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 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.

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 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.

Labile soil carbon fractions as indicators of soil quality improvement under short-term grassland set-aside

Soil Research ◽  
2020 ◽  
Vol 58 (4) ◽  
pp. 364
Author(s):  
Jason M. Lussier ◽  
Maja Krzic ◽  
Sean M. Smukler ◽  
Katarina R. Neufeld ◽  
Chantel J. Chizen ◽  
...  
Keyword(s):  
Soil Quality ◽  
Quality Indicators ◽  
Crop Production ◽  
Low Cost ◽  
Size Fraction ◽  
Aggregate Stability ◽  
Aggregate Size ◽  
Soil Quality Indicators ◽  
Short Term ◽  
Soil C

Grassland set-asides (GLSA) are fields that are taken out of intensive annual crop production and seeded with a mixture of grasses and legumes for one to four years to improve soil quality. The objectives of this study were to evaluate (i) the relationships among soil organic carbon (SOC), permanganate oxidisable C (POXC), dilute-acid extractable polysaccharides (DAEP) and aggregate stability to determine if they may be used as proxies for one another, (ii) whether these indicators could be used to predict aggregate stability, (iii) if differences in soil quality after short-term GLSAs, detected with aggregate stability, could instead be detected with POXC or DAEP and (iv) potential use of diffuse Fourier transform spectroscopy (FT-MIR) to predict POXC, DAEP and aggregate stability in the Fraser River Delta region of British Columbia, Canada. There were strong relationships among SOC, POXC and DAEP, but the relationship between DAEP and SOC (R2 = 0.60, P < 0.0001) was less strong than that observed between POXC and SOC (R2 = 0.71, P < 0.0001). All three soil C fractions were significantly predicted with the 2–6 mm aggregate size fraction but the correlations for DAEP (R2 = 0.43) and POXC (R2 = 0.36) were stronger than that for SOC (R2 = 0.29). Predictions of soil quality indicators using FT-MIR produced R2 = 0.92 for POXC, R2 = 0.93 for DAEP and R2 = 0.62 for the 2–6 mm aggregate size fraction. These results suggest that FT-MIR holds promise as a low-cost method to determine labile soil C fractions that are better proxy soil quality indicators for aggregate stability than SOC.

scholarly journals Field Scale Studies on the Spatial Variability of Soil Quality Indicators in Washington State, USA

2011 ◽  
Vol 2011 ◽  
pp. 1-7 ◽  
Author(s):  
Jeffrey L. Smith ◽  
Jonathan J. Halvorson
Keyword(s):  
Spatial Variability ◽  
Soil Quality ◽  
Crop Production ◽  
Arable Land ◽  
Microbial Biomass C ◽  
Soil Conditions ◽  
Spatial And Temporal Variability ◽  
Biological Parameters ◽  
Field Scale ◽  
Soil Quality Index

Arable lands are needed for sustainable agricultural systems to support an ever-growing human population. Soil quality needs to be defined to assure that new land brought into crop production is sustainable. To evaluate soil quality, a number of soil attributes will need to be measured, evaluated, and integrated into a soil-quality index using the multivariable indicator kriging (MVIK) procedure. This study was conducted to determine the spatial variability and correlation of indicator parameters on a field scale with respect to soil quality and suitability for use with MVIK. The variability of the biological parameters decreased in the order of respiration > enzyme assays and qCO2> microbial biomass C. The distribution frequency of all parameters except respiration were normal although the spatial distribution across the landscape was highly variable. The biological parameters showed little correlation with each other when all data points were considered; however, when grouped in smaller sections, the correlations were more consistent with observed patterns across the field. To accurately assess soil quality, and arable land use, consideration of spatial and temporal variability, soil conditions, and other controlling factors must be taken into account.

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