Developing a wild brassica diversity panel for canola improvement

Ms Jane Brownlee1, Mr Chris Herrmann1, Mr Matthew Nelson1 

1CSIRO, Floreat, Australia

Canola (Brassica napus L.) is a young species (<7500 years old) with a short history of crop improvement. Its narrow genetic base and limited diversity provides a significant challenge for canola breeders to make continual productivity gains while increasing the resilience of varieties to a range of environmental stresses and disease pressures.  In contrast, the wild relatives of canola are extremely diverse but have rarely been used effectively in canola improvement. This project is assembling a diverse panel of primarily diploid Brassica species focusing on wild accessions and oilseed types. The aim of this work is to identify useful traits and to rapidly transfer these to canola using efficient crossing and gene editing strategies.

We have mapped the origins of 922 wild Brassica accessions from 38 taxa held in international seed collections (and listed in the Eurisco database). With collaborators in the USA and Australia we are identifying key gaps in seed collections to prioritise regions for future seed collection activities. With the assistance of the Australian Grains Genebank, we are importing a broad representation of wild Brassica and non-B. napus oilseed species from international seed collections. So far, we have grown out 135 accessions from 19  different taxa from 23 countries for seed multiplication and preliminary phenotypic evaluation (including vernalisation requirement, self-incompatibility, colour, leaf waxiness, days to flowering and maturity). Intensive phenotypic and genotypic evaluations are planned. This genetic resource will be a valuable source of useful genetic and trait diversity for the genetic advancement and research of canola.


Jane Brownlee completed a Bachelor of Environmental Science (Honours), at Carleton University, Ottawa, Canada in 2014. During her honours she worked as a Research Assistant at the Ottawa Research and Development Centre, Agriculture and Agri-Food Canada and explored the relationship between plant height and severity of infection of Fusarium Head Blight in spring wheat. In 2015 she moved to Australia as Senior Research Technician at Sugar Research Australia, Townsville, where she worked alongside Dr Frikkie Botha, QAAFI and the University of Queensland investigating the causes of Yellow Canopy Syndrome in sugarcane. Jane later moved to Perth, Australia, to work as a Glasshouse Lead Coordinator at Intergrain, working with early-stage breeders to improve wheat and barley crop performance in new varieties.  Jane joined CSIRO in Perth, Western Australia, in 2019 and is involved in research exploring chilling tolerance in wild cicer where she is measuring phenology, biomass production and partitioning, water-use, stress onset, and the traits that mitigate these. Jane is also involved in work with wild brassicas to improve genetic diversity in canola and is particularly interested in using wild germplasm to broaden the adaptive and genetic base of elite, modern crops.



An automated canola phenology detection system based on drone imaging and deep learning

Jing Wang1, Chris Helliwell1, Shannon Dillon1, Geoff Bull1, Julianne Lilley1 

1Csiro, Canberra, Australia

Phenotyping plants in the field is currently performed manually, which is expensive for both researchers and breeding companies. Additionally, human scoring of plants is inevitably affected by subjective judgement and level of expertise. The growth stages of canola are mainly defined by visual properties, such as the number of leaves and the appearance of buds and flowers. Drones mounted with high resolution cameras can rapidly image large agricultural fields. With the availability of big data and parallel computing, deep learning has achieved great success in recognizing patterns from images. We proposed to combine drone imaging and deep learning methods to automate canola phenology detection. The system consists of 1) canola field imaging and annotation, 2) deep learning model design, training and validation, and 3) trained model deployment. We choose a Sony A7iii camera with a 35mm lens mounted on a DJI M600 drone to collect high resolution images of leaves, buds, and flowers. The collected images are labelled by human annotators with the plant locations and key stage categories. In step two, deep object detection networks are designed, trained, and validated on the acquired image datasets. The networks are pretrained on the Imagenet dataset and finetuned on the canola image datasets. In step three, the well-trained model is deployed to detect canola growth stages on new trials. We have designed the imaging acquisition scheme, and preliminary results on collected images show promising detection performance. The system is expected to be completed by 2022 and provide efficient automatic phenotyping.


Jing is a postdoctoral fellow with Ag&Food CSIRO. His works involve phonemics, deep learning and computer vision.



Identification of Seedling Resistance to Blackleg in the Brassica oleracea C-Genome

Ms Denise Barbulescu1, Dr Joshua C. O.  Koh1, Assoc. Prof. Phil A.  Salisbury1, Dr Surya  Kant1,2 

1DJPR- Agriculture Victoria, HORSHAM, Australia,
2School of Applied Systems Biology, La Trobe University, Bundoora, Australia

Blackleg disease caused by the fungus Leptosphaeria maculans can result in significant yield losses in canola (Brassica napus) in Australia and worldwide. Genetic resistance to blackleg in canola cultivars can be easily eroded leading to crop failure. As a result, there is a constant need to discover novel sources of blackleg resistance, particularly in Brassica species closely related to canola. Seedling resistance to blackleg has been discovered in the A- and B-genomes of Brassica, with recent discoveries reported in the C-genome. This study aimed to identify novel sources of blackleg resistance in the C-genome of B. oleracea and examine their effectiveness in the Australian field environment.

Resistant accessions were identified from 37 diverse B. oleracea accessions following an initial screening with two single spore L. maculans isolates. These were further characterized using a differential set of 16 single spore isolates and evaluated in field trials across two locations over two years. Two B. oleracea accessions showed phenotypic resistance patterns in seedlings identical to known major Rlm genes in canola, and had effective blackleg field resistance in one or both sites.

The identification of these putative novel blackleg resistance genes allows their introduction into canola in pre-breeding and breeding programmes.


Denise Barbulescu is a research scientist at Agriculture Victoria, Australia, where she has been working on canola trait improvement since 2003. Denise has Bachelor and Honours degrees in Biomedical Sciences from the University of South Australia.  She has been instrumental in optimising and scaling up the doubled haploid program for Brassica napus and Brassica juncea in early 2000s.  Since 2010, Denise’s research has been in the discovery of novel major genes for resistance to blackleg and, in evaluating the quantitative resistance to blackleg in Australian cultivars, working across national, state, and private research projects.  Denise has also been involved in projects evaluating agronomical traits in canola and wheat.



Female reproductive organs of Brassica napus are more sensitive than male to transient heat stress

Dr Sheng Chen1,2, Dr Renu Saradadevi1,2, Dr Miriam Vidotti3, Dr Roberto Fritsche-Neto3, Dr Jose Crossa4, Professor Kadambot H. M. Siddique1,2, Professor Wallace A. Cowling1,2 

1The UWA Institute of Agriculture, The University of Western Australia, Perth, Australia,
2UWA School of Agriculture and Environment, The University of Western Australia, Perth, Australia,
3Department of Genetics, Luiz de Queiroz Agriculture College, University of São Paulo, Piracicaba, Brazil,
4International Maize and Wheat Improvement Center (CIMMYT), Mexico City, Mexico

Oilseed rape (Brassica napus L.) is sensitive to heat stress during the reproductive stage, but it is not clear whether the male and female reproductive organs differ in their sensitivity to heat stress. In this study, full diallel crossing experiments were conducted among four genotypes of B. napus under control, moderate and high heat stress conditions for five days immediately before and two days after crossing. General combining ability (GCA), specific combining ability (SCA) and reciprocal effects were analyzed to evaluate the genetic basis of heat stress tolerance in male and female reproductive organs. High female temperature (Tf) and high male temperature (Tm) reduced the number of fertile pods and seeds set per floret, and the significant Tf × Tm interaction indicated that female reproductive organs were more sensitive to heat stress than male reproductive organs. There were no overall GCA, SCA or reciprocal effects across all combinations of Tf and Tm. However, a significant reciprocal × Tf effect was found, suggesting that genotypes differed in their ability to set fertile pods and seeds as Tf increased. The relative heat tolerance of G1 as a female increased as Tf increased, and the relative heat tolerance of G2 as a male decreased as Tf increased. In summary, reciprocal diallel crossing has demonstrated that female reproductive organs of B. napus are more sensitive than male to transient heat stress at the early flowering stage, and genotypes differ in relative heat tolerance in the male and female reproductive organs as Tf increases.


Sheng obtained his PhD in 2000 from Huazhong Agricultural University, China, and had postdoc research experiences at the University of Montpellier, France, followed by CSIRO Plant Industry in Perth, Australia.  Sheng joined Prof Wallace Cowling’s canola group at The University of Western Australia in 2005 and since then he has been working on canola germplasm evaluation and utilization, with particular interest in the physiology and genetics of canola tolerance to drought and heat stress.  He is currently leading a national research project on heat tolerant canola funded by GRDC.



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