Dr Bangyou  Zheng1, Dr Jeremy Whish1, Dr Julianne Lilley2, Mr Alexandre Boyer2, Dr Alec Zwart3, Dr Chris  Helliwell2, Dr Shannon Dillon2 

1CSIRO Agriculture and Food, Queensland Biosciences Precinct 306 Carmody Road, St Lucia, Australia,

2CSIRO Agriculture and Food, GPO Box 1700, Canberra, Australia,

3CSIRO Data61, GPO Box 1700, Canberra, Australia


Canola phenology is a major determinant of the adaptation of canola to different environments; the productivity of canola can be maximised by targeting phenology to the optimal flowering window. Although many crop models have been parameterised to predict canola phenology, these models are reliant on estimation of model parameter values from phenology scores for a given variety obtained in multiple environments. Canola breeding and agronomy research would benefit from robust models that predict phenology based on known genes or single nucleotide polymorphisms (SNPs). Here, we present a framework that blends genomic prediction and crop process modelling to predict phenology for a given variety across a range of environments based on its genome. Genome wide SNP data from more than 300 canola cultivars were integrated into a canola model in APSIM Next Generation (APSIM NG) to predict phenology traits (e.g. green bud and first flower). Datasets collected from controlled environments and field experiments with several time of sowing and locations were used to train and validate the canola model in APSIM NG (APSIM-Release) and the new integrated genomic model (APSIM-GP). In APSIM-GP the model parameter values are estimated using global optimisations directly from genotypic information (i.e. SNP data) to phenotypic values (i.e. observations).  Both APSIM-Release and APSIM-GP could predict canola phenology of all cultivars with an acceptable level of accuracy. APSIM-GP can be used by breeders and farmers to predict canola phenology for new cultivars with only known SNP information to accelerate breeding programs and close the yield gap.


Dr Bangyou Zheng is a data and experimental scientist at CSIRO in Brisbane, Australia. He received his PhD degree in agriculture science at China Agricultural University. His research focuses on the crop physiology, crop genotype to phenotype prediction, crop modelling, climate adaptation, high throughput phenotyping, big data management, processing and visualization.



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