Version 1
: Received: 10 August 2023 / Approved: 11 August 2023 / Online: 11 August 2023 (03:39:54 CEST)
How to cite:
Arunachalam, R.; Madhaiyan, R. Estimating Multivariate models for Association between soil Major and minor Micronutrients. Preprints2023, 2023080886. https://doi.org/10.20944/preprints202308.0886.v1
Arunachalam, R.; Madhaiyan, R. Estimating Multivariate models for Association between soil Major and minor Micronutrients. Preprints 2023, 2023080886. https://doi.org/10.20944/preprints202308.0886.v1
Arunachalam, R.; Madhaiyan, R. Estimating Multivariate models for Association between soil Major and minor Micronutrients. Preprints2023, 2023080886. https://doi.org/10.20944/preprints202308.0886.v1
APA Style
Arunachalam, R., & Madhaiyan, R. (2023). Estimating Multivariate models for Association between soil Major and minor Micronutrients. Preprints. https://doi.org/10.20944/preprints202308.0886.v1
Chicago/Turabian Style
Arunachalam, R. and Ramji Madhaiyan. 2023 "Estimating Multivariate models for Association between soil Major and minor Micronutrients" Preprints. https://doi.org/10.20944/preprints202308.0886.v1
Abstract
An empirical investigation was carried out to study the relationship between the soil characteristics, including the Nitrogen (N), Phosphorus (P), Potassium (K), potential of Hydrogen (pH), Electrical conductivity (EC), Sulfur (S,), Zinc (Zn), Iron (Fe), Copper (Cu), Manganese (Mn) and Boron (B) using principal component analysis (PCA), Factor Analysis (FA), and Canonical Correlation analysis (CCA) for data reduction multivariate techniques. The first five main components accounted for 24.22%, 18.52%, 16.1%, 9.97%, and 9.27% of sample variances, respectively. All five principal components (PCs) accounted for 78.07% of the sample variance. The soil characteristics viz., P and K have highly dominated the first PC; the soil characteristics N and pH have dominated the second PC; K, EC, and Cu have dominated the third PC; Fe, Mn, and pH have dominated in the fourth PC; whereas the parameter Cu dominated the fifth PC. The first factor showed a strong negative loading on S and a strong favorable loading on Cu; P, K, and EC have substantial positive loadings in the second factor. Significant positive loadings on Mn, Fe, Cu, pH, N, and K are present in the third component. The fourth factor had a significantly positive pH, Fe, N, P, K, and B loadings. Fe, P, and B have much weight in the fifth factor. The soil characteristics viz., N, Zn, pH, K, Fe, and Mn have dominated the first PC; the soil characteristics B, P, S, Zn, and Fe have dominated the second PC; B and P have dominated the third PC whereas the only character Cu dominated the fourth PC. Cu and OC have much weight in the fourth factor. The canonical redundancies for dependent and independent sets are 12% and 17%, respectively. The Stewart-Love canonical redundancy index is 70.58%, which means that the first linear combination of the X-set explains 70.58% of the total variance in the Y-set.
Keywords
Kaiser-Meyer-Olkin Test; Pearson correlation; Eigenvalue; Score plot; PCA; FA; CCA
Subject
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
