Browsing by Author "Avenot, Herve F."

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    Conidial production and viability of Calonectria pseudonaviculata on infected boxwood leaves as affected by temperature, wetness, and dryness periods
    Avenot, Herve F.; Baudoin, Antonius B.; Hong, Chuanxue (Wiley, 2021-10-31)
    Calonectria pseudonaviculata causes lesions on boxwood leaves and twigs. Controlled-environment experiments were conducted to determine the effects of temperature and leaf wetness period on C. pseudonaviculata sporulation on diseased (cv. Suffruticosa) leaves and of dryness periods and high temperature on conidial survival. Infected leaves were incubated in moist chambers and subjected to six temperatures (9, 13, 17, 21, 25, and 29°C) and six leaf wetness periods (0, 12, 24, 40, 48, and 72 h). Spore production was influenced significantly by wetness period, temperature, and their interaction. Increasing duration of leaf wetness and increasing temperature generally increased sporulation, with no sporulation occurring at 29°C or 9 and 13°C, except at 72 h of wetness exposure, while it was optimal at 21°C. Detached leaves with profuse conidia were subjected to a range of drying (relative humidity at 65%) times (0, 2, 4, 6, and 8 h) at two temperatures of 21 and 29°C. Conidia were then harvested and plated on water agar. Germinating conidia were counted to measure the spore viability. Spore mortality increased with increasing dryness duration at both temperatures but occurred more quickly and severely at 29 than 21°C. Overall, this study extended biological knowledge of conditions required for crucial stages of the C. pseudonaviculata disease cycle and the obtained results will be vital for developing boxwood blight forecasting and management tools.
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    Epidemiology of boxwood blight in western North Carolina and Virginia and evaluation of the boxwood blight infection risk model
    Khaliq, Ihsanul; Avenot, Herve F.; Baudoin, Anton; Coop, Leonard; Hong, Chuanxue (Nature Portfolio, 2024-11-05)
    Boxwood blight, caused by Calonectria pseudonaviculata, is a highly invasive emerging disease. Since the first US report in North Carolina and Connecticut in 2011, boxwood blight has spread to over 30 US states, risking more than 90% of boxwood production. Our study investigated the disease field epidemiology and evaluated the boxwood blight infection risk model’s prediction by analysing weekly blight monitoring data collected on detector plants exposed to the prevailing environmental conditions at two different locations (western Virginia and North Carolina) from spring through fall of 2014 to 2017. Boxwood blight was recorded in 61 of 86 weeks, with the highest infected leaf counts recorded in late summer or early fall. Rainfall, high relative humidity outside rainy periods and optimal temperatures (13.6–22.7 °C) during prolonged leaf wetness (> 65 h per week) had a significant positive effect on boxwood blight development. Classification analyses showed that disease predictions from the model using leaf wetness estimated by leaf wetness sensor were more closely aligned with observations from the field than predictions based on algorithms. This study improved our understanding of disease field epidemiology, provided leads to improve the existing model, and generated essential knowledge for formulating effective strategies for blight mitigation.