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Decoding Plant–Environment Interactions That Influence Crop Agronomic Traits
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 appe...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7434589/ https://www.ncbi.nlm.nih.gov/pubmed/32392328 http://dx.doi.org/10.1093/pcp/pcaa064 |
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author | Mochida, Keiichi Nishii, Ryuei Hirayama, Takashi |
author_facet | Mochida, Keiichi Nishii, Ryuei Hirayama, Takashi |
author_sort | Mochida, Keiichi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7434589 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-74345892020-08-24 Decoding Plant–Environment Interactions That Influence Crop Agronomic Traits Mochida, Keiichi Nishii, Ryuei Hirayama, Takashi Plant Cell Physiol Special Issue - Mini Reviews 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. Oxford University Press 2020-05-11 /pmc/articles/PMC7434589/ /pubmed/32392328 http://dx.doi.org/10.1093/pcp/pcaa064 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Special Issue - Mini Reviews Mochida, Keiichi Nishii, Ryuei Hirayama, Takashi Decoding Plant–Environment Interactions That Influence Crop Agronomic Traits |
title | Decoding Plant–Environment Interactions That Influence Crop Agronomic Traits |
title_full | Decoding Plant–Environment Interactions That Influence Crop Agronomic Traits |
title_fullStr | Decoding Plant–Environment Interactions That Influence Crop Agronomic Traits |
title_full_unstemmed | Decoding Plant–Environment Interactions That Influence Crop Agronomic Traits |
title_short | Decoding Plant–Environment Interactions That Influence Crop Agronomic Traits |
title_sort | decoding plant–environment interactions that influence crop agronomic traits |
topic | Special Issue - Mini Reviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7434589/ https://www.ncbi.nlm.nih.gov/pubmed/32392328 http://dx.doi.org/10.1093/pcp/pcaa064 |
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