An assessment of pasture soils quality based on multi-indicator weighting approaches in semi-arid ecosystem

Karaca, Siyami; Dengi̇z, Orhan; Turan, İ̇nci Demirağ; Özkan, Barış; Dedeoğlu, Mert; Gülser, Füsun; Sarğin, Bulut; DEMİRKAYA, Salih; Ay, Abdurahman

doi:10.1016/j.ecolind.2020.107001

Cited by 69 publications

(36 citation statements)

References 74 publications

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“…All these hazards reduce plant choice or require moderate, easy-to-use conservation practices. Similar studies have determined SQI using soil properties in semi-arid ecosystems but used different approaches with GIS and RS such as Multi-Criteria Decision Analysis (MCDA) [43,44], PCA, geostatistic, AHP-Fuzzy [45], and integration of type-2 fuzzy sets with AHP [46].…”

Section: Status Of the Study Areamentioning

confidence: 99%

Development of a Spatial Model for Soil Quality Assessment under Arid and Semi-Arid Conditions

et al. 2021

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Food security has become a global concern for humanity with rapid population growth, requiring a sustainable assessment of natural resources. Soil is one of the most important sources that can help to bridge the food demand gap to achieve food security if well assessed and managed. The aim of this study was to determine the soil quality index (SQI) for El Fayoum depression in the Western Egyptian Desert using spatial modeling for soil physical, chemical, and biological properties based on the MEDALUS methodology. For this purpose, a spatial model was developed to evaluate the soil quality of the El Fayoum depression in the Western Egyptian Desert. The integration between Digital Elevation Model (DEM) and Sentinel-2 satellite image was used to produce landforms and digital soil mapping for the study area. Results showed that the study area located under six classes of soil quality, e.g., very high-quality class represents an area of 387.12 km2 (22.7%), high-quality class occupies 441.72 km2 (25.87%), the moderate-quality class represents 208.57 km2 (12.21%), slightly moderate-quality class represents 231.10 km2 (13.5%), as well as, a low-quality class covering an area of 233 km2 (13.60%), and very low-quality class occupies about 206 km2 (12%). The Agricultural Land Evaluation System for arid and semi-arid regions (ALESarid) was used to estimate land capability. Land capability classes were non-agriculture class (C6), poor (C4), fair (C3), and good (C2) with an area 231.87 km2 (13.50%), 291.94 km2 (17%), 767.39 km2 (44.94%), and 416.07 km2 (24.4%), respectively. Land capability along with the normalized difference vegetation index (NDVI) used for validation of the proposed model of soil quality. The spatially-explicit soil quality index (SQI) shows a strong significant positive correlation with the land capability and a positive correlation with NDVI at R2 0.86 (p < 0.001) and 0.18 (p < 0.05), respectively. In arid regions, the strategy outlined here can easily be re-applied in similar environments, allowing decision-makers and regional governments to use the quantitative results achieved to ensure sustainable development.

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Section: Status Of the Study Areamentioning

confidence: 99%

Development of a Spatial Model for Soil Quality Assessment under Arid and Semi-Arid Conditions

et al. 2021

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“…According to these results, the TC was the most effective parameter, and the silt content was evaluated as the least effective parameter for the soil quality index value. In similar studies, it was stated that the organic matter content has the highest weight coefficient than any other property, and the silt content has the lowest weight coefficient (Karaca et al 2021;Zhao et al 2021).…”

Section: Resultsmentioning

confidence: 97%

Determining the effects of the forest stand age on the soil quality index in afforested areas: A case study in the Palandöken Mountains

Çomaklı¹,

Turgut²

2021

Soil Water Res.

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Afforestation is an essential strategy for erosion control. The objective of this study was to determine the soil quality index (SQI) in established afforested areas of different ages for erosion control in Erzurum, Turkey. Three afforested areas were selected as plots considering their establishment periods: + 40 years old (AA>40), 10–40 years old (AA10–40), and less than 10 years old (AA<10). Forty soil samples were taken in each plot area over the 0–15 and 15–30 cm depths. The soil samples were analysed for the texture, mean weight diameter, aggregate stability, pH, electrical conductivity, total nitrogen, total carbon, and total sulfur contents. These properties were used as the soil quality indicators, whereby the analytic hierarchy process (AHP) and principal component analysis (PCA) were used to establish their relative importance for describing the soil quality. The indicators were scored using the linear score functions of “more is better” and “optimum value”. For determining the SQI, the additive method (SQIA), the weighted method with AHP (SQIAHP), and the weighted method with PCA (SQIPCA) were used. The SQI scores of the plots showed statistically significant differences. In all three methods, the highest SQI value was obtained from the AA>40 plots.

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“…Among the chemical properties, lime is an important parameter in terms of determining the usefulness of plant nutrients. Lime parameter is also used in soil quality studies (Abuzaid et al, 2021;Karaca et al, 2021). In their study, Santos-Francés et al (2019) classified the indicators for soil quality as the best value, less good and optimal value.…”

Section: Distribution Of the Soil Quality Index In The Basinmentioning

confidence: 99%

“…One of the most commonly used approaches to create a minimum data set is principal components analysis. Principal components analysis is widely used in order to reduce criteria in different areas (Shukla et al, 2006;Karaca et al, 2021). In this context, while Şenol et al (2020) created minimum data set with soil texture, penetration resistance, available water capacity, pH, and some plant nutrient elements for SQI, Uzner and Dengiz (2020) used soil texture, parent material slope, depth, drainage and coarse material as soil indicators for SQI based on MEDALUS Semi-arid and arid areas, which are more vulnerable to environmental variations and are therefore more fragile, constitute about 65% of the total surface area of Turkey.…”

Section: Introductionmentioning

confidence: 99%

Spatio analysis of soil quality assessment in semi-arid ecosystem using a minimum data set

Turan¹

2021

Eurasian Journal of Soil Science (Ejss)

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Article InfoLand degradation especially as a result of the rapid increase in demand and pressure in the population, have emerged as one of the most important problems. Many model approaches, especially regarding land degradation and desertification, have been developed and continue to be developed on the world. The MEDALUS (Mediterranean Desertification and Land Use) which is one of the famous models has been developed for an environment assessment program contributed to by 31 groups and 10 countries in 1999. This model includes the Environmental Sensitive Areas (ESA) index include climate, soil, land use-land cover, and management index. This current study aims to evaluate the soil quality indexes by using the total data set (TDS) and minimum data set (MDS) of the lands in Konya Basin, which is an arid and semiarid terrestrial ecosystem, with MEDALUS approach. The TDS consisted of nine soil quality parameters measured on 1019 samples: pH, Electrical Conductivity (EC), Organic Matter (OM), texture, CaCO3, depth, parent material, and slope. In addition, Principal components analysis (PCA) was used to determine which parameters were to be selected in the MDS. The MDS parameters consisted of slope, pH, OM, CaCO3. After those process, different interpolation models in order to create spatial distribution maps of parameters and SQI (Soil Quality Index). Results classified 13.59% of the total area has as low, while 84.110% of the study area' soil indicated area moderate by the SQI in terms of. Accordingly MDS, it was determined that 80.41% of the study area was in the mediumquality category, while only 2.01% was in the high-quality category. In the study area, minimum data set is seen that similar to the soil quality distribution in the total data set.

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An assessment of pasture soils quality based on multi-indicator weighting approaches in semi-arid ecosystem

Cited by 69 publications

References 74 publications

Development of a Spatial Model for Soil Quality Assessment under Arid and Semi-Arid Conditions

Development of a Spatial Model for Soil Quality Assessment under Arid and Semi-Arid Conditions

Determining the effects of the forest stand age on the soil quality index in afforested areas: A case study in the Palandöken Mountains

Spatio analysis of soil quality assessment in semi-arid ecosystem using a minimum data set

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