摘要
以海滨锦葵优良单系杂交的F2代群体为材料,对产量性状进行了广义遗传力、相关性和主成分分析,以期为提高海滨锦葵产量提供有益信息.结果表明:F2家系单株种子产量平均值为12.84 g,比2001年在滩涂海滨锦葵自然生长群体中随机选择的80个单系的平均单株产量(4.25 g/单系)有显著提高;在10个产量性状中,广义遗传力较高的依次是结果枝比、种子成熟度、结果枝数、分枝数和果实数;6个因子(果实数、地径、结果枝数、株高、分枝数和结果枝比)与单株产量高度正相关,且这6个因子彼此高度相关;结果枝高度与上述6个因子及产量均呈显著负相关.主成分分析表明,影响单株产量的依次是结果枝数、地径、分枝数、株高和果实数.对海滨锦葵而言,地径和分枝数的增加是获得高产的关键,而结果枝高度的负选择也可显著提高种子产量.
In order to provide useful information for improving yield of Kosteletzkya virginica, analysis of broad heritability, correlation and principal component of F2 yield traits of hybrids of fine lines were conducted. The results showed the average seed yield of F2 lines was 12.84 g, it was significant higher than that (4.25 g) of 80 lines randomly selected from the naturalized population of Kosteletzkya virginica in tideland. Among ten yield traits, the order of traits with higher broad hefitability was the ratio of bearing branches to branches, degree of seed mature, the number of bearing branches, the number of branches and the number of fruits. Six traits ( the number of fruits, collar diameter, the number of bearing branches, plant height, the number of branches and ratio of bearing branches to branches) were significantly positively correlated with seed yield, and there are significantly positive correlations each other. The above six traits and seed yield were significantly negatively correlated with the height of bearing branches. Principal component analysis indicated that traits influencing seed yield orderly the number of bearing branches, collar diameter, the number of branches, plant height and the number of fruits. A key factor to obtain high yield for Kosteletzkya virginica was the increases of collar diameter and the number of branches, and it is also significantly enhanced the seed yield to negative select the height of bearing branches.
出处
《大连民族学院学报》
CAS
2007年第5期62-65,共4页
Journal of Dalian Nationalities University
基金
"863"计划基金资助项目(2002AA629210)
关键词
海滨锦葵
产量性状
广义遗传力
相关分析
主成分分析
Kosteletzkya virginica
yield traits
broad heritability
correlation analysis
principal component analysis