Are There Arid Land Soilscapes in Southwestern Europe?

Ibáñez, Juan José; Pérez-Gómez, Rufino; Oyonarte, Cecilio; Brevik, Eric C.

doi:10.1002/ldr.2451

Cited by 29 publications

(20 citation statements)

References 48 publications

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“…Khaledian et al Catena 158 (2017) 194-200 soils have been ploughed for millennia, leading to soil erosion and degradation (Ibáñez et al, 2015). The results for the pasture categories and for the USA and Iraq were consistent with previous studies.…”

Section: Discussionsupporting

confidence: 85%

Modeling soil cation exchange capacity in multiple countries

Khaledian

Brevik

Pereira

et al. 2017

CATENA

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A B S T R A C TCation 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 (R 2 = 0.82 and Root Mean Squared Error (RMSE) = 6.32) and ANNs (R 2 = 0.82 and RMSE = 5.53) were the best models to estimate CEC, PSO (R 2 = 0.80 and RMSE = 5.54) and PCR (R 2 = 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.

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Section: Discussionsupporting

confidence: 85%

Modeling soil cation exchange capacity in multiple countries

Khaledian

Brevik

Pereira

et al. 2017

CATENA

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“…Spatial heterogeneity has been reported in urban soils (Howard & Orlicki, ; Ibáñez et al, ; Howard & Orlicki, ). Spatially significant variations were apparent in this study as indicated by CA, showing remarkable spatial heterogeneity in the urban soils due to anthropogenic activities such as urbanization and home construction around the Zirata village (Tajgardan et al, ; Khaledian et al, ).…”

Section: Discussionmentioning

confidence: 77%

Assessment and Monitoring of Soil Degradation during Land Use Change Using Multivariate Analysis

et al. 2016

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Not only are soil erosion and overall loss of soil fertility serious issues for loess‐derived soils of developing nations, such as Iran, but they are also global problems. This research investigated the role of land use change and its effect on soil degradation in cultivated, pasture, and urban lands, when compared with native forest in terms of declining of soil quality and fertility. Multivariate statistical methods including principal component analysis and cluster analysis were employed to determine the relative magnitude of anthropogenic and natural influences on soil quality. Partial least squares (PLS), principal component regression, and ordinary least squares regression were used to predict soil cation exchange capacity using soil characteristics. Principal component analysis identified five primary components of soil quality. PC1 explained 41·01% of the total variance, PC2 accounted for 15·29%, and PC3 accounted for 11·3%. PC4 and PC5 accounted for 7·37% and 4·19% of the total variance, respectively. Cluster analysis revealed that the lowest soil quality occurred in urban soils. An exponential semivariogram using PLS predictions revealed stronger spatial dependence among cation exchange capacity [r2 = 0·80, root mean square error (RMSE) = 1·99] than the other methods, principal component regression [r2 = 0·84, RMSE = 2·45] and ordinary least squares [r2 = 0·84, RMSE = 2·45]. Therefore, the PLS method provided the best model for the data. In stepwise regression analysis, mean weight diameter and labile carbon were selected as influential variables in all soils. This study quantified reductions in numerous soil quality parameters resulting from extensive land‐use changes and urbanization in the Ziarat watershed in northern Iran. Copyright © 2016 John Wiley & Sons, Ltd.

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“…The soil subgroup is an intergrade between Lithic and Typic Rhodoxeralfs (García del Barrio et al, 1971;Soil Survey Staff, 1999), with a loamy sand texture and a maximum depth of 1.2 m to the calcarenite bedrock. In areas with weakly developed soils, such as the Setenil region, the rugged relief often leads to partial loss of the topsoil layer (Ibañez et al, 2015;Symeonakis et al, 2014). This is a natural process aggravated by certain agricultural practices (eg tillage) (Gómez et al, 2009;Keesstra et al, 2016;Taguas and Gómez, 2015;Vanwalleghem et al, 2011), leading to outcropping of the bedrock and to the appearance of localised zones where the humus-rich horizon rarely exceeds 0.1 m. The climate is Mediterranean, with a mean annual precipitation of 700 mm, where 75% of the rainfall occurs from October to May.…”

Section: Methodsmentioning

confidence: 99%

Temporal stability of electrical conductivity in a sandy soil

Pedrera‐Parrilla

Brevik

Giráldez

et al. 2016

International Agrophysics

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Understanding of soil spatial variability is needed to delimit areas for precision agriculture. Electromagnetic induction sensors which measure the soil apparent electrical conductivity reflect soil spatial variability. The objectives of this work were to see if a temporally stable component could be found in electrical conductivity, and to see if temporal stability information acquired from several electrical conductivity surveys could be used to better interpret the results of concurrent surveys of electrical conductivity and soil water content. The experimental work was performed in a commercial rainfed olive grove of 6.7 ha in the ‘La Manga’ catchment in SW Spain. Several soil surveys provided gravimetric soil water content and electrical conductivity data. Soil electrical conductivity values were used to spatially delimit three areas in the grove, based on the first principal component, which represented the time-stable dominant spatial electrical conductivity pattern and explained 86% of the total electrical conductivity variance. Significant differences in clay, stone and soil water contents were detected between the three areas. Relationships between electrical conductivity and soil water content were modelled with an exponential model. Parameters from the model showed a strong effect of the first principal component on the relationship between soil water content and electrical conductivity. Overall temporal stability of electrical conductivity reflects soil properties and manifests itself in spatial patterns of soil water content.

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Are There Arid Land Soilscapes in Southwestern Europe?

Cited by 29 publications

References 48 publications

Modeling soil cation exchange capacity in multiple countries

Modeling soil cation exchange capacity in multiple countries

Assessment and Monitoring of Soil Degradation during Land Use Change Using Multivariate Analysis

Temporal stability of electrical conductivity in a sandy soil

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