Premature senescence in annual crops reduces yield, while delayed senescence, termed stay-green, imposes positive and negative impacts on yield and nutrition quality. Despite its importance, scant information is available on the genetic architecture of senescence in maize (Zea mays) and other cereals. We combined a systematic characterization of natural diversity for senescence in maize and coexpression networks derived from transcriptome analysis of normally senescing and stay-green lines. Sixty-four candidate genes were identified by genome-wide association study (GWAS), and 14 of these genes are supported by additional evidence for involvement in senescence-related processes including proteolysis, sugar transport and signaling, and sink activity. Eight of the GWAS candidates, independently supported by a coexpression network underlying stay-green, include a trehalose-6-phosphate synthase, a NAC transcription factor, and two xylan biosynthetic enzymes. Source–sink communication and the activity of cell walls as a secondary sink emerge as key determinants of stay-green. Mutant analysis supports the role of a candidate encoding Cys protease in stay-green in Arabidopsis (Arabidopsis thaliana), and analysis of natural alleles suggests a similar role in maize. This study provides a foundation for enhanced understanding and manipulation of senescence for increasing carbon yield, nutritional quality, and stress tolerance of maize and other cereals.

Fusarium head blight (FHB) is one of the most economically destructive diseases of wheat (Triticum aestivum L.), causing substantial yield and quality loss worldwide. Fusarium graminearum is the predominant causal pathogen of FHB in the U.S., and produces deoxynivalenol (DON), a mycotoxin that accumulates in the grain throughout infection. FHB results in kernel damage, a visual symptom that is quantified by a human observer enumerating or estimating the percentage of Fusarium-damaged kernels (FDK) in a sample of grain. To date, FDK estimation is the most efficient and accurate method of predicting DON content without measuring presence in a laboratory. For this experiment, 1266 entries collectively representing elite varieties and SunGrains advanced breeding lines encompassing four inoculated FHB nurseries were represented in the analysis. All plots were subjected to a manual FDK count, both exact and estimated, near-infrared spectroscopy (NIR) analysis, DON laboratory analysis, and digital imaging seed phenotyping using the Vibe QM3 instrument developed by Vibe imaging analytics. Among the FDK analytical platforms used to establish percentage FDK within grain samples, Vibe QM3 showed the strongest prediction capabilities of DON content in experimental samples, R² = 0.63, and higher yet when deployed as FDK GEBVs, R² = 0.76. Additionally, Vibe QM3 was shown to detect a significant SNP association at locus S3B_9439629 within major FHB resistance quantitative trait locus (QTL) Fhb1. Visual estimates of FDK showed higher prediction capabilities of DON content in grain subsamples than previously expected when deployed as genomic estimated breeding values (GEBVs) (R² = 0.71), and the highest accuracy in genomic prediction, followed by Vibe QM3 digital imaging, with average Pearson’s correlations of r = 0.594 and r = 0.588 between observed and predicted values, respectively. These results demonstrate that seed phenotyping using traditional or automated platforms to determine FDK boast various throughput and efficacy that must be weighed appropriately when determining application in breeding programs to screen for and develop resistance to FHB and DON accumulation in wheat germplasms.

Alfalfa ( Medicago sativa L.) is a globally vital forage crop valued for its perennial growth and multiple annual harvests. A breeding effort is underway to improve the crop for productivity and persistence against biotic and abiotic stresses using “creeping rootedness,” a trait where plants exhibit horizontal root growth, similar to rhizomes, with increased vegetative ground surface area. In this study, we genotyped a breeding population of 648 alfalfa lines segregating for creeping rootedness using the 3K DArTag marker panel to identify trait‐associated genomic loci and evaluate the feasibility of genomic prediction to accelerate breeding cycles. Using genome‐wide association studies (GWAS), we identified three quantitative trait loci (QTLs), with one major QTL located on chromosome 6.1 associated with this trait. Genomic prediction showed moderate predictive ability ( r = 0.68) for creeping rootedness. A significant advancement in this study was the development and utilization of the Breeding Insight Genomics Application (BIGapp), an R Shiny application designed to streamline the processing of genomic data through an intuitive interface. This tool makes integrating genomics into existing breeding programs accessible, regardless of species ploidy or the researcher's coding proficiency. The identified QTL will be essential in future efforts to develop new alfalfa cultivars with the creeping rootedness trait and accelerate the breeding cycle, with BIGapp playing a pivotal role in these advancements.

ISHS International Workshop on Intensive Cultivation of Protea COOPERATIVE PRODUCTION OF PROTEA FLOWERING POT PLANTS SELECTED FOR RAPID PRODUCTION