Of buds and bits: a meta-QTL study identifies stable QTL for berry quality and yield traits in cranberry mapping populations ( Ait.).

Introduction: For nearly two centuries, cranberry ( Ait.) breeders have improved fruit quality and yield by selecting traits on fruiting stems, termed "reproductive uprights." Crop improvement is accelerating rapidly in contemporary breeding programs due to modern genetic tools and high-throughput phenotyping methods, improving selection efficiency and accuracy.

Quantitative Trait Loci Analysis and Marker Development for Fruit Rot Resistance in Cranberry Shows Potential Genetic Association with Epicuticular Wax.

Fruit rot is a fungal disease complex that threatens cranberry yields in North American growing operations. Management of fruit rot is especially difficult because of the diversity of the infecting fungal species, and although infections take place early in the season, the pathogens usually remain latent in the ovary until the fruit ripen. Control methods heavily rely on fungicide applications, a practice that may be limited in viability long term.

Blueberry and cranberry pangenomes as a resource for future genetic studies and breeding efforts.

Domestication of cranberry and blueberry began in the United States in the early 1800s and 1900s, respectively, and in part owing to their flavors and health-promoting benefits are now cultivated and consumed worldwide. The industry continues to face a wide variety of production challenges (e.g.

Blueberry and cranberry pangenomes as a resource for future genetic studies and breeding efforts.

Domestication of cranberry and blueberry began in the United States in the early 1800s and 1900s, respectively, and in part owing to their flavors and health-promoting benefits are now cultivated and consumed worldwide. The industry continues to face a wide variety of production challenges (e.g.

Multiple Leaf Sample Extraction System (MuLES): A tool to improve automated morphometric leaf studies.

Premise: The measurement of leaf morphometric parameters from digital images can be time-consuming or restrictive when using digital image analysis softwares. The Multiple Leaf Sample Extraction System (MuLES) is a new tool that enables high-throughput leaf shape analysis with minimal user input or prerequisites, such as coding knowledge or image modification.

Methods And Results: MuLES uses contrasting pixel color values to distinguish between leaf objects and their background area, eliminating the need for color threshold-based methods or color correction cards typically required in other software methods.