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Showing 2 results for Principal Component Analysis (pca)

M. Azimi, S. Massiha, M. Moghaddam, M. Valizadeh,
Volume 3, Issue 4 (1-2000)
Abstract

In order to study the genetic variation among local varieties of onion in Iran, an experiment was conducted in the Research Center, Faculty of Agriculture, Tabriz University. Sixteen populations were evaluated for agronomic characteristics and also total seed proteins via SDS-PAGE. Cluster analysis and principal component analysis were used to group the onion populations under study.

 Analysis of variance showed significant differences among varieties for leaf color, leaf length, texture tightness, onion yield per plant, and number of edible layers. No significant differences were observed for the number of twin onions, bulb diameter, and onion dry weight. Hamadan (98-148), Arak (98-95, 98-96, 98-97, 98-103), and Zanjan (98-223) populations acquired the highest onion yield per plant. The significant differences between populations for the majority of characteristics proved the existence of genetic variation in the Iranian onion germplasm. The results from cluster analysis for agronomic characteristics were the same as those from the cluster analysis for the onion yield per plant. The 16 populations were divided into 4 groups. Cluster analysis for the electrophoresis banding pattern resulted in two groups, which was not similar to the dendrogram of agronomic traits. Using principal component analysis, the first principal components determined 97.57% of the total variation. Onion yield per plant was the most important trait in the first principal component and onion dry weight was the second trait in the rank.


L. Divband Hafshejani, M. Mirnaseri, A. A. Naseri,
Volume 29, Issue 4 (12-2025)
Abstract

Soil, as one of the vital natural resources, plays a fundamental role in ecosystem sustainability and global food security; however, degradation caused by unsustainable management, intensive agriculture, and pollution threatens its capacity. The use of organic amendments such as hydrochar is considered an innovative approach to improve soil physicochemical properties and enhance the Soil Quality Index (SQI). This study aimed to investigate the effects of different levels of hydrochar on soil properties and evaluate SQI. The treatments included control and three hydrochar levels (H10, H20, and H50). Soil properties such as pH, porosity, bulk density, electrical conductivity, organic carbon, total nitrogen, and available phosphorus were measured and normalized, and parameter weighting was conducted using entropy and principal component analysis (PCA). Results showed that nitrogen and organic carbon had the greatest importance in soil quality. The H50 treatment recorded the highest SQI (0.815), significantly greater than other treatments, while H20 (0.546) and H10 (0.336) also showed positive effects compared to the control (0.159). Hydrochar application improved organic carbon, nitrogen, and phosphorus and reduced bulk density. Although an increase in electrical conductivity was observed in H50. Overall, hydrochar application had a positive and gradual effect on SQI, with H20 recommended as an optimal level to improve fertility and reduce long-term salinity risks.


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