Optimising Canola Phenology for Australian Growing Environments

Dr Chris Helliwell1, Dr Alec  Zwart1, Alex Boyer1, Andrew Gock1, Dr Bangyou Zheng2, Dr Bill Bovill1, Brett Cocks2, Emmett Leyne1, Dr Ian Greaves1, Dr Jeremy Whish2, Dr Jing Wang1, Dr Julianne Lilley1, Dr Matthew Nelson3, Dr Susie Sprague1, Dr  Shannon Dillon1 

1CSIRO Agriculture And Food, Canberra, Australia, 2CSIRO Agriculture and Food, Brisbane, Australia, 3CSIRO Agriculture and Food, Perth, Australia

Increased production and profitability of canola can be achieved by better matching phenology with the growing environment. Improved understanding of the factors determining canola phenology will better enable matching of location-specific growing conditions with variety, and direct breeding strategies for improved high yielding canola varieties that match the optimum flowering window. In this project we are taking the novel approach of combining existing crop modelling and knowledge of flowering processes with large-scale phenomic, ‘omic and environmental data to deliver (1) predictive tools to better inform management of canola genetic resources for optimal productivity across a range of environments, and (2) knowledge of genetic and environmental factors underpinning variation in phenology. The team have assessed phenological traits in a diverse set of ~350 modern Australian and globally important canola genotypes across the range of Australian canola growing environments as well as controlled environments. To complement the phenology data, dense genomic SNP and transcriptome data were generated. Genetic diversity in the panel reflected geographic and crop/maturity type and supports earlier reports of the prominence of Asian germplasm in the pedigrees of Australian cultivars. Genome wide associations of preliminary transcriptome, SNP and phenotype data in these experiments identify known and novel factors underpinning phenology traits. Results from a range of model frameworks to predict phenology and parameter traits using SNP data suggest it is possible to predict these traits based on genome data within sites. The new information and tools generated will better enable management of canola genetic resources for optimal productivity.


Biography:

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