winter crops as of May 1. As decisions on planting of winter crops were made before the conflict started, we would expect to see effects only through either destruction of a field’s crop cover or a failure to manage crops that had already been planted, expecting us to expect this effect to be relatively modest.

Conflict-related damages to fields or input shortages or price expectations may affect the ability to manage crops properly, thus leading to reductions in expected yield. To assess such effects for fields that, based on our crop classification are planted with winter cereal,<sup>4</sup> we use the NDVI, computed on the GEE platform, as a proxy for crop conditions and expected yield. This is motivated by the fact that recent agronomic studies including Dhillon _et al._ (2020) for the US, Vannoppen and Gobin (2021) for western Europe, and Nagy _et al._ (2021) for Eastern Europe show that, for winter wheat, the NDVI, ideally combined weather data (Aula _et al._ 2021), provides rather accurate yield prediction. An early application to the US and Ukraine is Becker-Reshef _et al._ (2010).<sup>5</sup> We can use the parameters obtained by some of these studies to illustrate the order of magnitude of expected yield effects: For data from the 2012-16 growing seasons in Poland, the Czech Republic, and Eastern Germany, Panek and Gozdowski (2020) find that an increase of 0.1 in early season (until mid-May) NDVI increased grain yields by between 1.1 and 2.6 t/ha or by 25%-50% with R<sup>2</sup> of between 0.5 and 0.85. For Argentina, Lopresti _et al._ (2015) find an increase of yield of 109 kg/ha with each additional point of peak NDVI as measured by MODIS data (R<sup>2</sup> of 0.53). For10 seasons in the 2000-2013 period from South Africa’s Free State, Mashaba _et al._ (2017) find that MODIS-based NDVI values from up to 30 days before anthesis have the highest predictive power (with an R<sup>2</sup> of 0.73) with a 0.1 point change in the NDVI predicted to increase wheat yield by 1.21 t/ha (from a mean of about 2.2 t/ha).

# **2.3 Ukraine’s context and motivation**

With 41.5 million ha of highly fertile land, Ukraine has traditionally been a major source of food grains. Before the war, agriculture contributed about 10% to GDP and 42% of the county’s exports. After decollectivization in the early 2000s, when some 7 mn. land owners were provided with land shares of about 4 ha each, the agricultural sector’s value added doubled. Some 20 mn. ha of Ukraine’s agricultural land is farmed by large farms, often with links to foreign capital (Deininger _et al._ 2018); 12 mn. ha is cultivated by small and household farms; and some 9.2 mn. ha was originally under state or communal land, a sector the size of which has been reduced significantly via disposal of land. was in non-transparent ways.

The war disrupted implementation of far-reaching structural reforms relating to agricultural land in Ukraine. To increase investment, diversification and climate resilience by making property rights more secure,<sup>6</sup>

> 4 As wheat accounts for more than 90% of winter cereals, we use winter wheat and winter cereals interchangeably.

> 5 Noting that the NDVI for winter wheat attains its maximum shortly before anthesis, most studies use the peak NDVI, Johnson _et al._ (2021) or the value of this variable just prior to anthesis although Panek and Gozdowski (2020) obtain the best fit with NDVI values from a slightly earlier date. 6 Several factors, including the lack of a unified lease registry, a mix of digital and paper documents, corruptible registrars, courts and police forces, and legislation prohibiting registration of leases shorter than 7 years that tends to drive landowners into informality, further reduce users’ ability to enforce leases, diverting resources from investment or reducing incentives for it.

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