Evaluation of soil quality in a cornell-based polye ecosystem in a Karstic Area: Integration of geostatistics and machine learning

Year 2026, Volume: 15 Issue: 2 , 291 - 301 , 01.04.2026

Aktop Aktop Sevda Altunbaş Beritanli , Bayram Çağdaş Demirel

https://doi.org/10.18393/ejss.1901800

https://izlik.org/JA88CE38LB

Abstract

Assessing soil quality and establishing processes to control quality is critically important in environmentally sensitive karst systems. This study aimed to analyze the spatial behavior of soil quality and the dominant soil properties controlling it in the karstic polje system of Gembos Plain (Southern Turkey) using the Cornell Soil Quality Index (SQI). SQI values were calculated using physical and chemical data obtained from 72 soil samples (0–30 cm). According to analysis results SQI values ranged from 53 to 59, with an average value of approximately 56. Semivariogram analysis showed that SQI exhibited moderate spatial dependence; the Gaussian variogram model was selected as the most appropriate model due to lowest error values and RMSSE close to 1. The spatial distribution obtained by ordinary kriging revealed that low-to-medium quality classes were dominant across the area and that geochemical micro-scale patterns prevailed over distinct morphological zoning. In addition, Random Forest and XGBoost models showed the highest performance in machine learning analyses (R² = 0.74 and 0.71). Variable importance analysis has shown that soil reaction (pH) is the primary control factor determining SQI variability, while P, Fe, and K are effective at a secondary level. Also soil organic matter plays a secondary role in explaining SQI variability due to its narrow range of variation. The results reveal that soil quality in karstic polje conditions is controlled by chemical constraints rather than physical properties, and that the combined use of geostatistical modeling and machine learning significantly improves the interpretability of soil quality analyses.

Keywords

Soil quality , Karstic polje , Cornell Soil Quality Index , Geostatistical modeling , Machine learning

References

• Adyanova, A., Buluktaev, A., Mukabenova, R., Mandzhieva, S., Rajput, V., Sayanov, V., Djimbee,N., Sushkova, S., 2023. Characterization of arid soil quality: Physical and chemical parameters. Eurasian Journal of Soil Science 12(2): 151-158.
• Alloway, B.J., 2008. Micronutrients and Crop Production: An Introduction. In: Micronutrient Deficiencies in Global Crop Production. Alloway, B.J. (Ed.). Springer, Dordrecht, pp. 1–39.
• Amoakwah, E., Rahman, M.A., Nketia, K.A., Djouaka, R., Didenko, N.O., Islam, K.R., 2021. Impact of deforestation and subsequent land-use change on soil quality. Eurasian Journal of Soil Science 10(2): 150-160.
• Amsili, J.P., van Es, H.M., Aller, D.M., Schindelbeck, R.R., 2023. Empirical approach for developing production environment soil health benchmarks. Geoderma Regional 32: e00688.
• Bandyopadhyay, S., Maiti, S.K., 2021. Application of statistical and machine learning approach for prediction of soil quality index formulated to evaluate trajectory of ecosystem recovery in coal mine degraded land. Ecological Engineering 159: 106124.
• Becker, W., Saisana, M., Paruolo, P., Vandecasteele, I., 2017. Weights and importance in composite indicators: Closing the gap. Ecological Indicators 80: 12–22.
• Bouyoucos, G.J., 1951. A recalibration of the hydrometer method for making mechanical analysis of soils. Agronomy Journal 43(9): 434–438.
• Bremner, J.M., Mulvaney, C.S., 1982. Nitrogen-Total. In: Methods of soil analysis. Part 2. Chemical and microbiological properties. Page, A.L., Miller, R.H., Keeney, D.R., (Eds.). American Society of Agronomy, Soil Science Society of America, Madison, Wisconsin, USA. pp. 595-624.
• Bünemann, E.K., Bongiorno, G., Bai, Z., Creamer, R.E., De Deyn, G., de Goede, R., Fleskens, L., Geissen, V., Kuyper, T.W., Mäder, P., Pulleman, M., Sukkel, W., van Groenigen, J.W., Brussaard, L., 2018. Soil quality – A critical review. Soil Biology and Biochemistry 120: 105–125.
• De Caires, S.A., Martin, C.S., Atwell, M.A., Kaya, F., 2025. Advancing soil mapping and management using geostatistics and integrated machine learning and remote sensing techniques: A synoptic review. Discover Soil 5: 82.
• De Waele, J., Gutiérrez, F., 2022. Karst Hydrogeology, Geomorphology and Caves. John Wiley & Sons Ltd. 912p.
• Demir, Z., Gülser, C. 2015. Effects of rice husk compost application on soil quality parameters in greenhouse conditions. Eurasian Journal of Soil Science 4(3): 185-190.
• Demirağ Turan, İ., 2021. Spatio analysis of soil quality assessment in semi-arid ecosystem using a minimum data set. Eurasian Journal of Soil Science 10(3): 222-235.
• Dengiz, O., Alaboz, P., Saygın, F., Adem, K., Yüksek, E., 2024. Evaluation of soil quality of cultivated lands with classification and regression-based machine learning algorithms optimization under humid environmental condition. Advances in Space Research 74: 5514–5529.
• Fine, A.K., van Es, H.M., Schindelbeck, R.R., 2017. Statistics, scoring functions, and regional analysis of a comprehensive soil health database. Soil Science Society of America Journal 81: 589–601.
• Gökkaya, E., 2016. Geomorphological evolution of the upper Manavgat River basin: Geomorphology of the Kembos and Eynif poljes. Unpublished M.Sc. Thesis, Ankara University, Graduate School of Social Sciences, Ankara, Türkiye.
• Gökkaya, G., Gutiérrez, F., 2022. Poljes in the Sivas gypsum karst, Turkey. Geomorphology 398: 108024.
• Gugino, B.K., Abawi, G.S., Idowu, O.J., Schindelbeck, R.R., Smith, L.L., Thies, J.E., Wolfe, D.W., van Es, H.M., 2009. Cornell Soil Health Assessment Training Manual. Cornell University College of Agriculture and Life Sciences, Ithaca, NY. 58p.
• Gutiérrez, F., Gutiérrez, M., 2016. Karst landforms. In: Landforms of the Earth. Gutiérrez, F., Gutiérrez, M. (Eds.). Springer, Cham, pp. 63–86.
• Hastie, T., Tibshirani, R., Friedman, J., 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. Springer, New York, USA. 745p.
• Hazelton, P., Murphy, B., 2007. Interpreting soil test results. What do the numbers mean? CSIRO Publishing, Melbourne. Australia. 152p.
• Heepngoen, P., Thoumazeau, A., Renevier, M.S., Sajjaphan, K., Gay, F., Brauman, A., 2021. Relationships between physico-chemical, biological and functional approaches for soil quality assessment: A case study along a gradient of disturbance. European Journal of Soil Biology 104: 103300.
• Kapoor, S., Narayanan, A., 2023. Leakage and the reproducibility crisis in machine-learning-based science. Patterns 4: 100804.
• Karlen, D.L., Andrews, S.S., Wienhold, B.J., Zobeck, T.M., 2008. Soil quality assessment: Past, present and future. Journal of Integrative Biosciences 6: 3–14.
• Kuhn, M., Johnson, K., 2013. Applied Predictive Modeling. Springer, New York, USA. 600p.
• Lehmann, J., Bossio, D.A., Kögel-Knabner, I., Rillig, M.C., 2020. The concept and future prospects of soil health. Nature Reviews Earth & Environment 1: 544–553.
• Lindsay, W.L., Norvell, W.A., 1978. Development of a DTPA soil test for zinc, iron, manganese, and copper. Soil Science Society of America Journal 42(3): 421–428.
• Moebius-Clune, B.N., Moebius-Clune, D.J., Gugino, B.K., Idowu, O.J., Schindelbeck, R.R., Ristow, A.J., van Es, H.M., 2016. Comprehensive assessment of soil health – The Cornell framework manual. 3rd ed. Cornell University, Geneva, NY, USA. 123p.
• Nelson, D.W., Sommers, L.E., 1982. Total carbon, organic carbon, and organic matter. In: Methods of Soil Analysis, Part 2, Chemical and Microbiological Properties, Second Edition. Number 9, Page, A.L., Keeney, D. R., Baker, D.E., Miller, R.H., Ellis, R. Jr., Rhoades, J.D. (Eds.). ASA-SSSA, Madison, Wisconsin, USA. pp. 539–580.
• Olsen, S.R., 1954. Estimation of available phosphorus in soils by extraction with sodium bicarbonate, Circular No. 939. US Department of Agriculture, Washington, D.C. USA. 19p.
• Panigrahi, B., Razavi, S., Doig, L., Cordell, B., Gupta, H., Liber, K., 2025. On robustness of the explanatory power of machine learning models: Insights from a new explainable AI approach using sensitivity analysis. Water Resources Research 61(1): e2024WR037398.
• Prasad, R., Shivay, Y.S., 2021. Phosphorus × Other plant nutrient ınteractions, reaction products, anion exchange and phosphate fixation in soil and strategies to increase availability of the native and applied p to crop plants-A mini review and critique. Agricultural Reviews 42(2): 220–224.
• Qin, C., Li, S.L., Yu, G.H., Bass, A.M., Yue, F.J., Xu, S., 2022. Vertical variations of soil carbon under different land uses in a karst critical zone observatory, SW China. Geoderma 412: 115741.
• Qiu, J., Liu, F., Wang, D., Yan, K., Guo, J., Huang, W., Feng, Y., 2025. Mapping key soil properties in low relief areas using integrated machine learning and geostatistics. Ecological Indicators 171: 113228.
• Raiesi, F., Salek-Gilani, S., 2020. Development of a soil quality index for characterizing effects of land-use changes on degradation and ecological restoration of rangeland soils in a semi-arid ecosystem. Land Degradation and Development 31(17): 2382–2397.
• Sanad, H., Moussadek, R., Mouhir, L., Oueld Lhaj, M., Dakak, H., El Azhari, H., Yachou, H., Ghanimi, A., Zouahri, A., 2024. Assessment of soil spatial variability in agricultural ecosystems using multivariate analysis, soil quality index (SQI), and geostatistical approach: A case study of the Mnasra region, Gharb Plain, Morocco. Agronomy 14(6): 1112.
• Soil Survey Staff, 1992. Procedures for collecting soil samples and methods of analysis for soil survey. Soil Survey Investigations Reports U.S. Govermentan Print Office, Washington D.C., USA.
• Şenol, H., Alaboz, P., Demir, S., Dengiz, O., 2020. Computational intelligence applied to soil quality index using GIS and geostatistical approaches in semiarid ecosystem. Arabian Journal of Geosciences 13(23): 1235.
• Vasu, D., Tiwary, P., Chandran, P., 2024. A novel and comprehensive soil quality index integrating soil morphological, physical, chemical, and biological properties. Soil and Tillage Research 244: 106246.
• Wang, Y., Yuan, F., Cammarano, D., Liu, X., Tian, Y., Zhu, Y., Cao, W., Cao, Q., 2025. Integrating machine learning with spatial analysis for enhanced soil interpolation: Balancing accuracy and visualization. Smart Agricultural Technology 11: 101032.
• Webster, R., Oliver, M.A., 2007. Geostatistics for Environmental Scientists. 2nd ed. John Wiley & Sons, Chichester, UK. 336p.
• Yertayeva, Z., Kızılkaya, R., Kaldybayev, S., Seitkali, N., Abdraimova, N., Zhamangarayeva, A., 2019. Changes in biological soil quality indicators under saline soil condition after amelioration with alfalfa (Medicago sativa L.) cultivation in meadow Solonchak. Eurasian Journal of Soil Science 8(3): 189-195.
• Yu, P., Zhang, K., Xiao, D., Li, C., Wang, S., 2023. Establishing a soil quality index to evaluate soil quality after afforestation in a karst region of Southwest China. Catena 220: 106675.
• Zeraatpisheh, M., Ayoubi, S., Jafari, A., Tajik, S., Finke, P., 2019. Digital mapping of soil properties using multiple machine learning in a semi-arid region, central Iran. Geoderma 338: 445–452.