Variability Among Forecast Models for the Apple Sooty Blotch/Flyspeck Disease Complex

Area of Science:

• Plant Pathology
• Agricultural Meteorology
• Horticultural Science

Background:

• Empirical disease forecast models are used for managing apple sooty blotch and flyspeck (SBFS).
• These models typically rely on leaf wetness duration (LWD) from a biofix near petal fall to time fungicide applications.
• Existing SBFS models exhibit considerable variation in biofix timing and LWD thresholds.

Purpose of the Study:

• To investigate the sources of variability among different SBFS forecast models.
• To identify factors influencing the consistency and accuracy of SBFS disease prediction.
• To highlight areas for improvement in developing more reliable SBFS management strategies.

Main Methods:

• Comparison of multiple SBFS forecast models using a single dataset.
• Analysis of leaf wetness (LW) data acquisition methods (on-site vs. remote sensing).
• Examination of horticultural and orchard site factors influencing SBFS risk.

Main Results:

• Model recommendations for initial fungicide applications varied by up to five weeks.
• Leaf wetness (LW) measurement methods (sensor type, placement, remote estimation) significantly impact model inputs.
• Discrepancies exist between model thresholds and data acquisition methods, potentially leading to application failures.

Conclusions:

• Inconsistencies in SBFS models stem from variable LWD thresholds and data collection methods.
• Horticultural factors (tree size, canopy density, cultivar) and site factors (inculum proximity) require integration into forecast models.
• Further research is needed on the epidemiology of diverse SBFS fungi to enhance model development and adoption.