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

Zhao, Yusheng; Thorwarth, Patrick; Jiang, Yong; Philipp, Norman; W. Schulthess, Albert; Gils, Mario; H. G. Boeven, Philipp; Friedrich H. Longin, C.; Schacht, Johannes; Ebmeyer, Erhard; Korzun, Viktor; Mirdita, Vilson; Dörnte, Jost; Avenhaus, Ulrike; Horbach, Ralf; Cöster, Hilmar; Holzapfel, Josef; Ramgraber, Ludwig; Kühnle, Simon; Varenne, Pierrick; Starke, Anne; Schürmann, Friederike; Beier, Sebastian; Scholz, Uwe; Fang, Liu; Schmidt, Renate; C. Reif, Jochen

doi:10.1126/sciadv.abf9106

Public

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

Published in Science Advances • Jun 11, 2021

Authors:

Yusheng Zhao

Patrick Thorwarth

Yong Jiang

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 sing...

Subject

Yield (engineering)

Grain yield

Big data

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