2024), compounding the damages sustained in 2014 (Bluszcz and Valente 2022), while Russia had at most modest losses or may have gained market share (Rose et al. 2023).

# **2.2 Measuring data on the conflict that are relevant for agricultural production**

Studies have traditionally used self-reported household survey data on conflict experience (Muriuki et al. 2023) or physical proximity to war zones (Akresh et al. 2022) to measure conflict exposure, although the subjective and static nature of these measures has long been recognized as a drawback. An important source of <mark>independent,</mark> time-varying data on <mark>conflict is the ACLED project (</mark> Raleigh et al. 2010), which relies on media reports on different types of violent events, together with the number of fatalities as a measure of severity, georeferenced to the nearest locality. Although it is not specific to the agriculture sector, the data it provides, combined with georeferenced household data, have been widely used to draw inferences on the household-level impacts of conflict. For example, in Ethiopia, exposure to an additional battle was found to increase food insecurity, mainly through reduced scope for nonfarm activities (Abay et al. 2023b). In Nigeria, more intense conflicts, measured by the number of fatalities, reduced the quantity and variety of foods eaten (George et al. 2020) and impeded medium-scale farmers’ area expansion (Adelaja et al. 2023). In Mali, conflict significantly reduced the likelihood of using inputs, and cash transfers failed to reverse this (Sessou and Henning 2024).

<mark>To create a measure of time-varying conflict exposure that is relevant for agricultural production, we use</mark> p <mark>ublicly available Sentinel-2 imagery with medium spatial (10-meter pixel size) and temporal (five-day revisit cycle) resolution to identify conflict-induced damage to agricultural fields. We constructed a measure of the area of fields damaged by military actions such as crop burning, heavy vehicle movement, or artillery shelling in nine oblasts (districts) affected by fighting for every two-week period since the start of the war.</mark><sup>4</sup> <mark>During the growing season, field damage is often visible only for a short period, although it may change a field’s level of productivity in the long term, especially if unexploded ordnance is left behind.</mark> To ensure that our measure focuses on agricultural field <mark>s, w</mark> e mask out areas that were not cultivated with agricultural crops in at least one of the pre-war years during 2019–21.<sup>5</sup> <mark>We complement this with a measure that indicates if a VC was o</mark> ccupied, involved in active fighting, or had troop presence <mark>, based on online daily situational reports from local and regional military administrations.</mark><sup>6</sup>

> 4 The work involved <mark>visual inspection by trained university students who, in the training phase and in cases of doubt, were also provided with higher resolution imagery. The presence of field damage was recorded as a zero/one dummy.</mark> Duncan et al. (2023) use a machine learning approach with higher resolution imagery to detect transitory artillery craters and draw out potential longer-term implications in the Ukrainian context. For Cambodia, Lin et al. (2021) discuss the longer-term damage wrought by unexploded ordnance and farmers’ adjustment to the associated dangers. In Ukraine, the oblasts included are <mark>Kyiv, Chernihiv, Sum, Kharkiv, Luhansk, Donetsk, Zaporizja, Kherson, and Mykolaiv.</mark>

> 5 While the measure used here is based on manual classification, Kussul et al. (2023) discuss the methodology and use of the data as an input into a machine learning model in more detail.

> 6 Open-source data (https://novy.tv/ru/news/2022/06/02/karta-bojovyh-dij-v-ukrayini/ and https://liveuamap.com/ru/time).

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