60 obtain district-level tea production data from the Sri Lanka Tea Board’s annual reports for 20192022.

Production is measured in kilograms (kg) and cultivated area is measured in hectares (ha). For crops with harvests in both Yala and Maha seasons (maize, rice, and groundnuts), we compute total production and total cultivated area in each year and district by summing across the two seasons. For each crop, we obtain yields (in kg/ha) as the ratio between production and cultivated area.

To estimate fertilizer coefficients in our model’s Cobb-Douglas agricultural production functions (see Section VI.A.3), we use national production data for each crop in 2022, in metric tons (MT), from the Central Bank of Sri Lanka’s Economic and Social Statistics 2023 report.<sup>100</sup> We use national production data for cinnamon, cloves, groundnuts, maize, onions, potato, rice, and tea.

# **A.C.2 Remote Sensing Estimates**

We produce novel remote sensing estimates of rice and tea yields and of cultivated rice areas at a highly granular level. For rice, the methodology described in Ozdogan et al. (2024) relies on an expert-based image classification algorithm on satellite observations from Landstat and Sentinel2 enhanced to isolate the rice signal based on the premise that paddy rice has specific growing conditions in Sri Lanka. The presence of rice-growing areas is determined by factors related to climate, water availability, topographic position, and farmer decision-making and technical expertise. The methodology monitors rice-related vegetation and water management from space and distinguishes it from other land use types using spectral (color) and temporal information included in the satellite signal. Since water and vegetation indexes are more visible for rice than for other crops, remote sensing tools can more accurately map crop type and predict yields for the former than for the latter (Dong et al., 2016). To estimate rice yields, we use a statistical model that correlates district-level rice yields from government statistics (described in Appendix A.C.1) with the satellite-derived vegetation index (green chlorophyll index), known to be sensitive to rice yields for the pixels identified as rice areas. A random forest-based machine learning model is then added to incorporate additional environmental variables known to affect rice yields, resulting in pixel-level rice yield estimates across two decades (2000-2022). These remote sensing estimates, aggregated to the DS level, are shown to be highly consistent with government-reported measures of rice area (from survey-based crop-cutting experiments) and rice yield (from production statistics).

For tea, we regress district-level yield data from Sri Lanka Tea Board reports (see Appendix A.C.1) on a district-level vegetation index derived from Planet Satellite Data for pixels deemed to be tea plantations for the period 2014-2022. Since the Tea Board reports only production, not yields, we divide total production by planted area in each district to obtain yields (in kg/ha). In

> 100The report is available at: https://www.cbsl.gov.lk/en/publications/other-publications/statisticalpublications/economic-and-social-statistics-of-sri-lanka/ess-2023.