The genus Rubus, part of the rose family (Rosaceae), includes economically and ecologically important plants such as blackberries, raspberries, and dewberries. Despite their familiarity, many Rubus species are extremely difficult to distinguish due to frequent hybridization, apomixis (asexual seed production), and overlapping morphological traits. This research examines three species in the rose family from the Rubus genus: Rubus alumnus, Rubus allegheniensis, and Rubus rosa. Through a combination of field observations, specimen analysis, and comparative classification with the help of measuring software, this study focuses on contrasting the leaf and inflorescence structures of primocanes (first year growth shoots) and floricanes (second year growth shoots which bear fruit). The aim is to distinguish key traits that define species boundaries and improve differentiation among these closely related taxa. These findings will help clarify distinguishing characteristics, support species identification, and guide further research into species variation within Rubus.

Kentucky Bluegrass (Poa pratensis) is a cool-season turfgrass used widely on athletic fields, golf courses, and homelawns. A drawback to using Kentucky bluegrass is its slow and sporadic germination rate. Germination stimulants are frequently used to improve germination rate, but little is known on their effects on Kentucky bluegrass. One such stimulant is gibberellins, a class of plant hormones found endogenously in seeds. Smoke has also been found to be a germination stimulant as it is abundant in karrikin hormones. Kentucky bluegrass (‘Tirem’) seeds were imbibed in varying concentrations of liquid smoke, pure karrikin (KAR1), gibberellin A1 (GA1), GA3, GA4, or GA7. Seeds were also imbibed in water (negative control) 0.20% KNO3 (positive control). Following imbibition, seeds were placed on germination blotter paper in honeycomb boxes within a growth chamber (25°C day, 20°C night, 12 hr. Photoperiod, 100 µmol m-2 s-1) to monitor germination rate. As the concentration of liquid smoke increased, the germination rate decreased. KAR1 showed a similar effect to GA4 and KNO3 showed the largest germination rate. All gibberellins produced an increase in germination except for GA1. Future experiments will examine the interaction between KNO3 and GA4/7 due to their positive, independent, effects on germination.

The cultivated potato (Solanum tuberosum) is typically bred as a tetraploid clonal crop and propagated asexually. However, tetraploid breeding often results in low genetic gain due to high genetic load and heterozygosity. Diploid breeding offers a simpler genetic structure, higher genetic gain, and a faster response to emerging challenges in potato production. In this study, we compared two populations derived from the Red Norland potato variety: 200 selfed tetraploid progeny and 200 dihaploid progeny generated using the IVP101 dihaploid inducer. All plants were grown in a greenhouse for tuber multiplication prior to field evaluation. In total, 400 genotypes, 187 of which were replicated, were established in the field to assess phenotypic differences associated with changes in ploidy level. We are collecting data on key morphological and agronomic traits, including date of row closure, flowering time, flower color, leaf color, and chlorophyll content. By analyzing these traits, we aim to better understand how reducing ploidy from tetraploid to diploid affects plant development and phenotype. These findings will help inform breeding decisions, facilitate the use of diploid lines in potato improvement programs, and contribute to broader efforts to develop more efficient and genetically tractable potato cultivars.

American hazelnut (Corylus americana Marshall) is gaining attention as a climate-resilient, regionally adapted nut crop with growing economic potential in the Upper Midwest. Hazelnut processing generates husks as a byproduct, which are typically discarded despite their potential value as mulch. This study evaluated the effectiveness of American hazelnut husks as a sustainable mulch alternative in container plant production. A greenhouse experiment using a randomized complete block design with seven blocks compared five mulch treatments: control (no mulch), American hazelnut husks, rice hulls, bark, and sawdust. Each block included one replicate of each treatment, one pot filled with a commercial substrate, planted with petunia, and inoculated with ryegrass (Lolium spp.) and white clover (Trifolium repens). Over 30 days, we recorded substrate moisture, substrate temperature, surface temperature, each weed emergence, and plant growth. Data were analyzed using ANOVA and Tukey’s HSD for mean separation (α = 0.05). Compared to the commonly used mulch materials, hazelnut husks were as effective, or superior, in suppressing weed emergence while maintaining moisture and temperature and supporting plant growth comparable to conventional materials. Our findings highlight the possibility of repurposing American hazelnut husks as an effective, locally sourced mulch material for use in container production.

Efficiently identifying prominent grapevine phenotypic traits such as canopy, fruit color, and fruit size is a critical part of grape breeding. Phenotyping is both time-intensive and relies on direct observations to determine the severity of disease symptoms. The phenotyping process can be expedited by using high-resolution images that can be manually annotated for valuable traits and subsequently trained on a convolutional neural network (CNN) to enable future prediction of disease symptoms/scores based on the acquired images. This would allow quicker, efficient, and objective phenotyping. At the University of Minnesota’s Horticulture Research Center, this technology is being used to phenotype diseases and other breeding traits, including berry color and cluster size.  A utility vehicle fitted with a stereo camera was used to photograph a family of 955 grapevines. Select photographs were evaluated using image-based plant phenotyping to assess and annotate the breeding traits. In the future, the annotated images and field observations will be used to train a CNN model that can identify common diseases and other valuable traits with less time and resources for accelerating breeding programs.