sunflower to the detriment of dairy, horticulture, and agri-processing, all of which would require landattached investment. To address tenure security constraints, the government recently passed legislation that would allow farmers to sell or buy agricultural land.<sup>7</sup> Subsidy programs to promote diversification of crops and a move towards higher value addition have also been put in place. Partly due to lack of data to underpin appropriate designs, these have been ill-targeted and ineffective.

# **3. Data, descriptive statistics, and econometric approach**

While national statistical data suggest that the diversity of the country’s agricultural output mix has decreased over time, they provide no information on mono-cropping that would allow to assess the incidence and impact of crop rotations. To generate such data, we combine a crop cover map generated from freely available satellite imagery via machine learning with statistical data for 2016, 2017, and 2018. With some 24% of area in maize, 17% in soybeans, 10% in sunflower, and 20% in cereals having been cropped in at least two consecutive seasons on the same field, detailed study of associated yield effects seems warranted and we describe the econometric approach for doing so.

# **3.1 Data sources and key outcome variables**

Remotely sensed data provide objective information at low cost and the number, resolution and revisit frequency of optical or radar sensors to provide such data have expanded significantly. Imagery covering the entire world at 10 m spatial resolution every 5 days is available freely from the European Space Agency Sentinel constellation since 2013. Access to cloud computing platforms to process these at scale implies that location-specific training data are rapidly emerging as a binding constraint to generation of crop cover maps at field-scale. To generate such training data for Ukraine, _in-situ_ data collection along main roads was undertaken every year during the 2016-18 period along different routes as displayed in figure 1. Experts covered these routes two times each year to capture images of winter as well as summer crops to serve as training data following standard guidelines. Appendix table 1 provides information on the distribution of fields by crop type.

To generate crop cover estimates for 2016, 2017, and 2018, all optical data from Sentinel-2 and SAR data from Sentinel-1 during the vegetation period were used to run a convoluted neural network on the Amazon Web Services cloud computing platform.<sup>8</sup> For classifier training, half of the data was randomly assigned to training and independent validation samples and accuracies obtained based on independent test sets for each of the crops received are in appendix table 1. Resulting maps for the four crops of interest are displayed in figure 2 with differences across years visible even at this scale.

> 7 Recently passed legislation would partly address this constraint through a phased opening of agricultural land purchase markets starting on July 1, 2021 when agricultural land purchases will be allowed only by individuals with an ownership limit of 100 ha per person.

> 8 See Kussul _et. al_ (2018 and 2019) for a detailed description of methodology.

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