profits and output values between small and large producers, suggesting that the war also impaired farmers’ ability to invest in higher value crops (von Cramon-Taubadel and Nivievskyi 2023). Removing technical, financial, or market-related constraints to diversification in locations not directly affected by conflict could thus be a high priority for support to complement or, if active fighting continues, even precede activities such as de-mining, which aim to restore the productive capacity of areas directly affected by the conflict.

We contribute to three strands of literature. First, we show that freely available medium-resolution imagery can be used to construct an indicator of conflict-related damage to agricultural fields at high levels of spatial and temporal resolution. This indicator is particularly suitable if the affected areas are large and damage is visible at least for some period of time. It builds on a body of studies that use imagery with lower resolution to assess the effects of war (Alix-Garcia et al. 2013), land use change (Meyfroidt et al. 2016), grazing intensity (Dara et al. 2020), or changes in crop cultivation <mark>(Munteanu et al. 2017), often o</mark> ver longer periods of time (Matasov et al. 2019). This indicator can also complement data from the commonly used <mark>Armed Conflict Location and Events Data (ACLED) project (</mark> Raleigh et al. 2010), which uses the number of casualties, a measure that may be less meaningful in agricultural settings, as a proxy for conflict intensity. In the Ukrainian context, our measure improves on the use of press reports or nighttime light data (Zhukov 2023), which are less relevant for agriculture, and it can be applied consistently at a larger scale without the computational and cost burden that would be associated with scaling up the use of data with higher frequency and resolution (Duncan et al. 2023). Developing an automated classifier for conflict-related damage to rural fields would be a straightforward extension over the manual classification adopted here.

Second, we build on a growing body of work that combines high-quality training data and machine learning with remotely sensed data for analysis of changes in land cover not only at the global level (Potapov et al. 2022) or country level (Lark et al. 2017), but also at a more granular scale (Lobell et al. 2020), including the field level (Deines et al. 2021). This allows us to demonstrate the scope for using spatial data to assess the impact of the conflict (Witmer 2015) in a way that uses customized (Ben Yishay et al. 2024) or readily available information (Chiovelli et al. 2018). Use of remotely sensed data to assess effects of conflict on preservation of environmentally sensitive areas or yields can rely on similar techniques in principle if ground-truth data is available.

Third, we add to the literature arguing that the effects of conflict vary over time and extend beyond areas directly affected by violence (Miguel and Roland 2011). Studies show that long-term effects of conflict may arise from displacement (Chiovelli et al. 2021; Ibanez et al. 2022) as exposed individuals may reduce investment in human capital (Akresh et al. 2023; Büttner et al. 2022) or physical capital (Rockmore 2020; Sinha et al. 2022) and alter their risk perceptions (Federle et al. 2022) or activity choices (Arias et al. 2019; Fergusson et al. 2020). Conflict may also affect the cost of trade (Amodio and Di Maio 2018), the level of 3