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草业学报 ›› 2025, Vol. 34 ›› Issue (11): 205-216.DOI: 10.11686/cyxb2025032

• 研究论文 • 上一篇    

小黑麦及其近缘种花序及穗部特征差异

郭常英(), 杜文华()   

  1. 甘肃农业大学草业学院,草业生态系统教育部重点实验室,甘肃省草业工程实验室,中-美草地畜牧业可持续发展研究中心,甘肃 兰州 730070
  • 收稿日期:2025-02-01 修回日期:2025-03-11 出版日期:2025-11-20 发布日期:2025-10-09
  • 通讯作者: 杜文华
  • 作者简介:E-mail: duwh@gsau.edu.cn
    郭常英(1997-),女,甘肃张掖人,在读博士。E-mail: gchangying2021@163.com
  • 基金资助:
    国家自然科学基金项目(32260339);甘肃省高等教育产业支撑计划项目(2022CYZC-49);甘肃省重点研发计划项目(20YF8NA129);农业生物育种重大专项(2023ZD0402605-02)

Differences in inflorescence and spike characteristics between triticale and its allied species

Chang-ying GUO(), Wen-hua DU()   

  1. College of Pratacultural Science,Gansu Agricultural University,Key Laboratory of Pratacultural Ecosystem,Ministry of Education,Pratacultural Engineering Laboratory of Gansu Province,Sino-US Center for Grazingland Ecosystem Sustainability,Lanzhou 730070,China
  • Received:2025-02-01 Revised:2025-03-11 Online:2025-11-20 Published:2025-10-09
  • Contact: Wen-hua DU

摘要:

植物的花序和穗部形态特征是植物分类、种群区分及种质资源鉴定的重要依据,且这些特征的差异直接影响植物的繁殖效率和遗传传播模式。小黑麦是由小麦与黑麦远缘杂交而成的新物种,为了揭示小黑麦与其近缘种小麦和黑麦在花序和穗部特征上的差异,并为小黑麦独立成属提供分类学依据,以10个小黑麦、10个小麦和10个黑麦品种(系)为研究材料,通过观察其花序与穗部特征,分析其差异并进行主成分分析与聚类分析,最后通过正交偏最小二乘判别分析对研究结果进行验证。结果显示:小黑麦的花序长度大于黑麦,宽度大于小麦和黑麦,且基部小穗小花数显著高于小麦和黑麦。小黑麦的上位护颖长、下位护颖长,以及第1和2小花的芒长、外稃长、内稃长均显著大于小麦和黑麦。小黑麦的小穗数少于黑麦,但每小穗的花朵数更多,籽粒质量较重,使其具有在相同环境条件下较高的千粒重。综合分析表明:小黑麦的花序和穗部特征与小麦和黑麦存在显著差异,可有效区分三者,其中花序长、花序宽、籽粒厚和籽粒宽可作为区分小黑麦与小麦及黑麦的主要形态特征,正交偏最小二乘判别分析再次验证了上述结果。总体而言,花序和穗部特征可有效区分小黑麦与小麦及黑麦,研究结果可为小黑麦属的建立提供基础依据。

关键词: 小黑麦, 花序, 穗部, 结构特征, 主成分分析, 正交偏最小二乘判别分析

Abstract:

The morphological characteristics of the inflorescence and spike are important criteria for plant classification, population differentiation, and germplasm resource identification. These characteristics also directly affect the reproductive efficiency and genetic dissemination patterns of plants. Triticale, a new species derived from the distant hybridization between wheat and rye, exhibits distinct differences in inflorescence and spike features compared with its progenitor species, wheat and rye. These differences provide taxonomic evidence for the establishment of triticale as an independent genus. This study aimed to examine in detail the differences in inflorescence and spike characteristics among triticale, wheat, and rye, to provide a taxonomic basis for the establishment of triticale as a separate genus. We compared 10 varieties (lines) of triticale, 10 of wheat, and 10 of rye. Their inflorescence and spike traits were observed and quantified, the differences among them were evaluated by principal component analysis and cluster analysis, and the results were validated by orthogonal partial least squares discriminant analysis (OPLS-DA). The results show that the inflorescence length of triticale was greater than that of rye, and its width was greater than that of both wheat and rye. Additionally, the number of florets at the base spikelet was significantly higher in triticale than in wheat and rye (P<0.05). The lemma length of the upper and lower glumes, as well as the awn length, lemma length, and palea length of the first and second florets, were all significantly greater in triticale than in wheat and rye. Although triticale had fewer spikelets than rye, it had more florets per spikelet and heavier grains, resulting in a higher thousand-grain weight under the same environmental conditions. These comprehensive analyses indicate that the inflorescence and spike characteristics of triticale are significantly different from those of wheat and rye, allowing for effective differentiation among the three. The inflorescence length, inflorescence width, grain thickness, and grain width can be used as the main morphological characteristics to distinguish triticale from wheat and rye. The results of the OPLS-DA validated these findings. In summary, the inflorescence and spike characteristics can effectively differentiate triticale from wheat and rye. These findings provide a fundamental basis for the establishment of Triticale as a taxonomic genus.

Key words: triticale, inflorescence, spike, structural traits, principal component analysis, orthogonal partial least squares discriminant analysis