We're harnessing genomics and machine-learning to develop crops better adapted to Australian conditions.

The challenge

A new era of plant breeding

The breeding of crops to improve yield, survival or quality can be slow and laborious. New technologies, such as machine-learning and advances in genome biology, are set to revolutionise the process of breeding improved crops.

Crops growing in a field with letters in rows amongst the green crop

Just as the sequencing of the human genome has improved our understanding of genetic disorders, the sequencing of crop genomes, such as for wheat, barley and canola, will allow us to boost agricultural productivity and improve global food security. New breeding technologies can identify genetic traits that increase yields and improve climatic resilience to challenges such as drought.

To make full use of new advances in genome technology we need to understand how variation in genomes corresponds to changes in the performance of crops. One challenge in doing this is the immense scale and volume of data that must be analysed. Crop genomes are typically large; much larger than the human genome. Interpretation also requires genome data to be integrated with environmental data and crop performance indicators, further expanding the scale of the data challenge.

Our response

Wheat for now

We are developing new data analysis platforms that harness genome biology to improve crops and predict crop performance.

Our first work in this area has focused on wheat but we are expanding this to other important crops in the future. We're initially focusing on OzWheat, a collection of historically important Australian wheats that spans from 1860s colonial wheats to varieties that dominate production today.

The results

Uncovering secrets from a century of breeding

Using genome sequencing technologies we have been able to investigate gene variation in the OzWheat collection. Then, by applying machine learning, we have developed new ways to integrate this genome information with climate records and field performance data. This has changed the way we link variation in genes and gene pathways to crop performance, and has provided insights into how a century of selective breeding has adapted wheat to Australian conditions.

This emerging capability in being able to link genomes to performance across different field environments will be relevant to a wide range of important crops, and we are already starting to look at canola. A priority for this work is to deliver genomics tools and knowledge to the crop breeding industry in a user friendly format.

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