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 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 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 ≤ 0,05 - < 0,01) and Principal Component Analysis with high value, eigenvalues > 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 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 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.

Principal Component Analysis of Correlated WEDM Performance Characteristics during Machining ZC63/SiCP MMCs

2014 ◽  
Vol 14 (1) ◽  
pp. 13-21 ◽  
Author(s):  
Thella Babu Rao ◽  
A. Gopala Krishna
Keyword(s):  
Principal Component Analysis ◽  
Quality Index ◽  
Optimization Problem ◽  
Principal Component ◽  
Component Analysis ◽  
Machining Process ◽  
Total Quality ◽  
Process Variables ◽  
Optimal Process ◽  
Pulse On Time

AbstractThe present investigation proposes the optimization of the wire electrical discharge machining process for machining ZC63/SiCP metal matrix composite. SiC particulate size and its percentage with the matrix are considered as the process variables along with the most significant WEDM variables such as pulse-on time, pulse-off time and wire tension. In view of quality cut, surface roughness, metal removal rate and kerf are considered as the process responses. Since, these responses are correlated with each other and they need to be optimized simultaneously. Therefore, the problem is treated as multi-response optimization problem. Principal component analysis (PCA) has been implemented to convert the multi-objective optimization problem in to single objective optimization problem by converting the multiple correlated responses in to the total quality index. Taguchi's robust optimization technique has been adopted to derive the set optimal process parameters which maximize the total quality index. The derived optimal process responses are confirmed with the experimental validation tests. ANOVA is conducted find the importance of the chosen process variables on the overall quality of the machined component. The practical possibility of the obtained optimal process performance is observed using SEM studies.

scholarly journals Using the Dermatology Life Quality Index to Assess How Breast Radiodermatitis Affects Patients’ Quality of Life

2019 ◽  
Vol 13 ◽  
pp. 117822341983554 ◽  
Author(s):  
Laura Curr Beamer ◽  
Marcia Grant
Keyword(s):  
Quality Of Life ◽  
Principal Component Analysis ◽  
Internal Consistency ◽  
Dermatology Life Quality Index ◽  
Quality Index ◽  
Life Quality ◽  
Principal Component ◽  
Component Analysis ◽  
Life Quality Index

Purpose: The purpose of this study is to report the initial validation process for using the Dermatology Life Quality Index (DLQI) for radiodermatitis of the breast. Methods: This is an additional analysis of a study designed to report a longitudinal study in skin-related and global quality of life in women with breast radiodermatitis. A total of 40 participants completed the DLQI instrument weekly while receiving external radiotherapy of the female breast. At week 5 on treatment, 31 (78%) participants provided narrative feedback on how each DLQI item affected her life. Agreement between participant DLQI numerical ratings and narrative feedback on items was assessed. Construct validity was estimated using principal component analysis (PCA). Internal consistency of the DLQI was assessed using Cronbach alpha. Results: Percentage of agreement between participant DLQI ratings and narratives ranged from 71% to 98%. Each participant responded “no” to the work and study item leading to zero variance and removal from our analyses. Principal component analysis supported the inclusion of all of the remaining items. The DLQI with nine remaining items demonstrated moderately good internal consistency (α = .69). Conclusions: The results of our examination of the DLQI when used for breast radiodermatitis are promising. Next steps include additional larger studies among more diverse populations.

scholarly journals Water quality index and hydrogeochemistry of Otukpo area, Benue State, North Central Nigeria

2021 ◽  
Vol 6 (1) ◽  
pp. 4-14
Author(s):  
Buba Apagu Ankidawa ◽  
Ujah Linus Sunday ◽  
Ibrahim Vanke
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Water Samples ◽  
Water Quality Index ◽  
Quality Index ◽  
Principal Component ◽  
Component Analysis ◽  
Research Area ◽  
Water Type ◽  
Surface And Groundwater

The research is aimed to assess the surface and groundwater quality in Otukpo area and environs, Benue State, North Eastern Nigeria. Sixteen water samples were collected from 7 boreholes, 7 hand duck wells and 2 rivers. The water samples were analysed chemically and bacteriologically using spectrophotometric, titrimetric and membrane filtration methods. Analytical results indicated that the groundwater in the area is acidic, fresh and moderately hard. The order of abundance of the cations were in Na+<K+<Mg2+<Ca2+ while the anions were in the order of Cl-<HCO3->SO42-<NO3-. Principal Component Analysis (PCA) identified four factors that accounts for 69.73% of the total variance. Correlation analysis, Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) revealed pollution from application of agricultural fertilizers, anthropogenic contamination and rock-water interaction as the major processes responsible for the modification of surface and groundwater chemistry of the research area. The Gibbs diagram plot shows that, the sample points fall under rock dominance and weathering zones, which suggested precipitation, induced chemical weathering with the dissolution of rock-forming minerals. The piper diagram classified groundwater samples as Ca-Mg-HCO3 water type. Water Quality Index (WQI) values range from 22.05 to 56.13 which indicated good and excellent water category. The SAR values range from 0.02 to 0.66 the values belong to the excellent category and is suitable for irrigation. The overall result revealed that, the water in the research area is suitable for domestic, industrial and irrigation activities.

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