scholarly journals Using Soil Survey Database to Assess Soil Quality in the Heterogeneous Taihang Mountains, North China

2018 ◽  
Vol 10 (10) ◽  
pp. 3443 ◽  
Author(s):  
Shoubao Geng ◽  
Peili Shi ◽  
Ning Zong ◽  
Wanrui Zhu
Keyword(s):  
Land Cover ◽  
Soil Quality ◽  
Soil Type ◽  
Quality Evaluation ◽  
Vegetation Index ◽  
Controlling Factors ◽  
Soil Survey ◽  
Boosted Regression Trees ◽  
Soil Quality Index ◽  
Data Set

Soil quality evaluation is an effective pathway to understanding the status of soil function and ecosystem productivity. Numerous studies have been made in managed ecosystems and land cover to quantify its effects on soil quality. However, little is coincident regarding soil quality assessment methods and its compatibility in highly heterogeneous soil. This paper used the soil survey database of Taihang Mountains as a case study to: (i) Examine the feasibility of soil quality evaluation with two different indicator methods: Total data set (TDS) and minimum data set (MDS); and (ii) analyze the controlling factors of regional soil quality. Principal component analysis (PCA) and the entropy method were used to calculate soil quality index (SQI). SQI values assessed from the TDS and MDS methods were both significantly correlated with normalized difference vegetation index (p < 0.001), suggesting that both indices were effective to describe soil quality and reflect vegetation growth status. However, the TDS method represented a slightly more accurate assessment than MDS in terms of variance explanation. Boosted regression trees (BRT) models and path analysis showed that soil type and land cover were the most important controlling factors of soil quality, within which soil type had the greatest direct effect and land cover had the most indirect effect. Compared to MDS, TDS is a more sensitive method for assessing regional soil quality, especially in heterogeneous mountains. Soil type is the fundamental factor to determining soil quality. Vegetation and land cover indirectly modulate soil properties and soil quality.

scholarly journals Spatial resolution and regionalization of airborne flux measurements using environmental response functions

2012 ◽  
Vol 9 (11) ◽  
pp. 15937-16003 ◽  
Author(s):  
S. Metzger ◽  
W. Junkermann ◽  
M. Mauder ◽  
K. Butterbach-Bahl ◽  
B. Trancón y Widemann ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Boosted Regression Trees ◽  
Environmental Response ◽  
Catchment Scale ◽  
River Catchment ◽  
Flux Measurements ◽  
Xilin River Catchment ◽  
Airborne Flux Measurements

Abstract. The goal of this study is to characterize the sensible (H) and latent (LE) heat exchange for different land covers in the heterogeneous steppe landscape of the Xilin River Catchment, Inner Mongolia, China. Eddy-covariance flux measurements at 50–100 m above ground were conducted in July 2009 using a weight-shift microlight aircraft. Wavelet decomposition of the turbulence data enables a spatial discretization of 90 m of the flux measurements. For a total of 8446 flux observations during 12 flights, MODIS land surface temperature (LST) and enhanced vegetation index (EVI) in each flux footprint are determined. Boosted regression trees are then used to infer an environmental response function (ERF) between all flux observations (H, LE) and biophysical- (LST, EVI) and meteorological drivers. Numerical tests show that ERF predictions covering the entire Xilin River Catchment (&amp;approx; 3670 km2) are accurate to ≤ 18%. The predictions are then summarized for each land cover type, providing individual estimates of source strength (36 W m−2 < H < 364 W m−2, 46 W m−2 < LE < 425 W m−2) and spatial variability (11 W m−2 < σH < 169 W m−2, 14 W m−2 < σLE < 152 W m−2) to a precision of ≤ 5%. Lastly, ERF predictions of land cover specific Bowen ratios are compared between subsequent flights at different locations in the Xilin River Catchment. Agreement of the land cover specific Bowen ratios to within 12 ± 9% emphasizes the robustness of the presented approach. This study indicates the potential of ERFs for (i) extending airborne flux measurements to the catchment scale, (ii) assessing the spatial representativeness of long-term tower flux measurements, and (iii) designing, constraining and evaluating flux algorithms for remote sensing and numerical modelling applications.

scholarly journals A GIS-Based Approach for the Quantitative Assessment of Soil Quality and Sustainable Agriculture

2021 ◽  
Vol 13 (23) ◽  
pp. 13438
Author(s):  
Mostafa A. Abdellatif ◽  
Ahmed A. El Baroudy ◽  
Muhammad Arshad ◽  
Esawy K. Mahmoud ◽  
Ahmed M. Saleh ◽  
...  
Keyword(s):  
Soil Quality ◽  
Environmental Sustainability ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Kriging Interpolation ◽  
Quality Model ◽  
Soil Quality Index ◽  
Gis And Remote Sensing ◽  
Geostatistical Approach ◽  
Total Study Area

Assessing soil quality is considered one the most important indicators to ensure planned and sustainable use of agricultural lands according to their potential. The current study was carried out to develop a spatial model for the assessment of soil quality, based on four main quality indices, Fertility Index (FI), Physical Index (PI), Chemical Index (CI), and Geomorphologic Index (GI), as well as the Geographic Information System (GIS) and remote sensing data (RS). In addition to the GI, the Normalized Difference Vegetation Index (NDVI) parameter were added to assess soil quality in the study area (western part of Matrouh Governorate, Egypt) as accurately as possible. The study area suffers from a lack of awareness of agriculture practices, and it depends on seasonal rain for cultivation. Thus, it is very important to assess soil quality to deliver valuable data to decision makers and regional governments to find the best ways to improve soil quality and overcome the food security problem. We integrated a Digital Elevation Model (DEM) with Sentinel-2 satellite images to extract landform units of the study area. Forty-eight soil profiles were created to represent identified geomorphic units of the investigated area. We used the model builder function and a geostatistical approach based on ordinary kriging interpolation to map the soil quality index of the study area and categorize it into different classes. The soil quality (SQ) of the study area, classified into four classes (i.e., high quality (SQ2), moderate quality (SQ3), low quality (SQ4), and very low quality (SQ5)), occupied 0.90%, 21.87%, 22.22%, and 49.23% of the total study area, respectively. In addition, 5.74% of the study area was classified as uncultivated area as a reference. The developed soil quality model (DSQM) shows substantial agreement (0.67) with the weighted additive model, according to kappa coefficient statics, and significantly correlated with land capability R2 (0.71). Hence, the model provides a full overview of SQ in the study area and can easily be implemented in similar environments to identify soil quality challenges and fight the negative factors that influence SQ, in addition to achieving environmental sustainability.

scholarly journals Soil quality index as affected by temporal long term tea cultivation in Jorhat District of Assam

2020 ◽  
Vol 41 (6) ◽  
pp. 1685-1695
Author(s):  
N. Baruah ◽  
◽  
B.K. Medhi ◽  
Sanjay Swami ◽  
R.K. Thakuria ◽  
...  
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Age Groups ◽  
Minimum Data Set ◽  
Soil Quality Index ◽  
Available Nitrogen ◽  
Systems Methodology ◽  
Data Set ◽  
Minimum Data ◽  
Tea Plantations

Aim: The investigation was undertaken to identify the Minimum Data Set (MDS) for Soil Quality Index (SQI) assessment in continuouslong-term tea cultivation systems. Methodology: In the study under very deep, fine loamy, well-drained soil, five age groups of tea plantations viz. less than 15 years, 15-30 years, 30-45 years, 45-60 years and more than 60 years were selected and identified minimum data set and soil quality index. Results: In very deep, fine loamy, well-drained soil under continuous tea cultivation, SQI, 14.74 was obtained for less than 15 years, 14.06 for 15-30 years, 11.12 for 30-45 years, 12.94 for 45-60 years and 11.37 for more than 60 years of plantation, respectively. Interpretation: The most sensitive soil quality indicators identified in very deep, fine loamy, well-drained soil were pH for less than 15 years, total nitrogen for 15-30 years, available nitrogen for 30-45 years, organic carbon for 45-60 years and exchangeable aluminium for more than 60 years of continuous tea cultivation.

scholarly journals The Assessment of Soil Quality Index for Paddy Fields with Indicator Biology in Jatipurno Districts, Wonogirl

2019 ◽  
Vol 14 (1) ◽  
pp. 20
Author(s):  
Supriyadi Supriyadi ◽  
Widyatmani Sih Dewi ◽  
Desmiasari Nugrahani ◽  
Adila Azza Rahmah ◽  
Haryuni Haryuni ◽  
...  
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Management Practices ◽  
Principal Component ◽  
Biological Indicators ◽  
Paddy Fields ◽  
Soil Quality Index ◽  
Data Set ◽  
Organic Rice ◽  
Minimum Data

Increased rice needs in an extensive use of paddy fields in the Jatipurno, Wonogiri. Managing rice fields can reduce soil quality. Proper management can improve soil quality, Jatipurno has management such as organic, semi-organic and inorganic paddy field management which have a real effect on soil quality. Assessment of soil quality is measured by physical, chemical and biological indicators, where each factor has a different effect. The chemical indicators are often used as the main indicators for determining soil quality, whereas every parameter has the opportunity to be the main indicator. So, biological indicators can play indicators. The main indicators are obtained from the correlation test (p-values &le; 0,05 - &lt; 0,01) and Principal Component Analysis with high value, eigenvalues &gt; 1 have the potential to be used as Minimum Data Sets. The result is biological can be able to use as the Minimum Data Set such as microbial carbon biomass, respiration, and total bacterial colonies. The Soil Quality Index (SQI) of various paddy management practices shows very low to low soil quality values. The management of organic rice systems shows better Soil Quality Index with a score of 0,20 compared to other management. The practice of organic rice management shows that it can improve soil quality.

scholarly journals SOIL QUALITY EVALUATION IN RANGELAND SOILS IN RELATION TO HEAVY METALS POLLUTION

2006 ◽  
Vol 28 (63) ◽  
Author(s):  
E. U. Onweremadu ◽  
I. C. Okoli ◽  
O. O. Emenalom ◽  
M. N. Opara ◽  
E. T. Eshett
Keyword(s):  
Heavy Metals ◽  
Organic Matter ◽  
Soil Quality ◽  
Quality Evaluation ◽  
Anthropogenic Activities ◽  
Soil Survey ◽  
Survey Technique ◽  
Morphological Index ◽  
Heavy Metals Pollution ◽  
Positive Correlations

Heightened anthropogenic activities at the study site necessitated this investigation on soil quality. A transect soil survey technique was used to link sampled points from an open dumpsite towards the river valley. Three minipedons were dug and sampled per location and collected samples were used for various laboratory analyses. Results showed that soils of the dumpsite (OB1) had the highest soil quality morphological index (SQMI) value of 3.82, indicating best quality while the least SQMI was found in the ranch (SQMI = 1.27). The SQMI had significant correlations with OM (organic matter) (r=0.82; p=0.001; n=72), clay (r=65; p=0.01; n=72), pH(r=0.58; p=0.01; n=72) and bulk density (r=0.71; p=0.05; p=0.05; n=72). Although there were positive correlations between SQMI and copper and cadmium, heavy metals were poor predictors of SQMI, indicating that soil quality evaluation by SQMI assesses mainly soil physical fertility.

scholarly journals Soil Quality at Rice Fields with Organic, Semi-organic and Inorganic Management in Wonogiri Regency, Indonesia

2021 ◽  
Vol 36 (2) ◽  
pp. 259
Author(s):  
Supriyadi Supriyadi ◽  
Intan Lestari Prima Vera ◽  
Purwanto Purwanto
Keyword(s):  
Soil Quality ◽  
Rice Field ◽  
Public Awareness ◽  
Pearson Correlation ◽  
Principal Component ◽  
Rice Fields ◽  
Soil Conditions ◽  
Soil Quality Index ◽  
Data Set ◽  
Organic Rice

The high demand of rice is fulfilled by intensification, particularly with the use of chemical fertilizer that allegedly causes land and environmental problems in a long term. As public awareness of environmental health rises, more rice fields are managed organically and semi-organically, but there are still many that manage rice fields inorganically. Assessment of soil quality of the three types of rice field management is important to prove that organic rice fields have better soil quality than semi-organic and inorganic rice fields, as well as to evaluate soil conditions on the location. This research was conducted in Girimarto, Wonogiri, Indonesia, using a descriptive explorative method with a survey approach on three points of each management system of rice fields, which are organic, semi-organic and inorganic rice fields. Statistical analysis was performed by Pearson correlation analysis and principal component analysis (PCA) to determine the indicators affecting soil quality, which are called the minimum data set (MDS). There were selected indicators in this research, including total microbes, base saturation, cation exchangeable capacity and organic carbon. Based on the results of the study, organic rice fields have the best soil quality with a score of soil quality index (SQI) of 2.3, compared to semi-organic rice field SQI (2.2) and inorganic rice field SQI (1.7). The results indicate that organic management contributes to better soil quality and environment.

scholarly journals Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data

2020 ◽  
Vol 12 (7) ◽  
pp. 1115 ◽  
Author(s):  
Shuai Wang ◽  
Qianlai Zhuang ◽  
Xinxin Jin ◽  
Zijiao Yang ◽  
Hongbin Liu
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Quality ◽  
Vegetation Index ◽  
Forest Ecosystems ◽  
Remotely Sensed ◽  
Northeastern China ◽  
Boosted Regression Trees ◽  
Land Policy ◽  
The Government

Forest ecosystems play an important role in regional carbon and nitrogen cycling. Accurate and effective monitoring of their soil organic carbon (SOC) and soil total nitrogen (STN) stocks provides important information for soil quality assessment, sustainable forestry management and climate change policy making. In this study, a geographical weighted regression (GWR) model, a multiple stepwise regression (MLSR) model, and a boosted regression trees (BRT) model were compared to obtain the best prediction of SOC and STN stocks of the forest ecosystems in northeastern China. Five-hundred and thirteen topsoil (0–30 cm) samples (10.32 kg m−2 (±0.53) for SOC, 1.21 kg m−2 (±0.32) for STN), and 9 remotely-sensed environmental variables were collected and used for the model development and verification. By comparing with independent verification data, the best model (BRT) achieved R2 = 0.56 and root mean square error (RMSE) = 00.85 kg m−2 for SOC stocks, R2 = 0.51 and RMSE = 0.22 kg m−2 for STN stocks. Of all the remotely-sensed environment variables, soil adjusted vegetation index (SAVI) and normalized difference vegetation index (NDVI) are of the highest relative importance in predicting SOC and STN stocks. The spatial distribution of the predicted SOC and STN stocks gradually decreased from northeast to southwest. This study provides an attempt to rapidly predict SOC and STN stocks in the dense vegetation covered area. The results can help evaluate soil quality and facilitate land policy and regulation making by the government in the region.

scholarly journals Degradation of soil quality by the waste leachate in a Mediterranean semi-arid ecosystem

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sh. Yeilagi ◽  
Salar Rezapour ◽  
F. Asadzadeh
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Crop Yields ◽  
Wheat Yield ◽  
Sensitivity Index ◽  
Soil Quality Index ◽  
Data Set ◽  
Total N ◽  
Quality Degradation ◽  
Waste Leachate

AbstractThe assessment of soil quality indices in waste leachate-affected soils is vital to understand the threats of land quality degradation and how to control it. In this respect, a study was conducted on the effects of uncontrolled landfill leachate on soil quality index (SQI) in calcareous agricultural lands using 28 soil variables. Using the total data set (TDS) and minimum data set (MDS) approaches, the SQI was compared between leachate-affected soils (LAS) and control soils by the integrated quality index (IQI) and nemoro quality index (NQI) methods. The results revealed that LAS were significantly enriched by soil salinity-sodicity indices including electrical conductivity (EC), sodium adsorption ratio (SAR), and exchangeable sodium percentage (ESP), fertility indices including total N, available P and K, organic carbon, and cation exchange capacity (CEC), exchangeable cations (Ca, Mg, K, and Na), the available and total fractions of heavy metals (Zn, Cu, Cd, Pb, Ni). After the leachate got its way into the soil, the values of IQI and NQI were dropped ranging 5–16% and 6.5–13% for the TDS approach and 5–15.2% and 7.5–12.2 for the MDS approach, respectively. Clearly, the data showed that soil quality degradation was encouraged and stimulated by the leachate. Among the different models of SQI applied in the present study, IQI determined by MDS was the optimal model to estimate soil quality and predict crop yields given the analysis of the correlations among the SQI models, the correlations between the SQI models and wheat yield, and sensitivity index values.

scholarly journals Spatio-temporal Soil Quality Assessment under Crop Rotation Irrigated with Treated Urban Wastewater using Fuzzy Modelling

2014 ◽  
Vol 28 (3) ◽  
pp. 291-302 ◽  
Author(s):  
Marjan Ghaemi ◽  
Ali R. Astaraei ◽  
Mehdi Nassiri Mahalati ◽  
Hojat Emami ◽  
Hossein H. Sanaeinejad
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Biological Significance ◽  
Minimum Data Set ◽  
Spatial And Temporal Variability ◽  
Urban Wastewater ◽  
Soil Quality Index ◽  
Data Set ◽  
Minimum Data ◽  
Total Data

Abstract Quantifying soil quality is important for assessing soil management practices effects on spatial and temporal variability of soil quality at the field scale. We studied the possibility of defining a simple and practical fuzzy soil quality index based on biological, chemical and physical indicators for assessing quality variations of soil irrigated with well water and treated urban wastewater during two experimental years. In this study 6 properties considered as minimum data set were selected out of 18 soil properties as total data set using the principal component analysis. Treated urban wastewater use had greater impact on biological and chemical quality. The results showed that the studied minimum data set could be a suitable representative of total data set. Significant correlation between the fuzzy soil quality index and crop yield (R2= 0.72) indicated the index had high biological significance for studied area. Fuzzy soil quality index approach (R2= 0.99) could be effectively utilized as a tool leading to better understanding soil quality changes. This is a first trial of creation of a universal index of soil quality undertaken.

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