NSW Department of Primary Industries, Orange Agricultural Institute, Orange, NSW 2800, Australia.
NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, NSW 2650, Australia.
Plant Sci. 2023 Nov;336:111852. doi: 10.1016/j.plantsci.2023.111852. Epub 2023 Sep 1.
With the increasing population, there lies a pressing demand for food, feed and fibre, while the changing climatic conditions pose severe challenges for agricultural production worldwide. Water is the lifeline for crop production; thus, enhancing crop water-use efficiency (WUE) and improving drought resistance in crop varieties are crucial for overcoming these challenges. Genetically-driven improvements in yield, WUE and drought tolerance traits can buffer the worst effects of climate change on crop production in dry areas. While traditional crop breeding approaches have delivered impressive results in increasing yield, the methods remain time-consuming and are often limited by the existing allelic variation present in the germplasm. Significant advances in breeding and high-throughput omics technologies in parallel with smart agriculture practices have created avenues to dramatically speed up the process of trait improvement by leveraging the vast volumes of genomic and phenotypic data. For example, individual genome and pan-genome assemblies, along with transcriptomic, metabolomic and proteomic data from germplasm collections, characterised at phenotypic levels, could be utilised to identify marker-trait associations and superior haplotypes for crop genetic improvement. In addition, these omics approaches enable the identification of genes involved in pathways leading to the expression of a trait, thereby providing an understanding of the genetic, physiological and biochemical basis of trait variation. These data-driven gene discoveries and validation approaches are essential for crop improvement pipelines, including genomic breeding, speed breeding and gene editing. Herein, we provide an overview of prospects presented using big data-driven approaches (including artificial intelligence and machine learning) to harness new genetic gains for breeding programs and develop drought-tolerant crop varieties with favourable WUE and high-yield potential traits.
随着人口的增长，对粮食、饲料和纤维的需求迫在眉睫，而不断变化的气候条件给全球农业生产带来了严峻挑战。水是作物生产的生命线；因此，提高作物水分利用效率（WUE）和改善作物品种的抗旱性对于克服这些挑战至关重要。通过基因驱动提高产量、WUE 和耐旱性特征可以缓冲气候变化对旱地作物生产的最坏影响。虽然传统的作物育种方法在提高产量方面取得了令人瞩目的成果，但这些方法仍然耗时，并且往往受到种质中现有等位基因变异的限制。在智能农业实践的推动下，育种和高通量组学技术的显著进展为利用大量基因组和表型数据来极大地加速性状改良过程创造了途径。例如，可以利用个体基因组和泛基因组组装，以及表型水平上的种质收集的转录组、代谢组和蛋白质组数据，来识别标记-性状关联和优良单倍型，以促进作物遗传改良。此外，这些组学方法还可以识别参与导致性状表达途径的基因，从而了解性状变异的遗传、生理和生化基础。这些基于数据的基因发现和验证方法对于基因组育种、快速育种和基因编辑等作物改良途径至关重要。本文综述了利用大数据驱动方法（包括人工智能和机器学习）挖掘新的遗传增益，为育种计划和开发具有有利 WUE 和高产潜力特征的抗旱作物品种提供了前景。
