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Alfredo J. Morales

Yolanda Torres Subscribe SciFeed Recommended Articles Related Info Link Google Scholar More by Authors Links on DOAJ Kaya, F. Mishra, G. Francaviglia, R. Keshavarzi, A. on Google Scholar Kaya, F. Mishra, G. Francaviglia, R. Keshavarzi, A. on PubMed Kaya, F. Mishra, G. Francaviglia, R. Keshavarzi, A. /ajax/scifeed/subscribe Article Views Citations - Table of Contents Altmetric share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up ... Endorse textsms ... Comment Need Help? Support

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Get Information clear JSmol Viewer clear first_page settings Order Article Reprints Font Type: Arial Georgia Verdana Font Size: Aa Aa Aa Line Spacing:    Column Width:    Background: Open AccessArticle Combining Digital Covariates and Machine Learning Models to Predict the Spatial Variation of Soil Cation Exchange Capacity by

Fuat Kaya 1 ORCID

Gaurav Mishra 2 ORCID

Rosa Francaviglia 3,* ORCID

and

Ali Keshavarzi 4 ORCID

1 Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Isparta University of Applied Sciences, Isparta 32260, Türkiye 2 Centre of Excellence on Sustainable Land Management, Indian Council of Forestry Research and Education, Dehradun 248006, Uttarakhand, India 3 Research Centre for Agriculture and Environment, Council for Agricultural Research and Economics, 00184 Rome, Italy 4 Laboratory of Remote Sensing and GIS, Department of Soil Science, University of Tehran, P.O. Box 4111, Karaj 31587-77871, Iran * Author to whom correspondence should be addressed. Land 2023, 12(4), 819; https://doi.org/10.3390/land12040819 Received: 27 February 2023 / Revised: 27 March 2023 / Accepted: 31 March 2023 / Published: 3 April 2023 (This article belongs to the Special Issue Machine Learning and Data Science Techniques for Remote Sensing and Social Media Data) Download Download PDF Download PDF with Cover Download XML Download Epub Browse Figures Versions Notes

Abstract: Cation exchange capacity (CEC) is a soil property that significantly determines nutrient availability and effectiveness of fertilizer applied in lands under different managements. CEC’s accurate and high-resolution spatial information is needed for the sustainability of agricultural management on farms in the Nagaland state (northeast India) which are fragmented and intertwined with the forest ecosystem. The current study applied the digital soil mapping (DSM) methodology, based on the CEC values determined in soil samples obtained from 305 points in the region, which is mountainous and difficult to access. Firstly, digital auxiliary data were obtained from three open-access sources, including indices generated from the time series Landsat 8 OLI satellite, topographic variables derived from a digital elevation model (DEM), and the WorldClim dataset. Furthermore, the CEC values and the auxiliary were used data to model Lasso regression (LR), stochastic gradient boosting (GBM), support vector regression (SVR), random forest (RF), and K-nearest neighbors (KNN) machine learning (ML) algorithms were systematically compared in the R-Core Environment Program. Model performance were evaluated with the square root mean error (RMSE), determination coefficient (R2), and mean absolute error (MAE) of 10-fold cross-validation (CV). The lowest RMSE was obtained by the RF algorithm with 4.12 cmolc kg−1, while the others were in the following order: SVR (4.27 cmolc kg−1)

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