Optimizing crop rotations for sustainable land management and resilience of the agricultural sector of economy
Keywords:
Resilience of agroecosystems, resilience of agriculture, sustainable soil management, crop rotation planning, diversification, modelling project solutionsSynopsis
Ensuring the resilience of any national economy depends significantly on the strength of its constituent sectors. Given the agricultural sector’s dominant share in Ukraine’s GDP and foreign exchange earnings from exports, the resilience of the Ukrainian economy depends to a large extent on the resilience and sustainability of its agricultural sector. In turn, the resilience and sustainability of Ukraine’s agricultural sector are contingent upon its ability to withstand environmental, military and other threats. Soil degradation poses a threat to food and environmental security, as well as to ecological resilience and the achievement of certain Sustainable Development Goals. Although soil degradation is a serious challenge, proactive land-use management can not only mitigate its vulnerability to climate change, but also prevent and avert soil degradation, reduce the risk of erosion and ensure improved resilience for agricultural producers. One aspect of this proactive management approach should be the optimization of crop rotation planning. In this study, a bibliometric landscape of the global knowledge base on agricultural resilience was first established as a theoretical foundation for ensuring the resilience of Ukraine’s agricultural sector. An economic-mathematical model was then formulated for crop rotation planning using a combinatorial approach. Finally, solutions to the crop rotation optimization problems were proposed, taking into account the insufficient constraints on environmental conditions. The proposed economic-mathematical model and the results of crop rotation optimization will be useful (1) for policymakers when developing and implementing agricultural and environmental policies regarding the optimal crop mix in crop rotations, and (2) for managers of agricultural enterprises when planning cropland areas and making management decisions.
References
Ivaniuk, U. V. (2024). Resilience of the social and economic system of Ukraine under conditions of global instability. Kyiv: Agrarnа nauka, 308. https://doi.org/10.31073/978-966-540-630-3
Berbeć, A. K. (2024). Agricultural resilience and agricultural sustainability – which is which? Current Agronomy, 53 (1), 10–22. https://doi.org/10.2478/cag-2024-0002
Resilience in agriculture and food systems. OECD. Available at: https://www.oecd.org/en/topics/resilience-in-agriculture-and-food-systems.html
Zou, Y., Liu, Z., Chen, Y., Wang, Y., Feng, S. (2024). Crop Rotation and Diversification in China: Enhancing Sustainable Agriculture and Resilience. Agriculture, 14 (9), 1465. https://doi.org/10.3390/agriculture14091465
Sutton, E., Jain, M., Connell, K., Wang, H., Zhou, W., Deshpande, M., Blesh, J. (2025). Increasing crop rotation diversity with cover crops builds climate resilience on farms. Environmental Research Letters, 20 (12), 124025. https://doi.org/10.1088/1748-9326/ae1c53
Sitienei, R., Qi, Z., Grant, B., Vanderzaag, A. C., Jégo, G., Lafond, J. (2025). Simulating Climate Change Impacts and Management Strategies on Crop Yield and Soil Organic Carbon Dynamics in Eastern Canada. Asabe Annual International Meeting, 2500367. https://doi.org/10.13031/aim.202500367
Wang, S., Xiong, J., Yang, B., Yang, X., Du, T., Steenhuis, T. S. et al. (2023). Diversified crop rotations reduce groundwater use and enhance system resilience. Agricultural Water Management, 276, 108067. https://doi.org/10.1016/j.agwat.2022.108067
Yu, T., Mahe, L., Li, Y., Wei, X., Deng, X., Zhang, D. (2022). Benefits of Crop Rotation on Climate Resilience and Its Prospects in China. Agronomy, 12 (2), 436. https://doi.org/10.3390/agronomy12020436
Liu, C., Plaza-Bonilla, D., Coulter, J. A., Randy Kutcher, H., Beckie, H. J., Wang, L. et al. (2022). Diversifying crop rotations enhances agroecosystem services and resilience. Advances in Agronomy, 173, 299–335. https://doi.org/10.1016/bs.agron.2022.02.007
Kovalenko, N. P. (2007). Optymizatsiia struktury posivnykh ploshch i spetsializovanykh sivozmin metodom ekonomiko-matematychnoho modeliuvannia. Naukovi pratsi instytutu bioenerhetychnykh kultur i tsukrovykh buriakiv, 9, 245–251.
Kovalenko, N. P. (2014). Stanovlennia ta rozvytok naukovo-orhanizatsiinykh osnov zastosuvannia vitchyznianykh sivozmin u systemakh zemlerobstva (druha polovyna XIX – pochatok XXI st.). Kyiv: TOV «Nilan-LTD», 490.
Smirnova, B. O. (2018). Optimization of structure of sowing areas and crop rotations for development of protecting soil agriculture in the economies of the Poltava region at the beginning ХХІ of century. Bulletin of Agrarian History, 25-26, 288–296.
Boyko, P. I., Litvinov, D. V., Tsymbal, Ya. S., Kudrya, S. O. (2018). Pryntsypy rozroblennia system riznorotatsiinykh sivozmin v Ukraini. Zbirnyk naukovykh prats NNTs «Instytut zemlerobstva NAAN», 1, 3–14. Available at: https://zemlerobstvo.com/wp-content/uploads/2021/04/znp-1-2018.pdf
Minkova, O., Kachanenko, Ye., Berestniev, D. (2018). Applying the models of combination of agricultural production sectors in the strategic management of enterprise. Agrosvit, 19, 11. https://doi.org/10.32702/2306-6792.2018.19.11
Mellaku, M. T., Reynolds, T. W., Woldeamanuel, T. (2018). Linear Programming-Based Cropland Allocation to Enhance Performance of Smallholder Crop Production: A Pilot Study in Abaro Kebele, Ethiopia. Resources, 7 (4), 76. https://doi.org/10.3390/resources7040076
Kochetkov, Yu.O. (2018). Upravlinnia zemlekorystuvanniam silskohospodarskykh pidpryiemstv v umovakh hlobalnykh zmin navkolyshnoho seredovyshcha. [PhD Thesis; Lugansk National Agrarian University].
Putyatyn, V. P., Kovalenko, S. N. (2007). Modeli zadach kombinatornoi optimizatcii dlia priniatiia reshenii v APK. Systemy obrobky informatsii, 2 (60), 71–75. Available at: http://www.hups.mil.gov.ua/periodic-app/article/5501
Putyatin, V.P., Chalyi, I.V., & Kovalenko, S.M. (2015). Combinatorial problems of crop rotation planning. Market transformation of the economy: state, problems, prospects: VI International Scientific-Practical International Conference, (Kharkiv, April 8-10, 2015). Available at: http://khntusg.com.ua/wp-content/uploads/2019/11/materiali_6_konferencii_hntusg_08-10.04.15.pdf
Kovalenko, S. N., Kovalenko, S. V., Levkin, A. V. (2017). Chislennaia realizatciia matematicheskikh modelei zadach kombinatornoi optimizatcii v APK. Bulletin of National Technical University "KhPI". Series: System Analysis, Control and Information Technologies, 4, 190–194. Available at: http://otp-journal.com.ua/index.php/2079-0023/article/view/117094
Megel, Yu.E., Rudenko, A.P., Kovalenko, S.M., & Danilko, I.V. (2013). Mathematical models of the functioning of economic, production and technical systems and methods of their research. Kharkiv: Miskdruk.
Yaskov, G. (2019). Methodology to Solve Multi-Dimentional Sphere Packing Problems. Journal of Mechanical Engineering, 22 (1), 67–75. https://doi.org/10.15407/pmach2019.01.067
Bezlyubchenko, A. V., Menyailov, E. S., Ugryumov, M. L., Ugryumova, K. M., Chernysh, S. V. (2018). Metod synteza reshenyi mnohokryteryalnikh zadach stokhastycheskoi optymyzatsyy so smeshannimy uslovyiamy. Visnyk Kharkivskoho natsionalnoho universytetu imeni V. N. Karazina, 39, 14–25. Available at: https://periodicals.karazin.ua/mia/article/download/11643/11044/
Erdős, P., Rényi, A. (1959). On random graphs I. Publicationes Mathematicae Debrecen, 6, 290–297. Available at: https://snap.stanford.edu/class/cs224w-readings/erdos59random.pdf
Kucher, A. V., Ulko, Ye. M. (2023). Economics of soil erosion and sustainable management of eroded land. Plovdiv: Academic Publishing House “Talent”. https://doi.org/10.13140/RG.2.2.17929.86888
Kuzmenko, I. M. (2020). Teoriia hrafiv. Kyiv: Igor Sikorsky Kyiv Polytechnic Institute, 71. Available at: https://ela.kpi.ua/bitstream/123456789/35854/1/Teoriia_hrafiv.pdf
Kovalenko, S. M. (2008). Mathematical models and methods for solving combinatorial optimization problems in the agrotechnical system. [Abstract of PhD thesis; Kharkiv National University of Radioelectronics]. Available at: http://www.irbis-nbuv.gov.ua/aref/2009020300112120090203001121
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. https://doi.org/10.1007/s10559-020-00295-w
Bar-Yam, Y. (2019). Dynamics of complex systems. New York: CRC Press, 864. https://doi.org/10.1201/9780429034961
Tymofieva, N. K. (2010). Liniine tsilochyslove prohramuvannia ta zadachi kombinatornoi optymizatsii. Control Systems and Computers, 1, 28–37. Available at: http://usim.org.ua/arch/2010/1/5.pdf
de Miranda, B. S. (2020). Optimization techniques in agriculture: the crop rotation problem. Campinas: University of Campinas. Available at: https://www.researchgate.net/publication/342178265_Optimization_Techniques_in_Agriculture_The_Crop_Rotation_Problem
dos Santos, L. M. R., Michelon, P., Arenales, M. N., Santos, R. H. S. (2008). Crop rotation scheduling with adjacency constraints. Annals of Operations Research, 190 (1), 165–180. https://doi.org/10.1007/s10479-008-0478-z
Filho, A. A., Florentino, H. D. O., Pato, M. V. (2014). Metaheuristics for a crop rotation problem. International Journal of Metaheuristics, 3 (3), 199–222. https://doi.org/10.1504/ijmheur.2014.065169
Miranda, B. S., Yamakami, A., Rampazzo, P. C. B.; Camarinha-Matos, L., Almeida, R., Oliveira, J. (Eds.) (2019). A new approach for crop rotation problem in farming 4.0. Technological Innovation for Industry and Service Systems. DoCEIS 2019. IFIP Advances in Information and Communication Technology, vol. 553. Cham: Springer, 99–111. https://doi.org/10.1007/978-3-030-17771-3_9
Aliano Filho, A., Florentino, H. D. O., Pato, M. V. (2018). Metodologias de escalarizações para o problema de rotação de culturas biobjetivo. Proceeding Series of the Brazilian Society of Computational and Applied Mathematics, 6 (1). https://doi.org/10.5540/03.2018.006.01.0386
Mehrabian, A. R., Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics, 1 (4), 355–366. https://doi.org/10.1016/j.ecoinf.2006.07.003
Forrester, R. J., Rodriguez, M. (2018). An integer programming approach to crop rotation planning at an organic farm. The UMAP Journal, 38 (4), 5–23. Available at: https://www.comap.com/membership/member-resources/item/an-integer-programming-approach-to-crop-rotation-planning-at-an-organic-farm
Fendji, J. L. E. K., Kenmogne, C. T., Fotsa-Mbogne, D. J., Förster, A. (2021). Improving Farmers’ Revenue in Crop Rotation Systems with Plot Adjacency Constraints in Organic Farms with Nutrient Amendments. Applied Sciences, 11 (15), 6775. https://doi.org/10.3390/app11156775
Haneveld, W. K. K., Stegeman, A. W. (2005). Crop succession requirements in agricultural production planning. European Journal of Operational Research, 166 (2), 406–429. https://doi.org/10.1016/j.ejor.2004.03.009
Deep, K., Singh, K. P., Kansal, M. L., Mohan, C. (2009). A real coded genetic algorithm for solving integer and mixed integer optimization problems. Applied Mathematics and Computation, 212 (2), 505–518. https://doi.org/10.1016/j.amc.2009.02.044
Mehrabi, Z.; Armstrong, L. (Ed.) (2020). Developing decision-support systems for crop rotations. Improving data management and decision support systems in agriculture. Cambridge: Burleigh Dodds Science Publishing. https://doi.org/10.19103/AS.2020.0069.15
Regis Mauri, G. (2019). Improved mathematical model and bounds for the crop rotation scheduling problem with adjacency constraints. European Journal of Operational Research, 278 (1), 120–135. https://doi.org/10.1016/j.ejor.2019.04.016
Brankatschk, G., Finkbeiner, M. (2015). Modeling crop rotation in agricultural LCAs – Challenges and potential solutions. Agricultural Systems, 138, 66–76. https://doi.org/10.1016/j.agsy.2015.05.008
dos Santos, L. M. R., Michelon, P., Arenales, M. N., Santos, R. H. S. (2008). Crop rotation scheduling with adjacency constraints. Annals of Operations Research, 190 (1), 165–180. https://doi.org/10.1007/s10479-008-0478-z
Li, S., Juhász-Horváth, L., Pintér, L., Rounsevell, M. D. A., Harrison, P. A. (2018). Modelling regional cropping patterns under scenarios of climate and socio-economic change in Hungary. Science of the Total Environment, 622-623, 1611–1620. https://doi.org/10.1016/j.scitotenv.2017.10.038
Kang, M., Wang, F.-Y. (2017). From parallel plants to smart plants: intelligent control and management for plant growth. IEEE/CAA Journal of Automatica Sinica, 4 (2), 161–166. https://doi.org/10.1109/jas.2017.7510487
dos Santos, L. M. R., Costa, A. M., Arenales, M. N., Santos, R. H. S. (2010). Sustainable vegetable crop supply problem. European Journal of Operational Research, 204 (3), 639–647. https://doi.org/10.1016/j.ejor.2009.11.026
Ridier, A., Chaib, K., Roussy, C. (2016). A Dynamic Stochastic Programming model of crop rotation choice to test the adoption of long rotation under price and production risks. European Journal of Operational Research, 252 (1), 270–279. https://doi.org/10.1016/j.ejor.2015.12.025
Ridier, A., Chaib, K., Roussy, C.A. (2012). The adoption of innovative cropping systems under price and production risks: a dynamic model of crop rotation choice. AgEcon Search. https://doi.org/10.22004/ag.econ.207985
Carpentier, A., Letort, E. (2011). Accounting for Heterogeneity in Multicrop Micro‐Econometric Models: Implications for Variable Input Demand Modeling. American Journal of Agricultural Economics, 94 (1), 209–224. https://doi.org/10.1093/ajae/aar132
Dury, J., Schaller, N., Garcia, F., Reynaud, A., Bergez, J. E. (2011). Models to support cropping plan and crop rotation decisions. A review. Agronomy for Sustainable Development, 32 (2), 567–580. https://doi.org/10.1007/s13593-011-0037-x
Brankatschk, G., Finkbeiner, M. (2015). Modeling crop rotation in agricultural LCAs – Challenges and potential solutions. Agricultural Systems, 138, 66–76. https://doi.org/10.1016/j.agsy.2015.05.008
Yaramasu, R., Bandaru, V., Pnvr, K. (2020). Pre-season crop type mapping using deep neural networks. Computers and Electronics in Agriculture, 176, 105664. https://doi.org/10.1016/j.compag.2020.105664
Osman, J., Inglada, J., Dejoux, J.-F. (2015). Assessment of a Markov logic model of crop rotations for early crop mapping. Computers and Electronics in Agriculture, 113, 234–243. https://doi.org/10.1016/j.compag.2015.02.015
Quinton, F., Landrieu, L. (2021). Crop Rotation Modeling for Deep Learning-Based Parcel Classification from Satellite Time Series. Remote Sensing, 13 (22), 4599. https://doi.org/10.3390/rs13224599
Deininger, K., Ali, D.A., Kussul, N., Lavreniuk, M., Nivievskyi, O. (2020). Using machine learning to assess yield impacts of crop rotation: combining satellite and statistical data for Ukraine. Working Paper No. 9306. Washingtone: World Bank. https://doi.org/10.1596/1813-9450-9306
Hu, Y. (2005). Efficient and high quality force-directed graph drawing. The Mathematica Journal, 10, 37–71. Available at: https://cir.nii.ac.jp/crid/1370004237453048078
Mohler, C. L.; Mohler, C. L., Johnson, S. E. (Eds.) (2009). A crop rotation planning procedure. Crop rotation on organic farms a planning manual. New York: NRAES. Available at: https://www.sare.org/publications/crop-rotation-on-organic-farms/a-crop-rotation-planning-procedure/
Published
May 11, 2026
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Copyright (c) 2026 Anatolii Kucher, Yevhenii Ulko, Olga Anisimova
How to Cite
Optimizing crop rotations for sustainable land management and resilience of the agricultural sector of economy. (2026). In A. Kucher, Y. Ulko, & O. Anisimova, In press. ECONOMICS OF RESILIENCE: ADAPTATION OF NATIONAL ECONOMIES TO THREATS. Kharkiv: TECHNOLOGY CENTER PC. Retrieved from https://monograph.com.ua/catalog/chapter/1281/4176
