基于图像的现象预测可以在小麦育种中提供有价值的决策支持
Lukas Roth1, Dario Fossati2, Patrick Krähenbühl3
1Institute of Agricultural Sciences, ETH Zurich, Universitätstrasse 2, 8092, Zurich, Switzerland. lukas.roth@usys.ethz.ch.
概括
使用二次特征的现象选择 (PS) 显示了冬季小麦育种中早期代试验的潜力. 这种方法提供了对生理特征的洞察,有助于选择具有更好的产量和蛋白质含量的基因型.
科学领域:
- 农业科学 农业科学
- 植物育种 植物育种
- 遗传学 是一个遗传学.
背景情况:
- 传统的育种依赖于早期世代的视觉评估.
- 基因组测序和高通量表型化的进步使得数据驱动的选择成为可能.
- 基因型与环境 (GxE) 相互作用影响特征表达和繁殖成功.
研究的目的:
- 为了调查二次特征的GxE相互作用是否比目标特征 (例如,收益率) 复杂度小.
- 评估现象选择 (PS) 在早期世代中识别优异基因型的有效性.
- 探索目标特征的生理基础,并识别有益的意识形态.
主要方法:
- 在5个不同的环境中分析了45种冬季小麦品种.
- 使用线性和因子分析 (FA) 混合模型来估计GxE相互作用.
- 用无人机获得的数据 (植物高度,叶面积,耕密度) 来进行动态特征评估.
主要成果:
- 其次性特征和谷物蛋白质含量表现出最小的GxE相互作用.
- 收益率GxE建模需要一个更复杂的FA模型与两个因素.
- 一个PS模型实现了产量性能 (0.43),产量稳定性 (0.30) 和蛋白质含量 (0.34) 的适度预测相关性.
结论:
- 现象选择,利用次要特征的不那么复杂的GxE相互作用,对早期代试验是可行的.
- PS提供了超越简单预测准确性的生理洞察力,补充了基因组选择 (GS).
- 确定了一种可以减轻产量和蛋白质含量之间的负面类效应的观念型.
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