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在多环境试验 (MET) 中选择高性能和稳定的豆类基因型:应用AMMI,GGE-Biplot和BLUP程序

Sintayehu D Daba¹, Alecia M Kiszonas¹, Rebecca J McGee²

¹USDA-ARS Western Wheat & Pulse Quality Laboratory, Pullman, WA 99164, USA.

Plants (Basel, Switzerland)

|June 28, 2023

概括

BLUP模型为豆繁殖提供了卓越的预测准确性,但AMMI和GGE工具对于识别具有广泛适应性在各种环境中的基因型至关重要. 这项研究有助于种植者进行品种选择,并增强豆子生产战略.

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:

作物育种计划产生了广泛的特征数据,这对于优化改进管道非常有价值.
先进产量试验 (AYT) 为评估基因型性能提供了关键数据.

研究的目的:

用十年的先进产量试验数据分析和测试豆子育种的关键方面.
评估最佳线性无偏预测 (BLUP) 和AMMI (添加主效应和乘法相互作用) 模型的预测成功.
确定用于理解基因型与环境相互作用 (GE) 的统计工具,并选择可适应的豆品种.

主要方法:

利用10年 (2012-2021) 绿色,黄色和冬季豆类的AYT数据.
利用六个平衡数据集通过交叉验证来比较BLUP和AMMI家族模型.
采用AMMI和GGE (基因型+GE相互作用) 双图分析GE相互作用和基因型适应性.

主要成果:

BLUP模型显示出比AMMI模型更高的预测准确度.
AMMI和GGE双图有效地确定了具有特定或广泛适应性的基因型.
在不利的环境中观察到显著的产量减少 (80-87%),这与天气变化 (高温,低降水) 有关.

结论:

对于预测,BLUP是准确的,但AMMI/GGE对于选择广泛适应的基因型至关重要.
了解转基因相互作用对于优化豆品种选择至关重要.
环境因素,特别是天气,显著影响豆种子产量变化.

关键词:

这就是WAASBB的意思.进行交叉验证.在指令后进行评估.预测性评估是一种预测性评估.稳定的稳定性稳定的稳定性

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