# **4. CH4 Concentration Anomalies over Irrigated Rice Production Areas**

The current version of our database provides monthly mean CH4 concentration anomalies in 266,884 terrestrial grid cells at 25 km resolution for 2020, 2021, and the first half of 2022. The means are calculated from daily observations that are pre-filtered to remove global background concentrations, trends, and latitudinal clustering. We overlay the IFPRI shapefile on the global grid to extract cells with irrigated rice production. For this exercise, CH4 observations in each cell are limited to the months in which fields are actually flooded. We identify those months using RiceAtlas, a spatial database of global rice calendars developed by Laborte et al. (2017). RiceAtlas distinguishes up to three rice cultivation seasons and identifies the first flooding and harvest days for each season by day-of-year.

For each pixel in RiceAtlas, we use this information to identify months that lie fully within periods lasting from first field flooding to ten days before harvest (to account for pre-harvest drainage (IRRI, 2022)). Then we intersect the RiceAtlas shapefile with our SP5/IFPRI spatial grid and assign each grid cell the cultivation calendar with the greatest representation in that cell.<sup>9</sup> For grid cells with IFPRI irrigated rice areas but no RiceAtlas information, we assign the cultivation calendar in the nearest RiceAtlas cell. We compute mean grid cell CH4 anomalies for all within-calendar monthly observations during the period 2020-2022.

# **5.  Sources of Variation in Concentration Anomalies**

# **5.1 The Effect of Field Scale**

We posit a monotone increasing relationship between CH4 anomalies and the scale of IFPRI flooded fields in a grid cell and perform a linear regression using percentile values.<sup>10</sup> To test for robustness, we use alternative estimators that incorporate different assumptions about the structure of the stochastic error term (εit) in the model. These techniques produce the same point estimates for model parameters, but their differing estimates of standard errors (and the accompanying t-statistics) may lead to very different inferences about the statistical significance of model variables. Table 1 reports our results; we replicate the point estimates in columns (1)– (3) to aid interpretation of the t-statistics. We include standard linear regression results along with results for robust standard errors (SE), and standard errors adjusted for 1,149 clusters defined by level-1 administrative units (states, provinces, etc.) in the 99 countries in the regression database.

The results in Table 1 are all highly robust, suggesting that an increase of one percentile in the extent of IFPRI-identified irrigated rice fields is associated with a percentile increase of 0.5 in the

> 9 Most grid cells have only one cultivation calendar; this assignment rule applies to grid cells that straddle two or more RiceAtlas calendars.

> 10 Percentile value regressions are robust to alternative specifications of functional forms, imposing only the assumption that dependent and independent variables have a monotone increasing relationship. Rank regressions also have these characteristics.