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Predictive ability of perennial ryegrass spaced-plant nurseries for turfgrass and seed production swards in Minnesota

Citation

Heineck, Garett (2021), Predictive ability of perennial ryegrass spaced-plant nurseries for turfgrass and seed production swards in Minnesota, Dryad, Dataset, https://doi.org/10.5061/dryad.95x69p8h7

Abstract

Turf-type perennial ryegrass (Lolium perenne L.) success depends both on adequate turfgrass quality and economic seed yield. In most breeding programs, spaced plants are the initial unit of selection in which observations of related individuals dictate selections of superior germplasm for further testing. As such, spaced plants must be predictive of both seed production and turfgrass growing environments. This study investigated the effectiveness of both standard (3 plants m-2) and competitive (23 plants m-2) spaced-plant nurseries as selection environments with respect to two sward environments as well as employed a novel image analysis technique for several key traits. Seed production, turfgrass, and the two spaced-plant growing environments were tested at two locations in Minnesota. Turfgrass quality traits were measured in 2017 and 2018 and seed production traits were measured in 2018. Automated image analysis was able to predict the traditional visual scoring values at both locations for crown rust severity (rp > 0.79, P < 0.001), winter injury (rp > 0.89, P < 0.001), and texture (rp > 0.88, P < 0.001). Increasing competition in between spaced plants altered plant phenotype and improved accuracy for vegetative biomass, crown rust severity, seed yield, and at one location, turfgrass quality. There was no benefit of increasing competition for several traits such as genetic color, fertile tillers, and spikelet number. While the competitive design was not useful for all traits, from a feasibility standpoint the competitive design took up less space and often made measurements and observations much easier for bunch-type grasses.

Methods

The attached file contains the metadata explaining haw data is organized and was collected.