Normative monetary valuation (NMV) of agricultural land in mountain districts of Ukraine currently neglects two spatially determined economic factors: soil quality heterogeneity and geomorphological hazard. This study proposes an integrated GIS-based methodology that incorporates both factors into a risk-adjusted valuation coefficient within the existing Ukrainian NMV regulatory framework. The study area encompasses Kosiv and Verkhovyna districts of Ivano-Frankivsk Region, located in the Outer Carpathians – a region with documented high landslide activity and pronounced soil variability.
A Soil Quality Index (SQI) was constructed from five satellite-derived soil variables obtained from the SIORA Ukraine Open Soil Dataset (2020): total nitrogen (N), organic carbon (OC), available phosphorus (P), exchangeable potassium (K), and pH at 250 m resolution. Physiology-grounded non-linear normalisation functions were applied – Michaelis-Menten saturation curves for N, OC, P, K, and a Gaussian optimum function for pH – followed by rescaling to a uniform [0;1] interval to ensure comparability. Landslide risk was decomposed into natural and technogenic components, each normalised independently. Component weights (wnatural = 0.60; wtech = 0.40) were determined via the Analytic Hierarchy Process (AHP), consistent with the documented dominance of lithological and morphometric controls in Carpathian landslide dynamics. An additive aggregation model was applied. The resulting SQI and Riskintegral rasters serve as inputs for a risk-adjusted land value correction coefficient Kr, proposed as a spatially continuous modifier to the standard Ukrainian NMV formula, bounded within [0.82; 1.00] to reflect economically defensible geological risk discounting.
Andrews, S. S., Karlen, D. L., & Cambardella, C. A. (2004). The soil management assessment framework: A quantitative soil quality evaluation method. Soil Science Society of America Journal, 68(6), 1945–1962. https://doi.org/10.2136/sssaj2004.1945
Kasiyanchuk, D., Atamaniuk, Y., Deputat, M., Gavdey, S., & Horishevskyi, P. (2026). From suitability to vulnerability: A GIS-based framework for assessing environmental sensitivity of mountain tourism landscapes in the Ukrainian Carpathians. Journal of the Bulgarian Geographical Society, 54, 59–92. https://doi.org/10.3897/jbgs.e177296
Kasiyanchuk, D., & Davybida, L. (2025). Integrated geoinformation modelling for the siting of industrial solar power plants considering landslide processes in mountainous areas. Geodynamics, 2(39)2025(2(39)), 83–98. https://doi.org/10.23939/jgd2025.02.083
Jalhoum, M. E. M., Abdellatif, M. A., Mohamed, E. S., Kucher, D. E., & Shokr, M. (2024). Multivariate analysis and GIS approaches for modeling and mapping soil quality and land suitability in arid zones. Heliyon, 10(5), e27577. https://doi.org/10.1016/j.heliyon.2024.e27577
Johnson, K. A., & Goody, R. S. (2011). The original Michaelis constant: Translation of the 1913 Michaelis–Menten paper. Biochemistry, 50(39), 8264–8269. https://doi.org/10.1021/bi201284u
Kaufman, S., Rosset, S., Perlich, C., & Stitelman, O. (2012). Leakage in data mining: Formulation, detection, and avoidance. ACM Transactions on Knowledge Discovery from Data, 6(4), Article 15. https://doi.org/10.1145/2382577.2382579
Michaelis, L., & Menten, M. L. (1913). Die Kinetik der Invertinwirkung. Biochemische Zeitschrift, 49, 333–369.
Pourghasemi, H. R., Pradhan, B., & Gokceoglu, C. (2012). Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural Hazards, 63(2), 965–996. https://doi.org/10.1007/s11069-012-0217-2
Saaty, T. L. (1980). The Analytic Hierarchy Process. McGraw-Hill.
Skopp, J. (2009). Derivation of the Freundlich adsorption isotherm from a model of soil kinetics and equilibria. Soil Science Society of America Journal, 73(2), 360–364. https://doi.org/10.2136/sssaj2008.0025
SIORA. (2020). Ukraine Open Soil Dataset: Satellite-derived maps of N, P, K, OC, and pH at 250 m resolution (2020). Retrieved from https://siora.ai/open-soil-datasets/ukraine-dataset
Vasu, D., Singh, S. K., Ray, S. K., Duraisami, V. P., Tiwary, P., Chandran, P., Nimkar, A. M., & Anantwar, S. G. (2016). Soil quality index (SQI) as a tool to evaluate crop productivity in semi-arid Deccan plateau, India. Geoderma, 282, 70–79. https://doi.org/10.1016/j.geoderma.2016.07.010
Yalcin, A. (2008). GIS-based landslide susceptibility mapping using analytical hierarchy process and bivariate statistics in Ardesen (Turkey). Catena, 72(1), 1–12. https://doi.org/10.1016/j.catena.2007.04.003