indicators affect changes in winter crop area; in fact, reductions in winter crop area in VCs unaffected by conflict are higher (though not significantly so) than in those affected by conflict. Determining whether the decline in winter crop area is due to conflict-related shortages of labor, fuel, or other inputs needed to carry out the necessary crop management activities or climatic factors will require econometric analysis.

As the literature suggests that for winter wheat, satellite-based estimates of NDVI can reliably approximate yield,<sup>16</sup> we use the crop masks obtained from the above land cover analysis for 2019-22, respectively, to compute NDVI on all fields planted to winter wheat for each of the 10-day periods ending on May11, 21, 31, and June 11 and aggregate these values at village level. Calculations were performed on the Google Earth Engine cloud computing platform.<sup>17</sup> Figure 3 illustrates NDVI densities for different periods in each year. For all years except 2021, peak NDVI values are attained in the first 10 days of June. Tabulating VClevel figures in panel C of table 2 confirms that NDVI in 2022 was uniformly lower than in earlier years.

# **3.3 Weather conditions**

<mark>To control for time-varying effects that are unrelated to conflict, we use information on precipitation as well as minimum and maximum temperature derived from the United States National Centers for Environmental Prediction Global Forecast System (GFS) that produces weather forecasts every 6 hours. For the 2019-22 growing seasons, we use the first 6-hour interval of each forecast as a “nowcast” to derive daily aggregated values a</mark> t rayon level using GEE scripts <mark>. Temperature data is then converted into growing degree days using the sinusoidal distribution as suggested by d'Agostino and Schlenker (2016).</mark> Data on cumulated GDD>0 days computed based on daily values of _Tmin_ and _Tmax_ in figure 4 illustrates a marked seasonal pattern and clear differences across years.<sup>18</sup> Most relevant for our analysis are a warm winter and spring in 2020; a mild winter followed by a cool spring that provided favorable growing conditions in 2021; and 2022 starting with a cold fall and remaining the coolest of the four growing seasons thus far, leading to delayed crop development and harvest dates.

Data on precipitation in figure 5 highlights that, in addition to having been unseasonably warm, the 2020 season was very dry, with abnormally low winter rainfall followed by a spring with long dry spells that contributed to a cumulative water deficit of 70-100 mm compared to 2019. This resulted in widespread winter crop failure, especially in the South and Center due to insufficient soil moisture. By contrast, in the 2021 season, ample spring and subsequent rainfall provided favorable growing conditions, contributing to what was widely considered a record harvest. Fall precipitation (i.e., until Dec. 1) was even lower in 2022 than in 2020 and, by end May, had approached levels of total rainfall similar to those observed in 2020.

> 16 Dhillon _et al._ (2020) report that the best fit is obtained most to the Feekes scale (Large 1954), i.e., between 97 and 112 GDD>0

> 17 Due to a change in Sentinel 2 data processing that was introduced with version 4.0.0. end of January 2022, GEE’s S2_SR_HARMONIZED collection was used for 2022 NDVI values.

> 18 In line with the empirical pattern, we define fall as the period between Oct 1 and Dec 1; winter as the period between Dec. 1 and Mar. 1; and spring as the period thereafter.

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