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解析影响作物农艺性状的植物-环境相互作用。

Decoding Plant-Environment Interactions That Influence Crop Agronomic Traits.

机构信息

RIKEN Center for Sustainable Resource Science, Tsurumi-ku, Yokohama, Japan.

Kihara Institute for Biological Research, Yokohama City University, Totsuka-ku, Yokohama, Japan.

出版信息

Plant Cell Physiol. 2020 Aug 1;61(8):1408-1418. doi: 10.1093/pcp/pcaa064.

Abstract

To ensure food security in the face of increasing global demand due to population growth and progressive urbanization, it will be crucial to integrate emerging technologies in multiple disciplines to accelerate overall throughput of gene discovery and crop breeding. Plant agronomic traits often appear during the plants' later growth stages due to the cumulative effects of their lifetime interactions with the environment. Therefore, decoding plant-environment interactions by elucidating plants' temporal physiological responses to environmental changes throughout their lifespans will facilitate the identification of genetic and environmental factors, timing and pathways that influence complex end-point agronomic traits, such as yield. Here, we discuss the expected role of the life-course approach to monitoring plant and crop health status in improving crop productivity by enhancing the understanding of plant-environment interactions. We review recent advances in analytical technologies for monitoring health status in plants based on multi-omics analyses and strategies for integrating heterogeneous datasets from multiple omics areas to identify informative factors associated with traits of interest. In addition, we showcase emerging phenomics techniques that enable the noninvasive and continuous monitoring of plant growth by various means, including three-dimensional phenotyping, plant root phenotyping, implantable/injectable sensors and affordable phenotyping devices. Finally, we present an integrated review of analytical technologies and applications for monitoring plant growth, developed across disciplines, such as plant science, data science and sensors and Internet-of-things technologies, to improve plant productivity.

摘要

为了应对由于人口增长和城市化进程推进导致的全球需求不断增加,确保粮食安全,至关重要的是要整合多学科的新兴技术,以加速基因发现和作物培育的整体通量。由于植物在其一生中与环境的相互作用的累积效应,其农艺性状通常在其后期生长阶段出现。因此,通过阐明植物在整个生命周期中对环境变化的时间生理响应来解码植物-环境相互作用,将有助于识别影响复杂终点农艺性状(如产量)的遗传和环境因素、时间和途径。在这里,我们讨论了采用生命历程方法监测植物和作物健康状况在提高作物生产力方面的预期作用,通过增强对植物-环境相互作用的理解来实现。我们回顾了基于多组学分析监测植物健康状况的分析技术的最新进展,并介绍了整合来自多个组学领域的异构数据集的策略,以识别与感兴趣性状相关的信息因子。此外,我们展示了新兴的表型组学技术,这些技术可以通过各种手段实现对植物生长的非侵入性和连续监测,包括三维表型分析、植物根系表型分析、可植入/可注射传感器和经济实惠的表型分析设备。最后,我们综合回顾了跨学科(如植物科学、数据科学以及传感器和物联网技术)开发的用于监测植物生长的分析技术和应用,以提高植物生产力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a998/7434589/f71def23cbb3/pcaa064f1.jpg

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