Cargando…
MBCAST: A Forecast Model for Marssonina Blotch of Apple in Korea
A disease forecast model for Marssonina blotch of apple was developed based on field observations on airborne spore catches, weather conditions, and disease incidence in 2013 and 2015. The model consisted of the airborne spore model (ASM) and the daily infection rate model (IRM). It was found that m...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Society of Plant Pathology
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6901243/ https://www.ncbi.nlm.nih.gov/pubmed/31832039 http://dx.doi.org/10.5423/PPJ.OA.09.2019.0236 |
Sumario: | A disease forecast model for Marssonina blotch of apple was developed based on field observations on airborne spore catches, weather conditions, and disease incidence in 2013 and 2015. The model consisted of the airborne spore model (ASM) and the daily infection rate model (IRM). It was found that more than 80% of airborne spore catches for the experiment period was made during the spore liberation period (SLP), which is the period of days of a rain event plus the following 2 days. Of 13 rain-related weather variables, number of rainy days with rainfall ≥ 0.5 mm per day (L(day)), maximum hourly rainfall (P(max)) and average daily maximum wind speed (W(avg)) during a rain event were most appropriate in describing variations in air-borne spore catches during SLP (S(i)) in 2013. The ASM, Ŝ(i) = 30.280+5.860×L(day)×P(max)–2.123×L(day)×P(max)×W(avg) was statistically significant and capable of predicting the amount of airborne spore catches during SLP in 2015. Assuming that airborne conidia liberated during SLP cause leaf infections resulting in symptom appearance after 21 days of incubation period, there was highly significant correlation between the estimated amount of airborne spore catches (Ŝ(i)) and the daily infection rate (R(i)). The IRM, R̂(i) = 0.039+0.041×Ŝ(i), was statistically significant but was not able to predict the daily infection rate in 2015. No weather variables showed statistical significance in explaining variations of the daily infection rate in 2013. |
---|