Unlocking big data doubled the accuracy in predicting the grain yield in hybrid wheat
Yusheng Zhao, Patrick Thorwarth, Yong Jiang, Norman Philipp, Albert W. Schulthess, Mario Gils, Philipp H. G. Boeven, C. Friedrich H. Longin, Johannes Schacht, Erhard Ebmeyer, Viktor Korzun, Vilson Mirdita, Jost Dörnte, Ulrike Avenhaus, Ralf Horbach, Hilmar Cöster, Josef Holzapfel, Ludwig Ramgraber, Simon Kühnle, Pierrick Varenne, Anne Starke, Friederike Schürmann, Sebastian Beier, Uwe Scholz, Liu Fang, Renate Schmidt, Jochen C. Reif
Abstract
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 ~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.