Developing a soil quality index model for assessing landscape-level soil quality along a toposequence in almond orchards using factor analysis

2022 ◽  
Author(s):  
Fayez Raiesi ◽  
Mehran Tavakoli
Keyword(s):  
Factor Analysis ◽  
Soil Quality ◽  
Quality Index ◽  
Soil Quality Index ◽  
Index Model ◽  
Almond Orchards ◽  
Landscape Level

DETERMINING THE SOIL QUALITY INDEX IN THE BATUMI DELTA, GEORGIA

Agrociencia ◽  
2021 ◽  
Vol 55 (1) ◽  
pp. 1-18
Author(s):  
Bülent Turgut ◽  
Merve Ateş ◽  
Halil Akıncı Akıncı
Keyword(s):  
Factor Analysis ◽  
Soil Quality ◽  
Quality Index ◽  
Organic Matter Content ◽  
Aggregate Stability ◽  
Clay Content ◽  
Matter Content ◽  
Soil Quality Index ◽  
Analytic Hierarchy ◽  
Score Functions

The soil quality index is a quantitative assessment concept and it is used in the evaluation of ecosystem components. Because of the high potential for agriculture and biodiversity, deltas are the most valuable parts of the ecosystem. This study aimed to determine the soil quality index (SQI) in the Batumi Delta, Georgia. For this purpose, the study area was divided into five plots due to their morphological positions (L1, L2, L3, L4, and L5). A total of 125 soil samples were taken for analysis including clay content (CC), silt content (SC), sand content (SaC), mean weight diameter (MWD), aggregate stability (AS), amount of water retained under -33 kPa (FC) and -1500 kPa (WP) pressures and organic matter content (OM). These properties were used as the main criteria, and the Analytic Hierarchy Process (AHP) and Factor Analysis were used for weighting them. Sub-criteria were scored using expert opinion and the linear score functions, such as “more is better” and “optimum value”. For determining SQI, the additive method (SQIA), the weighted method with AHP (SQIAHP), and the weighted method with factor analysis (SQIFA) were used. The resulting SQI scores of the three methods were ordered as SQIAHP>SQIA>SQIFA, but these differences were not significant. However, the SQI scores of the plots (p≤0.01) showed statistically significant differences and were ordered as L5>L4>L3>L2>L1.

Soil-quality-index model for assessing the impact of groundwater on soil in an intensively farmed coastal area of E China

2013 ◽  
Vol 177 (3) ◽  
pp. 330-342 ◽  
Author(s):  
Rong-Jiang Yao ◽  
Jing-Song Yang ◽  
Peng Gao ◽  
Jian-Bin Zhang ◽  
Wen-Hui Jin ◽  
...  
Keyword(s):  
Soil Quality ◽  
Coastal Area ◽  
Quality Index ◽  
Soil Quality Index ◽  
Index Model ◽  
The Impact

Soil vital signs: A new Soil Quality Index (SQI) for assessing forest soil health

2007 ◽  
Author(s):  
Michael C. Amacher ◽  
Katherine P. O'Neil ◽  
Charles H. Perry
Keyword(s):  
Forest Soil ◽  
Soil Quality ◽  
Quality Index ◽  
Soil Health ◽  
Vital Signs ◽  
Soil Quality Index

A new approach to elaborate a multifunctional soil quality index

2004 ◽  
Vol 4 (3) ◽  
pp. 201-204 ◽  
Author(s):  
Giancarlo Barbiroli ◽  
Giovanni Casalicchio ◽  
Andrea Raggi
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Soil Quality Index ◽  
New Approach

scholarly journals Land Degradation Vulnerability Mapping in a Newly-Reclaimed Desert Oasis in a Hyper-Arid Agro-Ecosystem Using AHP and Geospatial Techniques

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1426
Author(s):  
Ahmed S. Abuzaid ◽  
Mohamed A. E. AbdelRahman ◽  
Mohamed E. Fadl ◽  
Antonio Scopa
Keyword(s):  
Remote Sensing ◽  
Soil Quality ◽  
Land Degradation ◽  
Quality Index ◽  
Management Strategies ◽  
Remote Sensing Data ◽  
Western Desert ◽  
Soil Quality Index ◽  
Climate Conditions ◽  
Sensing Data

Modelling land degradation vulnerability (LDV) in the newly-reclaimed desert oases is a key factor for sustainable agricultural production. In the present work, a trial for usingremote sensing data, GIS tools, and Analytic Hierarchy Process (AHP) was conducted for modeling and evaluating LDV. The model was then applied within 144,566 ha in Farafra, an inland hyper-arid Western Desert Oases in Egypt. Data collected from climate conditions, geological maps, remote sensing imageries, field observations, and laboratory analyses were conducted and subjected to AHP to develop six indices. They included geology index (GI), topographic quality index (TQI), physical soil quality index (PSQI), chemical soil quality index (CSQI), wind erosion quality index (WEQI), and vegetation quality index (VQI). Weights derived from the AHP showed that the effective drivers of LDV in the studied area were as follows: CSQI (0.30) > PSQI (0.29) > VQI (0.17) > TQI (0.12) > GI (0.07) > WEQI (0.05). The LDV map indicated that nearly 85% of the total area was prone to moderate degradation risks, 11% was prone to high risks, while less than 1% was prone to low risks. The consistency ratio (CR) for all studied parameters and indices were less than 0.1, demonstrating the high accuracy of the AHP. The results of the cross-validation demonstrated that the performance of ordinary kriging models (spherical, exponential, and Gaussian) was suitable and reliable for predicting and mapping soil properties. Integrated use of remote sensing data, GIS, and AHP would provide an effective methodology for predicting LDV in desert oases, by which proper management strategies could be adopted to achieve sustainable food security.

scholarly journals Determining the impacts of deforestation and corn cultivation on soil quality in tropical acidic red soils using a soil quality index

2021 ◽  
Vol 125 ◽  
pp. 107580
Author(s):  
Wuping Huang ◽  
Mingming Zong ◽  
Zexin Fan ◽  
Yuan Feng ◽  
Shiyu Li ◽  
...  
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Soil Quality Index ◽  
Red Soils

Trace Metal Availability and Soil Quality Index Relationships under Different Land Uses

2015 ◽  
Vol 79 (6) ◽  
pp. 1629-1637 ◽  
Author(s):  
Vladimir Ivezić ◽  
Bal Ram Singh ◽  
Vlatka Gvozdić ◽  
Zdenko Lončarić
Keyword(s):  
Trace Metal ◽  
Soil Quality ◽  
Quality Index ◽  
Metal Availability ◽  
Land Uses ◽  
Soil Quality Index
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