Modeling Biological Responses to Meteorological Variables to Predict Disease Risk for European Canker ( Neonectria ditissima ) in Apple

, causes serious tree health and production problems in many apple-producing regions, particularly in high-rainfall climates. Prediction models based on biological and epidemiological understanding of disease processes can improve the efficiency of disease management practices. We developed a mathematical model for the EC disease cycle that targets critical mechanisms in the epidemiology of the disease, especially the pathogen response to meteorological parameters. The model enables infection risk comparisons between regions and seasons and facilitates decision support for disease management. The responses of disease cycle processes to environmental factors were determined from experimental and published data, including infection response to temperature, rainfall, and tree disease incidence and spore production potential in relation to lesion drying. Sensitivity analyses showed that predicted risk was most sensitive to changes in the optimal temperature parameter, whereas changing rainfall and drying thresholds had relatively little effect. Indices for daily and annual accumulated infection risk revealed variation between regions and years, helping to illustrate the changing pressures to which management actions need to adapt. Field validation was not part of this project, and we recommend international comparisons for better calibration and validation to support application opportunities for EC infection risk prediction. [Formula: see text] Copyright © 2026 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.