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.

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.