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Breeder friendly phenotyping

Authors :
Francisco de Assis de Carvalho Pinto
Diego N. L. Pequeno
Carolina Rivera-Amado
Gemma Molero
Carolina Saint Pierre
Leonardo Crespo-Herrera
Matthew P. Reynolds
Jesse Poland
Sivakumar Sukumaran
Suchismita Mondal
Scott Chapman
Francisco J. Piñera-Chavez
Source :
Plant science : an international journal of experimental plant biology. 295
Publication Year :
2019

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.

Details

ISSN :
18732259
Volume :
295
Database :
OpenAIRE
Journal :
Plant science : an international journal of experimental plant biology
Accession number :
edsair.doi.dedup.....0566248b6ec7c0e8cffe82877e833872