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多年生黑麦草种质资源物候期和形态性状 被引量:4

Analysis of phenophase and morphological traits of perennial ryegrass germplasm
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摘要 为评价并筛选具有优异特性的多年生黑麦草(Lolium perenne)种质资源,本研究选取99份多年生黑麦草种质资源,对其物候期及8个形态性状进行观测和分析,并依据各形态指标开展主成分分析和聚类分析。结果表明:供试种质的全生育期为252.0~310.0 d,形态性状均表现出了丰富的变异,其变异系数为22.63%~44.11%。根据主成分分析,将8个形态性状抽取成可代表原有指标87.2%信息量的4个成分,构建评价函数模型并对所有种质进行评分。其中种质W620468得分最高(1.334),而自主选育的R2-12得分最低(0.140)。基于8个形态性状的聚类分析将多年生黑麦草种质分为4大类群,各类群间形态差异明显,其中第Ⅰ类群和第Ⅳ类群可依据牧草和草坪草的不同育种目标进一步考察作为亲本育种材料或新品种推广。综上所述,本研究中多年生黑麦草种质间的形态性状变异丰富,其中第Ⅰ和第Ⅳ类群种质可作为优良亲本以选育牧草型和草坪草型新品种。 To evaluate and screen the germplasm resources of perennial ryegrass(Lolium perenne)for excellent characteristics,99 perennial ryegrass germplasm resources were selected to observe and analyze their phenological periods and eight morphological traits,and principal component analysis and cluster analysis were performed based on the morphological indicators.The results showed that the whole growth period of the tested germplasm was 252.0~310.0 days,their morphological characteristics showed abundant variation with a coefficient of variation of 22.63%~44.11%.The cluster analysis of eight morphological characteristics,perennial ryegrass germplasms were extracted into four groups with obvious morphological differences.The germplasms of groupsⅠandⅣcan be further investigated as parent breeding materials or released as new varieties for forage-or turf-type breeding objectives.In summary,there are rich variations in the morphological characteristics of the perennial ryegrass germplasms used in this study,among which,germplasms of groupsⅠandⅣcan be used as elite parental materials to breed new forage and turf-type varieties.
作者 张晨晨 曾令霜 张敬 徐彬 ZHANG Chenchen;ZENG Lingshuang;ZHANG Jing;XU Bin(College of Agro-grassland Science,Nanjing Agricultural University,Nanjing 210095,Jiangsu,China)
出处 《草业科学》 CAS CSCD 北大核心 2022年第2期290-299,共10页 Pratacultural Science
基金 国家自然科学基金(31971757、31772659)。
关键词 多年生黑麦草 物候期 形态性状 种质筛选 种质评价 主成分分析 聚类分析 perennial ryegrass phenological phase morphological traits germplasm screening germplasm evaluation principal component analysis cluster analysis
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