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Management and Characterization of Abiotic Stress via PhénoField(®), a High-Throughput Field Phenotyping Platform

In order to evaluate the impact of water deficit in field conditions, researchers or breeders must set up large experiment networks in very restrictive field environments. Experience shows that half of the field trials are not relevant because of climatic conditions that do not allow the stress scen...

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Autores principales: Beauchêne, Katia, Leroy, Fabien, Fournier, Antoine, Huet, Céline, Bonnefoy, Michel, Lorgeou, Josiane, de Solan, Benoît, Piquemal, Benoît, Thomas, Samuel, Cohan, Jean-Pierre
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6646674/
https://www.ncbi.nlm.nih.gov/pubmed/31379897
http://dx.doi.org/10.3389/fpls.2019.00904
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author Beauchêne, Katia
Leroy, Fabien
Fournier, Antoine
Huet, Céline
Bonnefoy, Michel
Lorgeou, Josiane
de Solan, Benoît
Piquemal, Benoît
Thomas, Samuel
Cohan, Jean-Pierre
author_facet Beauchêne, Katia
Leroy, Fabien
Fournier, Antoine
Huet, Céline
Bonnefoy, Michel
Lorgeou, Josiane
de Solan, Benoît
Piquemal, Benoît
Thomas, Samuel
Cohan, Jean-Pierre
author_sort Beauchêne, Katia
collection PubMed
description In order to evaluate the impact of water deficit in field conditions, researchers or breeders must set up large experiment networks in very restrictive field environments. Experience shows that half of the field trials are not relevant because of climatic conditions that do not allow the stress scenario to be tested. The PhénoField(®) platform is the first field based infrastructure in the European Union to ensure protection against rainfall for a large number of plots, coupled with the non-invasive acquisition of crops’ phenotype. In this paper, we will highlight the PhénoField(®) production capability using data from 2017-wheat trial. The innovative approach of the PhénoField(®) platform consists in the use of automatic irrigating rainout shelters coupled with high throughput field phenotyping to complete conventional phenotyping and micrometeorological densified measurements. Firstly, to test various abiotic stresses, automatic mobile rainout shelters allow fine management of fertilization or irrigation by driving daily the intensity and period of the application of the desired limiting factor on the evaluated crop. This management is based on micro-meteorological measurements coupled with a simulation of a carbon, water and nitrogen crop budget. Furthermore, as high-throughput plant-phenotyping under controlled conditions is well advanced, comparable evaluation in field conditions is enabled through phenotyping gantries equipped with various optical sensors. This approach, giving access to either similar or innovative variables compared manual measurements, is moreover distinguished by its capacity for dynamic analysis. Thus, the interactions between genotypes and the environment can be deciphered and better detailed since this gives access not only to the environmental data but also to plant responses to limiting hydric and nitrogen conditions. Further data analyses provide access to the curve parameters of various indicator kinetics, all the more integrative and relevant of plant behavior under stressful conditions. All these specificities of the PhénoField(®) platform open the way to the improvement of various categories of crop models, the fine characterization of variety behavior throughout the growth cycle and the evaluation of particular sensors better suited to a specific research question.
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spelling pubmed-66466742019-08-02 Management and Characterization of Abiotic Stress via PhénoField(®), a High-Throughput Field Phenotyping Platform Beauchêne, Katia Leroy, Fabien Fournier, Antoine Huet, Céline Bonnefoy, Michel Lorgeou, Josiane de Solan, Benoît Piquemal, Benoît Thomas, Samuel Cohan, Jean-Pierre Front Plant Sci Plant Science In order to evaluate the impact of water deficit in field conditions, researchers or breeders must set up large experiment networks in very restrictive field environments. Experience shows that half of the field trials are not relevant because of climatic conditions that do not allow the stress scenario to be tested. The PhénoField(®) platform is the first field based infrastructure in the European Union to ensure protection against rainfall for a large number of plots, coupled with the non-invasive acquisition of crops’ phenotype. In this paper, we will highlight the PhénoField(®) production capability using data from 2017-wheat trial. The innovative approach of the PhénoField(®) platform consists in the use of automatic irrigating rainout shelters coupled with high throughput field phenotyping to complete conventional phenotyping and micrometeorological densified measurements. Firstly, to test various abiotic stresses, automatic mobile rainout shelters allow fine management of fertilization or irrigation by driving daily the intensity and period of the application of the desired limiting factor on the evaluated crop. This management is based on micro-meteorological measurements coupled with a simulation of a carbon, water and nitrogen crop budget. Furthermore, as high-throughput plant-phenotyping under controlled conditions is well advanced, comparable evaluation in field conditions is enabled through phenotyping gantries equipped with various optical sensors. This approach, giving access to either similar or innovative variables compared manual measurements, is moreover distinguished by its capacity for dynamic analysis. Thus, the interactions between genotypes and the environment can be deciphered and better detailed since this gives access not only to the environmental data but also to plant responses to limiting hydric and nitrogen conditions. Further data analyses provide access to the curve parameters of various indicator kinetics, all the more integrative and relevant of plant behavior under stressful conditions. All these specificities of the PhénoField(®) platform open the way to the improvement of various categories of crop models, the fine characterization of variety behavior throughout the growth cycle and the evaluation of particular sensors better suited to a specific research question. Frontiers Media S.A. 2019-07-16 /pmc/articles/PMC6646674/ /pubmed/31379897 http://dx.doi.org/10.3389/fpls.2019.00904 Text en Copyright © 2019 Beauchêne, Leroy, Fournier, Huet, Bonnefoy, Lorgeou, de Solan, Piquemal, Thomas and Cohan. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Beauchêne, Katia
Leroy, Fabien
Fournier, Antoine
Huet, Céline
Bonnefoy, Michel
Lorgeou, Josiane
de Solan, Benoît
Piquemal, Benoît
Thomas, Samuel
Cohan, Jean-Pierre
Management and Characterization of Abiotic Stress via PhénoField(®), a High-Throughput Field Phenotyping Platform
title Management and Characterization of Abiotic Stress via PhénoField(®), a High-Throughput Field Phenotyping Platform
title_full Management and Characterization of Abiotic Stress via PhénoField(®), a High-Throughput Field Phenotyping Platform
title_fullStr Management and Characterization of Abiotic Stress via PhénoField(®), a High-Throughput Field Phenotyping Platform
title_full_unstemmed Management and Characterization of Abiotic Stress via PhénoField(®), a High-Throughput Field Phenotyping Platform
title_short Management and Characterization of Abiotic Stress via PhénoField(®), a High-Throughput Field Phenotyping Platform
title_sort management and characterization of abiotic stress via phénofield(®), a high-throughput field phenotyping platform
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6646674/
https://www.ncbi.nlm.nih.gov/pubmed/31379897
http://dx.doi.org/10.3389/fpls.2019.00904
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