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 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

Evaluation of the Aging Behavior of High Density Polyethylene in Thermal Oxidative Environment by Principal Component Analysis

2017 ◽  
Vol 727 ◽  
pp. 447-449 ◽  
Author(s):  
Jun Dai ◽  
Hua Yan ◽  
Jian Jian Yang ◽  
Jun Jun Guo
Keyword(s):  
Principal Component Analysis ◽  
Impact Strength ◽  
High Density Polyethylene ◽  
Tensile Modulus ◽  
Principal Component ◽  
Component Analysis ◽  
High Density ◽  
Aging Behavior ◽  
Data Set ◽  
The Impact

To evaluate the aging behavior of high density polyethylene (HDPE) under an artificial accelerated environment, principal component analysis (PCA) was used to establish a non-dimensional expression Z from a data set of multiple degradation parameters of HDPE. In this study, HDPE samples were exposed to the accelerated thermal oxidative environment for different time intervals up to 64 days. The results showed that the combined evaluating parameter Z was characterized by three-stage changes. The combined evaluating parameter Z increased quickly in the first 16 days of exposure and then leveled off. After 40 days, it began to increase again. Among the 10 degradation parameters, branching degree, carbonyl index and hydroxyl index are strongly associated. The tensile modulus is highly correlated with the impact strength. The tensile strength, tensile modulus and impact strength are negatively correlated with the crystallinity.

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.

Comparisons of Environmental Ordinations with Principal Component Vegetational Ordinations for Sets of Data Having Different Degrees of Complexity

1971 ◽  
Vol 1 (2) ◽  
pp. 99-112 ◽  
Author(s):  
J. K. Jeglum ◽  
C. F. Wehrhahn ◽  
J. M. A. Swan
Keyword(s):  
Principal Component Analysis ◽  
Water Level ◽  
Principal Component ◽  
Low Complexity ◽  
Component Analysis ◽  
Two Dimensional ◽  
High Complexity ◽  
Water Conductivity ◽  
Data Set ◽  
Total Data

Data from a survey of lowland, mainly peatland, vegetation were subjected to environmental ordination based on measurements of water level and water conductivity, and to vegetational ordination derived from principal component analysis (P.C.A.). Analyzed were the total set of the data ("all types"), half sets ("nonwoody" and "woody types") and quarter sets (stands of "marshes", "meadows", "shrub fens", and "other woody types"); the number of distinct physiognomic groups in a set of data, and presumably the amount of contained heterogeneity, decreased at each segmentation.The effectiveness of the ordination models was tested by correlating measured distances in two-dimensional ordination models with 2W/(A + B) indices of vegetational similarity for randomly selected pairs of types or stands. As the physiognomic complexity decreased, the effectiveness of the P.C.A. vegetational ordination increased whereas that of the environmental ordination decreased. The environmental ordination seemed most appropriate to the data encompassing high complexity (total data set), while the P.C.A. vegetational ordination seemed most appropriate to data with low complexity (quarter sets of the data).

Sign in / Sign up

Export Citation Format

Share Document