TY - JOUR
T1 - Breeder friendly phenotyping
AU - Reynolds, Matthew
AU - Chapman, Scott
AU - Crespo-Herrera, Leonardo
AU - Molero, Gemma
AU - Mondal, Suchismita
AU - Pequeno, Diego N.L.
AU - Pinto, Francisco
AU - Pinera-Chavez, Francisco J.
AU - Poland, Jesse
AU - Rivera-Amado, Carolina
AU - Saint Pierre, Carolina
AU - Sukumaran, Sivakumar
N1 - Generated from Scopus record by KAUST IRTS on 2022-09-13
PY - 2020/6/1
Y1 - 2020/6/1
N2 - 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.
AB - 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.
UR - https://linkinghub.elsevier.com/retrieve/pii/S0168945219315699
UR - http://www.scopus.com/inward/record.url?scp=85079068008&partnerID=8YFLogxK
U2 - 10.1016/j.plantsci.2019.110396
DO - 10.1016/j.plantsci.2019.110396
M3 - Article
SN - 1873-2259
VL - 295
JO - Plant Science
JF - Plant Science
ER -