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.

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 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 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 Modeling Land Suitability for Rice Crop Using Remote Sensing and Soil Quality Indicators: The Case Study of the Nile Delta

2020 ◽  
Vol 12 (22) ◽  
pp. 9653
Author(s):  
Ahmed A. El Baroudy ◽  
Abdelraouf. M. Ali ◽  
Elsayed Said Mohamed ◽  
Farahat S. Moghanm ◽  
Mohamed S. Shokr ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Food Security ◽  
Soil Quality ◽  
Quality Indicators ◽  
Nile Delta ◽  
Point Of View ◽  
Soil Quality Indicators ◽  
Global Food Security ◽  
Increasing Demand ◽  
Global Food

Today, the global food security is one of the most pressing issues for humanity, and, according to Food and Agriculture Organisation (FAO), the increasing demand for food is likely to grow by 70% until 2050. In this current condition and future scenario, the agricultural production is a critical factor for global food security and for facing the food security challenge, with specific reference to many African countries, where a large quantities of rice are imported from other continents. According to FAO, to face the Africa’s inability to reach self-sufficiency in rice, it is urgent “to redress to stem the trend of over-reliance on imports and to satisfy the increasing demand for rice in areas where the potential of local production resources is exploited at very low levels” The present study was undertaken to design a new method for land evaluation based on soil quality indicators and remote sensing data, to assess and map soil suitability for rice crop. Results from the investigations, performed in some areas in the northern part of the Nile Delta, were compared with the most common approaches, two parametric (the square root, Storie methods) and two qualitative (ALES and MicrioLEIS) methods. From the qualitative point of view, the results showed that: (i) all the models provided partly similar outputs related to the soil quality assessments, so that the distinction using the crop productivity played an important role, and (ii) outputs from the soil suitability models were consistent with both the satellite Sentinel-2 Normalize Difference Vegetation Indices (NDVI) during the crop growth and the yield production. From the quantitative point of view, the comparison of the results from the diverse approaches well fit each other, and the model, herein proposed, provided the highest performance. As a whole, a significant increasing in R2 values was provided by the model herein proposed, with R2 equal to 0.92, followed by MicroLES, Storie, ALES and Root as R2 with value equal to 0.87, 0.86, 0.84 and 0.84, respectively, with increasing percentage in R2 equal to 5%, 6% and 8%, respectively. Furthermore, the proposed model illustrated that around (i) 44.44% of the total soils of the study area are highly suitable, (ii) 44% are moderately suitable, and (iii) approximately 11.56% are unsuitable for rice due to their adverse physical and chemical soil properties. The approach herein presented can be promptly re-applied in arid region and the quantitative results obtained can be used by decision makers and regional governments.

scholarly journals Quantitative Evaluation of Soil Quality Using Principal Component Analysis: The Case Study of El-Fayoum Depression Egypt

2021 ◽  
Vol 13 (4) ◽  
pp. 1824
Author(s):  
Mohamed K. Abdel-Fattah ◽  
Elsayed Said Mohamed ◽  
Enas M. Wagdi ◽  
Sahar A. Shahin ◽  
Ali A. Aldosari ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Soil Quality ◽  
Quality Index ◽  
Principal Component ◽  
Component Analysis ◽  
Soil Samples ◽  
Western Desert ◽  
Geographical Information ◽  
Soil Quality Index

Soil quality assessment is the first step towards precision farming and agricultural management. In the present study, a multivariate analysis and geographical information system (GIS) were used to assess and map a soil quality index (SQI) in El-Fayoum depression in the Western Desert of Egypt. For this purpose, a total of 36 geo-referenced representative soil samples (0–0.6 m) were collected and analyzed according to standardized protocols. Principal component analysis (PCA) was used to reduce the dataset into new variables, to avoid multi-collinearity, and to determine relative weights (Wi) and soil indicators (Si), which were used to obtain the soil quality index (SQI). The zones of soil quality were determined using principal component scores and cluster analysis of soil properties. A soil quality index map was generated using a geostatistical approach based on ordinary kriging (OK) interpolation. The results show that the soil data can be classified into three clusters: Cluster I represents about 13.89% of soil samples, Cluster II represents about 16.6% of samples, and Cluster III represents the rest of the soil data (69.44% of samples). In addition, the simulation results of cluster analysis using the Monte Carlo method show satisfactory results for all clusters. The SQI results reveal that the study area is classified into three zones: very good, good, and fair soil quality. The areas categorized as very good and good quality occupy about 14.48% and 50.77% of the total surface investigated, and fair soil quality (mainly due to salinity and low soil nutrients) constitutes about 34.75%. As a whole, the results indicate that the joint use of PCA and GIS allows for an accurate and effective assessment of the SQI.

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