通过基于智能气候-土壤预测和经济重要性的模型,优化美国南部大米带的多环境试验
Melina Prado1, Adam Famoso2, Kurt Guidry2
1Department of Genetics, "Luiz de Queiroz" College of Agriculture/University of São Paulo, Piracicaba, Brazil.
Frontiers in plant science
|November 7, 2024
概括
优化养多环境试验 (MET) 可以在不损失准确性的情况下将地点减少28%. 这通过使用环境共变量和有效地集群试验场所来提高繁殖效率.
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:
- 全球大米育种的目标是生产,气候智能品种,但遗传收益受到表型化成本和试验分配挑战的阻碍.
- 优化多环境试验 (MET) 的数量和放置对于高效的米育种计划至关重要.
研究的目的:
- 开发一种具有成本效益和准确的方法,以优化在米育种中的MET设计.
- 确定影响大米谷物产量和基因型与环境相互作用的关键环境共变量 (EC).
主要方法:
- 利用美国大米带的历史天气和土壤数据,将其转化为大米响应指标.
- 应用特征选择算法来识别基于环境相似性的重要EC和聚类试验地点.
- 在各种试验场景下使用基于预测的模型进行联合分析,以评估优化策略.
主要成果:
- 八个EC解释了58%的谷物产量变化和53%的基因型与环境相互作用.
- 在没有显著的准确性损失的情况下,可以减少28%的试验地点.
- 美国大米带被划分为四个不同的环境集群,具有不同的经济重要性.
结论:
- 开发的方法优化了MET分配,降低了成本,提高了米育种的效率.
- 环境共变量和空间聚类是设计更具信息性和成本效益的试验的有效工具.
- 这一战略有助于更好地提前分配试验,提高育种计划的准确性.
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