The Big BIT maize experiment: A large multi‐location, multi‐year, multi‐tester, multi‐population predictive breeding validation study

The Big Breeding Innovation Team (Big BIT) maize (Zea mays L.) experiment was one of the largest genomic data-informed predictive breeding validation studies ever conducted. The experiment was a multi-location, multi-year, multi-tester, multi-population study involving F1 maize hybrids created by crossing individual doubled haploids to inbred testers. The purpose of the study, performed by DuPont Pioneer/Corteva Agriscience in 2017, 2018, and 2019, was to build comprehensive datasets to help answer a wide range of practical questions focused on optimizing predictive breeding strategies in maize. The purpose of our study is to (1) describe the design and unique features of our study and (2) discuss learnings with practical implications for plant breeders. Since the same F1 maize hybrids were grown across three distinct years, we use basic descriptive summary statistics to discuss our learnings. We provide a technical justification for the use of basic statistics and discuss the expected theoretical prediction accuracy of genomic estimated breeding values (GEBVs) of Big BIT individuals and families, and predictive abilities obtained by performing large-scale cross-validations. Our study provides multi-year field data-based evidence that, for inbred/variety development focused plant improvement efforts, early-stage genetic evaluation should be based on GEBVs generated from wide-area testing training datasets. This holds true for candidates for selection with or without own phenotypic records.