| 任瑞玉,李亚伟,董孔军,何继红,刘天鹏,张磊,杨天育.谷子种质资源农艺性状与品质的多样性分析及综合评价[J].干旱地区农业研究,2025,(5):1~14 |
| 谷子种质资源农艺性状与品质的多样性分析及综合评价 |
| Diversity analysis and comprehensive evaluation of agronomic traits and quality of foxtail millet germplasm resources |
| |
| DOI：10.7606/j.issn.1000-7601.2025.05.01 |
| 中文关键词: 谷子 种质资源 农艺性状 品质 多样性分析 综合评价 |
| 英文关键词:foxtail millet germplasm resources agronomic traits quality diversity analysis comprehensive evaluation |
| 基金项目:甘肃省农业科学院区域协同创新项目（2024GAAS03）；甘肃省农业科学院重点研发计划项目（2022GAAS41）；国家现代农业产业技术体系（CARS-06-14.5-A8）；甘肃省科技计划项目（23CXNA0036) |
|
| 摘要点击次数: 442 |
| 全文下载次数: 172 |
| 中文摘要: |
| 为挖掘谷子优异种质资源，从国家和省资源库及国内相关单位选取126份谷子种质资源，开展2年农艺性状调查，并从中筛选100份测定其营养品质，采用多种分析方法进行研究及综合评价。结果表明：农艺性状方面，谷子种质资源变异丰富，16个性状变异系数介于9.30%~66.66%之间，主茎高变异最小，幼苗色变异最大。相关性分析显示株草质量与株穗质量、小区产量、株粒质量等指标间存在显著正相关关系（P<0.05）。聚类分析将所有材料划分为五大类群，第 I 类群高杆大穗，可用于筛选饲草谷子；第II类群穗颈最短，可用于筛选矮秆宜机收谷子；第 III 类群平均主茎高最低，株穗和株粒质量及小区产量的均值最大，可筛选矮秆高产谷子；第Ⅳ类群千粒重最大，可作粒用育种材料。主成分分析将数量性状划分为4个主成分，累计贡献率77.493%；根据各性状贡献率的权重计算综合得分F值，筛选出‘60天红酒谷’、‘黄酒谷’等10份优异材料。品质分析中，微量元素Fe和Se变异系数较大，分别为44.97%和70.00%，总淀粉与支链淀粉含量显著正相关（r=0.83），而淀粉和粗蛋白含量则表现出负相关关系（r=-0.98）。主成分分析将营养品质划分为3个主成分，其累计贡献率达到72.559%，构建函数表达式综合评价营养品质，得分领先的‘济糯谷1号’等10份资源可作优良育种材料。 |
| 英文摘要: |
| In order to explore the excellent germplasm resources of foxtail millet, 126 foxtail millet germplasm accessions were selected from national and provincial resource banks and relevant domestic institutions. A two\|year agronomic trait investigation was conducted, and 100 accessions were selected for nutritional quality analysis. Various analytical methods were employed for research and comprehensive evaluation. The results showed that, in terms of agronomic traits, there was abundant variation among the millet germplasm accessions. The coefficient of variation for 16 traits ranged from 9.30% to 66.66%. The variation of main stem height was the smallest and the variation of seedling color was the largest. Correlation analysis revealed significant positive correlations between straw per plant mass and various factors such as spike mass per plant, community production, grain mass per plant (P<0.05). Cluster analysis divided the materials into five major groups, each with distinct characteristics. Group I was characterized by tall plants and large panicles, which could be used for screening forage millet. Group II, with the shortest peduncle, could be employed for selecting short\|statured millet suitable for mechanical harvesting. Group III had the lowest average main stem height and the highest average spike mass per plant, grain mass per plant, and community production, making it suitable for selecting short yet high\|yielding millet. Group IV had the largest thousand\|seed weight, ideal for grain breeding materials. Principal component analysis divided quantitative traits into four principal components, with a cumulative contribution rate of 77.493%. According to the comprehensive score (F value) calculated by the weight of contribution rates of each trait,the top 10 excellent materials, such as ‘60\|day hongjiugu’ and ‘Huangjiugu’, were selected. In the quality analysis, the coefficients of variation for the trace elements Fe and Se were relatively large, at 44.97% and 70.00%, respectively. There was a significant positive correlation between total starch content and amylopectin content (r=0.83), while starch content and crude protein content exhibited a negative correlation (r=-0.98). Principal component analysis divided nutritional quality into three principal components, with a cumulative contribution rate of 72.559%. A functional expression was constructed to comprehensively evaluate nutritional quality, and 10 resources, including ‘Jinuogu No.1’ with the highest score, could be used as excellent breeding materials. |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| | |