ZHANG Lei-lei
Academy of Agricultural Sciences in Xinjiang Bayingoleng Mongolia Autonomous Prefecture/Xinjiang Scientific Observation and Experimental Station of Medium and Early Maturing Upland Cotton and Long Fiber Cotton,Ministry of Agriculture and Rural Areas, Korla 841000FAN A-qi
Academy of Agricultural Sciences in Xinjiang Bayingoleng Mongolia Autonomous Prefecture/Xinjiang Scientific Observation and Experimental Station of Medium and Early Maturing Upland Cotton and Long Fiber Cotton,Ministry of Agriculture and Rural Areas, Korla 841000HONG Mei
Academy of Agricultural Sciences in Xinjiang Bayingoleng Mongolia Autonomous Prefecture/Xinjiang Scientific Observation and Experimental Station of Medium and Early Maturing Upland Cotton and Long Fiber Cotton,Ministry of Agriculture and Rural Areas, Korla 841000MA Zhi-hua
Academy of Agricultural Sciences in Xinjiang Bayingoleng Mongolia Autonomous Prefecture/Xinjiang Scientific Observation and Experimental Station of Medium and Early Maturing Upland Cotton and Long Fiber Cotton,Ministry of Agriculture and Rural Areas, Korla 841000CHEN Jin-rui
Academy of Agricultural Sciences in Xinjiang Bayingoleng Mongolia Autonomous Prefecture/Xinjiang Scientific Observation and Experimental Station of Medium and Early Maturing Upland Cotton and Long Fiber Cotton,Ministry of Agriculture and Rural Areas, Korla 841000ZHAO Shuang-yin
Academy of Agricultural Sciences in Xinjiang Bayingoleng Mongolia Autonomous Prefecture/Xinjiang Scientific Observation and Experimental Station of Medium and Early Maturing Upland Cotton and Long Fiber Cotton,Ministry of Agriculture and Rural Areas, Korla 841000ZHENG Kai
Agricultural College of Xinjiang Agricultural University, Urumqi 830052Tuer-hong Tuer-xun
Academy of Agricultural Sciences in Xinjiang Bayingoleng Mongolia Autonomous Prefecture/Xinjiang Scientific Observation and Experimental Station of Medium and Early Maturing Upland Cotton and Long Fiber Cotton,Ministry of Agriculture and Rural Areas, Korla 8410001.Academy of Agricultural Sciences in Xinjiang Bayingoleng Mongolia Autonomous Prefecture/Xinjiang Scientific Observation and Experimental Station of Medium and Early Maturing Upland Cotton and Long Fiber Cotton,Ministry of Agriculture and Rural Areas, Korla 841000;2.Agricultural College of Xinjiang Agricultural University, Urumqi 830052
Foundation project: The Innovation Environment (Talent, Base ) Construction Project of Xinjiang(PT2011)
The variation coefficient analysis, genetic diversity analysis, correlation analysis, principal component analysis and cluster analysis of 647 island cotton germplasm resources were carried out in order to screen more diverse types of island cotton germplasm resources for parent selection and variety breeding in the future. The variation range of quantitative index of 647 sea island cotton germplasm resources was 2.4608%~36.4320%, indicating the rich diversity among sea island cotton germplasm resources. The number of stem hairs, leaf color, leaf hairs, petal basal spot size, main stem hardness, fruit branch type and style length of island cotton germplasm resources were variable, and these external descriptive traits could be directly used for the improvement of plant morphology. Genetic diversity analysis of quantitative indicators showed that the diversity of indicators reflecting fiber quality was more abundant than that reflecting yield, and germplasm resources could be used for improving fiber quality and maturity. Correlation analysis revealed a significant correlation between different quantitative traits. Among them, the first fruit branch node was significantly negatively correlated with the average length of the upper half, the uniformity index and the breaking strength, the sub-index was significantly negatively correlated with the micronaire value, and the lint percentage was significantly negatively correlated with the average length of the upper half. The above correlation is consistent with previous research results on upland cotton, The complicated interaction mode implied a comprehensive evaluation by integrating multiple datasets in germplasm innovation. The principal component analysis showed that the cumulative contribution rate of the first five eigenvalues reached 75.761%. The first principal component was related to fiber quality, the second principal component was related to seed cotton yield, the third principal component was related to elongation, the fourth principal component was related to maturity, and the fifth principal component was related to lint percentage. When the genetic distance was 10, the germplasm resources were divided into 6 groups by cluster analysis. The comprehensive performance of cluster II was better. In actual breeding, targeted selection and improvement can be carried out according to breeding objectives.