The above process generated raster data that provide information on crops grown in 2016, 2017 and 2018 for every 10*10 m pixel in Ukraine. Combining pixel-level data across years provides the basis for computing the area devoted to different types of crop rotations just by counting pixels. To keep the regressions manageable, we focus on maize, sunflower, cereals (wheat and barley) and soybean as the crops of interest.<sup>9</sup> For each crop cultivated in 2017 or 2018, we identify the immediate predecessor crop from this set plus an ‘other’ category and, for the 2018 data with mono-cropping (i.e., cultivation of the same crop in 2017) also whether the pixel was mono cropped in 2016 as well. Overlaying the resulting raster file with boundaries for some 7,600 rural village councils (VC) from the State Service of Geodesy and Cadaster (SGC) allows us to obtain the total area with different rotational practices at VC level which will be a key dependent variable in the regressions reported below.

We use statistical and remotely sensed information as our two outcome variables. Information on area cultivated and output by crop is collected annually by the State Statistics Service of Ukraine (SSSU). In contrast to more detailed information on input use and output prices that is only available for a subset of producers in a way that makes it impossible to infer the actual location where production took place,<sup>10</sup> these data are obtained from the universe of registered producers and reported by village councils where production took place. Aggregation and merging these data with rotational information from satellite imagery allows us to add information on village level yields for the crops of interest as key dependent variables in our regressions.

As statistical data may still be affected by biases or measurement error, we complement statistical yield data with field-level indices of vegetative health and soil water content for pixels with different crop rotations as alternative outcome variables. To do so, we consider the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) at 250m and the Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) and Land Surface Water Index (LSWI) at 500m resolution from MODIS data.<sup>11</sup> To avoid unequal number of observations due to clouds or shadows, a median filter was applied to produce 16-day composites for each of the indices that were then summed over relevant crops’ vegetation periods. The crop classification map was then converted to the same spatial resolution as these indices and for each VC, crop, and index, the overlap

> 9 As budget-related factors precluded collection of enough training data in all years to distinguish wheat and barley in the imagery, we aggregate the two and note that, based on statistical data reported below, an increasing share of cereal is devoted to wheat. 10 The data used come from ‘form F29’. Detailed information on inputs and outputs is supposed to be collected from a sample of those farming fewer than 200 ha (or a threshold of value added) via ‘form F2’ and for the universe of farms above 200 ha via ‘form F50’. As these data are collected at the enterprise level and as even for those farming only one operational unit, fields are generally dispersed across several VCs, matching becomes much more difficult. See Deininger _et al._ (2018) for a more detailed description of the data. 11 As fields are relatively large and to ease computation burden and capitalize on higher temporal frequency of MODIS as compared to Sentinel, these indicators were produced at the field level by aggregating daily MODIS data.

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