About Flagship 2
Traditional crop breeding is based on the assessment of aboveground plant traits because these are the easiest to observe and measure. Aboveground traits are also much easier to assess by high-throughput phenotyping, using sensors mounted on drones or satellites.
However, the root network and its associated microbiome play the key role in nutrient and water acquisition. Root traits are therefore just as important in terms of selection targets as the more visible and accessible aboveground organs, but they are rarely considered in breeding programs.
Flagship Project 2 is developing methods for the comprehensive characterization of root traits, the root microbiome and soil properties, and is integrating the resulting datasets to improve breeding programs. To overcome the laborious and expensive process of sampling and measuring the root microbiome across many different genotypes in multiple environments, we are using a more streamlined approach to identify plant genes that control the properties of the microbiome, allowing those genes to be introduced into broader breeding programs. We are also developing an open-source computer program that will allow anyone to explore and select for microbes that will benefit crops on their land, thus optimizing belowground traits to maximize crop sustainability.
- Rapid screening of canola lines for bacteria that promote yield stability.
- Screening of field soils for microbiomes that provide consistent yields.
- A pencil core sampling method for the non-destructive collection of soil around plant roots.
- Spectroscopic methods for the rapid assessment of pencil cores for beneficial microbiomes.
- Rapid screening of canola lines for bacteria that promote nutrient use efficiency.
- Screening of field soils for microbiomes that enhance nutrient use efficiency.
- Analysis of key microbiome networks that promote nutrient use efficiency and yield stability.
- New unsupervised machine learning methods.
The following projects are currently underway within Flagship 2:
Identifying properties of the microbiome linked to value-added activities of crop breeding such as yield stability and nitrogen use efficiency, and determining whether these aspects of the microbiome are heritable.
Microbes associated with yield responses in crops can be isolated from soil using pencil cores, which are much simpler and less destructive than traditional sampling methods. This approach has been used to construct microbiome datasets to identify microbes linked to yield, nitrogen use efficiency and aboveground phenotypes in canola and wheat. Spectroscopic methods are also being used to link spectral properties of rhizosphere soil samples with soil fertility, microbiome structure and canola/wheat performance. Finally, the heritability of microbiome-associated traits are being assessed to allow their use in canola and wheat breeding programs.
Microbial Data Analytics and Machine Learning
Using data analytics and machine learning to evaluate the value of the microbiome in breeding programs, and identifying direct links between these microbiome constituents and specific plant genes.
Winnowing, an agricultural technique to separate grain from chaff, is the name of our analytical pipeline to derive useful information from microbiome data. Important data are selected on the basis of external factors such as yield and nitrogen use efficiency, to identify microbes associated with these factors. We are now developing an abundance-based network approach that will help to determine whether the microbiome has a causal effect on crop yields. Having defined important microbiome components, we are developing new data transformation methods linking those components to plant genes for use in breeding programs. We are converting our Python-based winnowing pipeline into a QIIME2 plug-in for distribution to the global plant–microbe and crop-breeding community.