Predictive breeding for maize: Making use of molecular phenotypes, machine learning, and physiological crop models
Predictive breeding for maize: Making use of molecular phenotypes, machine learning, and physiological crop models
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DOI：
10.1002/csc2.20052
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发表时间：
2020-03-01
期刊：
影响因子：
2.3
通讯作者：
Franco, Jose A. Valdes
中科院分区：
文献类型：
--
作者：
Washburn, Jacob D.;Burch, Merritt B.;Franco, Jose A. Valdes
玉米(Zea Mays L.)一个多世纪以来一直是科学研究和育种的重点。它也是世界上最重要的经济作物之一，仅在美国每年的价值就约为500亿美元。此外，玉米长期以来一直是研究和开发杂交优势的模式物种，它仍然是世界上将光合作用能量转化为淀粉的最有效的转化器之一。本文从以基因为中心的方法和以基因×环境×管理互作为重点的方法两个方面讨论了玉米预测育种的历史和未来。报告强调了当前的预测挑战，以及在技术、方法、数据集、跨学科合作和科学文化方面的重要进展，这些进步将使预测玉米(和其他作物品种)育种在未来几年加速取得进展。
Maize (Zea mays L.) has been a focus of scientific research and breeding for over a century. It is also one of the most economically important crops in the world, with a value of approximately US$50 billion per year in the United States alone. Additionally, maize has long been the model species of choice for the study and exploitation of hybrid vigor, and it continues to be one of the world's most efficient converters of photosynthetic energy into starch. This review discusses the history and future of maize predictive breeding in the context of both genotype centric methods, and those focusing on genotype x environment x management interactions. Current prediction challenges are highlighted, as well as important advances in technology, methods, datasets, interdisciplinary collaborations, and scientific culture that will enable accelerated progress in predictive maize (and other crop species) breeding for years to come.