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This study of genetic diversity in a wild ancestor of the cultivated strawberry was undertaken to describe patterns of variation in nature, assess worth of existing germplasm collections, and identify promising locations for future collection. Previous work reported a similar study of octaploid strawberry ranging east to west across North America. This complementary study focused on variation from north to south in the Rocky Mountains. The morphological diversity of 16 populations of Fragaria virgininia were characterized for morphological and molecular traits. Two clones of each of 133 genotypes from these populations were grown in a common environment in a greenhouse. Eighteen morphological traits, such as leaf area, runner color, and days to flowering, were measured and analyzed with principal components and canonical discriminant analyses. Molecular diversity data were obtained using seven randomly amplified polymorphic DNA primers. Resulting population marker frequencies were also subjected the previously describe anlayses. Differences due to latitude, longitude, and altitude were observed. Implications of the results will be discussed.
We conducted choice experiments with both strawberry producers and consumers. Consumer and producer willingness to pay (WTP) for the fruit attributes were estimated using mixed logit models. Through simulation using the mixed logit model results, we derived the market equilibrium prices, supply and demand curve, as well as quantities demanded and supplied for every fruit attribute. We found the highest equilibrium price was for strawberry internal color followed by flavor. Strawberry breeders can use the information when setting breeding targets, allocating resources appropriately during their breeding process and focusing on the improvement of attributes that produce the highest social surplus and total revenue.
Breeding programs around the world continually collect data on large numbers of individuals. To be able to combine data collected across regions, years, and experiments, research communities develop standard operating procedures for data collection and measurement. One such method is a crop ontology, or a standardized vocabulary for collecting data on commonly measured traits. The ontology is also computer readable to facilitate the use of data management systems such as databases. Blueberry breeders and researchers across the United States have come together to develop the first standardized crop ontology in blueberry (Vaccinium spp.). We provide an overview and report on the construction of the first blueberry crop ontology and the 178 traits and methods included within. Researchers of Vaccinium species—such as other blueberry species, cranberry, lingonberry, and bilberry—can use the described crop ontology to collect phenotypic data of greater quality and consistency, interoperability, and computer readability. Crop ontologies, as a shared data language, benefit the entire worldwide research community by enabling collaborative meta-analyses that can be used with genomic data for quantitative trait loci, genome-wide association studies, and genomic selection analysis.