Literature DB >> 32534615

Breeder friendly phenotyping.

Matthew Reynolds1, Scott Chapman2, Leonardo Crespo-Herrera3, Gemma Molero3, Suchismita Mondal3, Diego N L Pequeno3, Francisco Pinto3, Francisco J Pinera-Chavez3, Jesse Poland4, Carolina Rivera-Amado3, Carolina Saint Pierre3, Sivakumar Sukumaran3.   

Abstract

The word phenotyping can nowadays invoke visions of a drone or phenocart moving swiftly across research plots collecting high-resolution data sets on a wide array of traits. This has been made possible by recent advances in sensor technology and data processing. Nonetheless, more comprehensive often destructive phenotyping still has much to offer in breeding as well as research. This review considers the 'breeder friendliness' of phenotyping within three main domains: (i) the 'minimum data set', where being 'handy' or accessible and easy to collect and use is paramount, visual assessment often being preferred; (ii) the high throughput phenotyping (HTP), relatively new for most breeders, and requiring significantly greater investment with technical hurdles for implementation and a steeper learning curve than the minimum data set; (iii) detailed characterization or 'precision' phenotyping, typically customized for a set of traits associated with a target environment and requiring significant time and resources. While having been the subject of debate in the past, extra investment for phenotyping is becoming more accepted to capitalize on recent developments in crop genomics and prediction models, that can be built from the high-throughput and detailed precision phenotypes. This review considers different contexts for phenotyping, including breeding, exploration of genetic resources, parent building and translational research to deliver other new breeding resources, and how the different categories of phenotyping listed above apply to each. Some of the same tools and rules of thumb apply equally well to phenotyping for genetic analysis of complex traits and gene discovery.
Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.

Keywords:  Climate resilience; Disease resistance; Phenotyping; Plant breeding; Translational research

Year:  2020        PMID: 32534615     DOI: 10.1016/j.plantsci.2019.110396

Source DB:  PubMed          Journal:  Plant Sci        ISSN: 0168-9452            Impact factor:   4.729


  17 in total

Review 1.  Capturing crop adaptation to abiotic stress using image-based technologies.

Authors:  Nadia Al-Tamimi; Patrick Langan; Villő Bernád; Jason Walsh; Eleni Mangina; Sónia Negrão
Journal:  Open Biol       Date:  2022-06-22       Impact factor: 7.124

Review 2.  Trends and Limits for Quinoa Production and Promotion in Pakistan.

Authors:  Irfan Afzal; Shahzad Maqsood Ahmed Basra; Hafeez Ur Rehman; Shahid Iqbal; Didier Bazile
Journal:  Plants (Basel)       Date:  2022-06-18

Review 3.  Breeding for drought and heat tolerance in wheat.

Authors:  Peter Langridge; Matthew Reynolds
Journal:  Theor Appl Genet       Date:  2021-03-14       Impact factor: 5.699

Review 4.  Scaling up high-throughput phenotyping for abiotic stress selection in the field.

Authors:  Daniel T Smith; Andries B Potgieter; Scott C Chapman
Journal:  Theor Appl Genet       Date:  2021-06-02       Impact factor: 5.699

5.  Toward a Better Understanding of Genotype × Environment × Management Interactions-A Global Wheat Initiative Agronomic Research Strategy.

Authors:  Brian L Beres; Jerry L Hatfield; John A Kirkegaard; Sanford D Eigenbrode; William L Pan; Romulo P Lollato; James R Hunt; Sheri Strydhorst; Kenton Porker; Drew Lyon; Joel Ransom; Jochum Wiersma
Journal:  Front Plant Sci       Date:  2020-06-16       Impact factor: 5.753

6.  High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation.

Authors:  Leonardo Volpato; Francisco Pinto; Lorena González-Pérez; Iyotirindranath Gilberto Thompson; Aluízio Borém; Matthew Reynolds; Bruno Gérard; Gemma Molero; Francelino Augusto Rodrigues
Journal:  Front Plant Sci       Date:  2021-02-16       Impact factor: 5.753

7.  Field-based remote sensing models predict radiation use efficiency in wheat.

Authors:  Carlos A Robles-Zazueta; Gemma Molero; Francisco Pinto; M John Foulkes; Matthew P Reynolds; Erik H Murchie
Journal:  J Exp Bot       Date:  2021-05-04       Impact factor: 7.298

8.  Impact of Varying Light and Dew on Ground Cover Estimates from Active NDVI, RGB, and LiDAR.

Authors:  David M Deery; David J Smith; Robert Davy; Jose A Jimenez-Berni; Greg J Rebetzke; Richard A James
Journal:  Plant Phenomics       Date:  2021-05-27

9.  Harnessing translational research in wheat for climate resilience.

Authors:  Matthew P Reynolds; Janet M Lewis; Karim Ammar; Bhoja R Basnet; Leonardo Crespo-Herrera; José Crossa; Kanwarpal S Dhugga; Susanne Dreisigacker; Philomin Juliana; Hannes Karwat; Masahiro Kishii; Margaret R Krause; Peter Langridge; Azam Lashkari; Suchismita Mondal; Thomas Payne; Diego Pequeno; Francisco Pinto; Carolina Sansaloni; Urs Schulthess; Ravi P Singh; Kai Sonder; Sivakumar Sukumaran; Wei Xiong; Hans J Braun
Journal:  J Exp Bot       Date:  2021-07-10       Impact factor: 6.992

10.  Small "Nested" Introgressions from Wild Thinopyrum Species, Conferring Effective Resistance to Fusarium Diseases, Positively Impact Durum Wheat Yield Potential.

Authors:  Ljiljana Kuzmanović; Gloria Giovenali; Roberto Ruggeri; Francesco Rossini; Carla Ceoloni
Journal:  Plants (Basel)       Date:  2021-03-19
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