Addressing yield stability drivers of canola in a changing climate using high throughput phenotyping

Key Result

This project aims to utilize field trials of the B. napus NAM germplasm resource in contrasting climatic environments in order to acquire a sizably sufficient dataset to test and apply emerging phenotyping and selection techniques to improve canola yield stability for Canadian producers.

Project Summary


Crop tolerance to environmental stressors underlies the ability of a variety to produce satisfactory yield even when conditions are not ideal for crop growth. Given increasing variability in seasonal conditions, efficient selection of crop varieties able to consistently yield will continue to be a top priority for breeders and an increasingly important factor when producers select varieties to grow. Within crop breeding programs, identification of the optimal combination of yield potential and stability requires extensive field testing under many locations and multiple years. There is great potential to improve the efficiency of identifying yield stable breeding lines using two synergistic emerging plant breeding techniques: Digital Phenotyping and Genomic Selection. Phenotyping is the process of describing or quantifying characteristics of breeding lines within a given environment, which has occurred through manual measurements or observations from the dawn of strategic plant breeding. Digital Phenotyping, however, enhances or replaces this process with sensors and cameras to capture and process data and images collected remotely from ground or aerial-based units over the lifecycle of the crop. Digitalizing the phenotyping process offers unprecedented accuracy, precision and resolution.

Genomic Selection is the use of predictive analytics to identify ideal breeding lines prior to field testing based on the breeding line’s comprehensive genetic makeup. The Plant Phenotyping and Imaging Research Centre (P2IRC) at the University of Saskatchewan was founded in 2015 with the objective of establishing first-rate crop phenotyping research capacity in Canada. Through development of the center, multidisciplinary researchers from across plant breeding, genetics and genomics, agronomy, soil science, engineering and computer science are working to merge Digital Phenotyping with Genomic Selection in order to increase the efficiency of crop breeding programs to develop more resilient varieties. Canola breeding, genomics and physiology researchers at the Saskatoon Research and Development Centre of AAFC have been working with P2IRC to accomplish this task using a germplasm resource called the AAFC spring Brassica napus Nested Association Mapping (NAM) population which was developed specifically to study complex traits like yield stability and environmental stress tolerance.


This project has two main objectives:

  1. Large-scale nursery trials in the 2020 growing season of the spring Brassica napus NAM RIL population under dryland conditions in Saskatoon, SK and under irrigated conditions in Outlook, SK which expects to yield:
    a. Conventional agronomic and phenological phenotypic as well as grain yield data;
    b. A resource for advanced digital phenotyping and rhizosphere microbiome data collection through P2IRC.
  2. Harvested seed from 2020 field trials for the following purposes:
    a. Conventional seed quality analyses for seed size, contents of oil, protein and fiber, seed colour, fatty acid and glucosinolate profiles;
    b. A resource to be utilized for experimental testing of seed for physiological and/or new seed quality traits.

Project benefit

In the face of continuing climatic uncertainties, the increasing global population, with higher nutritional and calorific demands, is challenging the agriculture industry to actively respond to a potential food crisis. It is unrealistic to believe that substantive changes will be made to current agriculture practices. Thus, maximizing access to available genetic variation from within a species (and possibly close relatives) and breeding for the optimal crop genetic architecture tailored to the local environment could allow increased genetic gain reflected in higher and more stable yields. Yield stability describes the ability of a cultivar to produce high yield, even under stressful growing conditions e.g. high temperatures at flowering, environments that do not receive adequate precipitation during the growing season. Selecting for yield stability and resilience to environmental stresses can be difficult in early generations of breeding programs because genotype x environment (G x E) interaction masks genetic progress.

Plant phenotyping using high-throughput methods is an emerging field of study that has the potential to deliver plant breeders novel tools to support prediction of performance under stress conditions. This is expected to be accomplished through the project deliverables which include highly annotated phenomic datasets to support genetic dissection and breeding, association of genotype with phenotype for abiotic-stress tolerance traits, and characterized germplasm for directed crop improvement. Furthermore, results from this project could directly enable plant breeders to use high-throughput phenotyping tools to increase the size of breeding programs to enable higher selection intensity as well as enhancing the accuracy of selection leading to higher repeatability. Automated computer-recognition of plant growth, health, resilience and yield, rather than the traditional approach of subjective adjudication in the field, could increase speed, reliability and precision of trait identification. A realized benefit of this work could be application of predictive breeding tools to improve canola cultivars in the medium term (6-10 years), resulting in a significant benefit to the Canadian economy.