Impact of climate change on the area of major crops

1Yemelyanov, MO, 2Shelestov, AYu., 2Yailymova, HO, 1Shumilo, LL
1Space Research Institute of the NAS of Ukraine and SSA of Ukraine, Kyiv, Ukraine
2National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute», Kyiv, Ukraine
Space Sci. & Technol. 2022, 28 ;(2):30-38
Publication Language: Ukrainian
In this work, a statistical analysis of the time series of areas of majoritarian crops for 20 years (from 1998 to 2020) is carried out, and the influence of agro-climatic zones on the area of cultivation of major crops is analyzed. Climate change is acutely felt in the southern regions of Ukraine, increasing the production risk in the agricultural sector through changes in temperature, precipitation, and other extreme weather events. Historical climatic data indicate an increase in temperature on the territory of Ukraine, and climate forecasts suggest further warming, especially in the south of Ukraine.
     Using satellite and statistical data, changes in the earth’s surface are investigated for certain areas, which are characterized by the greatest changes in crop areas for the main types of crops. To analyze the dynamics of cultivated areas in relation to climatic zones, we used national statistical data for 1998–2019, maps of the classification of land cover from 2016—2020, data on climatic zones on the territory of Ukraine for 2000 and 2020, as well as the contours of administrative units of the NUTS2 level. Since statistical data for many districts are not available for the period 2019 – 2020 due to the reform of territorial boundaries, we used instead cultivated areas obtained from open satellite records. As additional and alternative information for the analysis of acreage, crop classification maps for 2016–2020 were used, obtained by specialists of the Space Research Institute of the National Academy of Sciences of Ukraine and the State Space Agency of Ukraine from their own in-depth training technologies.
     We used classification maps obtained using open satellite data of the Copernicus program: SAR Sentinel-1 and Sentinel-2 with a spatial resolution of 10 m. A comparison of statistical data and crop areas obtained from satellite data was carried out by applying the metric of statistical analysis of the correlation coefficient (r). To assess the accuracy, the coefficient of determination R2 between the statistical area of the main crops and the area according to satellite data was also applied.
Keywords: classification maps, climate change, deep learning, satellite data, Sentinel-1, Sentinel-2
1. Abstracts of the II International scientific-practical conference «Climate change and agriculture. Challenges for agricultural science and education», April 10—12, 2019. SI NMC «Agroosvita», Kyiv — Mykolaiv — Kherson, 2019. 495 p.
2. FAO, IIASA, ISRIC, ISS-CAS, and JRC (2009). Harmonized World Soil Database (version 1.1). Harmonized World Soil Database (version 1.1). FAO, Rome, Italy and IIASA, Laxenburg, Austria.
3. Keyzer M. A., Merbis M. D., Witt R., Heyets V., Borodina O., Prokopa I. (2012). Farming and rural development in Ukraine: making dualisation work. Farming and rural development in Ukraine: making dualisation work. Institute for Prospective Technological Studies, Joint Research Centre.
4. Kussul N., Lavreniuk M., Shelestov A., Skakun S. (2018). Crop inventory at regional scale in Ukraine: developing in season and end of season crop maps with multi-temporal optical and SAR satellite imagery. European J. Remote Sensing, 51, 627—636.
5. Kussul N., Shelestov A., Yailymov B., Yailymova H., Lavreniuk M., Shumilo L., Bilokonska Y. (2020). Crop monitoring technology based on time series of satellite imagery. IEEE 11th International Conference on Dependable Systems, Services and Technologies (DESSERT), 346—350.
6. Kussul N. (2020). Satellite Agricultural Monitoring in Ukraine at Country Level: World Bank Project. IGARSS 2020—2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE., 1050—1053.
7. Lesk C., Rowhani P., Ramankutty N. (2016). Influence of extreme weather disasters on global crop production. Nature, 529(7584), 84—87.
8. Lioubimtseva E., Beurs K. M., Henebry G. M. (2013). Grain Production Trends in Russia, Ukraine, and Kazakhstan in the Context of the Global Climate Variability and Change. Grain Production Trends in Russia, Ukraine, and Kazakhstan in the Context of the Global Climate Variability and Change / Eds T. Younos, C. A. Grady. Berlin: Springer, 121—141.
9. Nelson G. C., Valin H., Sands R. D., Havlík P., Ahammad H., Deryng D., Elliott J., Fujimori S., Hasegawa T., Heyhoe E., Kyle P., Von Lampe M., Lotze-Campen H., Mason d’Croz D., van Meijl H., van der Mensbrugghe D., M ller C., Popp A., Robertson R., Robinson S., Schmid E., Schmitz C., Tabeau A., Willenbockel D. (2014). Climate change effects on agriculture: Economic responses to biophysical shocks. Proceedings of the National Academy of Sciences, 111(9), 3274-9.
10. Porter J. R., Xie L., Challinor A. J., Cochrane K., Howden S. M., Iqbal M. M., Lobell D. B., Travasso M. I. (2014). Food security and food production systems. Food security and food production systems / Eds C. B. Field, V. R. Barros, D. J. Dokken, K. J. Mach, M. D.
11. Schlenker W., Roberts M. J. (2009). Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proceedings of the National Academy of Sciences, 106(37), 15594-98.
12. Shelestov A., Lavreniuk M., Vasiliev V., Shumilo L., Kolotii A., Yailymov B., Yailymova H. (2020). Cloud approach to automated crop classification using Sentinel-1 imagery. IEEE Transactions on Big Data, 6(3), 572—582.
13. State Statistics Service of Ukraine. URL: (Last accessed 10.05.2021).
14. Tack J., Barkley A., Nalley L. L. (2015). Effect of warming temperatures on US wheat yields. Proceedings of the National Academy of Sciences, 112(22), 6931—6936.
15. Tao F., Yokozawa M., Xu Y., Hayashi Y., Zhang Z. (2006). Climate changes and trends in phenology and yields of field crops in China, 1981—2000. Agricultural and forest meteorology, 138(1-4), 82—92.
16. World Bank (2016). World Development Indicators 2016. Washington, D.C.: The World Bank.