Soil quality indices and their application in the hilly loess plateau region of China

Soil Research ◽  
2006 ◽  
Vol 44 (3) ◽  
pp. 245 ◽  
Author(s):  
Mingxiang Xu ◽  
Yunge Zhao ◽  
Guobin Liu ◽  
Robert M. Argent
Keyword(s):  
Soil Quality ◽  
Quality Indicators ◽  
Loess Plateau ◽  
Quality Index ◽  
Shaanxi Province ◽  
Soil Quality Indicators ◽  
Soil Quality Index ◽  
Plateau Region ◽  
Significant Difference ◽  
Hilly Loess Plateau

Soil quality in the hilly Loess Plateau region of China is seriously degraded due to hillside cultivation and severe soil erosion. No established methods are available for evaluating the regional soil quality nor has integrated soil quality assessment been conducted in the region. Our objectives were to (i) develop soil quality models and assessment methods, (ii) verify the representativeness of selected soil quality indicators, and (iii) evaluate landuse effects on regional soil quality. The research was conducted on 707 km2 of typical hilly Loess Plateau in Shaanxi province, China. Soil samples (total 208) were taken from 5 catchments under 10 different landuse types. Two integrated evaluation methods (weighted summation and weighted product) and 2 indicator sets (a whole and a minimum set) were tested, each producing a soil quality index. Quantitative evaluation of soil quality in different landuse types was also performed. The results showed that the weighted product method provided better differentiation of soil quality between landuses. The minimum indicator set of 8 soil quality indicators, selected by factor analysis from a complete set of 29 soil attributes, reflected all or most of the information of the whole set in assessing regional soil quality. Soil quality index (SQI) values under different landuse types ranged from 0.842 for natural woodland to 0.150 for orchard. Index values for orchard, cropland, revegetated grassland, and planted grassland were significantly less than those for 6 other landuse types, whereas planted shrubland, planted woodland, and natural grassland indices were significantly less than those for greenhouse, natural shrubland, and natural woodland. No significant difference in SQI was found between orchard, cropland, revegetated grassland, and planted grassland, or between planted shrubland and planted woodland. Overall, it was found that soil quality was generally poor across the region, except for natural woodland, shrubland and greenhouse areas.

scholarly journals The use of multivariate statistical analysis and soil quality indices as tools to be included in regional management plans. A case study from the Mashhad Plain, Iran

2019 ◽  
Vol 45 (2) ◽  
pp. 687 ◽  
Author(s):  
J. Rodrigo-Comino ◽  
A. Keshavarzi ◽  
A. Bagherzadeh ◽  
E.C. Brevik
Keyword(s):  
Particle Size ◽  
Calcium Carbonate ◽  
Organic Carbon ◽  
Soil Properties ◽  
Bulk Density ◽  
Soil Quality ◽  
Quality Indicators ◽  
Quality Index ◽  
Soil Quality Indicators ◽  
Soil Quality Index

Several methods have been used to model reality and explain soil pedogenesis and evolution. However, there is a lack of information about which soil properties truly condition soil quality indicators and indices particularly at the pedon scale and at different soil depths to be used in land management planning. Thus, the main goals of this research were: i) to assess differences in soil properties (particle size, saturation point, bulk density, soil organic carbon, pH and electrical conductivity) at different soil depths (0-30 and 30-60 cm); ii) to check their statistical correlation with soil quality indicators (CEC, total N, Olsen-P, available K, exchangeable Na, calcium carbonate equivalent, Fe, Mn, Zn, and Cu); and, iii) to elaborate a soil quality index and maps for each soil layer. To achieve this, forty-eight soil samples were analysed in the laboratory and subjected to statistical analyses by ANOVA, Spearman Rank coefficients and Principal Component Analyses. Finally, a soil quality index was developed based on indicators of sensitivity. The study was conducted in a semiarid catchment in northeast Iran with irrigated farming and well-documented land degradation issues. We found that: i) organic carbon and bulk density were not similar in the topsoil and subsoil; ii) calcium carbonate and sand content conditioned organic carbon content and bulk density; iii) organic carbon showed the highest correlations with soil quality indicators; iv) particle size conditioned cation-exchange capacity; and, v) heavy metals such as Mn and Cu were highly correlated with organic carbon due to non-suitable agricultural practices. Based on the communality analysis to map of soil quality, CEC, Mn, Zn, and Cu had the highest weights (≥0.11) at both depths, coinciding with the same level of relevance in the multivariate analysis. Exchangeable Na, CaCO3, and Fe had the lowest weights (≤0.1) and N, P, and K had intermediate weights (0.1- 0.11). In general, the map of the soil quality index shows a lower soil quality in the subsoil increment than in the topsoil.

scholarly journals ANALISIS KUALITAS TANAH PADA LAHAN TEGALAN BERPASIR DI DAS MIKRO SUPITURUNG, KABUPATEN KEDIRI, JAWA TIMUR

2021 ◽  
Vol 8 (2) ◽  
pp. 527-537
Author(s):  
Mochamad Fikri Kurniawan ◽  
Mochtar Lutfi Rayes ◽  
Christanti Agustina
Keyword(s):  
Soil Quality ◽  
Quality Indicators ◽  
Soil Texture ◽  
Quality Index ◽  
Survey Method ◽  
Soil Quality Indicators ◽  
Soil Quality Index ◽  
N Content ◽  
Total N ◽  
Organic C

Soil quality is the ability of soil that plays a role in maintaining plant productivity, preserving and maintaining water availability and supporting human activities. Soil quality assessment is measured based on indicators that describe important soil processes based on the physical, chemical and biological properties of the soil. The level of soil quality in a plot of land is assessed based on the soil quality index. This research was conducted from August to December 2020 in the Supiturung Micro Watershed, Kediri Regency, East Java using a graphical survey method based on the Land Map Unit. Soil samples were taken at a depth of 0-20 cm at each observation point (20 points) for analysis in the laboratory. Soil quality indicators are determined based on key soil properties with the Minimum Data Set (MDS) method, with soil quality indicators from soil physical properties including texture, bulk density, porosity and soil chemical properties including pH, available-P, exchangeable-K, total-N, organic-C. Soil quality index was calculated by weighting soil quality indicators with criteria which divided into 5 classes, i.e. (i) very low class (0.00-0.19), (ii) low (0.20-0.39), (iii) moderate (0.40-0.59), (iv) good (0.60-0.79) and (v) very good (0.80-1.00). The results showed that the soil in land unit 2 had different limiting factor values on the percentage of sand and dust from the soil texture, the total-N content of the soil and the organic-C content of the soil which caused differences in soil quality. There are two indicators of soil quality, namely the percentage of dust from the soil texture and the total N content of the soil which has the most influence on the soil quality index.

scholarly journals Evaluation of Soil Quality in Arid Western Fringes of the Nile Delta for Sustainable Agriculture

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Ahmed M. Saleh ◽  
Mohamed M. Elsharkawy ◽  
Mohamed A. E. AbdelRahman ◽  
Sayed M. Arafat
Keyword(s):  
Soil Quality ◽  
Quality Indicators ◽  
Quality Index ◽  
Nile Delta ◽  
Western Desert ◽  
Scoring Functions ◽  
Soil Quality Indicators ◽  
Soil Quality Index ◽  
Quality Grade ◽  
Minimum Dataset

Egypt is currently witnessing an extensive desert greening plan with a target of adding one and a half million feddans to the agricultural area. The present study evaluates the soil quality in the western desert fringes of the Nile Delta using three indicator datasets, which involve the total dataset (TDS), the minimum dataset (MDS), and the expert dataset (EDS). Three quality index models are included: the Additive Soil Quality Index (SQI-A), the Weighted Additive Soil Quality Index (SQI-W), and the Nemoro Soil Quality Index (SQI-N). Linear and nonlinear scoring functions are evaluated for scoring soil and terrain indicators. Thirteen soil quality indicators and three terrain indicators were measured in 397 sampling sites for soil quality evaluation. Factor analyses determined five soil and terrain indicators for the minimum dataset and their associated weights. The linear scoring functions reflected the soil system functions more than nonlinear scoring functions. Soil quality estimation by the minimum dataset (MDS) and Weighted Additive Soil Quality Index (SQI-W) is more sensitive than that by SQI-A and SQI-N quality models to explain soil quality indicators. The moderate soil quality grade is the largest quality grade in the studied area. The minimum dataset of soil quality indicators could assist in reducing time and cost of evaluating soil quality and monitoring the temporal changes in soil quality of the region due to the increased agricultural development.

scholarly journals Spatial Variability of Physical Soil Quality Index of an Agricultural Field

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Sheikh M. Fazle Rabbi ◽  
Bina R. Roy ◽  
M. Masum Miah ◽  
M. Sadiqul Amin ◽  
Tania Khandakar
Keyword(s):  
Spatial Variability ◽  
Bulk Density ◽  
Soil Quality ◽  
Quality Indicators ◽  
Quality Index ◽  
Crop Production ◽  
Field Investigation ◽  
Agricultural Field ◽  
Soil Quality Indicators ◽  
Soil Quality Index

A field investigation was carried out to evaluate the spatial variability of physical indicators of soil quality of an agricultural field and to construct a physical soil quality index (SQIP) map. Surface soil samples were collected using10  m×10 m grid from an Inceptisol on Ganges Tidal Floodplain of Bangladesh. Five physical soil quality indicators, soil texture, bulk density, porosity, saturated hydraulic conductivity (KS), and aggregate stability (measured as mean weight diameter, MWD) were determined. The spatial structures of sand, clay, andKSwere moderate but the structure was strong for silt, bulk density, porosity, and MWD. Each of the physical soil quality indicators was transformed into 0 and 1 using threshold criteria which are required for crop production. The transformed indicators were the combined into SQIP. The kriged SQIPmap showed that the agricultural field studied could be divided into two parts having “good physical quality” and “poor physical soil quality.”

scholarly journals Influence of Treated Wastewater Irrigation on Soil Nutritional-Chemical Attributes Using Soil Quality Index

2021 ◽  
Vol 13 (4) ◽  
pp. 1952
Author(s):  
Salar Rezapour ◽  
Amin Nouri ◽  
Hawzhin M. Jalil ◽  
Shawn A. Hawkins ◽  
Scott B. Lukas
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Treated Wastewater ◽  
Corn Yield ◽  
Regression Equations ◽  
Soil Quality Index ◽  
Data Set ◽  
Significant Difference ◽  
Chemical Attributes ◽  
The Impact

Dwindling water resources have drawn global attention to the reuse of treated wastewater (TWW) for irrigation. However, the impact of continuous TWW applications on soil quality and the proper quantification and monitoring frameworks have not been well-understood. This study aims to provides an insight into the impact of flood irrigation of urban TWW on soil nutritional-chemical attributes and the potential application of multiple soil quality indices for a corn cropping system. To achieve that goal, we pursued the Total Data Set (TDS) and Minimum Data Set (MDS) approaches, as well as the Integrated Quality Index (IQI) and Nemoro Quality Index (NQI) models. A total of 17 soil nutritional-chemical indicators (0–50 cm depths) were determined for the soils irrigated with TWW (five sites) and well water (one site as control) in West Azerbaijan province in northwestern Iran. Results revealed a significant difference in the majority of soil nutritional-chemical attributes, IQI-TDS, NQI-TDS, IQI-MDS, NQI-MDS, and corn yield between the TWW-irrigated and well-irrigated soils. Irrigation with TWW resulted in a significant increase in the amount of organic matter and cation exchange capacity by 9–17% and 17–26%, respectively, macronutrients (N, P, K, Ca, and Mg) by 22–164%, and the majority of trace metals (Fe, Mn, Zn, and Cu) by 17–175%, suggesting an improvement in soil nutrients and an increase in productivity. Comparing to the soil in control sites, the TWW irrigation caused a notable increase in the values of IQI-TDS, NQI-TDS, IQI-MDS, and NQI-MDS models ranging 14.6–29.5%, 19.1–25.5%, 21.7–33.3%, and 18.4–23.7%, respectively. This implies that soil quality was ameliorated to a significant extent with TWW irrigation. These improvements resulted in a remarkable increase in corn yield ranging from 12.5% to 28.1%. The regression equations revealed that up to 78%, 47%, 72%, and 36% of the variance in the IQI-TDS, NQI-TDS, IQI-MDS, and NQI-MDS models, respectively, could be captured by corn yield. The results of the regression and correlation analyses showed that the IQI-MDS model was more accurate than the other models in assessing soil quality and predicting crop yield. These findings may be an effective and practical tool for policy making, implementation, and management of soil irrigated with TWW.

EVALUATION OF SOIL QUALITY INDICATORS BY A PARTICIPATORY METHOD IN RURAL SETTLEMENTS IN SOUTHERN BAHIA, BRAZIL

2015 ◽  
Vol 27 (3) ◽  
pp. 219-232
Author(s):  
Antônio W. O. Rocha Junior ◽  
Guilherme A. H. A. Loureiro ◽  
Quintino R. Araujo ◽  
George A. Sodré ◽  
Arlicélio Q. Paiva ◽  
...  
Keyword(s):  
Soil Quality ◽  
Quality Indicators ◽  
Rural Settlements ◽  
Soil Quality Indicators ◽  
Southern Bahia ◽  
Participatory Method
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