MULTIDIMENSIONAL ASSESSMENT OF THE AGRICULTURE IN UKRAINE IN A REGIONAL CONTEXT: WARTIME CHALLENGES AND RESILIENCE STRATEGIES
Keywords:
agricultural sector, taxonomic analysis, regional development, agricultural policy, region, resilience, Ukraine, agriculture, sustainability, multidimensional modeling, model, economic policy.
Abstract
Agriculture plays a vital role in Ukraine's economy, acting as a key driver of national economic development and food security. However, the war with russia has posed significant challenges. This study examines the state of Ukraine's agricultural sector, with a particular focus on regional disparities that have been exacerbated by the prolonged conflict. Given agriculture’s critical role in the economy and the severe challenges posed by the war, this study employs a taxonomic analysis to assess and rank Ukraine’s regions based on six key agricultural indicators: the value of agricultural products per capita, the average monthly wage of permanent agricultural workers, sown areas of grain and leguminous crops per capita, production volumes of grain and leguminous crops per capita, crop yield, and livestock weight per capita. The study found significant regional disparities in agricultural performance due to the war, with western regions (Khmelnytskyi, Poltava, Cherkasy) demonstrating greater resilience compared to the eastern and southern regions (Kherson, Zaporizhzhia, Donetsk, Luhansk), highlighting the need for targeted policy interventions, strategic support mechanisms, and data-driven approaches to address agricultural challenges in conflict-affected areas. These findings provide valuable insights for regional policy development and targeted support in post-war recovery. They also underscore the importance of data-driven approaches to addressing agricultural challenges in the context of geopolitical instability. The study's findings highlight the need for adaptive agricultural strategies that consider regional specificities to ensure the sustainable development of agriculture during Ukraine’s recovery process.
References
Andriushchenko, K., Kovtun, V., Shergina, L., Rozhko, O., & Yefimenko, L. (2020). Agro-based Clusters: A tool for effective management of regional development in the ERA of globalisation. TEM Journal, 9(1). 198-204.
2. Asheim, B.T. (2019). Smart specialisation, innovation policy and regional innovation systems: what about new path development in less innovative regions? Innovation: The European Journal of Social Science Research, 32(1). 8-25.
3. Bachev, H., Ivanov, B., & Sarov, A. (2021). Assessing governance aspect of agrarian sustainability in Bulgaria. Bulgarian Journal of Agricultural Sciences, 27(3). 429-440.
4. Bai, D., Ye, L., Yang, Z., & Wang, G. (2024). Impact of climate change on agricultural productivity: a combination of spatial Durbin model and entropy approaches. International Journal of Climate Change Strategies and Management, 16(4). 26-48.
5. Balakrishnan, S. (2019). Recombinant urbanization: Agrarian–urban landed property and uneven development in India. International Journal of Urban and Regional Research, 43(4). 617-632.
6. Bellon, M.R., Kotu, B.H., Azzarri, C., & Caracciolo, F. (2020). To diversify or not to diversify, that is the question. Pursuing agricultural development for smallholder farmers in marginal areas of Ghana. World Development, 125. 104682.
7. Biru, W.D., Zeller, M., & Loos, T.K. (2020). The impact of agricultural technologies on poverty and vulnerability of smallholders in Ethiopia: a panel data analysis. Social Indicators Research, 147(2). 517-544.
8. Boiko, V., Kwilinski, A., Misiuk, M., & Boiko, L. (2019). Competitive advantages of wholesale markets of agricultural products as a type of entrepreneurial activity: the experience of Ukraine and Poland. Економiчний часопис-XXI, 175(1-2). 68-72.
9. Bulut, E., & Bayraktar, Y. (2023). Do Agricultural Supports Affect Production? A Panel ARDL Analysis of Turkey. Journal of Agricultural Sciences, 29(1). 249-261.
10. Chandio, A.A., Jiang, Y., Fatima, T., Ahmad, F., Ahmad, M., & Li, J. (2022). Assessing the impacts of climate change on cereal production in Bangladesh: evidence from ARDL modeling approach. International Journal of Climate Change Strategies and Management, 14(2). 125-147.
11. Ding, J., Liu, B., & Shao, X. (2022). Spatial effects of industrial synergistic agglomeration and regional green development efficiency: Evidence from China. Energy Economics, 112. 106156.
12. Donkoh, S.A., Azumah, S.B., & Awuni, J.A. (2019). Adoption of improved agricultural technologies among rice farmers in Ghana: A multivariate probit approach. Ghana Journal of Development Studies, 16(1). 46-67.
13. Elavarasan, D., & Vincent, P.D. (2020). Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE access, 8. 86886-86901.
14. Feng, W., Liu, Y., & Qu, L. (2019). Effect of land-centered urbanization on rural development: A regional analysis in China. Land Use Policy, 87. 104072.
15. Gururani, S. (2023). Cities in a world of villages: Agrarian urbanism and the making of India's urbanizing frontiers. Changing Asian Urban Geographies. 97-115.
16. Hrytsiuk, P., Babych, T., Baranovsky, S., & Havryliuk, M. (2023). Assessing of Climate Impact on Wheat Yield using Machine Learning Techniques. ICST. 314-329.
17. Jiang, H., Hu, H., Zhong, R., Xu, J., Xu, J., Huang, J., & Lin, T. (2020). A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level. Global change biology, 26(3). 1754-1766.
18. Kozin, I.V., Maksyshko, N.K., & Perepelitsa, V.A. (2020). A Fragmented Model for the Problem of Land Use on Hypergraphs. Cybernetics and Systems Analysis, 56(5). 753-757.
19. Loizou, E., Karelakis, C., Galanopoulos, K., & Mattas, K. (2019). The role of agriculture as a development tool for a regional economy. Agricultural Systems, 173. 482-490.
20. Mao, L., Huang, Y., Zhang, X., Li, S., & Huang, X. (2022). ARIMA model forecasting analysis of the prices of multiple vegetables under the impact of the COVID-19. Plos one, 17(7). e0271594.
21. Ntim-Amo, G., Qi, Y., Ankrah-Kwarko, E., Ankrah Twumasi, M., Ansah, S., Boateng Kissiwa, L., & Ruiping, R. (2022). Investigating the validity of the agricultural-induced environmental Kuznets curve (EKC) hypothesis for Ghana: Evidence from an autoregressive distributed lag (ARDL) approach with a structural break. Management of Environmental Quality: An International Journal, 33(2). 494-526.
22. Paul, P.K., Bhuimali, A., Sinha, R.R., Tiwary, K.S., Baby, P., Rajesh, R., & Chancellor, P. V. (2020). Big Data and Data Analytics in Agricultural Space: Towards Sustainable and Intelligent Agro Sector Development. Management of Data in AI Age. 95-120.
23. Rahmati, O., Falah, F., Dayal, K. S., Deo, R.C., Mohammadi, F., Biggs, T., & Bui, D.T. (2020). Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia. Science of the total environment, 699. 134230.
24. Reardon, T., Echeverria, R., Berdegué, J., Minten, B., Liverpool-Tasie, S., Tschirley, D., & Zilberman, D. (2019). Rapid transformation of food systems in developing regions: Highlighting the role of agricultural research & innovations. Agricultural systems, 172. 47-59.
25. Santika, T., Wilson, K.A., Budiharta, S., Law, E.A., Poh, T.M., Ancrenaz, M., & Meijaard, E. (2019). Does oil palm agriculture help alleviate poverty? A multidimensional counterfactual assessment of oil palm development in Indonesia. World Development, 120. 105-117.
26. Schrijver, R., Westerink, J., de Jong, K., Smit, B., van der Meer, R., & Dijkshoorn, M. (2022). Regional development and spatial use biodiversity and policy performance and impact agrosectors, green economy and landuse.
27. Shpak, N., Kulyniak, I., Gvozd, M., Vveinhardt, J., & Horbal, N. (2021). Formulation of development strategies for regional agricultural resource potential: The ukrainian case. Resources, 10(6). 57.
28. Storm, H., Baylis, K., & Heckelei, T. (2020). Machine learning in agricultural and applied economics. European Review of Agricultural Economics, 47(3). 849-892.
29. Teixeira, D.S.J.A., Koblianska, I., & Kucher, A. (2023). Agricultural production in Ukraine: An insight into the impact of the Russo-Ukrainian war on local, regional and global food security. Journal of Agricultural Sciences (Belgrade), 68(2). 121-140.
30. Trusova, N., Hryvkivska, O., Kepko, V., Prystemskyi, O., & Yavorska, T. (2020). Innovative development and competitiveness of agribusiness subjects in the system of ensuring of economic security of the regions of Ukraine. Rivista di Studi sulla Sostenibilita, 2. 141-156.
31. Vdovyn, M., & Zomchak, L. (2022). Export in services of Ukraine: pre-pandemic period, Covid-19 and war. Věda a perspektivy, 8(15). 48-57
32. Wang, M., Xu, M., & Ma, S. (2021). The effect of the spatial heterogeneity of human capital structure on regional green total factor productivity. Structural Change and Economic Dynamics, 59. 427-441.
33. Wen, Q., Wang, Y., Zhang, H., & Li, Z. (2019). Application of ARIMA and SVM mixed model in agricultural management under the background of intellectual agriculture. Cluster computing, 22. 14349-14358.
34. Zomchak, L., & Kukhotska, T. (2023). Wheat market price dynamics in Ukraine: quantitative exploration and forecasting. European Journal of Economics and Management, 4(9). 14-22.
35. Zomchak, L., Hakava, S. (2025). Unveiling Disparities and Resilience in Ukrainian Regional Labor Markets: Multidimensional Ranking Approach. Developments in Information and Knowledge Management Systems for Business Applications. Studies in Systems, Decision and Control, 578. Springer, Cham. https://doi.org/10.1007/978-3-031-80935-4_23
36. Zomchak, L., Kukhotska, T. (2025). Building Food Security Resilience in Ukraine: The Autoregressive Approach to Food Price Forecasting. Developments in Information and Knowledge Management Systems for Business Applications. Studies in Systems, Decision and Control, 578. Springer, Cham. https://doi.org/10.1007/978-3-031-80935-4_19
2. Asheim, B.T. (2019). Smart specialisation, innovation policy and regional innovation systems: what about new path development in less innovative regions? Innovation: The European Journal of Social Science Research, 32(1). 8-25.
3. Bachev, H., Ivanov, B., & Sarov, A. (2021). Assessing governance aspect of agrarian sustainability in Bulgaria. Bulgarian Journal of Agricultural Sciences, 27(3). 429-440.
4. Bai, D., Ye, L., Yang, Z., & Wang, G. (2024). Impact of climate change on agricultural productivity: a combination of spatial Durbin model and entropy approaches. International Journal of Climate Change Strategies and Management, 16(4). 26-48.
5. Balakrishnan, S. (2019). Recombinant urbanization: Agrarian–urban landed property and uneven development in India. International Journal of Urban and Regional Research, 43(4). 617-632.
6. Bellon, M.R., Kotu, B.H., Azzarri, C., & Caracciolo, F. (2020). To diversify or not to diversify, that is the question. Pursuing agricultural development for smallholder farmers in marginal areas of Ghana. World Development, 125. 104682.
7. Biru, W.D., Zeller, M., & Loos, T.K. (2020). The impact of agricultural technologies on poverty and vulnerability of smallholders in Ethiopia: a panel data analysis. Social Indicators Research, 147(2). 517-544.
8. Boiko, V., Kwilinski, A., Misiuk, M., & Boiko, L. (2019). Competitive advantages of wholesale markets of agricultural products as a type of entrepreneurial activity: the experience of Ukraine and Poland. Економiчний часопис-XXI, 175(1-2). 68-72.
9. Bulut, E., & Bayraktar, Y. (2023). Do Agricultural Supports Affect Production? A Panel ARDL Analysis of Turkey. Journal of Agricultural Sciences, 29(1). 249-261.
10. Chandio, A.A., Jiang, Y., Fatima, T., Ahmad, F., Ahmad, M., & Li, J. (2022). Assessing the impacts of climate change on cereal production in Bangladesh: evidence from ARDL modeling approach. International Journal of Climate Change Strategies and Management, 14(2). 125-147.
11. Ding, J., Liu, B., & Shao, X. (2022). Spatial effects of industrial synergistic agglomeration and regional green development efficiency: Evidence from China. Energy Economics, 112. 106156.
12. Donkoh, S.A., Azumah, S.B., & Awuni, J.A. (2019). Adoption of improved agricultural technologies among rice farmers in Ghana: A multivariate probit approach. Ghana Journal of Development Studies, 16(1). 46-67.
13. Elavarasan, D., & Vincent, P.D. (2020). Crop yield prediction using deep reinforcement learning model for sustainable agrarian applications. IEEE access, 8. 86886-86901.
14. Feng, W., Liu, Y., & Qu, L. (2019). Effect of land-centered urbanization on rural development: A regional analysis in China. Land Use Policy, 87. 104072.
15. Gururani, S. (2023). Cities in a world of villages: Agrarian urbanism and the making of India's urbanizing frontiers. Changing Asian Urban Geographies. 97-115.
16. Hrytsiuk, P., Babych, T., Baranovsky, S., & Havryliuk, M. (2023). Assessing of Climate Impact on Wheat Yield using Machine Learning Techniques. ICST. 314-329.
17. Jiang, H., Hu, H., Zhong, R., Xu, J., Xu, J., Huang, J., & Lin, T. (2020). A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level. Global change biology, 26(3). 1754-1766.
18. Kozin, I.V., Maksyshko, N.K., & Perepelitsa, V.A. (2020). A Fragmented Model for the Problem of Land Use on Hypergraphs. Cybernetics and Systems Analysis, 56(5). 753-757.
19. Loizou, E., Karelakis, C., Galanopoulos, K., & Mattas, K. (2019). The role of agriculture as a development tool for a regional economy. Agricultural Systems, 173. 482-490.
20. Mao, L., Huang, Y., Zhang, X., Li, S., & Huang, X. (2022). ARIMA model forecasting analysis of the prices of multiple vegetables under the impact of the COVID-19. Plos one, 17(7). e0271594.
21. Ntim-Amo, G., Qi, Y., Ankrah-Kwarko, E., Ankrah Twumasi, M., Ansah, S., Boateng Kissiwa, L., & Ruiping, R. (2022). Investigating the validity of the agricultural-induced environmental Kuznets curve (EKC) hypothesis for Ghana: Evidence from an autoregressive distributed lag (ARDL) approach with a structural break. Management of Environmental Quality: An International Journal, 33(2). 494-526.
22. Paul, P.K., Bhuimali, A., Sinha, R.R., Tiwary, K.S., Baby, P., Rajesh, R., & Chancellor, P. V. (2020). Big Data and Data Analytics in Agricultural Space: Towards Sustainable and Intelligent Agro Sector Development. Management of Data in AI Age. 95-120.
23. Rahmati, O., Falah, F., Dayal, K. S., Deo, R.C., Mohammadi, F., Biggs, T., & Bui, D.T. (2020). Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia. Science of the total environment, 699. 134230.
24. Reardon, T., Echeverria, R., Berdegué, J., Minten, B., Liverpool-Tasie, S., Tschirley, D., & Zilberman, D. (2019). Rapid transformation of food systems in developing regions: Highlighting the role of agricultural research & innovations. Agricultural systems, 172. 47-59.
25. Santika, T., Wilson, K.A., Budiharta, S., Law, E.A., Poh, T.M., Ancrenaz, M., & Meijaard, E. (2019). Does oil palm agriculture help alleviate poverty? A multidimensional counterfactual assessment of oil palm development in Indonesia. World Development, 120. 105-117.
26. Schrijver, R., Westerink, J., de Jong, K., Smit, B., van der Meer, R., & Dijkshoorn, M. (2022). Regional development and spatial use biodiversity and policy performance and impact agrosectors, green economy and landuse.
27. Shpak, N., Kulyniak, I., Gvozd, M., Vveinhardt, J., & Horbal, N. (2021). Formulation of development strategies for regional agricultural resource potential: The ukrainian case. Resources, 10(6). 57.
28. Storm, H., Baylis, K., & Heckelei, T. (2020). Machine learning in agricultural and applied economics. European Review of Agricultural Economics, 47(3). 849-892.
29. Teixeira, D.S.J.A., Koblianska, I., & Kucher, A. (2023). Agricultural production in Ukraine: An insight into the impact of the Russo-Ukrainian war on local, regional and global food security. Journal of Agricultural Sciences (Belgrade), 68(2). 121-140.
30. Trusova, N., Hryvkivska, O., Kepko, V., Prystemskyi, O., & Yavorska, T. (2020). Innovative development and competitiveness of agribusiness subjects in the system of ensuring of economic security of the regions of Ukraine. Rivista di Studi sulla Sostenibilita, 2. 141-156.
31. Vdovyn, M., & Zomchak, L. (2022). Export in services of Ukraine: pre-pandemic period, Covid-19 and war. Věda a perspektivy, 8(15). 48-57
32. Wang, M., Xu, M., & Ma, S. (2021). The effect of the spatial heterogeneity of human capital structure on regional green total factor productivity. Structural Change and Economic Dynamics, 59. 427-441.
33. Wen, Q., Wang, Y., Zhang, H., & Li, Z. (2019). Application of ARIMA and SVM mixed model in agricultural management under the background of intellectual agriculture. Cluster computing, 22. 14349-14358.
34. Zomchak, L., & Kukhotska, T. (2023). Wheat market price dynamics in Ukraine: quantitative exploration and forecasting. European Journal of Economics and Management, 4(9). 14-22.
35. Zomchak, L., Hakava, S. (2025). Unveiling Disparities and Resilience in Ukrainian Regional Labor Markets: Multidimensional Ranking Approach. Developments in Information and Knowledge Management Systems for Business Applications. Studies in Systems, Decision and Control, 578. Springer, Cham. https://doi.org/10.1007/978-3-031-80935-4_23
36. Zomchak, L., Kukhotska, T. (2025). Building Food Security Resilience in Ukraine: The Autoregressive Approach to Food Price Forecasting. Developments in Information and Knowledge Management Systems for Business Applications. Studies in Systems, Decision and Control, 578. Springer, Cham. https://doi.org/10.1007/978-3-031-80935-4_19
Published
2025-02-26
How to Cite
ZOMCHAK, L., & DYDA, A. (2025). MULTIDIMENSIONAL ASSESSMENT OF THE AGRICULTURE IN UKRAINE IN A REGIONAL CONTEXT: WARTIME CHALLENGES AND RESILIENCE STRATEGIES. Herald of Kyiv Institute of Business and Technology, 52(1), 22-38. https://doi.org/10.37203/kibit.2025.52(1).02
Section
Статьи
