Enhancing predictive modeling accuracy in wheat ( Triticum aestivum ) breeding through the integration of high‐throughput phenotyping (HTP) data with genomic information is crucial for maximizing genetic gain. In this study, spanning four locations in the southeastern United States over 3 years, models to predict grain yield (GY) were investigated through different cross‐validation approaches. The results demonstrate the superiority of multivariate comprehensive models that incorporate both genomic and HTP data, particularly in accurately predicting GY across diverse locations and years. These HTP‐incorporating models achieve prediction accuracies ranging from 0.59 to 0.68, compared to 0.40–0.54 for genomic‐only models when tested under different prediction scenarios both across years and locations. The comprehensive models exhibit superior generalization to new environments and achieve the highest accuracy when trained on diverse datasets. Predictive accuracy improves as models incorporate data from multiple years, highlighting the importance of considering temporal dynamics in modeling approaches. The study reveals that multivariate prediction outperformed genomic prediction methods in predicting lines across years and locations. The percentage of top 25% lines selected based on multivariate prediction was higher compared to genomic‐only models, indicated by higher specificity, which is the proportion of correctly identified top‐yielding lines that matched the observed top 25% performance across different sites and years. Additionally, the study addresses the prediction of untested locations based on other locations within the same year and in new years at previously tested locations. Findings show the comprehensive models effectively extrapolate to new environments, highlighting their potential for guiding breeding strategies.

Fusarium head blight (FHB) 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. A historical dataset comprised of entries in the Southern Uniform Winter Wheat Scab Nursery (SUWWSN) from 2011 to 2021 was partitioned and used in genomic prediction. Two traits were curated from 2011 to 2021 in the SUWWSN: percent Fusarium damaged kernels (FDK) and deoxynivalenol (DON) content. Heritability was estimated for each trait‐by‐environment combination. A consistent set of check lines was drawn from each year in the SUWWSN, and k ‐means clustering was performed across environments to assign environments into clusters. Two clusters were identified as FDK and three for DON. Cross‐validation on SUWWSN data from 2011 to 2019 indicated no outperforming training population in comparison to the combined dataset. Forward validation for FDK on the SUWWSN 2020 and 2021 data indicated a predictive accuracy and , respectively. Forward validation for DON indicated a predictive accuracy of and , respectively. Forward validation using environments in cluster one for FDK indicated a predictive accuracy of and , respectively. Forward validation using environments in cluster one for DON indicated a predictive accuracy of and , respectively. These results indicated that selecting environments based on check performance may produce higher forward prediction accuracies. This work may be used as a model for utilizing public resources for genomic prediction of FHB resistance traits across public wheat breeding programs.

The presence or absence of awns—whether wheat heads are ‘bearded’ or ‘smooth’ – is the most visible phenotype distinguishing wheat cultivars. Previous studies suggest that awns may improve yields in heat or water-stressed environments, but the exact contribution of awns to yield differences remains unclear. Here we leverage historical phenotypic, genotypic, and climate data for wheat (Triticum aestivum) to estimate the yield effects of awns under different environmental conditions over a 12-year period in the southeastern USA. Lines were classified as awned or awnless based on sequence data, and observed heading dates were used to associate grain fill periods of each line in each environment with climatic data and grain yield. In most environments, awn suppression was associated with higher yields, but awns were associated with better performance in heat-stressed environments more common at southern locations. Wheat breeders in environments where awns are only beneficial in some years may consider selection for awned lines to reduce year-to-year yield variability, and with an eye towards future climates.

Stripe rust is a severe disease affecting wheat (Triticum aestivum L.) production in the United States Southeast region, necessitating the identification of resistant sources. The study was conducted at the University of Arkansas, Fayetteville, and utilized a Randomized Block Design over three years. A total of 1130 lines were inoculated annually, and their responses for stripe rust were recorded. The results revealed that 11%, 79%, and 77% of the lines showed resistance in the first, second, and third years, respectively, with an overall 50% of the whole population. Linear Mixed Model and Generalized Estimating Equation analyses highlighted environmental influences, with cooler, humid conditions in 2021 favoring stripe rust, while warmer, variable conditions in 2022 and stable weather in 2023 contributed to lower disease severity. Data analysis of infection rates and disease development indicated that the newer generations of wheat lines tested in 2022 and 2023 exhibited higher resistance lines, lower infection rates, and slower disease progression. The findings support targeted breeding strategies for durable stripe rust resistance, emphasizing the importance of multi-environment testing and selection of lines with adult-plant resistance traits. This research offers valuable insights for breeders, agronomists, and farmers aiming to mitigate stripe rust impact through improved cultivars and informed management practices.

The presence or absence of awns – whether wheat heads are ”bearded” or ”smooth”– is the most visible phenotype distinguishing wheat cultivars. Previous studies suggest that awns may improve yields in heat or water-stressed environments, but the exact contribution of awns to yield differences remains unclear. Here we leverage historical phenotypic, genotypic, and climate data to estimate the yield effects of awns under different environmental conditions over a 12-year period in the Southeast US. Lines were classified as awned or awnless based on sequence data, and observed heading dates were used to associate grain fill periods of each line in each environment with climatic data and grain yield. In most environments, awn suppression was associated with higher yields, but awns were associated with better performance in heat-stressed environments more common at southern locations. Wheat breeders in environments where awns are only beneficial in some years may consider selection for awned lines to reduce year-to-year yield variability, and with an eye towards future climates.