± 0.08 b 0.07 ± 0.01 c 0.91 ± 0.01 c 36.70 ± 0.14 a 30.06 ± 2.10 ab 玉米秸秆（MS） 1.04 ± 0.07 c 1.98 ± 0.00 a 1.98 ± 0.00 a 36.00 ± 0.14 b 34.59 ± 2.37 a 注：同列不同字母表示处理间差异显著（P < 0.05） 
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表 2 各处理水稻各生育期CH4排放量、CH4排放总量、水稻产量和单位产量CH4排放量
Table 2. CH4 emission in each period, total CH4 emission, rice grain yield and CH4 emission per unit output during rice season in 2020
处理
TreatmentCH4总排放量
CH4 total emission
（g m–2）CH4排放量（g m–2）
CH4 emission水稻产量
Yield
（g pot–1）单位水稻产量CH4排放量
CH4 emission per unit
（pot m–2）前期淹水
Water flooding烤田
Baking field干湿交替
Dry-wet alternation排水落干
Drain and dryCK 225.11 ± 20.47 b 46.05 ± 16.10 Bb 28.51 ± 12.78 Cb 77.72 ± 1.96 ABb 100.49 ± 6.43 Ab 101.80 ± 1.41 b 2.21 ± 0.25 b RS 877.13 ± 113.36 a 309.67 ± 58.31 Aa 134.70 ± 25.42 Ba 309.75 ± 55.93 Aa 203.08 ± 7.07 ABa 127.83 ± 1.89 a 6.86 ± 0.94 a WS 817.82 ± 159.58 a 385.16 ± 76.98 Aa 123.09 ± 22.91 Ba 238.09 ± 46.12 ABa 144.96 ± 26.91 Bab 130.97 ± 5.32 a 6.24 ± 1.39 a MS 1000.46 ± 173.00 a 459.14 ± 124.96 Aa 167.51 ± 45.23 Ba 291.87 ± 43.01 ABa 181.06 ± 23.07 Ba 118.30 ± 2.80 a 8.46 ± 1.38 a 注：同列用不同小写字母表示处理间数据差异显著，同行用大写字母表示数据差异显著不同水分管理时期数据差异显著（P < 0.05）。处理CK、RS、WS、MS分别代表空白对照处理、施用水稻秸秆处理、施用小麦秸秆处理、施用玉米秸秆处理。
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表 3 水稻生长季CH4排放与土壤微生物种群数量和微生物碳氮的相关性
Table 3. Correlation among CH4 emission, the abundance of soil microbial communities and microbial carbon and nitrogen
CH4 16S rRNA mcrA pmoA mcrA/pmoA MBC MBN MBC/MBN CH4 1 −0.200 0.400* −0.180 0.232 −0.174 0.054 −0.247 16S rRNA −0.200 1 −0.161 0.966** −0.146 0.425** 0.188 −0.008 mcrA 0.400* −0.161 1 −0.168 0.425** −0.137 0.057 −0.096 pmoA −0.180 0.966** −0.168 1 −0.146 0.454** 0.233 0.038 mcrA/pmoA 0.232 −0.146 0.425** −0.146 1 −0.059 0.027 −0.037 MBC −0.174 0.425** −0.137 0.454** −0.059 1 0.150 0.229 MBN 0.054 0.188 0.057 0.233 0.027 0.150 1 −0.319 MBC/MBN −0.247 −0.008 −0.096 0.038 −0.037 0.229 −0.319 1 注：皮尔逊双尾显著性检验，n = 48；*和**分别表示在0.05和0.01水平上相关性显著。CH4: CH4:排放通量; 16S rRNA: 细菌16S rRNA基因拷贝数; mcrA: 产CH4:菌mcrA基因拷贝数; pmoA: CH4:氧化菌pmoA基因拷贝数; mcrA/pmoA: 产CH4:菌mcrA基因拷贝数和CH4:氧化菌pmoA基因拷贝数的比值; MBC: 土壤微生物碳; MBN: 土壤微生物氮; MBC/MBN: 土壤微生物碳含量和土壤微生物氮含量的比值, 下同.
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