Soil quality index as affected by long-time continuous cultivation in a Mediterranean sub-humid region

Kafei, Fatemeh; Rezapour, Salar; Dalalian, Mohammad Reza; Sabbaghtazeh, Elnaz; Rafieyan, Omid

doi:10.1007/s12210-023-01148-2

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…Within the MDS of three distinct methods, Cation Exchange Capacity (CEC) had the highest influence on the Soil Quality Index calculation, followed by porosity, Exchangeable Sodium Percentage (ESP), organic carbon, CN ratio, and total nitrogen. These factors have been frequently recognised as significant and sensitive variables in the development of the SQI, as indicated in previous studies [24,30,36,38,62,[66][67][68].…”

Section: Discussionmentioning

confidence: 68%

“…Results on the correlation between crop yields and SQI methods revealed that the yields of all four crops were significantly correlated with all three SQI methodologies. Several studies [38,62,68,76,77] have also found a significant correlation between the yield components of various agricultural products and SQIs with different coefficients of variations. A relatively higher correlation coefficient was observed with SQI-1 and -2 than with SQI-3 (Table 17), which indicates the pedological significance and the effect of soil subsurface phenomena on the physiological conditions of plant systems, which take nutrients and water from the subsurface.…”

Section: Discussionmentioning

confidence: 95%

“…To determine the minimum data set (MDS) and key factors affecting soil quality index, PCA was carried out on the total data set. The PCA method is a common and accepted approach for determining MDS and has been widely used in previous studies [22,[60][61][62]. The SQI of this sub-watershed was assessed using three methodologies: (1) assessment of horizon-wise SQI by subjecting the soil properties of every horizon to PCA followed by calculating the weighted averages of the SQI of each soil profile (SQI-1); (2) weighted averages of the soil properties of each soil profile subjected to PCA and followed by SQI assessment (SQI-2); and (3) considering the Ap horizon properties of each soil profile for the SQI assessment (SQI-3).…”

Section: Physical Physico-chemical and Chemical Propertiesmentioning

confidence: 99%

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Comparative Studies on Soil Quality Index Estimation of a Hilly-Zone Sub-Watershed in Karnataka

Yadav,

Patil,

Hebbara

2023

Sustainability

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The assessment of soil quality aims to evaluate the utility and health of soils. In agricultural studies, soil productivity can be likened to soil quality. Evaluating the Soil Quality Index (SQI) solely based on surface properties offers an incomplete picture because productivity is influenced by both surface and subsurface characteristics, with the latter associated with pedogenic processes. Additionally, relying on weighted averages of soil properties from a soil profile for the SQI may offer an overall summary, but it can occasionally obscure variations that manifest across different soil horizons. Therefore, the present study was conducted to assess the SQI in the Ganjigatti sub-watershed using data from 27 soil profiles and three different methods: (1) assessment of horizon-wise SQI by subjecting the soil properties of every horizon to principal component analysis (PCA), followed by the calculation of the weighted averages of the SQI for each soil profile (SQI-1); (2) calculation of the weighted averages of the soil properties for each soil profile, subjected to PCA, and followed by an SQI assessment (SQI-2); and (3) SQI assessment considering the properties of the Ap horizon for each soil profile (SQI-3). Additionally, to validate SQI methodologies, correlation studies were conducted against major crop yields in the sub-watershed. The results showed that cation exchange capacity (CEC) has the most significant weight and contribution to the SQI determined using MDS, followed by porosity, exchangeable sodium percentage (ESP), organic carbon (OC), CN ratio, and total N. SQI-1 was most strongly correlated with crop yield; the correlation coefficient ranged from 0.69 to 0.74. Among all the three methodologies, SQI-1 and -2 were better methods for assessment of SQI compared to SQI-3. In the SQI-1 method, the soil quality of pedons ranged from 0.26 (pedon-26) to 0.74 (pedon-11). The majority of the area in the sub-watershed (72.40%) fell within the medium category of SQI (0.35–0.55), followed by the high category of SQI (>0.55), which comprised 12.92%, and the low SQI (<0.35), which comprised 6.45% of the sub-watershed.

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