Phenotype Diversity Analysis of Amomum tsao-ko in Lvchun County of Yunnan Province

DOI

10.2991/aiea-16.2016.30How to use a DOI?

Keywords

Amomum tsao-ko; Phenotype diversity; PCA.

Abstract

Genetic diversity analysis is very important for germplasm conservation and utilization. In this study, 13 quantitative traits of 50 Amomum tsao-ko plants were assessed by basic statistical parameters and principal component analysis (PCA). The results indicated that the phenotypic diversity was abundant in Amomum tsao-ko. Coef cients of variation (CV) ranged from 7.60% to 43.86%, and the largest of variation coefficient was the fruiting rate, while fresh fruit width was low. Shannon-Weaver diversity index (H') of 13 traits ranged from 1.71 to 2.22, the largest and the smallest H' values were observed in seed regiment weight and fresh fruit weight, respectively. The principal component analysis (PCA) explained 78.381% of the total variation in four components. The first principal component was determined by dry fruit weight, fresh fruit width, dry fruit peel weight and seed regiment weight. The second was determined by fresh fruit length, dry fruit length and ratio of dry fruit length and width. The third mainly represented number of seeds per fruit, and the fourth reflected fresh fruit weight. Increasing the first principal component factor will be favorable for increasing the fruit weight, while the second principal component of the change will affect the shape of fruit.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shaoze Duan
AU  - Kunlong Zhu
AU  - Wenqiang Li
AU  - Linyan Xie
AU  - Xianwang Zhou
AU  - Dong Shen
AU  - Tiantao Wang
AU  - Shenxuan Yang
AU  - Mengli Ma
AU  - Bingyue Lu
PY  - 2016/11
DA  - 2016/11
TI  - Phenotype Diversity Analysis of Amomum tsao-ko in Lvchun County of Yunnan Province
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence and Engineering Applications
PB  - Atlantis Press
SP  - 163
EP  - 166
SN  - 2352-538X
UR  - https://doi.org/10.2991/aiea-16.2016.30
DO  - 10.2991/aiea-16.2016.30
ID  - Duan2016/11
ER  -