Effects of Host and Weather Factors on the Growth Rate of Septoria nodorum Blotch Lesions on Winter Wheat.

Septoria nodorum blotch (SNB), caused by Parastagonospora nodorum, is a major disease of winter wheat that occurs frequently in the central and southeastern United States. Quantitative resistance to SNB in wheat is determined by various disease resistance components and their interaction with environmental factors. A study was conducted in North Carolina from 2018 to 2020 to characterize SNB lesion size and growth rate and to quantify the effects of temperature and relative humidity on lesion expansion in winter wheat cultivars with different levels of resistance. Disease was initiated in the field by spreading P. nodorum-infected wheat straw in experimental plots. Cohorts (groups of foliar lesions arbitrarily selected and tagged as an observational unit) were sequentially selected and monitored throughout each season. Lesion area was measured at regular intervals, and weather data were collected using in-field data loggers and the nearest weather stations. Final mean lesion area was approximately seven times greater on susceptible than on moderately resistant cultivars, and lesion growth rate was approximately four times higher on susceptible than on moderately resistant cultivars. Across trials and cultivars, temperature had a strong effect of increasing lesion growth rates (P < 0.001), while relative humidity had no significant effect (P = 0.34). Lesion growth rate declined slightly and steadily over the duration of cohort assessment. Our results demonstrate that restricting lesion growth is an important component of SNB resistance in the field and suggest that the ability to limit lesion size may be a useful breeding goal.

Multi-Year Field Plantings Evaluating Boxwood Cultivars for Susceptibility to the Blight Pathogens (Calonectria spp.) in Northern Germany.

Two multiyear field trials were conducted to evaluate boxwood cultivars for their susceptibility to the blight pathogens Calonectria pseudonaviculata and C. henricotiae in northern Germany. Fifteen cultivars were included in the first trial from 2007 to 2012, and 46 cultivars were included in the second trial from 2014 to 2017. Both trials were done in a naturally infested field that was supplemented with infected plant tissue added to the soil before planting. Each cultivar had three replicate hedge sections with 10 plants per section, and they were assessed annually for blight severity expressed as proportion of leaves blighted and fallen. Blight severity varied significantly among years (P < 0.0001) and cultivars (P < 0.05) within each trial. In the first trial, mean severity ranged from 0.03 to 0.11 for the most resistant cultivars and 0.35 to 0.96 for the most susceptible ones. Similarly, in the second trial, mean severity ranged from 0.06 to 0.27 and 0.71 to 0.97 for the most resistant and susceptible cultivars, respectively. 'Suffruticosa' was consistently the most susceptible cultivar, followed by 'Marianne', 'Myosotidifolia', 'Raket', and 'Morris Midget'. 'Herrenhausen' was the most resistant cultivar, followed by B. microphylla var. japonica, B. microphylla var. koreana, 'Green Mound', 'Faulkner', and 'Winter Beauty'. This study provides field data showing the performance of boxwood cultivars under different levels of disease pressure in an area where C. henricotiae was dominant. This knowledge will help boxwood growers and gardeners to choose less susceptible cultivars and help plant breeders to select for disease resistance.

Evaluation of a Model for Predicting Onset of Septoria nodorum Blotch in Winter Wheat.

Prediction models that aid growers in making decisions on timing of fungicide application are important components of integrated management programs for several foliar diseases of wheat. The risk of Septoria nodorum blotch (caused by Parastagonospora nodorum) onset in winter wheat has been reported to be influenced by location, amount of wheat residue in the field, and cumulative daily infection values 2 weeks prior to day of year (DOY) 102. A model previously developed based on these predictor variables was evaluated for its ability to predict disease onset under field conditions. An experiment was conducted at three locations in North Carolina in 2018, 2019, and 2020, where plots were either treated with >20% wheat residue or received no residue treatment. Plots were monitored for disease symptoms, and disease onset was defined to have occurred when mean disease incidence in a plot was 50%. Of the 298 disease cases recorded, disease onset occurred early (i.e., prior to DOY 102) in 257 cases, while onset was late (i.e., on or after DOY 102) in 41 cases. Model accuracy based on correct classification ranged from 0.67 to 0.95, with a mean of 0.87 across the study period. Similarly, sensitivity rates of the model ranged from 0.88 to 1.0 with a mean of 0.98 across all years. However, the model had low specificity, with a mean rate of 0.15 across the study period. Overall, there was no significant difference in the frequency of observed and predicted cases in the study (χ2 = 0.50, P = 0.7788, df = 2). Time to disease onset was significantly correlated with grain yield and explained 26% of variation in yield (P < 0.0001). Results indicated that the disease onset model performs well in predicting early disease onset but requires further evaluation and improvement, particularly in the Piedmont, where it over-predicted early onset in 2 successive years.