scholarly journals Principal Component Analysis on Soil Fertility Parameters in Vegetable Growing Locations of Kottayam District of Kerala

2021 ◽  
pp. 64-72
Author(s):  
A. Muhsina ◽  
Brigit Joseph ◽  
Vijayaraghava Kumar
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Organic Carbon ◽  
Soil Fertility ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Soil Testing ◽  
Score Plot

Present study utilizes Principal Component Analysis (PCA) of 13 soil testing variables obtained from 28 vegetable growing locations of Kottayam district and there were a total of 718 samples for analysis. Thirteen Principal Components (PCs) were generated and five PCs could explain the major share of variance (80%). Score plot was drawn based on PCA and the results indicated that none of the variables was predominant in Bharananganam, Kadanadu, Kozhuvanal, Kidangoor and Pallikkathode and also these panchayats had positive scores on both F1 and F2 when factor analysis was conducted. Boron (B), Copper (Cu) and Zinc (Zn) were predominant in Akalakkunnam, Kadalpalamattom, Meeaachil, Melukavu, Poonjar and Ramapuram panchayats. Elikulam, Erumeli, Karoor, Mundakkayam, Mutholi, Poonjar south, Thalapalm and Vakathanom were those panchayats where the contribution of Magnesium (Mg), Potassium (K) and pH was more. All other elements viz, Oxidisable Organic Carbon (OC), Sulphur (S), Phosphorus (P), Calcium (Ca), Manganese (Mn) and Iron (Fe) had significant importance in Ayarkkunnam, Aymanam, Chempu, Kaduthuruthy, Kurichi, Manjoor, Maravanthuruth, Puthuppally and Thalayazham panchayats.

scholarly journals Factor analysis for soil test data: a methodological approach in environment friendly soil fertility management

2007 ◽  
Vol 8 ◽  
pp. 8-19 ◽  
Author(s):  
Hari Dahal
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Soil Fertility ◽  
Test Data ◽  
Methodological Approach ◽  
Principal Component ◽  
Component Analysis ◽  
Soil Test ◽  
Soil Fertility Management ◽  
Fertility Management

Soil test data were used in factor analysis employing the Principal Component Analysis technique for the reduction and summarization of soil variables. Principal component analysis was found to be highly suggestive in analyzing soil test data on which a rational fertilizer nutrients recommendation can be made for a sustainable soil fertility management reign. The Journal of AGRICULTURE AND ENVIRONMENT Vol. 8, 2007, pp. 8-19

scholarly journals Principal Component Analysis on Soil Fertility Parameters of Vegetable Growing Panchayats/ Locations in Ernakulam District of Kerala

2020 ◽  
pp. 58-65
Author(s):  
A. Muhsina ◽  
Brigit Joseph ◽  
Vijayaraghava Kumar
Keyword(s):  
Principal Component Analysis ◽  
Soil Fertility ◽  
Soil Ph ◽  
Principal Component ◽  
Component Analysis ◽  
Organic Carbon Content ◽  
Score Plot ◽  
Five Factors ◽  
Total Variability ◽  
High Level

The present paper used Principal Component Analysis (PCA) on 13 soil fertility parameters including soil pH and electrical conductivity of 17 vegetable growing panchyat/locations in Ernakulam district of Kerala based on 583 soil samples. Soil pH of panchayats varied from 4.2- 5.8 with a coefficient of variation 3.16-12.23 per cent and it was inferred that most of the panchayats in the district had very strongly acidic (pH: 4.2-5) and strongly acidic soils (pH: 5-5.5). High level of organic carbon content was noticed in most of the panchayats except in four panchayats. The results of PCA revealed that five PC’s together explained a total variability of 80 per cent and the remaining PCs accounted for 20 per cent of the variability in the data which has been discarded from further analysis. First principal component accounted for 25 per cent variance followed by PC 2(21%), PC 3(14%), PC 4(10%) and PC 5(10%). Factor analysis generated five factors and they explained 85 per cent of variability. Score plot drawn as part of PCA showed that Chengamanadu, Manjapra and Thirumaradi panchayats had high content of soil available S and B. EC was also found to be higher in these panchayats. Amount of OC, Fe and Mn were more in Kalady, Keerampara and Mudakkuzha of Ernakulam district whereas Thuravur, Piravom and Pothanikkad had highly acidic and Mg rich soils. Amount of Zn was more in Vengoor panchayat. Available K, Ca, P and Cu were found to be higher in Kakkad, Nedumbassery, Vengola and Kadungalloor. Based on the fertility status of each panchayats, they could be classified into different groups.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

The Design of Index about Corporate Governance Based on PCA Method

2014 ◽  
Vol 926-930 ◽  
pp. 4085-4088
Author(s):  
Chuan Jun Li
Keyword(s):  
Principal Component Analysis ◽  
Corporate Governance ◽  
Principal Components ◽  
Principal Component ◽  
Component Analysis ◽  
Contribution Rate ◽  
Variance Contribution ◽  
Pca Method

This article uses the PCA method (Principal component analysis) to evaluate the level of corporate governance. PCA is used to analyze the correlation among 10 original indicators, and extract some principal components so that most of the information of the original indicators is extracted. The formulation of the index of corporate governance can be got by calculating the weight based on the variance contribution rate of the principal component, which can comprehensively evaluate corporate governance.

Identification of Liquor Brands Based on near Infrared Spectroscopy

2013 ◽  
Vol 834-836 ◽  
pp. 935-938
Author(s):  
Lian Shun Zhang ◽  
Chao Guo ◽  
Bao Quan Wang
Keyword(s):  
Principal Component Analysis ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Principal Components ◽  
Near Infrared ◽  
Scatter Correction ◽  
Principal Component ◽  
Component Analysis ◽  
Correction Method ◽  
Variance Contribution

In this paper, the liquor brands were identified based on the near infrared spectroscopy method and the principal component analysis. 60 samples of 6 different brands liquor were measured by the spectrometer of USB4000. Then, in order to eliminate the noise caused by the external factors, the smoothing method and the multiplicative scatter correction method were used. After the preprocessing, we got the revised spectra of the 60 samples. The difference of the spectrum shape of different brands is not much enough to classify them. So the principal component analysis was applied for further analysis. The results showed that the first two principal components variance contribution rate had reached 99.06%, which can effectively represent the information of the spectrums after preprocessing. From the scatter plot of the two principal components, the 6 different brands of liquor were identified more accurate and easier than the spectra curves.

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