Breeding programs generate vast amount of data which are often scattered in separate files. This hinders the application of modern breeding tools such as multienvironment analyses and genomic selection. This research work describes the process of consolidating 23 years of phenotypic, pedigree, and genomic records from the Uruguayan national rice (Oryza sativa L.) breeding program, and the features and structure of the resulting database. Using a custom-made R code, we gathered all the available data from 1997 to 2020 corresponding to field trials, blast disease evaluation nurseries, laboratory analyses of milling and cooking quality, pedigree information, and genomic information for selected advanced breeding lines, and organized it into a relational database. Records of 996 trials in 12 locations over a span of 23 years, 91,636 field plots with information on 14 phenotypic variables, pedigree for 19,447 genotypes, and genomic information regarding 61,260 single nucleotide polymorphism (SNP) markers for 965 genotypes were recovered. The dataset is structured in trials, phenotypes, lines, genomic information, and SNP tables in an easy-to-access relational database, which will be a valuable resource for rice breeding.

Genotype by environment interaction (GEI) is one of the main challenges in plant breeding. A complete characterization of it is necessary to decide on proper breeding strategies. Random regression models (RRMs) allow a genotype‐specific response to each regressor factor. RRMs that include selected environmental variables represent a promising approach to deal with GEI in genomic prediction. They enable to predict for both tested and untested environments, but their utility in a plant breeding scenario remains to be shown. We used phenotypic, climatic, pedigree, and genomic data from two public subtropical rice (Oryza sativa L.) breeding programs; one manages the indica population and the other manages the japonica population. First, we characterized GEI for grain yield (GY) with a set of tools: variance component estimation, mega‐environment (ME) definition, and correlation between locations, sowing periods, and MEs. Then, we identified the most influential climatic variables related to GY and its GEI and used them in RRMs for single‐step genomic prediction. Finally, we evaluated the predictive ability of these models for GY prediction in tested and untested years and environments using the complete dataset and within each ME. Our results suggest large GEI in both populations while larger in indica than in japonica. In indica, early sowing periods showed crossover (i.e., rank‐change) GEI with other sowing periods. Climatic variables related to temperature, radiation, wind, and precipitation affecting GY were identified and differed in each population. RRMs with selected climatic covariates improved the predictive ability in both tested and untested years and environments. Prediction using the complete dataset performed better than predicting within each ME.