Literature DB >> 31003610

Review: High-throughput phenotyping to enhance the use of crop genetic resources.

G J Rebetzke1, J Jimenez-Berni2, R A Fischer3, D M Deery3, D J Smith4.   

Abstract

Improved genetic, genomic and statistical technologies have increased the capacity to enrich breeding populations for key alleles underpinning adaptation and continued genetic gain. In turn, directed genomic selection together with increased heritability will reduce genetic variance to narrow the genetic base in many crop breeding programs. Diverse genetic resources (GR), including wild and weedy relatives, landraces and reconstituted synthetics, have potential to contribute novel alleles for key traits. Targeted trait identification may also identify genetic diversity in addressing new challenges including the need for modified root architecture, greater nutrient-use efficiency, and adaptation to warmer air and soil temperatures forecast with climate change. Yet while core collections and other GR sources have historically been invaluable for major gene control of disease and subsoil constraints, the mining of genetically (and phenotypically) complex traits in GR remains a significant challenge owing to reduced fertility, limited seed quantities and poor adaptation through linkage drag with undesirable alleles. High-throughput field phenomics (HTFP) offers the opportunity to capture phenotypically complex variation underpinning adaptation in traditional phenotypic selection or statistics-based breeding programs. Targeted HTFP will permit the reliable phenotyping of greater numbers of GR-derived breeding lines using smaller plot sizes and at earlier stages of population development to reduce the duration of breeding cycles and the loss of potentially important alleles with linkage drag. Two key opportunities are highlighted for use of HTFP in selection among GR-derived wheat breeding lines for greater biomass and stomatal conductance through canopy temperature.
Copyright © 2018 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Biomass; Breeding; Canopy temperature; Genomic selection; LiDAR; Selection

Mesh:

Year:  2018        PMID: 31003610     DOI: 10.1016/j.plantsci.2018.06.017

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


  20 in total

1.  Hyperspectral Technique Combined With Deep Learning Algorithm for Prediction of Phenotyping Traits in Lettuce.

Authors:  Shuan Yu; Jiangchuan Fan; Xianju Lu; Weiliang Wen; Song Shao; Xinyu Guo; Chunjiang Zhao
Journal:  Front Plant Sci       Date:  2022-06-30       Impact factor: 6.627

Review 2.  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 3.  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

4.  Transcriptome Analysis of MYB Genes and Patterns of Anthocyanin Accumulation During Seed Development in Wheat.

Authors:  Paulina Calderon Flores; Jin Seok Yoon; Dae Yeon Kim; Yong Weon Seo
Journal:  Evol Bioinform Online       Date:  2022-04-13       Impact factor: 2.031

Review 5.  Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm.

Authors:  Giao N Nguyen; Sally L Norton
Journal:  Plants (Basel)       Date:  2020-06-29

6.  Genetic variation for photosynthetic capacity and efficiency in spring wheat.

Authors:  Viridiana Silva-Pérez; Joanne De Faveri; Gemma Molero; David M Deery; Anthony G Condon; Matthew P Reynolds; John R Evans; Robert T Furbank
Journal:  J Exp Bot       Date:  2020-04-06       Impact factor: 6.992

Review 7.  Advances and Challenges in the Breeding of Salt-Tolerant Rice.

Authors:  Hua Qin; Yuxiang Li; Rongfeng Huang
Journal:  Int J Mol Sci       Date:  2020-11-09       Impact factor: 5.923

8.  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

9.  Deep Multiview Image Fusion for Soybean Yield Estimation in Breeding Applications.

Authors:  Luis G Riera; Matthew E Carroll; Zhisheng Zhang; Johnathon M Shook; Sambuddha Ghosal; Tianshuang Gao; Arti Singh; Sourabh Bhattacharya; Baskar Ganapathysubramanian; Asheesh K Singh; Soumik Sarkar
Journal:  Plant Phenomics       Date:  2021-06-23

10.  Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform.

Authors:  Danilo H Lyra; Nicolas Virlet; Pouria Sadeghi-Tehran; Kirsty L Hassall; Luzie U Wingen; Simon Orford; Simon Griffiths; Malcolm J Hawkesford; Gancho T Slavov
Journal:  J Exp Bot       Date:  2020-03-25       Impact factor: 6.992

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.