so improve resilience and prevent longer-term erosion of productive capacity. Beyond informing design of such measures, the data presented here can potentially also be useful to assess their impact.

The rest of the paper is structured as follows. Section two provides background and motivation and introduces the strategy to estimate direct and indirect impacts of conflict. Section three describes data sources and highlights descriptive evidence. Section four presents econometric results for winter crop cover and NDVI of cereals and discusses their implications. Section five concludes by highlighting potential policy relevance and areas for future research.

# **2. Motivation and approach**

# **2.1 Conflict and food security in the literature**

A large literature identified weather or price shocks that affect households’ relative endowments as potential triggers for violent conflict by a large body of literature (see Berman _et al._ (2021) or Blair _et al._ (2021) for a discussion and references). Studies of the impact of violence on food security focus almost exclusively on the consumption side; in fact the review by Martin-Shields and Stojetz (2019) includes no studies that directly analyze conflict impacts on food production. Thus, while such studies highlight the need for proper counterfactuals and use of panel data, Ukraine’s situation is sufficiently different from that analyzed in most studies on conflict and food security to limit the transferability of substantive insights and require a different analytical approach.<sup>1</sup> One advantage of agriculture is that remotely sensed covers large areas consistently. Satellite imagery is now routinely used to consistently quantify changes in land use such as deforestation (Hansen _et al._ 2013) or crop expansion (Potapov _et al._ 2022) globally. In the US, a Landsat-based crop data layer is available since the 1980s to allow parcel-level analysis (Lark _et al._ 2017). For developing countries, the use of such imagery to provide reliable output or productivity indicators down to the plot level has recently been demonstrated (Lobell _et al._ 2020) with access to ground-based training data a key constraint (Burke _et al._ 2020). Easier access to imagery and cloud computing platform to store data and provide computing power has greatly increased extent, scope, and granularity with which satellite imagery is used to assess damages due to conflict since the review of studies on this topic by Witmer (2015).

Documenting conflict effects in urban areas requires very high resolution imagery as illustrated by Mueller _et al._ (2021) who develop a machine learning algorithm applied to Syria.<sup>2</sup> Effects of war on agricultural production can rely on lower resolution imagery as illustrated by studies such as Alix-Garcia _et al._ (2013) for Darfur and a number of studies covering <mark>areas of the former Soviet Union including Ukraine</mark> to assess

> 1 As exposure to violence often leads the poorest to revert to subsistence as in Colombia (Arias _et al._ 2019), levels of food consumption may not decline linearly with conflict exposure as shown in Afghanistan (D’Souza & Jolliffe 2013). This has led to use of dietary diversity as a more appropriate measure of conflict impact there (D'Souza & Jolliffe 2014), in Nigeria (George _et al._ 2020), and Cote d’Ivoire (Dabalen & Paul 2014).

> 2 Wouters _et al._ (2021) similarly use UAV imagery in Malawi to assess flood-related damage to houses.

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