We then apply panel econometric methods to provide estimates of war effects on two outcome variables, namely (i) the extent of area grown with winter crops; and (ii) for winter crop fields, peak NDVI as a predictor of crop yield, both at VC level. Estimates of direct effects are obtained from the coefficients on war-related disturbances (damaged agricultural fields or conflict indicators) that we include as independent variables. Village fixed effects are then used to control for time invariant effects (e.g., soil quality, infrastructure access, or social cohesion and effectiveness of local leadership) and data on precipitation and accumulated temperature control for time varying effects, a 2022 dummy can be interpreted as an upper bound estimate of indirect conflict effects.

Our key finding is that direct conflict effects are relatively modest but that they are dwarfed by indirect ones: Conflict reduced the area grown with winter crops by up to 0.755 million ha, 88.4% of which due to indirect effects. On winter wheat fields, changes in NDVI suggest a conflict-induced drop in yield of up to 1.5%, a loss of 4.7 mn. t of which 4.4 mn t. are attributable to indirect and 0.3 mn. t to direct effects. Findings are robust across a range of alternative sensors (Landsat and MODIS). Also, expected winter wheat yields drop most in rayons with a larger share of small producers, suggesting that conflict hurts small farmers most given their more limited access to diversification options.

Our findings relate to and contribute to three strands of literature. First, while a large body of studies discusses the link between conflict and food security, the case most prevalent in developing countries is one where shocks to food production–or the value of agricultural endowments more generally–trigger conflict. We show that a case like of Ukraine where causality runs from conflict to food production, can be analyzed using satellite imagery to generate measures of agricultural production as outcome variables as well as indicators of conflict beyond what is routinely discussed in the literature and, for the latter, demonstrate that our measure outperforms measures of conflict used routinely in other studies.

Second, most of literature focused on direct impacts of conflict, sometimes on long-term outcomes such as human capital rather than production. The massive population movements, economic disruption, and input shortages experienced by Ukrainian farmers due to the war suggest that, in addition to direct effects, indirect ones need to be considered. Constructing a panel of VCs provides an expedient way to obtain an estimate of indirect effects in a way that avoids selection issues based on the assumption that all changes over time not explicitly controlled for can be directly or indirectly attributed to the conflict. While this implies our estimate is an upper bound, it provides a basis for further scrutiny or disaggregation, ideally complemented by a more structural approach towards identifying channels through which such effects materialize.

Finally, while much of existing analyses are ex post, often involving significant time lags, use of proxies such as NDVI at a high level of granularity allows our estimates to be used as a basis for measures (e.g., cash or in-kind transfers or opening up of markets) to help attenuate immediate conflict effects and in doing 3