In press. ECONOMICS OF RESILIENCE: ADAPTATION OF NATIONAL ECONOMIES TO THREATS
Keywords:
Resilience of agroecosystems, resilience of agriculture, sustainable soil management, crop rotation planning, diversification, modelling project solutionsSynopsis
This monograph examines the economic resilience of states and the mechanisms by which national economies adapt to contemporary threats and crises.
Chapters
References
Ivaniuk, U. V. (2024). Resilience of the social and economic system of Ukraine under conditions of global instability. Kyiv, Agrarnа nauka. https://doi.org/10.31073/978-966-540-630-3
Berbeć, A. (2024). Agricultural resilience and agricultural sustainability – which is which? Current Agronomy, 1(1), 10-22. https://doi.org/10.2478/cag-2024-0002
Resilience in agriculture and food systems. 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., ... & 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., ... Qian, B., Smith, W. (2025). Evaluating the resilience and sustainability of diverse crop rotations with long-term manure management under future climate change in Eastern Canada. Asabe Annual International Meeting, 2500367. https://doi.org/10.13031/aim.202500367
Wang, S., Xiong, J., Yang, B.,... Siddique, K.H.M., Kang, S. (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.,... Li, L., Gan, Y. (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). Optimization of the structure of sown areas and specialized crop rotations using the method of economic and mathematical modeling. Scientific works of the Institute of Bioenergy Crops and Sugar Beet, 9, 245–251. Available at: http://nbuv.gov.ua/UJRN/znpicb_2007_9_41
Kovalenko, N.P. (2014). Formation and development of scientific and organizational foundations for the application of domestic crop rotations in agricultural systems (second half of the 19th – beginning of the 21st century), V. A. Verhunov (Ed.). Kyiv, TOV «Nilan-LTD».
Smirnova, B.O. (2018). New optimization of the structure of sown areas and crop rotation for the development of soil conservation agriculture in farms of the Poltava region at the beginning of the 21st century. Bulletin of Agrarian History, 25-26, 281–289.
Boyko, P.I., Litvinov, D.V., Tsymbal, Ya.S., & Kudrya, S.O. (2018). Principles of developing systems of mixed crop rotations in Ukraine. Collection of scientific papers of the National Scientific Center "Institute of Agriculture of the National Academy of Sciences of Ukraine", 1, 3–14. Available at: http://nbuv.gov.ua/UJRN/znpzeml_2018_1_3
Minkova, O., Kachanenko, Ye. & Berestniev, D. (2018). Applying the models of combination of agricultural production sectors in the strategic management of enterprise. Agrosvit, 19, 11–23. https://doi.org/10.32702/2306-6792.2018.19.11
Mellaku, M.S., Reynolds, T.W., & Woldeamanuel, T. (2018). Liner 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). Land use management of agricultural enterprises in the context of global environmental change (PhD Thesis). Lugansk National Agrarian University, Tavria State Agrotechnological University, Melіtopol.
Putyatyn, V.P., & Kovalenko, S.N. (2007). Models of combinatorial optimization problems to support decision-making in the agro-industrial complex. Information processing systems, 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). Numerical implementation of mathematical models of combinatorial optimization problems in the agro-industrial complex. 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.N. (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). The method of synthesis of solutions to multi-criteria problems of stochastic optimization with mixed conditions. Bulletin of the V. N. Karazin Kharkiv National University. Series Mathematical modeling, information technologies, automated control systems, 39, 14–25. Available at: http://nbuv.gov.ua/UJRN/VKhIMAM_2018_39_4
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). Graph theory. Igor Sikorsky Kyiv Polytechnic Institute. 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, Kharkiv National University of Radioelectronics. Available at: http://www.irbis-nbuv.gov.ua/aref/2009020300112120090203001121
Kozin, I.V., Maksishko, N.K., & Perepilitsa, 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. (2018). Dynamics of complex systems. New York, Routledge. https://doi.org/10.1201/9780429034961
Tymofieva, N.K. (2010). Linear integer programming and combinatorial optimization problems. 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://core.ac.uk/download/pdf/326804445.pdf
Santos, L.M.R. dos, Michelon, Ph., Arenales, M.N., & Santos, R.H.S. (2011). Crop rotation scheduling with adjacency constraints. Annals of Operations Research, 190, 165–180. https://doi.org/10.1007/s10479-008-0478-z
Aliano, A., Florentino, H., & Pato, M. (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. (2019). A new approach for crop rotation problem in farming 4.0. In Camarinha-Matos, L., Almeida, R., Oliveira, J. (eds), Technological Innovation for Industry and Service Systems (pp. 99–111). DoCEIS 2019. IFIP Advances in Information and Communication Technology, vol. 553. Springer, Cham. https://doi.org/10.1007/978-3-030-17771-3_9
Aliano, A., Florentino, H., & Pato, M. (2018). Metodologias de escalarizações para o problema de rotação de culturas biobjetivo. Proceeding Series of the Brazilian Society of Applied and Computational 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., 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., & Stegeman, A.W. (2005). Crop succession requirements in agricultural production planning. European Journal of Operational Research, 166, 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, 1(1), 505–518. https://doi.org/10.1016/j.amc.2009.02.044
Mehrabi, Z. (2020). Developing decision-support systems for crop rotations. In Armstrong L. (ed.), Improving data management and decision support systems in agriculture. Cambridge, Burleigh Dodds Science Publishing. https://doi.org/10.19103/AS.2020.0069.15
Mauri, G.R. (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
Santos, L. M. R. (2008). Crop rotation scheduling with adjacency constraints. Springer Science+Business Media, LLC.
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. Journal of Automatica Sinica, 4(2), 161–166. https://doi.org/10.1109/JAS.2017.7510487
Santos dos Lana Mara, 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.A. (2016). 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. Working Paper SMART – LERECO, 12-07. https://doi.org/10.22004/ag.econ.207985
Carpentier, A., & Letort, E. (2012). 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. (2012). 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, 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. World Bank, Washingtone, DC. Available at: http://hdl.handle.net/10986/34021
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. (2009). A crop rotation planning procedure. In C.L. Mohler & S.E. Johnson (Eds.), Crop rotation on organic farms a planning manual. New York, Published by NRAES. Available at: https://saiplatform.org/uploads/Library/CropRotationOnOrganicFarms.pdf
