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Quinoa Phenotyping Methodologies: An International Consensus.

Clara S StanschewskiElodie ReyGabriele FieneEvan B CraineGordon WellmanVanessa J MelinoDilan S R PatiranageKasper JohansenSandra M SchmöckelHéctor Daniel BerteroHelena OakeyCarla Colque-LittleIrfan AfzalSebastian RaubachNathan MillerJared StreichDaniel Buchvaldt AmbyNazgol EmraniMark WarmingtonMagdi Ali Ahmed MousaDavid WuDaniel A JacobsonChristian AndreasenChristian JungKevin M MurphyDidier BazileMark A Testernull On Behalf Of The Quinoa Phenotyping Consortium
Published in: Plants (Basel, Switzerland) (2021)
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.
Keyphrases
  • high throughput
  • electronic health record
  • big data
  • single cell
  • climate change
  • palliative care
  • gene expression
  • artificial intelligence
  • risk assessment
  • clinical practice