ones such as 2016/17 (-0.14) that are unfavorable to development of fungi and the associated spread of stem rot so that soybean’s ability to fix nitrogen outweighs its role as potential disease vector. While other crops have a slightly larger negative effect, monocropping of sunflower is estimated to have the most negative effects with coefficients of -0.94 in 2016/17 when, in addition to disease permanence, water access is likely to have acted as a constraint, and -0.61 in 2017/18. With coefficients of -0.45 and -0.90 a three-year sunflower succession has a more negative impact than a two-year one.

As limited training data made it impossible to distinguish between wheat and barley as the main cereal crops in satellite imagery, cereals’ high levels of self-incompatibility, with coefficients of -0.30 and -0.79 for two-year cereal successions in 2016/17 and 2017/18, respectively are only slightly above those for maize (-0.22 and -0.19 in the two periods) and other crops (-0.29 and -0.51), reflecting the positive contribution of soybeans (the excluded category) as a predecessor crop due to N fixation. With -0.83, the estimated coefficient on a three-year cereal monocrop is not significantly different from that on a two-year sequence (-0.76).

# **4.2 Using alternative outcome variables**

Vegetation indices based on remote sensing data can complement statistical data two ways. First, rather than having one outcome variable (yield), they offer additional variables such as soil water content or leaf area at much higher temporal resolution. This allows to take corrective action in near real time<sup>13</sup> and to assess channels for observed effects _ex post_ . Second, by providing an independent source of information, they can allay concerns about bias in statistical data that may result from misreporting to statistical agencies (e.g., to avoid taxes); partial coverage such as the omission of nonregistered or household farms in the case of Ukraine; or other types of measurement error.<sup>14</sup> To address these issues within the confines of this paper,<sup>15</sup> we complement yield analysis with an aggregate analysis where the means/sums of four key vegetation indices over relevant crops’ entire vegetation period is used as outcome variables in regression equation (1). These indices, widely used in the literature and practical work are the enhanced vegetation index (EVI), the leaf area index (LAI), fraction of photosynthetically active radiation (FAPAR), and the Land Surface Water Index (LSWI).

The EVI is based on the normalized difference vegetation index (NDVI) that is widely used in the literature and agronomy but minimizes canopy-soil variations and improves sensitivity in dense vegetations. The

> 13 While several firms provide such services to large farms in Ukraine, they generally use satellite imagery to identify areas at risk and complement it with drone imagery or manual scouting.

> 14 In our case, inability to match all the village councils is a relevant concern in this respect.

> 15 While detailed discussion of temporal variation in these indices together with detailed climatic data would be of great interest to provide insights on the mechanisms through which crop rotation and other factors affect yields, it is well beyond the scope of this paper.

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