Assessing desertification by using soil indices

Khanamani, A; Fathizad, Hassan; Karimi, Haji; Shojaei, Saeed

doi:10.1007/s12517-017-3054-5

Cited by 28 publications

(12 citation statements)

References 15 publications

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“…By the end of 2014, the total area of soil erosion, land desertification, and land sandification in China was 2,949,000 km 2 , 2,611,600 km 2 , and 1,721,200 km 2 , respectively [ 11 ]. As soil degrades, the nature of soil is remarkably changed, including destroyed agglomerating structure and particles, lower organic matter content, and poorer microbial communities, which makes the water content and environmental buffer capacity drop [ 12 , 13 ]. The natural change of degraded soil also influences the adsorption behavior of pollutants.…”

Section: Introductionmentioning

confidence: 99%

Adsorption of Cadmium on Degraded Soils Amended with Maize-Stalk-Derived Biochar

Chen

et al. 2018

IJERPH

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Biochar has been extensively proven to distinctively enhance the sorption capacity of both heavy metal and organic pollutants and reduce the related environmental risks. Soil pollution and degradation widely coexist, and the effect of biochar addition on adsorption behavior by degraded soils is not well understood. Four degraded soils with different degrees of degradation were amended with maize-stalk-derived biochar to investigate the adsorption of cadmium using batch methods. The maximum adsorption capacity (Qm) of degraded soil remarkably decreased in comparison with undegraded soil (5361 mg·kg−1→170 mg·kg−1), and the Qm of biochar increased with increasing pyrolysis temperature (22987 mg·kg−1→49016 mg·kg−1) which was much higher than that of soil. The addition of biochar can effectively improve the cadmium adsorption capacity of degraded soil (36–328%). The improving effect is stronger when increasing either the degradation level or the amount of added biochar, or the pyrolysis temperature of biochar. Contrary to the general soil–biochar system, adsorption of Cd was not enhanced but slightly suppressed (7.1–36.6%) when biochar was incorporated with degraded soils, and the adsorptivity attenuation degree was found to be negatively linear with SOM content in the degraded soil–biochar system. The results of the present study suggest that more attention on the adsorption inhibition and acceleration effect difference between the soil–biochar system and the degraded soil–biochar system is needed.

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Section: Introductionmentioning

confidence: 99%

Adsorption of Cadmium on Degraded Soils Amended with Maize-Stalk-Derived Biochar

Chen

et al. 2018

IJERPH

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“…However, XRD data showed a significant increase in gypsum concentration over depth that coincides with a decrease in SOC content. This fact was observed by other authors in gypsiferous areas, and lead them to establish that gypsum concentration, lack of SOC and high salts concentration can be considered as an index of desertification intensity [74]. Recent studies [52] have set up gypsum prediction in laboratory models using the Normalized Differenced Gypsum Index (NDGI) and the Half-Area and Continuum Removed Absorption Depth (CRAD) spectral parameters.…”

Section: Discussionmentioning

confidence: 83%

Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

Marques

Álvarez

Carral

et al. 2020

Water

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Contents of soil organic carbon (SOC), gypsum, CaCO3, and quartz, among others, were analyzed and related to reflectance features in visible and near-infrared (VIS/NIR) range, using partial least square regression (PLSR) in ParLes software. Soil samples come from a sloping olive grove managed by frequent tillage in a gypsiferous area of Central Spain. Samples were collected in three different layers, at 0–10, 10–20 and 20–30 cm depth (IPCC guidelines for Greenhouse Gas Inventories Programme in 2006). Analyses were performed by C Loss-On-Ignition, X-ray diffraction and water content by the Richards plates method. Significant differences for SOC, gypsum, and CaCO3 were found between layers; similarly, soil reflectance for 30 cm depth layers was higher. The resulting PLSR models (60 samples for calibration and 30 independent samples for validation) yielded good predictions for SOC (R2 = 0.74), moderate prediction ability for gypsum and were not accurate for the rest of rest of soil components. Importantly, SOC content was related to water available capacity. Soils with high reflectance features held c.a. 40% less water than soils with less reflectance. Therefore, higher reflectance can be related to degradation in gypsiferous soil. The starting point of soil degradation and further evolution could be established and mapped through remote sensing techniques for policy decision making.

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“…We selected the most effective factors in soil quality assessment after field visits and review of the available literature (Khanamani, Fathizad, Karimi, & Shojaei, 2017). We then dug at least one 40-cmdeep soil profile in every work unit.…”

Section: Producing the Information Layers Of The Medalus Modelmentioning

confidence: 99%

“…We applied a process similar to Delphi method for this purpose (Clark, Applegate, Niles, & Dobkin, 2006;Liou, 1992). We conducted a comprehensive review of the literature (Aalders et al, 2011;Abbasi-Jandani & Malekinejad, 2012;Ekhtesasi & Ahmadi, 1995;Ekhtesasi & Sepehr, 2009;Jafari & Bakhshandehmehr, 2016;Khanamani et al, 2017;Yang, Abbaspour, & Zhang, 2002) and held meetings with an agrologist and a hydrologist to identify factors affecting soil quality, groundwater index, and wind erosion rate. We adopted the approach described by Marcot et al (2006) to develop influence diagrams based on the acquired information.…”

Section: Development Of Bn Modelsmentioning

confidence: 99%

Evaluating the potential of Bayesian networks for desertification assessment in arid areas of Iran

2018

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Spatiotemporal complexities increase the uncertainty of the results of various desertification assessment models. We integrated the Mediterranean desertification and land‐use (MEDALUS) model with Bayesian networks (BNs) to develop a novel method for desertification assessment in central Iran. We first derived the current desertification status of the study area from empirical observations of actual, current desertification conditions and mapped the obtained status based on the criteria of the MEDALUS model. We then utilized BNs to produce three separate causal models for the soil, groundwater, and wind erosion indicators of the MEDALUS model. We then integrated all the related indicators and indices into a desertification assessment BNs model. After the reevaluation and sensitivity analysis of the model, we compared its results with those of the MEDALUS model. The obtained results highlighted soil condition and wind erosion as the main determinants of desertification in the study area. Considering the ability of BNs to accommodate uncertainty in the assessment process, these models can assist relevant authorities in making informed decisions. They can also be applied as decision support tools for adaptive management in fragile ecosystems of arid areas.

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Assessing desertification by using soil indices

Cited by 28 publications

References 15 publications

Adsorption of Cadmium on Degraded Soils Amended with Maize-Stalk-Derived Biochar

Adsorption of Cadmium on Degraded Soils Amended with Maize-Stalk-Derived Biochar

Estimating Soil Organic Carbon in Agricultural Gypsiferous Soils by Diffuse Reflectance Spectroscopy

Evaluating the potential of Bayesian networks for desertification assessment in arid areas of Iran

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