A Comparison of Local Polynomial Interpolation and Experimental Bayesian Kriging Methods in Determining the Spatial Distribution of Some Soil Properties in Ergani Plain
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DOI:
https://doi.org/10.5281/zenodo.8396228Keywords:
Spatial distribution, interpolation, soil properties, pH, cation exchange capacity, limeAbstract
In this study, the spatial prediction performances of Local Polynomial Interpolation (LPI) and Experimental Bayesian Kriging (EBK) were compared. Soil pH, lime content, electrical conductivity (EC) and cation exchange capacity (CEC) analyses were performed on 622 soil samples in a study area of 18,143 ha in Ergani district of Diyarbakır in southeastern Turkey. The coefficient of variation between estimated and measured pH values in both models was quite similar. In the local polynomial interpolation method, the coefficient of variation of the estimated and measured data set in the lime content of soils was quite similar. The root mean square deviation (RMSE) values showed a decrease of 9.5%, 78.8% and 25.6% for pH, EC and CEC, respectively, in the EBK method compared to the LPI. The LPI-Lime method showed an RMSE improvement of 28.4% compared to EBK. The results showed that the error values of the estimations obtained with the EBK method were lower compared to the LPI method. On the other hand, the estimated pH and lime maps obtained with the LPI method were more compatible with the coefficient of variation of the sample dataset. This research is important to provide efficient spatial soil information for land management applications where rapid and accurate digital soil maps are required without the need for environmental variables.
References
Acar, S., Kazankaya, A., Doğan, A., 2018. Selection of almonds (P. amygdalus L.) naturally grown in Eğil and Ergani Towns in Diyarbakır Province. Yuzuncu Yil University Journal of Agricultural Sciences 28: 448–457.
Agyeman, P.C., John, K., Kebonye, N.M., Borůvka, L., Vašát, R., Drábek, O., 2021. A geostatistical approach to estimating source apportionment in urban and peri-urban soils using the Czech Republic as an example. Scientific Reports, 11.
Ahmed, M., El- Desoky, M., Gameh, M., Ahmed, E., Selmy, S., 2022. Soil Suitability Assessment for Twenty Crops in East Edfu Soils, Aswan. Assiut Journal of Agricultural Sciences 53: 203–223.
Akramkhanov, A., Martius, C., Park, S.J., Hendrickx, J.M.H., 2011. Environmental factors of spatial distribution of soil salinity on flat irrigated terrain. Geoderma 163: 55–62.
Alcaras, E., 2020. Interpolating single-beam data for sea bottom GIS modelling. International Journal of Emerging Trends in Engineering Research 8: 591–597.
Allison, L.E., Moodie, C.D., 1965. Carbonate. Methods of Soil Analysis, Agronomy Monographs.
Attorre, F., Alfo, M., De Sanctis, M., Francesconi, F., Bruno, F., 2007. Comparison of interpolation methods for mapping climatic and bioclimatic variables at regional scale. International Journal of Climatology 27: 1825–1843.
Bayona, V., Flyer, N., Fornberg, B., Barnett, G.A., 2017. On the role of polynomials in RBF-FD approximations: II. Numerical solution of elliptic PDEs. Journal of Computational Physics 332: 257–273.
Berrocal, V.J., Gelfand, A.E., Holland, D.M., 2010. A spatio-temporal downscaler for output from numerical models. Journal of Agricultural, Biological, and Environmental Statistics 15: 176–197.
Bradshaw, S.J., Cargill, P.J., 2013. The influence of numerical resolution on coronal density in hydrodynamic models of impulsive heating. Astrophysical Journal 770.
Budak, M., Günal, E., Kılıç, M., Çelik, İ., Sırrı, M., Acir, N., 2023. Improvement of spatial estimation for soil organic carbon stocks in Yuksekova plain using Sentinel 2 imagery and gradient descent–boosted regression tree. Environmental Science and Pollution Research 30: 53253–53274.
Budak, M., Günal, H., Çelik, İ., Acir, N., Sırrı, M., 2018. Dicle havzası toprak özelliklerinin yersel değişimlerinin jeoistatistik ve coğrafi bilgi sistemleri ile belirlenmesi ve haritalanması. Türkiye Tarımsal Araştırmalar Dergisi. 5(2): 103-115.
Charman, P.E., Murphy, B.W.. 2007. Soils: Their properties and management. Oxford University Press, New York, NY
Dharumarajan, S., Hegde, R., Singh, S. K. 2017. Spatial prediction of major soil properties using Random Forest techniques-A case study in semi-arid tropics of South India. Geoderma Regional, 10: 154-162.
Đurđević, B., Jug, I., Jug, D., Bogunović, I., Vukadinović, V., Stipešević, B., Brozović, B., 2019. Spatial variability of soil organic matter content in Eastern Croatia assessed using different interpolation methods. International Agrophysics 33: 31–39.
ESRI Inc., 2023. Local Polynomial Interpolation (Geostatistical Analyst) [www Document]. URL https://pro.arcgis.com/en/pro-app/latest/ tool-reference/geostatistical-analyst/loc al-polynomial-interpolation.htm /Erişim Tarihi: 10.04.2023)
Giordano, B. V., Kaur, S., Hunter, F.F., 2017. West Nile virus in Ontario, Canada: A twelve-year analysis of human case prevalence, mosquito surveillance, and climate data. PLoSONE, 12.
Gribov, A., Krivoruchko, K., 2011. Local polynomials for data detrending and interpolation in the presence of barriers. Stochastic Environmental Research and Risk Assessment 25: 1057–1063.
Goovaerts, P., 1999. Geostatistics in soil science: state-of-the-art and perspectives. Geoderma, 89(1-2): 1-45.
Goovaerts, P., 2001. Geostatistical modelling of uncertainty in soil science. Geoderma, 103(1-2): 3-26.
Günal, H., Miraç Kılıç, Mesut Altındal, recep Gündoğan. 2021. Rapid spatial estimation of soil ph using machine learning under limited covariate conditions. Levantine Journal of Applied Sciences, 1(1): 30-37.
İmamoğlu, M., Kavak, O., Kaya, M. 2014. Diyarbakır ili Ergani ilçesi ve çevresinin jeolojik özellikleri. Editörler: Güzel, C., Haspolat K. Tüm Yönleriyle Diyarbakır Ergani İlçesi ve Turizm. Amaç Matbaacılık, ss.258-330.
Hu, C., Wright, A., Lian, G., 2019. Estimating the spatial distribution of soil properties using environmental variables at a catchment scale in the loess hilly area, China. International Journal of Environmental Research and Public Health 16: 491.
Huang, Yajie, Li, Z., Ye, H., Zhang, S., Zhuo, Z., Xing, A., Huang, Yuanfang, 2019. Mapping soil electrical conductivity using ordinary kriging combined with back-propagation network. Chinese Geographical Science 29: 270–282.
Hussain, Azfar, Ali, H., Begum, F., Hussain, Azhar, Khan, M.Z., Guan, Y., Zhou, J., Saif-Ud-din, Hussain, K., 2021. Mapping of soil properties under different land uses in Lesser Karakoram range, Pakistan. Polish Journal of Environmental Studies 30: 1181–1189.
Iorio, D. Di, Walter, M., Lantinga, E., Kerckhoffs, H., Campbell, R.E., 2019. Mapping European canker spatial pattern and disease progression in apples using GIS, Tasman, New Zealand. New Zealand Plant Protection, 72: 176–184.
Isaaks, E.H., Srivastava, R.M., 1988. Spatial continuity measures for probabilistic and deterministic geostatistics. Mathematical Geology, 20: 313–341.
Krivoruchko, K., 2012. Deneysel Bayesian Kriging, ESRI Press.
Liao, Y., Li, D., Zhang, N., 2018. Comparison of interpolation models for estimating heavy metals in soils under various spatial characteristics and sampling methods. Transactions in GIS 22: 409–434.
Liu Q., Sun X., Wu W., Liu Z., Fang G., Yang, P., 2022.Agroecosystem services: A review of concepts, indicators, assessment methods and future research perspectives. Ecologival Indicators, 142:109218.
Luo, W., Taylor, M.C., Parker, S.R., 2008. A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales. International Journal of Climatology, 28: 947–959.
Magno, J.L., Budianta, W., 2022. Spatial distribution and pattern of heavy metals in urban soils of Yogyakarta, Indonesia. IOP Conference Series: Earth and Environmental Science 1071: 012032.
Summers M.E., Miller, W.P., 1996. Cation exchange capacity and exchange coefficient. In. Methods of soil analysis. SSSSA Book Series 5: 1201-1229.
Mishra, U., Lal, R., Liu, D., Van Meirvenne, M., 2010. Predicting the Spatial variation of the soil organic carbon pool at a regional scale. Soil Science Society of America Journal, 74: 906–914.
Moore, I.D., Gessler, P.E., Nielsen, G.A., Peterson, G.A., 1993. Soil Attribute prediction using terrain analysis. Soil Science Society of America Journal 57: 443–452.
Nelson, E., Mendoza, G., Regetz, J., Polasky, S., Tallis, H., Cameron, D.R., Chan, K.M.A., Daily, G.C., Goldstein, J., Kareiva, P.M., Lonsdorf, E., Naidoo, R., Ricketts, T.H., Shaw, M.R., 2009. Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Frontiers in Ecology and the Environment 7: 4–11.
Polasky, S., Nelson, E., Pennington, D., Johnson, K.A., 2011. The impact of land-use change on ecosystem services, biodiversity and returns to landowners: A case study in the state of Minnesota. Environmental and Resource Economics, 48: 219–242.
Ricchiuto, M., Filippini, A.G., 2014. Upwind residual discretization of enhanced Boussinesq equations for wave propagation over complex bathymetries. Journal of Computational Physics, 271: 306–341.
Rodrigues, M.S., Alves, D.C., De Souza, V.C., De Melo, A.C., Lima, A.M.N., Cunha, J.C., 2018. Spatial interpolation techniques for site-specific irrigation management in a mango orchard. Comunicata Scientiae 9: 93–101.
Sarı, H., 2019. Geostatistical Assessments for characteristics of soils around naip dam. International Journal of Scientific Research and Management 7.
Selmy, S., El-Aziz, S.A., El-Desoky, A., El-Sayed, M., 2022. Characterizing, predicting, and mapping of soil spatial variability in Gharb El-Mawhoub area of Dakhla Oasis using geostatistics and GIS approaches. Journal of the Saudi Society of Agricultural Sciences, 21: 383–396.
Sevilla, R., Fernández-Méndez, S., Huerta, A., 2008. NURBS-enhanced finite element method (NEFEM). International Journal for Numerical Methods in Engineering 76: 56–83.
Shabbir, W., Omer, T., Pilz, J., 2022. The impact of environmental change on landslides, fatal landslides, and their triggers in Pakistan (2003–2019). Environmental Science and Pollution Research.
Szymanowski, M., Kryza, M., 2009. GIS-based techniques for urban heat island spatialization. Climate Research, 38: 171–187.
Tesema, G.A., Teshale, A.B., 2021. Residential inequality and spatial patterns of infant mortality in Ethiopia: evidence from Ethiopian Demographic and Health Surveys. Tropical Medicine and Health, 49.
Tripathi, R., Nayak, A.K., Shahid, M., Raja, R., Panda, B.B., Mohanty, S., Kumar, A., Lal, B., Gautam, P., Sahoo, R.N., 2015. Characterizing spatial variability of soil properties in salt affected coastal India using geostatistics and kriging. Arabian Journal of Geosciences, 8: 10693–10703.
Wimalasiri, E.M., Jahanshiri, E., Suhairi, T., Mapa, R.B., Karunaratne, A.S., Vidhanarachchi, L.P., Udayangani, H., Nizar, N.M.M., Azam-Ali, S.N., 2020. The first version of nation-wide open 3D soil database for Sri Lanka. Data in Brief 33: 106342.
Xiao, Y., Gu, X., Yin, S., Shao, J., Cui, Y., Zhang, Q., Niu, Y., 2016. Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China. SpringerPlus 5.
Yang, Q., Luo, W., Jiang, Z., Li, W., Yuan, D., 2016. Improve the prediction of soil bulk density by cokriging with predicted soil water content as auxiliary variable. Journal of Soils and Sediments 16: 77–84.
Zhang, Z.G., Chan, S.C., Ho, K.L., Ho, K.C., 2008. On bandwidth selection in local polynomial regression analysis and ıts application to multi-resolution analysis of non-uniform data. Journal of Signal Processing Systems 52: 263–280.
Zhuo, Z., Xing, A., Li, Y., Huang, Y., Nie, C., 2019. Spatio-temporal variability and the factors influencing soil-available heavy metal micronutrients in different agricultural sub-catchments. Sustainability (Switzerland) 11.
Zimmerman, D., Pavlik, C., Ruggles, A., Armstrong, M.P. 1999. An experimental comparison of ordinary and universal kriging and inverse distance weighting. Mathematical Geology, 31: 375-390.
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