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Experts Use Big Data on Genetic Analysis to Level Up Crop Breeding

Statisticians from the University of Wollongong (UOW) in Australia, led by Senior Professor Brian Cullis are developing a new software called Dependent Wollongong Restricted Maximum Likelihood (DWReml) designed to analyze large datasets from crop breeding experiments conducted across multiple locations and years. This data will help improve crop yields by allowing researchers to identify plant varieties that thrive in a wide range of conditions or are suited to specific environments.

Statisticians from the University of Wollongong (UOW) in Australia, led by Senior Professor Brian Cullis are developing a new software called Dependent Wollongong Restricted Maximum Likelihood (DWReml) designed to analyze large datasets from crop breeding experiments conducted across multiple locations and years. This data will help improve crop yields by allowing researchers to identify plant varieties that thrive in a wide range of conditions or are suited to specific environments.

 

DWReml poses a big potential to crop breeding because of its capacity to analyze large sets of data instantly. The program will also develop models that can handle the changes in crop growth conditions in various parts of Australia.

 

“Breeders don't have eight weeks to analyze data – they'll miss the sowing season. They need the information immediately to be able to make selections for planting next year… We're putting together bigger data sets than I had ever thought were possible, providing much more accurate information and turning it around in real time.”

 

The project team has conducted beta testing of the program, and the results have been exemplary.

 

Read more from UOW.

See https://www.isaaa.org/kc/cropbiotechupdate/article/default.asp?ID=20731

 

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