Abstract
A dynamic model, called VenInf, was developed to forecast infection of pear leaves by conidia of Venturia nashicola. By simulating conidial infection processes following a rain event, the model estimates % conidia that successfully infected leaves at the end of an infection period. The model is mainly derived from logistic models developed from recent laboratory and glasshouse experimental results on infection of pear seedlings to estimate the rates of infection and mortality. It simulates the conidial infection process at 5 min intervals using temperature, relative humidity (RH), surface wetness and rainfall as input. The model was evaluated against pear scab in four unsprayed orchards in China over a 4-year period. In all orchards, all significant disease increases were associated with infection periods predicted by the model. In one orchard, in 2004 the incidence of leaf infection remained very low (<3%) during the entire season despite the model forecasting several severe infection periods. Results of orchard evaluation suggest that the model is able to identify all important potential infection periods. Thus, further field studies should be carried out to determine whether and how the model can be used in practice to assist farmers in making decisions on fungicide applications.
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Acknowledgements
This research was funded by European Union (contract number: ICA4-CT-2001-10001). We thank Li Jintong, Lian Sen, Liu Haifeng and Wang Zhongyun for their help in disease assessment.
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Li, B.H., Yang, J.R., Dong, X.L. et al. A dynamic model forecasting infection of pear leaves by conidia of Venturia nashicola and its evaluation in unsprayed orchards. Eur J Plant Pathol 118, 227–238 (2007). https://doi.org/10.1007/s10658-007-9138-4
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DOI: https://doi.org/10.1007/s10658-007-9138-4