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来自大米育种的NARO历史现象型数据集

Kei Matsushita^1,2, Akio Onogi^1,3, Jun-Ichi Yonemaru^1,2

¹Research Center for Agricultural Information Technology (RCAIT), National Agriculture and Food Research Organization (NARO), 3-1-1 Kannondai, Tsukuba, Ibaraki 305-8517, Japan.

Breeding science

|October 2, 2024

概括

历史的米育种数据显示,随着时间的推移,与产量相关的特征和存放抗性的显著改善. 较新的品种表现出更大的特征多样性,表明不断发展的育种目标,以提高作物表现.

科学领域:

农业科学农业科学
植物育种植物育种
遗传学遗传学是一个

背景情况:

现型数据和历史育种试验记录对于提高育种计划效率至关重要.
国家农业和食品研究组织 (NARO) 已经从其米育种计划中积累了大量数据.

研究的目的:

组织历史数据,并为NARO大米育种计划开发一个全面的表型数据集.
分析15种水特性的趋势,包括产量,以及它们与品种发布年之间的关系.

主要方法:

开发了一个历史的表型数据集,包含667个品种的6052个记录 (1991-2018).
使用最佳线性无偏预测 (BLUP) 和主要组件分析 (PCA) 来进行数据分析.
检查了特征和诸如品种释放年和繁殖地点等因素之间的关系.

主要成果:

随着时间的推移,观察到与产量相关的特征 (每谷的谷物,植物重量,谷物产量,千颗谷物重量) 显著增加.
显著降低子的数量,增加成熟时间,降低 lodging 程度和棕米蛋白质含量.
主要成分分析 (PCA) 表明,在较新的水品种中,特征多样性更大.

结论:

选择有利于面包重型的高产大米品种,具有更好的住宿阻力.
观察到的特征趋势表明不同NARO研究站的繁殖目标不同.
开发的历史数据集为长期的育种进展和大米的特征演变提供了宝贵的见解.

关键词:

蓝点蓝点蓝点蓝点纳罗 (NARO) 是一个名为纳罗 (NARO) 的语言.在PCA中,PCA是PCA.历史数据历史数据发布年份发布年份米米饭米饭米饭米饭.收益率收益率收益率收益率

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