Decoding Plant–Environment Interactions That Influence Crop Agronomic Traits. (11th May 2020)

• Record Type: Journal Article
• Authors: Mochida, Keiichi; Nishii, Ryuei; Hirayama, Takashi
• Is Part Of: Plant & cell physiology. Volume 61:Number 8(2020)
• Page Start: 1408
• Page End: 1418
• Publication Date: 2020-05-11
• Subjects: Genome to phenome -- Life-course approach -- Multi-omics -- Plant phenomics -- Sensor; Plant physiology -- Periodicals; Microbiology -- Periodicals; Cytology -- Periodicals; Cell Physiology -- Periodicals; Plant Physiological Phenomena -- Periodicals; Cytology; Microbiology; Plant physiology; Periodicals; 571.205
• DOI: 10.1093/pcp/pcaa064
• Languages: English
• ISSNs: 0032-0781

Abstract

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.