数据驱动的方法,以提高水利用效率和作物植物的抗干旱能力
Niharika Sharma1, Harsh Raman2, David Wheeler1
1NSW Department of Primary Industries, Orange Agricultural Institute, Orange, NSW 2800, Australia.
Plant science : an international journal of experimental plant biology
|September 2, 2023
概括
满足全球粮食需求需要提高作物用水效率 (WUE) 和耐旱能力. 大数据方法加速了气候适应性作物品种的发展,提高了产量.
科学领域:
- 农业科学 农业科学
- 遗传学 是一个遗传学.
- 植物育种 植物育种
背景情况:
- 全球人口增长和气候变化需要在作物生产方面取得进展.
- 缺水和干旱对全球农业构成了重大挑战.
- 传统的育种方法是缓慢的,并且受到现有的遗传多样性的限制.
研究的目的:
- 探索大数据驱动的方法如何加速作物遗传改进.
- 确定开发耐旱作物品种的战略,以提高用水效率 (WUE) 和产量.
- 为了利用omics技术和人工智能来更快地增强特征.
主要方法:
- 使用来自各种生殖质的高通量欧米克数据 (基因组学,转录组学,代谢组学,蛋白质组学).
- 应用人工智能 (AI) 和机器学习 (ML) 来分析大型数据集.
- 识别标记特征关联和优越的单元类型,以进行遗传改进.
- 整合基因组育种,速度育种和基因编辑技术.
主要成果:
- 奥米克数据与表型信息相结合,可以识别基因和特征变异的途径.
- 大数据分析加速了对干旱耐受性和WUE的遗传标记的发现.
- 人工智能和机器学习有助于用于繁殖计划的优质基因型的预测.
- 这些方法显著加快了改进作物品种的发展.
结论:
- 数据驱动的策略,包括AI和omics,对于快速改善作物至关重要.
- 开发耐干旱和高WUE的作物品种对于在不断变化的气候中实现粮食安全至关重要.
- 利用大数据为可持续农业和气候变化适应提供了新的遗传收益.
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