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.

scholarly journals Mapping of Soil Quality Index (SQI) for Paddy Fields Using Sentinel-2 Imagery, Laboratory Analysis, and Principal Component Analysis

2021 ◽  
Vol 6 (2) ◽  
pp. 173
Author(s):  
Putri Tunjung Sari ◽  
Indarto Indarto ◽  
Marga Mandala ◽  
Bowo Eko Cahyono
Keyword(s):  
Principal Component Analysis ◽  
Soil Quality ◽  
Paddy Field ◽  
Quality Index ◽  
Principal Component ◽  
Component Analysis ◽  
Paddy Fields ◽  
Organic Fertilizers ◽  
Soil Quality Index ◽  
Sentinel 2

The use of intensive chemical inputs causes lower availability of nutrients, organic matter, cation exchange capacity, and soil degradation.Therefore, this study aims to assess the soil quality index (SQI) for paddy fields in Jember, East Java, Indonesia. Input data for this study consist of land cover (interpreted from the Sentinel-2 image), soil type, and slope maps. Furthermore, the procedure to calculate soil quality index (SQI) include (1) spatial analysis to create the land unit, (2) preparation of soil sampling, (3) soil chemical analysis, (4) principal component analysis (PCA), and (5) reclassifying soil quality index (SQI).  The PCA results showed that three variables i.e., % sand, total- P, and % silt were strongly correlated to SQI, while three classes namely very low, low, and medium of SQI were sufficiently used to describe the spatial variability of the paddy field. Furthermore, approximately 41.14% of the paddy field area were classed as very low while 52.23%, and 6.63% were categorized as low and medium SQI respectively. Based on the results, about 93.37% of paddy fields in Jember Regency still require improvement in soil quality via the addition of ameliorants such as organic fertilizers to increase quality and productivity. This application needs to focus on areas with very low-low quality hence, the quality increased to the medium category. Keywords : Mapping; Soil Quality Index (SQI); PCA; Paddy field Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

scholarly journals No-tillage system participatory quality index

2020 ◽  
Vol 24 (2) ◽  
pp. 128-133 ◽  
Author(s):  
Tiago S. Telles ◽  
Ana J. Righetto ◽  
Marco A. P. Lourenço ◽  
Graziela M. C. Barbosa
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Crop Rotation ◽  
Quality Index ◽  
Pearson Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
Soil Tillage ◽  
No Tillage ◽  
Tillage System

ABSTRACT The no-tillage system participatory quality index aims to evaluate the quality and efficiency of soil management under no-tillage systems and consists of a weighted sum of eight indicators: intensity of crop rotation, diversity of crop rotation, persistence of crop residues in the soil surface, frequency of soil tillage, use of agricultural terraces, evaluation of soil conservation, balance of soil fertilization and time of adoption of the no-tillage system. The aim of this study was to assess the extent to which these indicators correlate with the no-tillage system participatory quality index and to characterize the farmers who participated in the research. The data used were provided by ITAIPU Binacional for the indicators of the no-tillage system participatory quality index II. Descriptive analyses were performed, and the Pearson correlation coefficient between the index and each indicator was calculated. To assess the relationship between the indicators and the farmers’ behavior toward the indicators, principal component analysis and cluster analysis were performed. Although all correlations are significant at p-value ≤ 0.05, some correlations are weak, indicating a need for improvement of the index. The principal component analysis identified three principal components, which explained 66% of the variability of the data, and the cluster analysis separated the 121 farmers into five groups. It was verified that the no-tillage system participatory quality index II has some limitations and should therefore be reevaluated to increase its efficiency as an indicator of the quality of the no-tillage system.

scholarly journals Elemental composition of surface soils in Nature Park Shumen Plateau and Shumen City, Bulgaria

2018 ◽  
Vol 5 (2) ◽  
pp. 68-76
Author(s):  
Vanya Koleva ◽  
Teodora Koynova ◽  
Asya Dragoeva ◽  
Nikolay Natchev
Keyword(s):  
Heavy Metals ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Elemental Composition ◽  
Principal Component ◽  
Component Analysis ◽  
Soil Samples ◽  
Surface Soils ◽  
Nature Park ◽  
And Cluster Analysis

Abstract Anthropogenic activities cause environmental pollution and alter biogeochemical cycles. Soils in cities and their vicinity are exposed to different pollutants. Nature Park Shumen Plateau is a protected area situated in the proximity of Shumen (Bulgaria). The aim of this research was to compare elemental composition of surface soil samples from Nature Park with two areas in Shumen city. Soil samples from seven sites on the territory of Nature Park and from two urban sites were collected. The elemental composition of the samples was determined using Energy Dispersive X-Ray Fluorescence technique. Principal component analysis and cluster analysis were performed to interpret the complex data. The content of 24 elements was determined: Br, Y, Zr, Mo, Ag, Cd, Sn, Sb, I, Cs, Ba, La, Ce Si, K, Ca, Ti, Mn, Fe, Cu, Zn, Rb, Sr, and Pb. Results presented here and previously showed that concentrations of heavy metals Cu, Zn, Cd and Pb are below the upper limit according to Bulgarian legislation. Concentrations of Mn and Fe in samples from Nature Park were comparable to the literature data reported for unpolluted areas. Principal component analysis and cluster analysis show similarity of the content of 24 elements between samples from Nature Park and from Shumen city. These findings are in accordance with our previous positive results from Allium-test: cytogenetic endpoints showed a presence of harmful compounds in Nature Park soils. The content of heavy metals in the surface soils studied show a lack of environmental risk for Nature Park. However, a similar distribution pattern of the investigated elements in the park and two anthropologically influenced areas in Shumen city indicated a potential hazard in Nature Park.

scholarly journals Soil Quality Assessment Using Multivariate Approaches: A Case Study of the Dakhla Oasis Arid Lands

Land ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1074
Author(s):  
Salman A.H. Selmy ◽  
Salah H. Abd Abd Al-Aziz ◽  
Raimundo Jiménez-Ballesta ◽  
Francisco Jesús García-Navarro ◽  
Mohamed E. Fadl
Keyword(s):  
Principal Component Analysis ◽  
Soil Quality ◽  
Quality Index ◽  
Soil Depth ◽  
Principal Component ◽  
Component Analysis ◽  
Data Set ◽  
Dakhla Oasis ◽  
Multivariate Approaches ◽  
Total Study Area

A precise evaluation of soil quality (SQ) is important for sustainable land use planning. This study was conducted to assess soil quality using multivariate approaches. An assessment of SQ was carried out in an area of Dakhla Oasis using two methods of indicator selection, i.e., total data set (TDS) and minimum data set (MDS), and three soil quality indices (SQIs), i.e., additive quality index (AQI), weighted quality index (WQI), and Nemoro quality index (NQI). Fifty-five soil profiles were dug and samples were collected and analyzed. A total of 16 soil physicochemical parameters were selected for their sensitivity in SQ appraising to represent the TDS. The principal component analysis (PCA) was employed to establish the MDS. Statistical analyses were performed to test the accuracy and validation of each model, as well as to understand the relationship between the used methods and indices. The results of principal component analysis (PCA) showed that soil depth, gravel content, sand fraction, and exchangeable sodium percentage (ESP) were included in the MDS. High positive correlations (r ≥ 0.9) occurred between SQIs calculated using TDS and/or MDS under the three models. Moreover, the findings showed highly significant differences (p < 0.001) among SQIs within and between TDS and MDS. Approximately 80 to 85% of the total study area based on TDS, as well as 70 to 75%, according to MDS, were identified as suitable soils with slight limitations on soil quality grade (Q3, Q2, and Q1), while the remaining 20 to 30% had high to severe limitations (Q4 and Q5). The highest sensitivity (SI = 2.9) occurred by applying WQI using MDS and indicator weights based on the variance of PCA. Furthermore, the highest linear regression value (R2 = 0.88) between TDS and MDS was recorded using the same model. Because of its high sensitivity, such a model could be used for monitoring SQ changes caused by agricultural practices and environmental factors. The findings of this study have significant guiding implications and practical value in assessing the soil quality using TDS and MDS in arid areas critically and accurately.

scholarly journals Determining the effects of the forest stand age on the soil quality index in afforested areas: A case study in the Palandöken Mountains

2021 ◽  
Author(s):  
Emre Çomaklı ◽  
Bülent Turgut
Keyword(s):  
Soil Quality ◽  
Quality Index ◽  
Aggregate Stability ◽  
Principal Component ◽  
Erosion Control ◽  
Soil Samples ◽  
Total Carbon ◽  
Stand Age ◽  
Soil Quality Index ◽  
Score Functions

Afforestation is an essential strategy for erosion control. The objective of this study was to determine the soil quality index (SQI) in established afforested areas of different ages for erosion control in Erzurum, Turkey. Three afforested areas were selected as plots considering their establishment periods: + 40 years old (AA<sub>&gt;40</sub>), 10–40 years old (AA<sub>10–40</sub>), and less than 10 years old (AA<sub>&lt;10</sub>). Forty soil samples were taken in each plot area over the 0–15 and 15–30 cm depths. The soil samples were analysed for the texture, mean weight diameter, aggregate stability, pH, electrical conductivity, total nitrogen, total carbon, and total sulfur contents. These properties were used as the soil quality indicators, whereby the analytic hierarchy process (AHP) and principal component analysis (PCA) were used to establish their relative importance for describing the soil quality. The indicators were scored using the linear score functions of “more is better” and “optimum value”. For determining the SQI, the additive method (SQI<sub>A</sub>), the weighted method with AHP (SQI<sub>AHP</sub>), and the weighted method with PCA (SQI<sub>PCA</sub>) were used. The SQI scores of the plots showed statistically significant differences. In all three methods, the highest SQI value was obtained from the AA<sub>&gt;40</sub> plots.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.

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