MBCAST: A Forecast Model for Marssonina Blotch of Apple in Korea
Hyo-Suk Kim1, Jung-Hee Jo1, Wee Soo Kang2
1Department of Agricultural Biotechnology, Seoul National University, Seoul 08826, Korea.
Abstract:
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 ), maximum hourly rainfall (P ) and average daily maximum wind speed (W ) during a rain event were most appropriate in describing variations in air-borne spore catches during SLP (S ) in 2013. The ASM, Ŝ = 30.280+5.860×L ×P -2.123×L ×P ×W 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 ) and the daily infection rate (R ). The IRM, R̂ = 0.039+0.041×Ŝ , 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.
Insights
A new disease forecast model for Marssonina blotch of apple was developed. This model uses airborne spore catches and weather data to predict disease risk, aiding in orchard management.
Area of Science:
- Plant Pathology
- Agricultural Meteorology
- Disease Forecasting
Background:
- Marssonina blotch is a significant apple disease.
- Accurate disease forecasting is crucial for effective orchard management.
- Existing models may not fully integrate weather and spore dynamics.
Purpose of the Study:
- To develop and validate a disease forecast model for Marssonina blotch of apple.
- To identify key weather variables influencing airborne spore release.
- To correlate spore release with daily infection rates.
Main Methods:
- Field observations of airborne spore catches, weather conditions, and disease incidence.
- Development of an airborne spore model (ASM) and a daily infection rate model (IRM).
- Statistical analysis of weather variables (rainfall, wind speed) and spore catches during spore liberation periods (SLP).
Main Results:
- The ASM, incorporating rainfall and wind speed, significantly predicted airborne spore catches during SLP.
- A high correlation was found between estimated airborne spore catches and daily infection rates.
- The IRM showed statistical significance but limited predictive power for daily infection rates in 2015.
Conclusions:
- The developed disease forecast model shows potential for predicting Marssonina blotch risk.
- Key weather variables effectively describe airborne spore variations during rain events.
- Further refinement of the infection rate model may improve predictive accuracy.
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