BIOCLIMATIC CONSTRAINTS AND EDAPHIC PREFERENCES OF WHEAT: IMPLICATIONS FOR ENVIRONMENTAL SUITABILITY FORECASTING UNDER CLIMATE CHANGE

Yurii Nykytiuk, Oksana Kravchenko, Dmytro Vyskushenko, Andriy Pitsil, Oksana Komorna, Igor Bezvershuck


DOI: http://dx.doi.org/10.30970/sbi.1904.859

Abstract


Background. Understanding how environmental factors influence the spatial suita­bility of wheat is critical for sustaining productivity under climate change. In regions like Ukrainian Polissia and the Forest-Steppe, where climatic and soil gradients are strong, changes in agroecological conditions may substantially affect cultivation potential. While global studies exist, regional assessments that integrate both climate and soil data remain limited. Identifying key environmental drivers and their response patterns supports targeted adaptation and land use planning, helping ensure food security in a changing climate.
Materials and Methods. The spatial suitability of wheat cultivation in the Polissia and Forest-Steppe regions of Ukraine was assessed using agroecological modelling. We compiled a dataset of observed wheat cover from official agricultural statistics. The environmental predictors included 19 bioclimatic variables (WorldClim), soil properties (texture, pH, and organic matter content), and topographic factors. Multicollinearity was reduced via principal component analysis and correlation filtering. Four modelling approaches: ordinary least squares (OLS), ridge regression, generalised additive models (GAM), and random forest (RF), were applied to identify key predictors and response patterns.
Results and Discussion. Among the tested models, random forest provided the highest accuracy, followed by GAM and ridge regression, while OLS lagged behind. Key predictors of wheat suitability included warm-quarter temperature (bio10), growing seasonal precipitation, and soil factors, such as pH, clay content, and bulk density. Wheat showed clear sensitivity to high summer temperatures, with response curves revealing nonlinear, bell-shaped patterns indicative of ecological optima. Climate projections suggest a northward shift and fragmentation of suitable areas, especially under SSP3-7.0 and SSP5-8.5 scenarios. While marginal gains are possible short-term, long-term suita­bility is likely to decline in the southern and central zones. These findings underscore the need to integrate climatic and soil data in regional planning and to support adaptation through targeted crop relocation and variety selection.
Conclusion. This study demonstrates that the spatial suitability of wheat in Ukraine’s Polissia and Forest-Steppe regions is strongly influenced by both bioclimatic and edaphic factors. Random forest modelling proved the most effective for capturing complex environmental responses. Climate change projections indicate a northward shift and reduction of suitable areas, emphasising the need for adaptive land-use strate­gies. Integrating climate and soil data into agroecological assessments is critical for anticipating risks, guiding crop management decisions, and ensuring long-term food security in vulnerable agricultural landscapes.


Keywords


crop suitability modelling, edaphic limitations, temperature–precipitation interactions, spatial regression, agroecological zoning, Shared Socioeconomic Pathways, Eastern European agriculture, land-use adaptation

Full Text:

PDF

References


An-Vo, D.-A., Radanielson, A. M., Mushtaq, S., Reardon-Smith, K., & Hewitt, C. (2021). A framework for assessing the value of seasonal climate forecasting in key agricultural decisions. Climate Services, 22, 100234. doi:10.1016/j.cliser.2021.100234
CrossrefGoogle Scholar

Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. doi:10.1023/a:1010933404324
CrossrefGoogle Scholar

Chawla, R., & Balasaheb, K. S. (2023). Optimizing water use efficiency and yield of wheat crops through integrated irrigation and nitrogen management: a comprehensive review. International Journal of Environment and Climate Change, 13(11), 4059-4067. doi:10.9734/ijecc/2023/v13i113585
CrossrefGoogle Scholar

Chetvertak, T., Diuzhykova, T., Hryshko, S., Nepsha, O., & Tutova, H. (2025). The precipitation levels during the warmest quarter are the primary factor influencing the spatial distribution of Opatrum sabulosum. Biosystems Diversity, 33(1), e2507. doi:10.15421/012507
CrossrefGoogle Scholar

Collins, B., Lai, Y., Grewer, U., Attard, S., Sexton, J., & Pembleton, K. G. (2024). Evaluating the impact of weather forecasts on productivity and environmental footprint of irrigated maize production systems. Science of The Total Environment, 954, 176368. doi:10.1016/j.scitotenv.2024.176368
CrossrefPubMedGoogle Scholar

Datta, P., Behera, B., & Rahut, D. B. (2022). Climate change and Indian agriculture: a systematic review of farmers' perception, adaptation, and transformation. Environmental Challenges, 8, 100543. doi:10.1016/j.envc.2022.100543
CrossrefGoogle Scholar

Fu, Y., Tian, Z., Amoozegar, A., & Heitman, J. (2019). Measuring dynamic changes of soil porosity during compaction. Soil and Tillage Research, 193, 114-121. doi:10.1016/j.still.2019.05.016
CrossrefGoogle Scholar

Ganesan, M., Andavar, S., & Raj, R. S. P. (2021). Prediction of land suitability for crop cultivation using classification techniques. Brazilian Archives of Biology and Technology, 64, e21200483. doi:10.1590/1678-4324-2021200483
CrossrefGoogle Scholar

Kayode Ayinde, O. O. A., & Nwosu, U. I. (2021). Solving multicollinearity problem in linear regression model: the review suggests new idea of partitioning and extraction of the explanatory variables. Journal of Mathematics and Statistics Studies, 2(1), 12-20. doi:10.32996/jmss.2021.2.1.2
CrossrefGoogle Scholar

Kettlewell, P., Byrne, R., & Jeffery, S. (2023). Wheat area expansion into northern higher latitudes and global food security. Agriculture, Ecosystems & Environment, 351, 108499. doi:10.1016/j.agee.2023.108499
CrossrefGoogle Scholar

Klemm, T., & McPherson, R. A. (2017). The development of seasonal climate forecasting for agricultural producers. Agricultural and Forest Meteorology, 232, 384-399. doi:10.1016/j.agrformet.2016.09.005
CrossrefGoogle Scholar

Kunah, O. M., Pakhomov, O. Y., Zymaroieva, А. А., Demchuk, N. I., Skupskyi, R. M., Bezuhla, L. S., & Vladyka, Y. P. (2018). Agroeconomic and agroecological aspects of spatial variation of rye (Secale cereale) yields within Polesia and the Forest-Steppe zone of Ukraine: the usage of geographically weighted principal components analysis. Biosystems Diversity, 26(4), 276-285. doi:10.15421/011842
CrossrefGoogle Scholar

Kunakh, O., Lisovets, O., & Zhukov, O. (2024). Hemeroby and naturalness differ in spatial patterns: the case of aquatic macrophytes. International Journal of Environmental Studies, 81(6), 2692-2706. doi:10.1080/00207233.2024.2379117
CrossrefGoogle Scholar

Lai, J., Tang, J., Li, T., Zhang, A., & Mao, L. (2024). Evaluating the relative importance of predictors in Generalized Additive Models using the gam.hp R package. Plant Diversity, 46(4), 542-546. doi:10.1016/j.pld.2024.06.002
CrossrefPubMedPMCGoogle Scholar

Lisovets, O., Khrystov, O., Kunakh, O., & Zhukov, O. (2024). Application of hemeroby and naturalness indicators for monitoring the aquatic macrophyte communities in protected areas. Biosystems Diversity, 32(2), 270-277. doi:10.15421/012429
CrossrefGoogle Scholar

Liu, Y., Liu, R., Feng, Z., Hu, R., Zhao, F., & Wang, J. (2024). Regulation of wheat growth by soil multifunctionality and metagenomic-based microbial functional profiles under mulching treatments. Science of The Total Environment, 920, 170881. doi:10.1016/j.scitotenv.2024.170881
CrossrefPubMedGoogle Scholar

Marino, S. (2023). Understanding the spatio-temporal behavior of crop yield, yield components and weed pressure using time series Sentinel-2-data in an organic farming system. European Journal of Agronomy, 145, 126785. doi:10.1016/j.eja.2023.126785
CrossrefGoogle Scholar

Molozhon, K. O., Lisovets, O. I., Kunakh, O. M., & Zhukov, O. V. (2023). Increased soil penetration resistance drives degrees of hemeroby in vegetation of urban parks. Biosystems Diversity, 31(4). doi:10.15421/012349
CrossrefGoogle Scholar

Mykhailyuk, T., Lisovets, O., & Tutova, H. (2023a). The importance of terrain factors in the spatial variability of plant cover diversity in a steppe gully. Biosystems Diversity, 31(4), 470-483. doi:10.15421/012356
CrossrefGoogle Scholar

Mykhailyuk, T., Lisovets, O., & Tutova, H. (2023b). Steppe vegetation islands in the gully landscape system: hemeroby, naturalness and phytoindication of ecological regimes. Regulatory Mechanisms in Biosystems, 14(4), 581-594. doi:10.15421/022385
CrossrefGoogle Scholar

Nykytiuk, Y., Kravchenko, O., Komorna, O., Bambura, V., & Seredniak, D. (2025). Global climate change will lead to a decrease in the erosion resistance of Polissya and Forest-Steppe soils. Biosystems Diversity, 33(1), e2502. doi:10.15421/012502
CrossrefGoogle Scholar

Panchenko, K., Podorozhnyi, S., & Diuzhykova, T. (2024). Predicting organic carbon in European soils: only in Southern Ukraine can we expect an increase in humus content. Regulatory Mechanisms in Biosystems, 15(1), 24-30. doi:10.15421/022403
CrossrefGoogle Scholar

Pavlou, M., Ambler, G., Seaman, S., De Iorio, M., & Omar, R. Z. (2016). Review and evaluation of penalised regression methods for risk prediction in low-dimensional data with few events. Statistics in Medicine, 35(7), 1159-1177. doi:10.1002/sim.6782
CrossrefPubMedPMCGoogle Scholar

Ponomarenko, O., Komlyk, Y., Tutova, H., & Zhukov, O. (2024). Landscape diversity mapping allows assessment of the hemeroby of bird species in a modern industrial metropolis. Biosystems Diversity, 32(4), 470-483. doi:10.15421/012449
CrossrefGoogle Scholar

Romashchenko, M., Bohaienko, V., Shatkovskyi, A., Saidak, R., Matiash, T., & Kovalchuk, V. (2023). Optimisation of crop rotations: a case study for corn growing practices in forest-steppe of Ukraine. Journal of Water and Land Development, 56(I-III), 194-202. doi:10.24425/jwld.2023.143760
CrossrefGoogle Scholar

Roustaei, N. (2024). Application and interpretation of linear-regression analysis. Medical Hypothesis Discovery and Innovation in Ophthalmology, 13(3), 151-159. doi:10.51329/mehdiophthal1506
CrossrefPubMedPMCGoogle Scholar

Ruane, A. C., Phillips, M., Jägermeyr, J., & Müller, C. (2024). Non-linear climate change impacts on crop yields may mislead stakeholders. Earth's Future, 12(4), e2023ef003842. doi:10.1029/2023ef003842
CrossrefGoogle Scholar

Sable, N. P., Shukla, V. K., Mahalle, P. N., & Khedkar, V. (2025). Optimizing agricultural yield: a predictive model for profitable crop harvesting based on market dynamics. Frontiers in Computer Science, 7, 1567333. doi:10.3389/fcomp.2025.1567333
CrossrefGoogle Scholar

Sanjaya, I., Mantoro, T., Asian, J., Kharisma, I. L., & Thohir, M. I. (2024). Forecasting cropping patterns to increase crop yields food and horticulture using a machine approach learning. BIO Web of Conferences, 148, 03002. doi:10.1051/bioconf/202414803002
CrossrefGoogle Scholar

Semenov, M. A., & Porter, J. R. (1995). Climatic variability and the modelling of crop yields. Agricultural and Forest Meteorology, 73(3-4), 265-283. doi:10.1016/0168-1923(94)05078-k
CrossrefGoogle Scholar

Stefanovska, T., Skwiercz, A., Pidlisnyuk, V., Newton, R. A., Zhukov, O., Ust'ak, S., Szczech, M., & Kowalska, B. (2025). The interactions between nematode and microbial communities offer significant insights into the impact of organic amendments on the productivity of Miscanthus × giganteus cultivated on marginal lands. Biosystems Diversity, 33(1), e2508. doi:10.15421/012508
CrossrefGoogle Scholar

Stefanovska, T., Skwierzc, A., Zhukov, O., & Pidlisnyuk, V. (2024). Soil nematodes as a monitoring tool of bioenergy crop production management: the case of Miscanthus giganteus cultivation on different soil types. Biosystems Diversity, 32(2), 217-224. doi:10.15421/012423
CrossrefGoogle Scholar

Tamasiga, P., Ouassou, E. houssin, Onyeaka, H., Bakwena, M., Happonen, A., & Molala, M. (2023). Forecasting disruptions in global food value chains to tackle food insecurity: the role of AI and big data analytics - a bibliometric and scientometric analysis. Journal of Agriculture and Food Research, 14, 100819. doi:10.1016/j.jafr.2023.100819
CrossrefGoogle Scholar

Tang, F. H. M., Nguyen, T. H., Conchedda, G., Casse, L., Tubiello, F. N., & Maggi, F. (2024). CROPGRIDS: a global geo-referenced dataset of 173 crops. Scientific Data, 11(1), 413. doi:10.1038/s41597-024-03247-7
CrossrefPubMedPMCGoogle Scholar

Thapa, S., Xue, Q., Jessup, K. E., Rudd, J. C., Liu, S., Marek, T. H., Devkota, R. N., Baker, J. A., & Baker, S. (2019). Yield determination in winter wheat under different water regimes. Field Crops Research, 233, 80-87. doi:10.1016/j.fcr.2018.12.018
CrossrefGoogle Scholar

Trifanova, M., Zadorozhna, G., Novitsky, R., Ponomarenko, O., Makhina, V., Khrystov, O., Ruchiy, V., & Zhukov, O. (2023). How much space is needed for biodiversity conservation? Biosystems Diversity, 31(4), 521-534. doi:10.15421/012362
CrossrefGoogle Scholar

Tutova, H., Ruchiy, V., Khrystov, O., Lisovets, O., Kunakh, O., & Zhukov, O. (2025). Influence of morphology and functional properties of floodplain water bodies on species diversity of macrophyte communities. Regulatory Mechanisms in Biosystems, 33(1), e25012. doi:10.15421/0225012
CrossrefGoogle Scholar

van Verseveld, C. J. W., & Gebert, J. (2020). Effect of compaction and soil moisture on the effective permeability of sands for use in methane oxidation systems. Waste Management, 107, 44-53. doi:10.1016/j.wasman.2020.03.038
CrossrefPubMedGoogle Scholar

Veen, B. W., & Boone, F. R. (1990). The influence of mechanical resistance and soil water on the growth of seminal roots of maize. Soil and Tillage Research, 16(1-2), 219-226. doi:10.1016/0167-1987(90)90031-8
CrossrefGoogle Scholar

Wang, J., Qian, R., Li, J., Wei, F., Ma, Z., Gao, S., Sun, X., Zhang, P., Cai, T., Zhao, X., Chen, X., & Ren, X. (2024). Nitrogen reduction enhances crop productivity, decreases soil nitrogen loss and optimize its balance in wheat-maize cropping area of the Loess Plateau, China. European Journal of Agronomy, 161, 127352. doi:10.1016/j.eja.2024.127352
CrossrefGoogle Scholar

Westerveld, J. J. L., van den Homberg, M. J. C., Nobre, G. G., van den Berg, D. L. J., Teklesadik, A. D., & Stuit, S. M. (2021). Forecasting transitions in the state of food security with machine learning using transferable features. Science of The Total Environment, 786, 147366. doi:10.1016/j.scitotenv.2021.147366
CrossrefPubMedGoogle Scholar

Yakovenko, V., & Zhukov, O. (2021). Zoogenic structure aggregation in steppe and forest soils. In: Y. Dmytruk & D. Dent (Eds.), Soils under stress (pp. 111-127). Springer International Publishing, Cham. doi:10.1007/978-3-030-68394-8_12
CrossrefGoogle Scholar

Yin, X., Kropff, M. J., McLaren, G., & Visperas, R. M. (1995). A nonlinear model for crop development as a function of temperature. Agricultural and Forest Meteorology, 77(1-2), 1-16. doi:10.1016/0168-1923(95)02236-q
CrossrefGoogle Scholar

Zelenova, V. O., Zelenov, P. V., & Tutova, G. F. (2024). Bioindication potentials of the grass stand and soil macrofauna for assessing the level of anthropogenic transformation of an urban park are complementary. Biosystems Diversity, 32(3), 306-313. doi:10.15421/012433
CrossrefGoogle Scholar

Zymaroieva, A., Zhukov, O., Fedonyuk, T., & Pinkin, A. (2019). Application of geographically weighted principal components analysis based on soybean yield spatial variation for agro-ecological zoning of the territory. Agronomy Research, 17(6), 2460-2473. doi:10.15159/ar.19.208
CrossrefGoogle Scholar

Zymaroieva, A., Zhukov, O., Romanchuck, L., & Pinkin, A. (2019). Spatiotemporal dynamics of cereals grains and grain legumes yield in Ukraine. Bulgarian Journal of Agricultural Science, 25(6), 1107-1113.
Google Scholar


Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Yurii Nykytiuk, Oksana Kravchenko, Dmytro Vyskushenko, Andriy Pitsil, Oksana Komorna, Igor Bezvershuck

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.