Fusarium head blight (FHB), also known as head scab, is an economically and environmentally concerning disease of wheat (Triticum aestivum L). A two-pronged approach of marker assisted selection coupled with genomic selection has been suggested when breeding for FHB resistance. In the present study, an historical dataset comprised of entries in the Southern Uniform Winter Wheat Nursery (SUWWSN) ranging from 2011-2021 was partitioned and used for cross validation within the dataset from the years 2011-2019 and forward validation on the 2020-2021 environments in the dataset. Two traits were curated: percent Fusarium damaged kernels (FDK) and Deoxynivalenol (DON) content in parts per million. Two methods of data partitioning were entertained to establish a training population for each trait: 1) sub-setting environments by genomic heritability and 2) sub-setting environments based on like performance of checks. Heritability was estimated for each trait-by-environment combination and data were partitioned on environments with greater heritability than 0.10, 0.25, 0.50, 0.75, and 0.90. For each trait assessed, a consistent set of check lines were drawn from each environment in the SUWWSN, and K-means clustering was performed to establish clusters with like-check performance; possible number of clusters from 2 to 15 were entertained and the optimal clustering was selected by multiple clustering criterion. By majority rule among the clustering criterion, two clusters were identified for FDK and three for DON. When using the combined data from 2011-2019 for training, forward validation for FDK on the SUWWSN 2020 and 2021 indicated a predictive accuracy R=0.58 and R=0.53, respectively. In comparison, forward validation using only the environments in the first cluster of like-check-performance environments for FDK indicated a predictive accuracy of R= 0.65 and R=0.60. Forward validation for DON using the total data available indicated a predictive accuracy of R=0.57 and R=0.45. Likewise, forward validation using only the like-check performance environments in cluster one for DON indicated a predictive accuracy of R=0.67 and R=0.60. These results indicate that selecting environments based on like-check performance may produce substantially higher forward prediction accuracies than using the total available data. This work may be used as a model to create a public resource for genomic prediction of FHB resistance traits across public wheat breeding programs.