Field crop phenomics: enabling breeding for radiation use efficiency and biomass in cereal crops.

Autor: Furbank RT; ARC Centre of Excellence for Translational Photosynthesis, Division of Plant Science, Australian National University, Canberra, 2601, ACT, Australia.; CSIRO Agriculture and Food, Canberra, 2601, ACT, Australia., Jimenez-Berni JA; CSIRO Agriculture and Food, Canberra, 2601, ACT, Australia.; Institute for Sustainable Agriculture (IAS), CSIC, Cordoba, 14004, Spain., George-Jaeggli B; Queensland Alliance for Agriculture & Food Innovation, Centre for Crop Science, The University of Queensland, Hermitage Research Station, Warwick, 4370, QLD, Australia.; Agri-Science Queensland, Queensland Department of Agriculture & Fisheries, Hermitage Research Facility, Warwick, 4370, QLD, Australia., Potgieter AB; Queensland Alliance for Agriculture & Food Innovation, Centre for Crop Science, The University of Queensland, Tor Street, Toowoomba, 4350, QLD, Australia., Deery DM; CSIRO Agriculture and Food, Canberra, 2601, ACT, Australia.
Jazyk: angličtina
Zdroj: The New phytologist [New Phytol] 2019 Sep; Vol. 223 (4), pp. 1714-1727. Date of Electronic Publication: 2019 Apr 26.
DOI: 10.1111/nph.15817
Abstrakt: Plant phenotyping forms the core of crop breeding, allowing breeders to build on physiological traits and mechanistic science to inform their selection of material for crossing and genetic gain. Recent rapid progress in high-throughput techniques based on machine vision, robotics, and computing (plant phenomics) enables crop physiologists and breeders to quantitatively measure complex and previously intractable traits. By combining these techniques with affordable genomic sequencing and genotyping, machine learning, and genome selection approaches, breeders have an opportunity to make rapid genetic progress. This review focuses on how field-based plant phenomics can enable next-generation physiological breeding in cereal crops for traits related to radiation use efficiency, photosynthesis, and crop biomass. These traits have previously been regarded as difficult and laborious to measure but have recently become a focus as cereal breeders find genetic progress from 'Green Revolution' traits such as harvest index become exhausted. Application of LiDAR, thermal imaging, leaf and canopy spectral reflectance, Chl fluorescence, and machine learning are discussed using wheat and sorghum phenotyping as case studies. A vision of how crop genomics and high-throughput phenotyping could enable the next generation of crop research and breeding is presented.
(© 2019 The Authors. New Phytologist © 2019 New Phytologist Trust.)
Databáze: MEDLINE
Externí odkaz: