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Modeling soil cation exchange capacity in multiple countries

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Modeling soil cation exchange capacity in multiple countries

dc.contributor.author	Khaledian, Yones
dc.contributor.author	Brevik, E. C.
dc.contributor.author	Pereira, Paulo
dc.contributor.author	Cerdà Bolinches, Artemi
dc.contributor.author	Fattah, Mohammed A.
dc.contributor.author	Tazikeh, Hossein
dc.date.accessioned	2018-04-17T10:39:41Z
dc.date.available	2018-04-17T10:39:41Z
dc.date.issued	2017
dc.identifier.citation	Khaledian, Yones Brevik, E. C. Pereira, Paulo Cerdà Bolinches, Artemi Fattah, Mohammed A. Tazikeh, Hossein 2017 Modeling soil cation exchange capacity in multiple countries Catena 158 194 200
dc.identifier.uri	http://hdl.handle.net/10550/65729
dc.description.abstract	Cation exchange capacity (CEC), as an important indicator for soil quality, represents soil's ability to hold positively charged ions. We attempted to predict CEC using different statistical methods including monotone analysis of variance (MONANOVA), artificial neural networks (ANNs), principal components regressions (PCR), and particle swarm optimization (PSO) in order to compare the utility of these approaches and identify the best predictor. We analyzed 170 soil samples from four different nations (USA, Spain, Iran and Iraq) under three land uses (agriculture, pasture, and forest). Seventy percent of the samples (120 samples) were selected as the calibration set and the remaining 50 samples (30%) were used as the prediction set. The results indicated that the MONANOVA (R2 =0.82 and Root Mean Squared Error (RMSE) =6.32) and ANNs (R2 = 0.82 and RMSE =5.53) were the best models to estimate CEC, PSO (R2 =0.80 and RMSE =5.54) and PCR (R2 =0.70 and RMSE =6.48) also worked well and the results were very similar to each other. While the most influential variables for the various countries and land uses were different and CEC was affected by different variables in different situations, clay (positively correlated) and sand (negatively correlated) were the most influential variables for predicting CEC for the entire data set. Although the MANOVA and ANNs provided good predictions of the entire dataset, PSO gives a formula to estimate soil CEC using commonly tested soil properties. Therefore, PSO shows promise as a technique to estimate soil CEC. Establishing effective pedotransfer functions to predict CEC would be productive where there are limitations of time and money, and other commonly analyzed soil properties are available.
dc.language.iso	eng
dc.relation.ispartof	Catena, 2017, num. 158, p. 194-200
dc.subject	Ecologia dels sòls
dc.title	Modeling soil cation exchange capacity in multiple countries
dc.type	journal article	es_ES
dc.date.updated	2018-04-17T10:39:41Z
dc.identifier.doi	10.1016/j.catena.2017.07.002
dc.identifier.idgrec	125684
dc.rights.accessRights	open access	es_ES