Predicting Soil Properties Using Topographic and Climatic Variables

Year 2021, Volume: 21 Issue: 3, 252 - 267, 31.12.2021

İsmet Yener Mehmet Küçük Aşkın Göktürk

https://doi.org/10.17475/kastorman.1049347

Abstract

Aim of study: The present study aimed to model soil physical and chemical properties through multiple linear and regression tree techniques.
Area of study: The study area is located between 41,07 – 41,33 N latitude and 41,74 – 42,27 E longitude in Artvin, which is in the Colchis part of the Black Sea Region of Turkey.
Material and methods: The multiple linear regression and regression tree models were used to predict soil properties using topographic and climatic features as independent variables. Besides, the relationships between soil properties and independent variables were determined by Pearson correlation.
Main results: The study results revealed that model accuracy by regression tree generally was higher than those of multiple linear regression. Up to 56% and 59% of the variance in soil properties was accounted for by multiple linear regression and regression tree, respectively. The easting, northing, elevation, and minimum temperature parameters were key drivers of both models. Increasing soil depth significantly increased the pH and reduced the organic carbon, total nitrogen, and carbon/nitrogen ratio.
Highlights: Topographic and climatic variables accounted for Up to 59% and 56% of the variance in soil properties such as texture, pH, organic carbon, total nitrogen, and carbon/nitrogen ratio by regression tree and multiple linear regression techniques. The most influential factors on soil properties were the minimum temperature, latitude, actual
evapotranspiration, mean temperature, distance to the ridge, and radiation index.

Keywords

Forest, Modeling, Multiple Linear Regression, Regression Tree.

Topoğrafik ve Klimatik Değişkenlerden Yararlanarak Toprak Özelliklerinin Tahmin Edilmesi

Abstract

Çalışmanın amacı: Bu çalışma ile toprağa ilişkin bazı fiziksel ve kimyasal özelliklerin çoklu doğrusal regresyon ve regresyon ağacı tekniklerinden yararlanılarak modellenmesi amaçlanmıştır.
Çalışma alanı: Çalışma alanı Karadeniz bölgesinin kolşik kesiminde yer alan Artvin ili sınırları içerisinde ve 41,07 – 41,33 K enlemleri ile 41,74 – 42,27 D boylamları arasında bulunmaktadır.
Materyal ve yöntem: Toprak özelliklerinin tahmin edilmesinde çoklu doğrusal regresyon ve regresyon ağacı modelleri kullanılırken, toprak özellikleri ile bağımsız değişkenler arasındaki ilişki ise Pearson korelasyonu ile belirlenmiştir.
Temel sonuçlar: Regresyon ağacı modellerinin doğruluğu çoklu doğrusal regresyon modellerininkine göre daha yüksek bulunmuştur. Toprak özelliklerindeki değişimin en fazla %56 ile %59’luk bir kısmı sırasıyla doğrusal regresyon ve regresyon ağacı modelleri ile açıklanabilmiştir. Her iki model için de en önemli değişkenler boylam, enlem, yükselti ve en düşük sıcaklık olarak belirlenmiştir. Toprak derinliğinin artmasına bağlı olarak pH anlamlı bir şekilde artarken, organik karbon, toplam azot ve karbon/azot oranı azalmıştır.
Araştırma vurguları: Regresyon ağacı modelleri toprak özelliklerindeki değişimi %59’a varan bir oranda, doğrusal regresyon modelleri ise %56’ya varan bir oranda açıklamıştır. Toprak özelliklerini tahminde en belirleyici değişkenler en düşük sıcaklık, enlem, gerçek evapotranspirasyon, ortalama sıcaklık, sırta olan uzaklık ve radyasyon indeksi olarak belirlenmiştir.

Keywords

Orman, Modelleme, Çoklu Doğrusal Regresyon, Regresyon Ağacı

References

• Aartsma, P. (2016). Climate, A Driving Factor Behind Soil Formation In Proglacial Areas In The European Alps? Master thesis. Master Earth and Environment Soil Geography and Landscape Group, Wageningen University.
• Adhikari, K. & Hartemink, A. E. (2016). Linking soils to ecosystem services—A global review. Geoderma, 262, 101-111.
• Aertsen, W., Kint, V., Muys, B. & Van Orshoven, J. (2012). Effects of scale and scaling in predictive modelling of forest site productivity. Environmental Modelling & Software, 31,19-27.
• Bishop, T. F. & Minasny, B. (2006). Digital soil-terrain modeling: the predictive potential and uncertainty. Environmental Soil-landscape Modeling, 185-213.
• Bolton, D. K., White, J. C., Wulder, M. A., Coops, N. C., Hermosilla, T. & Yuan, X. (2018). Updating stand-level forest inventories using airborne laser scanning and Landsat time series data. International Journal of Applied Earth Observation and Geoinformation, 66, 174-183.
• Brinkman, R. (1990). Resilience Against Climate Change? Pages 51-60, Developments in soil science. Elsevier.
• Buol, S. W., Southard, R. J., Graham, R. C. & McDaniel, P. A. (2011). Soil genesis and classification. John Wiley & Sons.
• Chai, T. & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific model development, 7, 1247-1250.
• Demircan, M., Alan, I. & Sensoy, S. (2011). Increasing resolution of temperature maps by using Geographic Information Systems (GIS) and topography information. 27-29 in 5th Atmospheric Science Symposium.
• Dick, W. & Gregorich, E. (2004). Developing and maintaining soil organic matter levels. Managing soil quality: Challenges in modern agriculture, 103-120.
• Eminagaoglu, O., Akyıldırım Begen, H. & Aksu, G. (2015). Flora and vegetation structure of Artvin (In Turkish). 27-51 in O. Eminagaoglu, editor. Native Plants of Artvin. Promat, Istanbul.
• ESRI. (2019). National geographic world map. http://services.arcgisonline.com/ARCGIS/rest/services/NatGeo_World_Map/MapServer.
• Grunwald, S. (2016). Environmental Soil-Landscape Modeling: Geographic Information Technologies and Pedometrics. CRC Press.
• Gunal, H. & Ransom, M. D. (2006). Clay illuviation and calcium carbonate accumulation along a precipitation gradient in Kansas. Catena, 68, 59-69.
• Gutiérrez, Á. G., Contador, F. L., & Schnabel, S. (2011). Modeling soil properties at a regional scale using GIS and Multivariate Adaptive Regression Splines. Geomorphometry, 79-82.
• Henderson, B. L., Bui, E. N., Moran, C. J. & Simon, D. A. P. (2005). Australia-wide predictions of soil properties using decision trees. Geoderma, 124, 383-398.
• Hengl, T., Heuvelink, G. & MacMillan, R. (2019). Statistical theory for predictive soil mapping.in T. Hengl and R. MacMillan, editors. Predictive Soil Mapping with R. OpenGeoHub Foundation, Wageningen, Netherlands.
• Hong, J. (2011). Modeling of soil properties in the Santa Fe River Watershed using exhaustive spatial environmental data. University of Florida.
• IBM, C. (2011). IBM SPSS statistics for Windows, version 20.0. New York, IBM Corp 440.
• Jenny, H. (1994). Factors of soil formation: a system of quantitative pedology. Courier Corporation.
• Jenny, H. (2012). The soil resource: origin and behavior. Springer Science & Business Media.
• Kapur, S., Akca, E. & Gunal, H. (2017). The Soils of Turkey. Springer International Publishing.
• Kirkham, M. B. (2014). Principles of soil and plant water relations. Academic Press.
• Kosiba, A. M., Schaberg, P. G., Rayback, S. A. & Hawley, G. J. (2018). The surprising recovery of red spruce growth shows links to decreased acid deposition and elevated temperature. Science of the Total Environment, 637, 1480-1491.
• Lal, R. & Stewart, B. A. (2018). Soil and climate. CRC Press.
• Lin, Y., Prentice III, S. E., Tran, T., Bingham, N. L., King, J. Y. & Chadwick, O. A. J. G. R. (2016). Modeling deep soil properties on California grassland hillslopes using LiDAR digital elevation models. Geoderma Regional, 7, 67-75.
• Liu, W., Zhang, H. R., Yan, D. P. & Wang, S. L. (2017). Adaptive surface modeling of soil properties in complex landforms. ISPRS International Journal of Geo-Information, 6(6), 178.
• Lourenço, V., Taniguchi, C., Costa, C. A., Toma, R. & Alencar, P. (2018). Using of regression tree for soil organic carbon prediction in the caatinga biome.
• Mason, E. & Sulaeman, Y. (2016). Comparison Of Three Models For Predicting The Spatial Distribution Of Soil Organic Carbon In Boalemo Regency, Sulawesi. Jurnal Ilmu Tanah dan Lingkungan, 18, 42-48.
• Miles, N., Antwerpen, R. V. & Ramburan, S. (2016). Soil organic matter under sugarcane: levels, composition and dynamics. Pages 161-169 in Proceedings of the Annual Congress-South African Sugar Technologists' Association. South African Sugar Technologists' Association.
• Miller, B. A., Koszinski, S., Wehrhan, M. & Sommer, M. J. G. (2015). Impact of multi-scale predictor selection for modeling soil properties. Geoderma, 239, 97-106.
• Mosleh, Z., Salehi, M. H., Jafari, A., Borujeni, I. E. & Mehnatkesh, A. (2016). The effectiveness of digital soil mapping to predict soil properties over low-relief areas. Environmental Monitoring and Assessment, 188.
• Motulsky, H. & Christopoulos, A. (2004). Fitting models to biological data using linear and nonlinear regression: a practical guide to curve fitting. Oxford University Press.
• MTA. (2019). Geological map of Turkey. General Directorate of Mineral Research and Exploration, Ankara.
• Narwal, S. (2004). Research Methods in Plant Sciences: Allelopathy, 1 (Soil Analysis). Scientific Publishers.
• NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2009). ASTER Global Digital Elevation Model [Data set]. NASA EOSDIS Land Processes DAAC. Accessed 2021-08-04 from https://doi.org/10.5067/ASTER/ASTGTM.002.
• Ozcan, H., Dengiz, O. & Ersahin, S. (2018). Alisols-Acrisols. 207-215 The Soils of Turkey. Springer.
• Padarian, J., Minasny, B. & McBratney, A. B. (2019). Using deep learning to predict soil properties from regional spectral data. Geoderma Regional, 16.
• Pahlavan-Rad, M. R. & Akbarimoghaddam, A. (2018). Spatial variability of soil texture fractions and pH in a flood plain (case study from eastern Iran). Catena, 160, 275-281.
• Patriche, C. V., Pîrnău, R. G. & Roşca, B. (2011). Comparing Linear Regression and Regression Trees for Spatial Modelling of Soil Reaction in Dobrovăţ Basin (Eastern Romania). Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Agriculture, 68.
• Peccerillo, A. & Taylor, S. R. (1975). Geochemistry of upper cretaceous volcanic rocks from the pontic chain, northern Turkey. Bulletin Volcanologique, 39, 557.
• Pham, H. (2019). A New Criterion for Model Selection. Mathematics, 7, 1215.
• Pinheiro, H. S. K., Carvalho Junior, W. D., Chagas, C. D. S., Anjos, L. H. C. D., & Owens, P. R. (2018). Prediction of topsoil texture through regression trees and multiple linear regressions. Revista Brasileira de Ciência do Solo, 42.
• Sariyildiz, T., Savaci, G. & Maral, Z. J. (2017). Effect of different land uses (mature and young fir stands-pasture and agriculture sites) on soil organic carbon and total nitrogen stock capacity in Kastamonu Region. Kastamonu University Journal of Forestry Faculty, 17, 132-142.
• Schoeneberger, P. J. (2012). Field book for describing and sampling soils. National Soil Survey Center, Natural Resources Conservation Service, U.S. Dept. of Agriculture.
• Seiler, B., Kneubühler, M., Wolfgramm, B. & Itten, K. (2007). Quantitative assessment of soil parameters in Western Tajikistan using a soil spectral library approach. 451-455 in Proceedings of the ISPRS Working Group VII/1 Workshop ISPMSRS'07. International Society for Photogrammery and Remote Sensing.
• Selhorst, A. L. (2011). Carbon Sequestration By Home Lawn Turfgrass Development and Maintenance in Diverse Climatic Regions of the United States. Ph.D. The Ohio State University.
• Sherrod, P. H. (2003). DTREG predictive modeling software. Software available at http://www. dtreg. com.
• Sindayihebura, A., Ottoy, S., Dondeyne, S., Van Meirvenne, M. & Van Orshoven, J. (2017). Comparing digital soil mapping techniques for organic carbon and clay content: Case study in Burundi's central plateaus. Catena, 156, 161-175.
• Team, R. C. (2013). R development core team. RA Lang Environ Stat Comput, 55, 275-286.
• Thornthwaite, C. W. (1948). An Approach toward a Rational Classification of Climate. Geographical Review, 38, 55-94.
• Van Breemen, N. & Buurman, P. (2007). Soil Formation. Springer Netherlands.
• Vogel, H. J., Bartke, S., Daedlow, K., Helming, K., Kogel-Knabner, I., Lang, B., Rabot, E., Russell, D., Stossel, B., Weller, U., Wiesmeier, M. & Wollschlager, U. (2018). A systemic approach for modeling soil functions. Soil, 4, 83-92.
• Zaimes, G., Kayiaoglu, K. & Kozanidis, A. (2017). Land-use/vegetation cover and soil erosion impacts on soil properties of hilly slopes in Drama Prefecture of Northern Greece. Kastamonu University Journal of Forestry Faculty, 17(3), 427-433.
• 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.
• Ziadat, F. (2005). Analyzing digital terrain attributes to predict soil attributes for a relatively large area. Soil Science Society of America Journal 69, 1590-1599.