We collected peer-reviewed papers on straw incorporation and CH4 emission from the China National Knowledge Infrastructure and Web of Science. These peer-reviewed papers were published in Chinese or English before October 2017. The studies had to meet the following criteria to be included in our dataset: (i) The experiment was conducted under field conditions with replicates; (ii) experimental duration was clearly stated; (iii) all management practices besides straw addition (e.g., N fertilizer rate and water management) needed to be the same between the treatment and control; (iv) crop straw was incorporated into the soils within the same rice season (i.e., less than 30 days before rice transplanting); (v) the application rate of straw was between 3 and 7.5 MT ha−1; and (vi) the rice paddies were under flooded conditions before the jointing stage. Criteria 4 to 6 were applied to ensure that all experiments in our dataset were representative of real-world conditions (18, 21, 22) and that results could be compared to IPCC estimates.

In total, 24 published papers including 94 observations met our criteria (table S2 and dataset S1). For each study, we tabulated rice growth data (that is, aboveground biomass or rice yield) if these were available. If a paper reported both data of aboveground biomass and rice yield, then we used the data of aboveground biomass. For each experiment in dataset S1, we quantified the effects of straw incorporation on CH4 emissions through calculating the natural logarithm of the response ratio (R)Embedded Imagewhere xs and xc are the values of the variables (CH4 emissions and rice growth) for the treatment with and without straw incorporation, respectively (35). We weighted lnR by the inverse of its variance and estimated missing variances using the average coefficient of variation across our dataset (36).

Several factors are known to affect CH4 emissions from rice paddies (7). To test whether these factors affected straw addition responses, we extracted the following information for each study in our dataset: SOC (gram per kilogram), mean annual temperature (degree Celsius), water management (continuous flooding, midseason drainage, or intermittent irrigation), rice cultivar (Japonica or Indica), inorganic N application rate (kilogram per hectare), cropping system (single rice, rice wheat, or double rice), straw application rate (metric ton per hectare), straw type (rice or wheat), and experimental duration (≤5 or >5 years). We used the “glmulti” package in R to determine the relative importance of the experimental factors in affecting treatment effects, analyzing our data with all possible models that could be constructed using combinations of the experimental factors (3638). The relative importance of the experimental factors was calculated as the sum of Akaike weights derived for all the models in which the factor occurred, where the Akaike weights represent the relative likelihood of a model. On the basis of the outcome of the model selection procedure, we used a Wald test to determine whether treatment effects were statistically different between experimental classes.

We used the rma.mv function in the “metafor” package (39) to perform a mixed-effects meta-analysis in R, including “paper” as a random effect because several papers contributed more than one effect size. To ease interpretation, the results of lnR were back-transformed and reported as the percentage change [(R − 1) × 100]. We used the logarithmic function of the statistical package SPSS 18.0 to describe correlation between treatment effects and durations in our dataset.

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