一项精确、高通量的茎结构特征分析加深了对高粱抗倒伏性的认识。

IF 4.3 2区 生物学 Q1 PLANT SCIENCES
Jianguo Li, Liyan Zhao, Hongzeng Fan, Falin Zhao, Dandan He, Bo Li, Jibin Wang, Guosheng Xie, Zhen Hu, Chuchuan Fan, Lingqiang Wang
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引用次数: 0

摘要

背景:植物茎秆结构特征是决定植物抗倒伏性的关键因素,而高粱茎秆结构特征的高通量快速调查方法尚缺乏。结果:在103份高粱材料中,对干法和水洗法两种茎粉进行了可见和近红外光谱采集,生成了16个茎结构特征模型(组合),表明支持向量机回归模型对茎结构特征的预测有显著的正向影响,而粉末类型和光谱预处理对茎结构特征的预测影响较小。此外,茎结构性状与农艺性状呈显著正相关,与倒伏指数呈显著负相关,倒伏指数是植物抗倒伏的负相关指标。结论:本研究首次为基于光谱的高粱茎秆结构特性预测提供了一种精确、高通量的方法,为作物育种中抗倒伏能力的提高提供了依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A precise and high-throughput assay for stem structural characteristics deepens understanding of lodging resistance in sorghum.

Background: Plant stem structural characteristics are crucial factors determining plant lodging resistance, while high throughput methods for rapid surveys of these traits are still lacking in sorghum.

Results: Among 103 sorghum accessions, two kinds of stem powders (dry and water-washed) were subject to visible and near-infrared spectra acquisition, and 16 models (combinations) for stem structural characteristics were generated, revealing that the support vector machine regression model has significant positive effects on the prediction of stem structural characteristics while powder type and pretreatment of spectra has minor effects on the prediction of stem structural characteristics. In addition, we found that stem structure characteristics were positively correlated with agronomic traits but negatively correlated with lodging index which is the criterion that negatively accounts for plant lodging resistance.

Conclusion: This study for the first time provided a precise and high throughput method for the prediction of sorghum stem structural characteristics based on spectra, which could facilitate the improvement of lodging resistance in crop breeding.

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来源期刊
BMC Plant Biology
BMC Plant Biology 生物-植物科学
CiteScore
8.40
自引率
3.80%
发文量
539
审稿时长
3.8 months
期刊介绍: BMC Plant Biology is an open access, peer-reviewed journal that considers articles on all aspects of plant biology, including molecular, cellular, tissue, organ and whole organism research.
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