Quinoa phenotyping methodologies: An international consensus

Quinoa Phenotyping Consortium

doi:10.3390/plants10091759

Quinoa phenotyping methodologies: An international consensus

Quinoa Phenotyping Consortium

Computational and Predictive Biology

Research output: Contribution to journal › Review article › peer-review

35 Scopus citations

Abstract

Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.

Original language	English
Article number	1759
Journal	Plants
Volume	10
Issue number	9
DOIs	https://doi.org/10.3390/plants10091759
State	Published - Sep 2021

Funding

The research reported in this publication from the lead author’s laboratory was supported by King Abdullah University of Science and Technology (KAUST), including with a grant from the Office of Sponsored Research (OSR) under Award No. CRG5-2966. A section of this publication is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. 1842493. As a disclaimer, any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation. Two authors are supported by the Center for Bioenergy Innovation, a US Department of Energy Bioenergy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science. The manuscript was coauthored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the US Department of Energy. The publisher, by accepting the article for publication, acknowledges that the US Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. Available online: http://energy.gov/downloads/doe-public-access-plan (accessed on 15 August 2021). Funding: The research reported in this publication from the lead author’s laboratory was supported by King Abdullah University of Science and Technology (KAUST), including with a grant from the Office of Sponsored Research (OSR) under Award No. CRG5‐2966. A section of this publication is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No. 1842493. As a disclaimer, any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors(s) and do not necessarily reflect the views of the National Science Foundation. Two authors are supported by the Center for Bioenergy Innovation, a US Department of Energy Bioenergy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science. The manuscript was coauthored by UT‐Battelle, LLC under Contract No. DE‐AC05‐00OR22725 with the US Department of Energy. The publisher, by accepting the article for publication, acknowledges that the US Government retains a nonexclusive, paid‐up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. Available online: http://energy.gov/downloads/doe‐public‐access‐plan (accessed on 15 August 2021).

Funders	Funder number
OSR	CRG5‐2966
Office of Sponsored Research
National Science Foundation	1842493
National Science Foundation
U.S. Department of Energy
Office of Science
Biological and Environmental Research
Center for Bioenergy Innovation
Government of South Australia
UT-Battelle	DE-AC05-00OR22725
UT-Battelle
King Abdullah University of Science and Technology

Keywords

Architecture
Chenopodium quinoa
Database
Descriptors
Disease
Genetic diversity
High throughput seed phenotyping
Panicle
Remote sensing
Scoring card

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3390/plants10091759

Cite this

@article{4253b75f4793492884887381a03639a7,

title = "Quinoa phenotyping methodologies: An international consensus",

abstract = "Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.",

keywords = "Architecture, Chenopodium quinoa, Database, Descriptors, Disease, Genetic diversity, High throughput seed phenotyping, Panicle, Remote sensing, Scoring card",

author = "{Quinoa Phenotyping Consortium} and Stanschewski, {Clara S.} and Elodie Rey and Gabriele Fiene and Craine, {Evan B.} and Gordon Wellman and Melino, {Vanessa J.} and Patiranage, {Dilan S.R.} and Kasper Johansen and Schm{\"o}ckel, {Sandra M.} and Daniel Bertero and Helena Oakey and Carla Colque-Little and Irfan Afzal and Sebastian Raubach and Nathan Miller and Jared Streich and Amby, {Daniel Buchvaldt} and Nazgol Emrani and Mark Warmington and Mousa, {Magdi A.A.} and David Wu and Daniel Jacobson and Christian Andreasen and Christian Jung and Kevin Murphy and Didier Bazile and Mark Tester",

note = "Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2021",

month = sep,

doi = "10.3390/plants10091759",

language = "English",

volume = "10",

journal = "Plants",

issn = "2223-7747",

publisher = "MDPI",

number = "9",

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TY - JOUR

T1 - Quinoa phenotyping methodologies

T2 - An international consensus

AU - Quinoa Phenotyping Consortium

AU - Stanschewski, Clara S.

AU - Rey, Elodie

AU - Fiene, Gabriele

AU - Craine, Evan B.

AU - Wellman, Gordon

AU - Melino, Vanessa J.

AU - Patiranage, Dilan S.R.

AU - Johansen, Kasper

AU - Schmöckel, Sandra M.

AU - Bertero, Daniel

AU - Oakey, Helena

AU - Colque-Little, Carla

AU - Afzal, Irfan

AU - Raubach, Sebastian

AU - Miller, Nathan

AU - Streich, Jared

AU - Amby, Daniel Buchvaldt

AU - Emrani, Nazgol

AU - Warmington, Mark

AU - Mousa, Magdi A.A.

AU - Wu, David

AU - Jacobson, Daniel

AU - Andreasen, Christian

AU - Jung, Christian

AU - Murphy, Kevin

AU - Bazile, Didier

AU - Tester, Mark

PY - 2021/9

Y1 - 2021/9

N2 - Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.

AB - Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.

KW - Architecture

KW - Chenopodium quinoa

KW - Database

KW - Descriptors

KW - Disease

KW - Genetic diversity

KW - High throughput seed phenotyping

KW - Panicle

KW - Remote sensing

KW - Scoring card

UR - http://www.scopus.com/inward/record.url?scp=85113306192&partnerID=8YFLogxK

U2 - 10.3390/plants10091759

DO - 10.3390/plants10091759

M3 - Review article

AN - SCOPUS:85113306192

SN - 2223-7747

VL - 10

JO - Plants

JF - Plants

IS - 9

M1 - 1759

ER -

Quinoa phenotyping methodologies: An international consensus

Abstract

Funding

Keywords

UN SDGs

Access to Document

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