determinants of land use–including land abandonment (Meyfroidt _et al._ 2016), grazing intensity (Dara _et al._ 2020), and crop cultivation <mark>(Munteanu</mark> _<mark>et al.</mark>_ <mark>2017)–</mark> over rathe r long time periods (Matasov _et al._ 2019). We build on insights from recent studies using satellite imagery by using interpretation of readily available imagery from the European Union’s Copernicus Sentinel 2 constellation to construct an indicator of whether or to what extent a VC is directly affected by the war. Imagery also allows us to construct the two main outcome variables considered in our analysis, i.e., the area grown with winter crops and, for fields growing winter cereals, the NDVI as a measure of yield, for the 2019-22 time span at VC level. At the same time, we follow the literature on conflict impacts in terms of using a robust econometric framework to estimate conflict impacts as discussed below.

# **2.2 Estimation strategy**

To estimate conflict effects at a high level of spatial disaggregation and in a way that allows appreciation of possible heterogeneity, we construct a panel data set covering 10,125 village councils (VCs) over the period 2019-2022. This allows using a difference-in-differences approach to estimate conflict impacts on a wide range of variables. Indexing village councils by and year by , the model to be estimated is v v vv v v tt (1) vv vv where v is the outcome variable of interest in village YY = αα + ββC + γγXX + λλ at year + εε ; C v is a an indicator for presence of different types of conflict at village level that takes a value of zero for years prior to 2022 and for villages YY vv tt that did not suffer direct conflict-related damages in 2022 based on either satellite imagery or public sources as discussed in more detail below; v is a vector of time varying weather variables that includes number of growing degree days (GDDs) and precipitation;<sup>3</sup> is a village fixed effects that controls for time XX invariant factors such as agronomic suitability, access to infrastructure and economic opportunities, and vv αα local leadership quality; s are time fixed effects; and v is random error term. vv Two coefficients in (1) are of particular interest: λλ measures the direct impact of conflict-related activities εε in affected villages while 2022 captures the deviation of 2022 from the common trend. To the extent that ββ time invariant (access to infrastructure, soil quality, village leadership) and time variant (weather) variables λλ are controlled for, this can be interpreted as an estimate of indirect conflict effects. Interpreting the time dummy in this way assumes there are no other relevant time-varying variables that would need to be controlled for; if such variables exist, they can be included in the regressions or otherwise adjusted for. Interacting the 2022 dummy with relevant characteristics would allow to explore heterogeneity of conflict effects among different types of producers or VCs.

To illustrate how this approach can be applied in practice, we estimate the impact of conflict on two outcome variables. To cover the extensive margin, we assess the impact of conflict on area growing with 3 We include levels and quadratic terms to allow for decreasing marginal effects from rain or temperature or negative flooding or drought effects. 5