We build on the price transmission framework to identify domestic wheat price effects of wheat export controls. We explicitly take into account that a harvest failure causes domestic price effects. Moreover, the analysis at the regional level provides further evidence of the functioning of export controls in a large country. Results suggest a pronounced regional heterogeneity in the strength of domestic price effects of the 2010/11 export ban in Russia. The wheat price dampening effects amount to up to 67% and are strongest in the major wheat exporting region with direct access to the world market. This effect is transmitted to other regions by increased and reversed interregional trade flows. In contrast, we find that regional variation of export controls’ domestic price effects in Ukraine is rather small.

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The Russian Federation’s efforts to expand its regional political influence culminated in launching a full‐scale war of aggression on Ukraine on 24 February 2022. As both countries are large exporters of commodities crucial for global food and energy security, the resulting abrupt supply chains disruptions created substantial uncertainty in commodity markets worldwide. This study quantifies to what extent this major shock induced global commodity prices to move more synchronously by gauging their time‐varying comovement. Using the concordance index, it analyses the development of 15 key global commodity price indices from January 2010 to July 2022. We find that the supply chains disruptions increased synchronisation of grain, energy and fertiliser prices at the global level in direction and magnitude. Moreover, they resulted in contagion across numerous food and non‐food markets, creating a global covariate shock to food and energy security. Notably, the increased synchronisation at broad scale restricts the ability of consumers to mitigate the adverse effects of food and energy price inflation by resorting to inexpensive alternatives. Hence, policymakers must improve the resilience of global food supply chains sustainably such that adverse effects of attaining the Sustainable Development Goals in crises can be minimised.

Crop rotation is an important determining factor of crop productivity. Sustainable agriculture requires correct rules of crop rotation. Failure to comply with these rules can lead to deterioration of soil biochemical characteristics and land degradation. In Ukraine as well as in many other countries, sunflower monocropping is common practice and the impact of this fact should be studied to find the most precise rules to save the economic potential of land and minimize land degradation factors. This research provides an evaluation of the sunflower monocropping effect on the vegetation indices obtained from MODIS vegetation indices datasets for Ukraine as one of the countries with the biggest sunflower export in Europe. The crop rotation schemes are represented by their area proportions at the village level calculated based on the crop classification maps for 2016 to 2020. This representation gives the possibility to use regression models and f-test feature importance analysis to measure the impact of 3-year and 5-year crop rotation sequences. For these purposes, we use several models: a four-year binary representation model (model A1) and a model with all possible three-year crop rotation scheme representations (model B). These models gave the possibility to evaluate crop rotation schemes based on their biophysical impact on the next sunflower plantings and found that sunflower planting with an interval of three or more years is optimal in terms of the sustainability of soil fertility.

The measurement of competitiveness in agriculture is often based on data for average or 'typical' farms. However, if the farms that are summarized in this manner are heterogeneous, inferences based on aggregated measures can be misleading. The authors propose the calculation of competitiveness indicator distributions based on micro-data. This procedure makes it possible to determine for each product (a) the proportion of the farms producing it that is competitive and (b) the proportion of the total production/output value of the product that derives from competitive/uncompetitive farms. Furthermore, the authors propose and contrast distribution-based and aggregate-based approaches for ranking activities.