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影响华南稻区常规籼稻产量水平的主要农艺性状分析μ 被引量:8

Analysis of Main Agronomic Traits Affecting Yield Level in Inbred indica Rice Cultivars in South China
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摘要 对近50年来华南地区常规籼稻主栽品种的农艺性状进行主成分分析,前6项主成分的方差总贡献达到85.1%,其中穗粒数因子的方差贡献率最高。回归分析结果则显示,生物产量因子得分与产量的标准化回归系数最大,表明其对产量的贡献最大。基于前6项主成分得分进行品种聚类,可以将品种分为5大类。进一步对不同品种类别的各项主成分得分以及各项农艺性状进行比较,结果显示生物产量是决定华南地区水稻产量水平的关键性因素。不同类别间穗粒数因子与粒重因子相互消长,其中任一项因子得分过低都将限制产量水平提高,只有两者达到比较理想的平衡时,才能在高生物产量水平的条件下实现库容量的提高,促使产量水平提高。以提高生物产量为前提,在现有大穗的基础上适当提高千粒重水平,使穗粒数因子和粒重因子在更高水平上形成新的平衡,将是进一步提高华南地区水稻产量水平的重要途径。 Principal component analysis was carried out on 20 agronomic traits of 68 inbred indica rice cultivars, which were widely planted in South China in recent 50 years. The results indicated 85. 1% of the total variations were contributed by the top six factors among which the factor of grain number per panicle made the greatest contribution. The regression analysis result showed that the standardized regression coefficient of biomass factor to yield was the greatest. It indicated that the biomass was the most important factor to high yield. Based on the scores of the top six factors, 68 cultivars could be classified into five groups. A comparison of the mean factor scores and the agronomic traits among the five groups indicated that biomass was a key factor which determined yield level. Spikelet number and grain weight followed an opposite trend among the cultivar groups, and if the score of one factor is too low, the yield level will be reduced significantly. Only when the two factors maintained balance at a high biomass yield, the sink size and yield could be increased to a high level. It should be an important approach to increase the grain yield potential in South China to maintain the balance between the grain number and grain weight at a higher level through increasing 1000-grain weight based on large panicle and high biomass.
出处 《中国水稻科学》 CAS CSCD 北大核心 2012年第2期182-188,共7页 Chinese Journal of Rice Science
基金 广东省重大科技专项(2006A20202004 2007A020400003)
关键词 华南地区 常规籼稻 产量水平 农艺性状 主成分分析 South China inbred indica rice yield level agronomic trait principal component analysis
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