Bacterial black node (BBN) due to Pseudomonas syringae pv. syringae (PSS) is the most serious bacterial disease of barley in Japan. To help growers determine when to apply control measures against BBN, we developed a disease-forecasting model using a hierarchical Bayesian model (HBM) based on 29 years of data from Kagawa Prefecture (1992–2020), 19 years from Okayama Prefecture (2002–2020), and 8 from Yamaguchi Prefecture (2013–2020). The model included the number of fields with BBN in May of the previous season and the number of days at a minimum temperature (≤ − 4 °C) in January of the current season as predictors. The model was validated using a fivefold cross-validation (CV) procedure and achieved an average accuracy of 0.713, suggesting that this model can be used to predict the BBN incidence in May of the current season. This is the first report on developing a disease-forecasting model for BBN incidence using HBM based on a total of 56 years of historical data from three prefectures.
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I am grateful to Drs. Sun Jianqiang and Shigeki Kishi (RCAIT, NARO, Tokyo, Japan) for much useful advice on statistical analyses using HBM. I also thank the staff at the Okayama Plant Protection Office (Akaiwa, Okayama, Japan), the Kagawa Plant Protection Office (Ayakawa, Kagawa, Japan), and the Yamaguchi Plant Protection Office (Yamaguchi, Yamaguchi, Japan) for gathering the historical data on the incidence of BBN.
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Supplementary Fig. S1. Mean total precipitation and air temperature (±SD) between January and March from 2002 to 2020 in Okayama (Okayama City), from 1992 to 2020 in Kagawa (Ayakawacho), and from 2013 to 2020 in Yamaguchi (Yamaguchi City)
Supplementary Fig. S2. Proportion of fields with bacterial black node in mid-May in Okayama (2002–2020), Kagawa (1992-2020), and Yamaguchi (2013–2020)
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Kawaguchi, A. Model-based forecasting of bacterial black node of barley using a hierarchical Bayesian model. J Gen Plant Pathol (2021). https://doi.org/10.1007/s10327-021-01035-4
- Hierarchical Bayesian model
- Bacterial black node
- Disease-forecasting model