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Basic quantitative and population genetics topics are typically taught in introductory plant breeding courses and are critical for success in upper-level study. Active learning, including simulations and games, may be useful for instruction of these concepts, which rely heavily on theory and may be more challenging for students. The statistical computing language "R" is now routinely used in the analysis of plant breeding experiments, but the command-line interface of the language may be unsuitable for an introductory course. Here we describe "qgshiny" (quantitative genetics in "shiny"), an interactive application for performing simulations to understand basic theory in quantitative and population genetics. The initial version of the application includes modules on three core topics in quantitative genetics: randomly mating populations, genetic variance, and response to selection. Students can specify parameters and initiate simulations to assess their impact on responses such as allele frequency, genetic variance, and genetic gain, which together can be used to reinforce more general learning objectives. Feedback collected from students after engaging with the application suggests this tool can have a positive impact on student learning. The application is bundled in an R package, "qgshiny," which is available through the Comprehensive R Archive Network (CRAN), on GitHub (https://github.com/neyhartj/qgshiny), or interactively through the shinyapps.io platform (http://neyhartj.shinyapps.io/qgshiny).

Hard fescue is an allo‐hexaploid, caespitose, cool‐season grass that is part of the fine‐leaved ovina complex of the Lolium–Festuca clade in Poaceae. It is predominantly used as a low‐maintenance turfgrass and soil stabilization grass in situations with reduced inputs of nitrogen and water. While often recommended for use under tree canopies, prolonged foliar shade can thin turf stands and reduce their functionality; furthermore, gray snow mold caused by Typhula incarnata can further affect hard fescue turfgrass functionality. In this study, we gathered a diverse population of hard fescue collections and varieties, which fit into four genetic structures. Because no genomic reference sequences exist for this species, a contig‐level pseudohaploid genomic reference was assembled from a plant of the hard fescue variety Beacon. With 98% benchmarking universal single‐copy orthologs (BUSCO) coverage from 241 contigs, this reference assembly was used to conduct genome‐wide association analysis. Thirty‐eight single‐nucleotide polymorphism (SNP) markers were associated with foliar shade response phenotypes, including SNPs near molybdate transporter and cytochrome P450 genes. Three SNP markers were associated with gray snow mold responses, with one located near a Protein Kinase G11 gene and corresponding to a 20% improvement in gray snow mold resistance. Core Ideas: A primary contig‐level genome of Beacon hard fescue was assembled.A GWAS for shade tolerance resulted in 38 significant loci, with some loci shared among traits.A GWAS for gray snow mold resistance found three loci, two of which were near candidate genes [ABSTRACT FROM AUTHOR]

Government rebates provide monetary incentives to encourage consumers' adoption of ecofriendly technologies. Understanding how consumers perceive the value of rebate is crucial to policy makers. We use the smart irrigation system as an example and design choice experiments that present rebates in two formats: the total device cost and the cost consumers needed to pay versus the total device cost and the rebate value. We find that consumers discount the value of the rebate more when presented with rebate value. Additionally, the framing of incentives has a spillover effect on the perceived value of a seemingly unrelated attribute (i.e., water saving). [ABSTRACT FROM AUTHOR]

Introduction: Traditional evaluation procedure in National Turfgrass Evaluation Program (NTEP) relies on visually assessing replicated turf plots at multiple testing locations. This process yields ordinal data; however, statistical models that falsely assume these to be interval or ratio data have almost exclusively been applied in the subsequent analysis. This practice raises concerns about procedural subjectivity, preventing objective comparisons of cultivars across different test locations. It may also lead to serious errors, such as increased false alarms, failures to detect effects, and even inversions of differences among groups. Methods: We reviewed this problem, identified sources of subjectivity, and presented a model-based approach to minimize subjectivity, allowing objective comparisons of cultivars across different locations and better monitoring of the evaluation procedure. We demonstrate how to fit the described model in a Bayesian framework with Stan, using datasets on overall turf quality ratings from the 2017 NTEP Kentucky bluegrass trials at seven testing locations. Results: Compared with the existing method, ours allows the estimation of additional parameters, i.e., category thresholds, rating severity, and within-field spatial variations, and provides better separation of cultivar means and more realistic standard deviations. Discussion: To implement the proposed model, additional information on rater identification, trial layout, rating date is needed. Given the model assumptions, we recommend small trials to reduce rater fatigue. For large trials, ratings can be conducted for each replication on multiple occasions instead of all at once. To minimize subjectivity, multiple raters are required. We also proposed new ideas on temporal analysis, incorporating existing knowledge of turfgrass. [ABSTRACT FROM AUTHOR]