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MBCAST: A Forecast Model for Marssonina Blotch of Apple in Korea
Plant Pathol. J. 2019;35:585-597
Published online December 1, 2019
© 2019 The Korean Society of Plant Pathology.

Hyo-suk Kim1 , Jung-hee Jo1, Wee Soo Kang2 , Yun Su Do3 , Dong Hyuk Lee4 , Mun-Il Ahn5 , Joo Hyeon Park5 , and Eun Woo Park1,6,7*

1Department of Agricultural Biotechnology, Seoul National University, Seoul 08826, Korea
2Department of Agro-food Safety and Crop Protection, National Institute of Agricultural Sciences, Rural Development Administration, Wanju 55365, Korea
3Research Policy Bureau, Rural Development Administration, Jeonju 54875, Korea
4Apple Research Institute, National Institute of Horticultural & Herbal Science, Gunwi 39000, Korea
5EPINET Corporation, Anyang 14056, Korea
6Interdisciplinary Program in Agricultural and Forest Meteorology, Seoul National University, Seoul 08826, Korea
7Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul 08826, Korea
Correspondence to: *Phone) +82-2-880-4672, FAX) +82-2-872-2317
E-mail) ewpark@snu.ac.kr
ORCID
Hyo-suk Kim
https://orcid.org/0000-0002-3031-9333
Wee Soo Kang
https://orcid.org/0000-0001-9162-4086
Yun Su Do
https://orcid.org/0000-0003-0362-6706
Dong Hyuk Lee
https://orcid.org/0000-0003-4468-5964
Mun-Il Ahn
https://orcid.org/0000-0002-2240-1804
Joo Hyeon Park
https://orcid.org/0000-0001-6218-4456
Eun Woo Park
https://orcid.org/0000-0001-8305-5709

Handling Editor : Park, Sook-Young
Received September 5, 2019; Revised September 18, 2019; Accepted September 19, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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 (Lday), maximum hourly rainfall (Pmax) and average daily maximum wind speed (Wavg) during a rain event were most appropriate in describing variations in airborne spore catches during SLP (Si) in 2013. The ASM, S^i=30.280+5.860×Lday×Pmax-2.123×Lday×Pmax×Wavg 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 (S^i) and the daily infection rate (Ri). The IRM, R^i= 0.039+0.041×S^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.
Keywords : airborne spores, disease forecast model, Marssonina blotch of apple
Figures
Fig. 1.

Illustration on the relationship between the airborne spore catches (Si) liberated during the SLP of individual rain events and the mean daily infection rate (Ri) that resulted from infection by airborne spores which had been liberated 21 days ago during SLP of the ith rain event. It was reported previously that the incubation period of Marssonina coronaria was 21 days or longer in apple orchards (Back and Jung, 2014; Lee et al., 2011). The daily infection rate (rt) was calculated from observed disease incidence data assuming that disease incidence increased at a rate of compound interest (Van der Plank, 1963).


Fig. 2.

A schematic plot of disease infection rate per day (rt). The dt (%) is the disease incidence assessed at tth rating.


Fig. 3.

Rainfall periods (yellow area) and daily airborne spore catches (black histogram) from June 27 to October 10, 2013 at Hwaseong in Gyeonggi-do (A), and from June 3 to October 21, 2015 at Gunwi in Gyeongsangbuk-do (B). aThe periods from 11 to 18 in June and from 13 to 27 in August in 2015 were excluded due to the power failure of the Burkard spore sampler.


Fig. 4.

The mean and percentile of accumulated airborne spore catches during days of rain events (DR) and the following dry days without rainfall of 0.5 mm after the end of individual rain events in 2013 (A) and 2015 (B). The periods of power failure in 2015 were excluded in the calculation.


Fig. 5.

Correlation matrix of observed airborne spore catches (Si) and the weather variables in 2013, which were selected for airborne spore model development.


Fig. 6.

Changes in amount of estimated airborne spore catches (Ŝi) affected by Lday, Pmax, and Wavg. The Ŝi is the number of airborne spore catches during spore liberation period, which is the days of rain event plus the following 2 days after the end of rain event. Note the difference in scales of the number of spores for different Lday.


Fig. 7.

Disease progress curves of Marssonina blotch of apple along with the mean daily infection rate (Ri) that occurred during the period of 21 days after individual rain events in 2013 (A) and 2015 (B).


Fig. 8.

Relationship between the airborne spore model (ASM)– estimated airborne spore catches (Ŝi) and the mean daily infection rate (Ri) calculated from the observed disease incidence in 2013.


Fig. 9.

Relationship between the airborne spore model (ASM)– estimated (Ŝi) and the observed (Si) airborne spore catches in 2015.


Tables

Weather factors during the period from the starting day of individual rain events until 2 days after the end of rain events

Weather variable Description
Lday (day) Number of rainy days with rainfall ≥ 0.5 mm/day
Lhour (h) Duration in hours with rainfall ≥ 0.5 mm/h
Pmax (mm) Maximum hourly rainfall
Psum (mm) Total amount of rainfall
Havg (%) Daily average relative humidity
Hmax (%) Maximum hourly relative humidity
Hmin (%) Minimum hourly relative humidity
Tavg (°C) Daily average air temperature
Tmax (°C) Maximum hourly temperature
Tmin (°C) Minimum hourly temperature
Wavg (m/s) Average daily maximum wind speed
Wmax (m/s) Maximum hourly wind speed
Wmin (m/s) Minimum hourly wind speed

These factors were used as independent variables for model development and evaluation.


Weather variables selected to explain variations in the number of airborne spore catches in 2013

Variable b Std. error R2 F P-value
Intercept 59.390 37.376 - 2.52 0.1404
Lday 17.984 5.545 0.380 10.52 0.0078
Pmax 4.217 1.440 0.217 8.57 0.0137
Wavg −34.062 17.765 0.101 3.68 0.0815
Model - - 0.698 8.45 0.0034

Stepwise regression analysis was applied using all weather variables listed in Table 1. The forward selection of PROC STEPWISE of SAS was used with the criterion of P ≤ 0.15 for entry of a variable into the model.


The airborne spore model (ASM) selected by stepwise regression analysis to esitmate airborne spore catches during the spore liberation perioda

Variable b Std. error R2 F P-value
Intercept 30.280 9.227 - 10.77 0.0066
Lday × Pmax 5.860 0.747 0.610 61.56 < 0.001
Lday × Pmax × Wavg −2.123 0.370 0.286 32.93 < 0.001
Model - - 0.896 8.45 < 0.001

aThe forward selection of PROC STEPWISE of SAS was used with the criterion of P ≤ 0.15 for entry of a variable into the model.


The infection rate model resulted from stepwise regression analysis on observed daily infection rate at 21 days after individual rain eventsa

Variable b Std. error R2 F P-value
Intercept 0.039 0.757 - 0.00 0.9604
Ŝi 0.041 0.007 0.788 37.13 < 0.001
Model - - 0.788 37.13 < 0.001

The stepwise regression analysis was conducted for each of 23 data sets representing conduciveness of weather conditions during 1, 2, 3, ····, and 23 days after the end of rainfall. All weather variables were not selected based on statistical significance of parameter estimates (P ≤ 0.05) and collinearity among independent variables measured in terms of the variance inflation factors (≤ 2) and the condition index (≤ 10). The forward selection of PROC STEPWISE of SAS was used with the criterion of P ≤ 0.15 for entry of a variable into the model.

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