The subject of this research is the changes in soil fertility within the Cherkasy region caused by natural, anthropogenic, and socio-economic factors. The objective of the study was to develop a scientifically grounded model for forecasting soil fertility, considering the spatial dynamics of land use, agrochemical parameters, and economic factors.
The study developed an integrated model combining the spatially explicit dynamic model CLUE-S (Conversion of Land Use and its Effects at Small regional extent) for analyzing land-use changes and an ANN (Artificial Neural Network) for predicting the agrochemical characteristics of soils. The findings indicate that humus content and soil pH are the key indicators of long-term soil productivity. Modeling results showed that, under a business-as-usual scenario preserving current land-use trends, humus content may decrease by 8–12 % by 2050, soil pH may decline to 5.7, and overall soil fertility may decrease by 15–20 %. In contrast, the sustainable management scenario predicts the maintenance of humus content at approximately 4 % and the stabilization of soil pH within the range of 6.3–6.5 through the optimization of crop rotations and the preservation of soil organic matter.
A distinctive feature of the proposed model is its incorporation of socio-economic factors, particularly government support for the agricultural sector and prevailing market conditions. The obtained results can be applied to agro-landscape planning, the development of sustainable land-use strategies, and the effective management of land resources.
Verburg Peter. (2024). «CLUE Full Version». DataverseNL, V1. https://doi.org/10.34894/CXAUYO [in English].
Liao G., He P., Gao X., Lin Z., Huang C., Zhou W., Deng L. (2022). Land use optimization of rural production–living–ecological space at different scales based on the BP–ANN and CLUE–S models. Ecological Indicators. 137. https://doi.org/10.1016/j.ecolind.2022.108710 [in English].
Chu H.-J., Wu C.-F., Lin Y.-P. (2013). Incorporating Spatial Autocorrelation with Neural Networks in Empirical Land-Use Change Models. Environment and Planning B: Planning and Design. 40(3). pp. 384–404. https://doi.org/10.1068/b37116 [in English].
Anputhas M., Janmaat J. J. A., Nichol C. F. & Wei X. (Adam). (2016). Modelling spatial association in pattern based land use simulation models. Journal of Environmental Management. 181. pp. 465–476. https://doi.org/10.1016/j.jenvman.2016.06.034 [in English].
Ecological Passport of Cherkasy Region (2023). Ministry of Environmental Protection and Natural Resources of Ukraine. https://mepr.gov.ua/wp-content/uploads/2024/10/Ekologichnyj-pasport-2023.zip [in Ukrainian].
Trokhymenko G., Litvak S., Litvak O., Andreeva A., Rabich O., Chumak L., Nalysko M., Troshyn M., Komarysta B., Sopov D. (2023) Assessment of iron and heavy metals accumulation in the soils of the combat zone. Eastern-European Journal of Enterprise Technologies. vol. 5. № 10 (125). pp. 6–16. https://doi.org/10.15587/1729-4061.2023.289289 [in English].
Pindozzi S. (2017). Predicting land use change on a broad area: Dyna-CLUE model application to the Litorale Domizio-Agro Aversano (Campania, South Italy). Journal of Agricultural Engineering. 48(s1). pp. 27–35. https://doi.org/10.4081/jae.2017.657 [in English].
Verburg P. H. & Overmars K. P. (2009). Combining top-down and bottom-up dynamics in land use modeling: exploring the future of abandoned farmlands in Europe with the Dyna-CLUE model. Landscape Ecology. 24(9). pp. 1167–1181. https://doi.org/10.1007/s10980-009-9355-7 [in English].
Parakhnenko V. G., Lyakhovska N. O., Blagopoluchna A. G. (2021). Ecological assessment of soil condition in Cherkasy region. Bulletin of the Uman National University of Horticulture. № 2. pp. 91–95. https://doi.org/10.31395/2310-0478-2021-2-91-95 [in Ukrainian].
Sopova N., Olepir R., Sopov D., Kyrpichova I., Berezenko K., Cherednychenko I., Maslova N., Buzina I., Makeeva L., Bubnikovich A. (2025) Comprehensive forecasting of geospatial changes in soil fertility in Cherkasy region using CLUE-S and ANN models. Visnyk of V. N. Karazin Kharkiv National University. Series Geology. Geography. Ecology. Issue 63. pp. 411–425. https://doi.org/10.26565/2410-7360-2025-63-30 [in Ukrainian].
Yu Z., Zhao M., Gao Y., Wang T., Zhao Z. & Wang S. (2023). Multiscenario Simulation and Prediction of Land Use in Huaibei City Based on CLUE-S and PLUS Models. Applied Sciences. 13(12). 7142. https://doi.org/10.3390/app13127142 [in English].
Anputhas M., Janmaat J. J. A., Nichol C. F. & Wei X. (Adam). (2016). Modelling spatial association in pattern based land use simulation models. Journal of Environmental Management. 181. pp. 465–476. https://doi.org/10.1016/j.jenvman.2016.06.034 [in English].