A minimum data set and soil quality index to quantify the effect of land use conversion on soil quality and degradation in native rangelands of upland arid and semiarid regions

Raiesi, Fayez

doi:10.1016/j.ecolind.2016.12.049

Cited by 232 publications

(141 citation statements)

References 70 publications

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“…The use of indices or surrogates for assessing soil quality is widespread, particularly under cultivated agriculture (e.g. Granatstein and Bezdicek, 1992;Sojka and Upchurch, 1999;Li et al, 2013;Izquierdo et al, 2005;Zornoza et al, 2015;Raiesi and Kabiri, 2016) but also in drylands (Li et al, 2013;Raiesi, 2017). The attributes used to assess quality vary substantially, from soil physical, biological, chemical and biochemical, to microbiological assays, and the advantages of different indices vary with land management type, soil type, environmental setting and available resources.…”

Section: Introductionmentioning

confidence: 99%

Grazing reduces the capacity of Landscape Function Analysis to predict regional-scale nutrient availability or decomposition, but not total nutrient pools

Eldridge

Delgado‐Baquerizo

2018

Ecological Indicators

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The Nutrient Cycling Index (hereafter 'Nutrient Index') derived from Landscape Function Analysis (LFA) is used extensively by land managers worldwide to obtain rapid and costeffective information on soil condition and nutrient status in terrestrial ecosystems. Despite its utility, relatively little is known about its reliability under different management conditions (e.g. grazing) or across different climatic zones (aridity). Here we correlated the Nutrient Index, comprising measures of biocrust cover, plant basal cover, soil roughness and three attributes of surface litter cover, with empirical data on measures of soil total nutrient pools (C and N), nutrient availability (labile C, inorganic N and P), and decompositionrelated enzymes at 151 locations from eastern Australia varying in grazing intensity and climatic conditions. Grazing intensity was assessed by measuring current grazing (dung production by the herbivores cattle, sheep/goats, kangaroos and rabbits), and historic grazing (the total area of livestock tracks leading from water). We used aridity (the relationship between precipitation and potential evapotranspiration) as a measure of climate. On average, the Nutrient Index was positively associated with total nutrient pools, nutrient availability and decomposition enzymes. However, further statistical modelling indicated that grazing intensity strongly reduced the link between the index and decomposition enzymes, labile C and inorganic P, but not with total nutrient pools. This grazing effect was predominantly due to cattle. Conversely, aridity had no significant effect on the predictive power of the index, suggesting that it could be used across different aridity conditions in natural ecosystems as a reliable predictor of soil health. Overall, our study reveals that the Nutrient Index is a robust predictor of total nutrient pools across different aridity and grazing conditions, but not for predicting nutrient availability or decomposition in environments heavily grazed by livestock.

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Section: Introductionmentioning

confidence: 99%

Grazing reduces the capacity of Landscape Function Analysis to predict regional-scale nutrient availability or decomposition, but not total nutrient pools

Eldridge

Delgado‐Baquerizo

2018

Ecological Indicators

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“…Selection of variables (indicators) for inclusion in a SQI may be simplified by statistical methods. Several multivariate statistical techniques and modelling approaches have been widely applied to evaluate soil quality for different soils under different management regimes (Biswas et al 2017;Brejda et al 2000;de Paul Obade and Lal 2016;Nabiollahi et al 2018a,b;Nosrati 2013;Raiesi 2017;Sánchez-Navarro et al 2015;Yu et al 2018b;Zuber et al 2017). While the number and diversity of soil quality studies has increased, there remains a need to explore the scope for developing new SQIs.…”

Section: Introductionmentioning

confidence: 99%

“…Soil quality degradation in Iran is widespread (Ayoubi et al 2014;Emadi et al 2009;Nosrati 2013;Raiesi 2017;Rezapour 2014). Current socioeconomic pressures in Iran pose a serious threat to soil quality and thereby both food security and environmental protection.…”

Section: Introductionmentioning

confidence: 99%

A soil quality index for evaluation of degradation under land use and soil erosion categories in a small mountainous catchment, Iran

Nosrati

Collins

2019

J. Mt. Sci.

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“…The dynamic soil properties such as soil organic matter and microbial attributes are sensitive to agricultural activities and land use changes [4], and land use alterations can have an adverse impact on soil function [5]. In addition, soil properties are often interdependent but under various agricultural and management activities may affect the soil quality and productivity and respond differently, so the interpretation of their effects under different management will be confusing [6].…”

Section: Introductionmentioning

confidence: 99%

Assessing the soil quality index as affected by two land use scenarios in Miandoab region

et al. 2020

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In this research, the physical, chemical and biological properties of 19 soil indicators using 80 samples (0-25 cm) were quantified to measure the soil quality index (SQI) in Miandoab region, Iran, across different land uses. These properties include aggregate stability (AS), bulk density (BD), soil moisture content (θ m), saturation percentage (SP), particle size fractions (clay, silt, sand), pH, EC, available P and K, OC, total N, C/N, CaCO 3 , sequestration of organic carbon (SOC), CEC, microbial respiration (MR) and microbial population (MP). Principal component analysis (PCA) was used as a dimension reduction method to separate farmlands and orchards as two different land uses. The linear SQI and nonlinear SQI were calculated, and the results showed a clear difference in SQI as affected by the aforementioned land uses. The specific contribution of each minimum data set also showed that soil OC, N, MR, MP, silt, CaCO 3 , AS and BD had the highest importance to SQI. The linear SQI value was more significantly affected by the physical properties than the chemical and biological ones. It varies from the maximum value of 35.17% in the farmland to 37.74% in the orchard. On the other hand, the nonlinear SQI showed the highest contribution of biological SQI in the farmland (73.94%) followed by the orchard (64.15%). Finally, both linear and nonlinear equations may be acceptable for assessing SQI using the aforementioned soil properties. Furthermore, it is the biological properties that have the main role in terms of evaluating the effect of land use conversion on soil quality.

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A minimum data set and soil quality index to quantify the effect of land use conversion on soil quality and degradation in native rangelands of upland arid and semiarid regions

Cited by 232 publications

References 70 publications

Grazing reduces the capacity of Landscape Function Analysis to predict regional-scale nutrient availability or decomposition, but not total nutrient pools

Grazing reduces the capacity of Landscape Function Analysis to predict regional-scale nutrient availability or decomposition, but not total nutrient pools

A soil quality index for evaluation of degradation under land use and soil erosion categories in a small mountainous catchment, Iran

Assessing the soil quality index as affected by two land use scenarios in Miandoab region

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