Can genomics assist the phenological adaptation of canola to new and changing environments?
Matthew N. Nelson A B C F , Julianne M. Lilley D , Chris Helliwell D , Candy M. Taylor A , Kadambot H. M. Siddique B , Sheng Chen A B , Harsh Raman E , Jacqueline Batley A B and Wallace A. Cowling B
+ Author Affiliations
- Author Affiliations
A School of Plant Biology, The University of Western Australia, Perth, WA 6009, Australia.
B The UWA Institute of Agriculture, The University of Western Australia, Perth, WA 6009, Australia.
C Natural Capital and Plant Health, Royal Botanic Gardens Kew, Wakehurst Place, Ardingly, West Sussex, RH17 6TN, UK.
D CSIRO Agriculture, GPO Box 1600, Canberra, ACT 2601, Australia.
E Graham Centre for Agricultural Innovation, NSW DPI, Agricultural Institute, Wagga Wagga, NSW 2650, Australia.
F Corresponding author. Email: m.nelson@kew.org
Crop and Pasture Science 67(4) 284-297 https://doi.org/10.1071/CP15320
Submitted: 18 September 2015 Accepted: 9 December 2015 Published: 29 March 2016
Abstract
Timing of life history events (phenology) is a key driver for the adaptation of grain crops to their environments. Anthesis (flowering) date is the critical phenological stage that has been most extensively studied. Maximum crop yield is achieved by maximising the duration of the pre-anthesis biomass accumulation phase and hence yield potential, while minimising the risk of water stress and temperature stress (heat and cold) during flowering and grain-filling stages. In this article, we review our understanding of phenology of the valuable oilseed crop canola (oilseed rape, Brassica napus L.) from the perspectives of biophysical modelling and genetics. In conjunction, we review the genomic resources for canola and how they could be used to develop models that can accurately predict flowering date in any given set of environmental conditions. Finally, we discuss how molecular marker tools can help canola breeders to continue to improve canola productivity in the light of climate changes and to broaden its adaptation into new agricultural areas.
Additional keywords: Arabidopsis, modelling phenology, next generation sequencing, photoperiod, thermal time to flowering, vernalisation.
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