Unlocking big data doubled the accuracy in predicting the grain yield in hybrid wheat

The potential of big data to support businesses has been demonstrated in financial services, manufacturing, and telecommunications. Here, we report on efforts to enter a new data era in plant breeding by collecting genomic and phenotypic information from 12,858 wheat genotypes representing 6575 single-cross hybrids and 6283 inbred lines that were evaluated in six experimental series for yield in field trials encompassing similar to 125,000 plots. Integrating data resulted in twofold higher prediction ability compared with cases in which hybrid performance was predicted across individual experimental series. Our results suggest that combining data across breeding programs is a particularly appropriate strategy to exploit the potential of big data for predictive plant breeding. This paradigm shift can contribute to increasing yield and resilience, which is needed to feed the growing world population.

Automated summary

The potential of big data in supporting businesses has been proven in various industries, including financial services, manufacturing, and telecommunications. By collecting genomic and phenotypic information from a large number of wheat genotypes and integrating data across breeding programs, the predictive ability in plant breeding can be significantly improved, leading to increased yield and resilience for feeding the growing world population.