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Many winter stresses affect the ability of a perennial grass to overwinter in cold, temperate climates. Freezing tolerance is one of the major component traits affecting winterhardiness. Although effective freezing tolerance assessment procedures have long been available for perennial ryegrass ( Lolium perenne L.), no work has been done to characterize plant collections in the United States that may possess novel variation for freezing tolerance and winterhardiness. In this study, 21 accessions sampled from a larger set of 300 accessions with known winterhardiness and 3 check varieties were subjected to environmentally-controlled, low temperature acclimation, followed by controlled freezing using a programmable laboratory freezer. Eight freezing treatments from −2 to −16°C were used to determine survival across the known spectrum of freezing tolerance in perennial ryegrass. LT50 values were estimated based on whole plant survival, as well as tiller survival at each of the temperature treatments in the evaluation. LT50 values for the accessions and checks ranged from −10.31 to −13.95°C, with 3 accessions possessing significantly greater freezing tolerance than the most freeze-tolerant check, ‘NK200’. LT50 values were well correlated with winterhardiness in St. Paul during the winters of 2004–2005 and 2005–2006. Lower LT50 values in this experiment were associated with greater spring growth following the stressful winter of 2004–2005 and upright growth habit. The lack of negative biological association between freezing tolerance and turf quality components indicates that freezing tolerance testing could be a useful tool in breeding and selection with landrace germplasm. [ABSTRACT FROM AUTHOR]

Italian ryegrass (Lolium perenne ssp. multiflorum (Lam) Husnot) is a troublesome weed species in wheat (Triticum aestivum) production in the United States, severely affecting grain yields. Spatial mapping of ryegrass infestation in wheat fields and early prediction of its impact on yield can assist management decision making. In this study, unmanned aerial systems (UAS)-based red, green and blue (RGB) imageries acquired at an early wheat growth stage in two different experimental sites were used for developing predictive models. Deep neural networks (DNNs) coupled with an extensive feature selection method were used to detect ryegrass in wheat and estimate ryegrass canopy coverage. Predictive models were developed by regressing early-season ryegrass canopy coverage (%) with end-of-season (at wheat maturity) biomass and seed yield of ryegrass, as well as biomass and grain yield reduction (%) of wheat. Italian ryegrass was detected with high accuracy (precision = 95.44 ± 4.27%, recall = 95.48 ± 5.05%, F-score = 95.56 ± 4.11%) using the best model which included four features: hue, saturation, excess green index, and visible atmospheric resistant index. End-of-season ryegrass biomass was predicted with high accuracy (R2 = 0.87), whereas the other variables had moderate to high accuracy levels (R2 values of 0.74 for ryegrass seed yield, 0.73 for wheat biomass reduction, and 0.69 for wheat grain yield reduction). The methodology demonstrated in the current study shows great potential for mapping and quantifying ryegrass infestation and predicting its competitive response in wheat, allowing for timely management decisions. [ABSTRACT FROM AUTHOR]