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<p>Alternaria leaf blotch incited by <i>Alternaria</i> sp., is an emerging foliar disease that threatens apple productivity in India. Field experiments were conducted during 2022 and 2023 to evaluate the response of 11 commercial apple cultivars under natural epiphytotic conditions and to determine the role of weather factors in epidemic development. None of the cultivars expressed resistance; however, clear differences in susceptibility were observed. Super Chief and Scarlet Spur-II were consistently the most susceptible, with final severities exceeding 70%, whereas Jeromine and Auvil Early Fuji maintained the lowest severity (&lt; 25%). The mean area under the disease progress curve (AUDPC) was similar across years (201.4 in 2022; 209.8 in 2023), though epidemics progressed slightly faster in 2023. Weather parameters strongly influenced epidemic trajectories. Minimum temperature (Tmin) and rainfall generally suppressed severity, while minimum relative humidity (RHmin) showed contrasting effects, being positively associated with disease in 2022 but negatively in 2023. Pooled analyses identified Tmin and rainfall as the most consistent suppressive factors, while regression confirmed rainfall as the dominant predictor of reduced severity. Time-series modeling demonstrated that conventional ARIMA and SARIMA captured baseline epidemic trends but failed to reflect weather-driven variability. In contrast, ARIMAX models incorporating Tmin, RHmin, and rainfall markedly improved forecasting accuracy (R² = 0.937 in 2022; 0.911 in 2023), reducing AIC values by nearly 400 units compared to ARIMA. The findings highlight that weather dynamics play a decisive role in Alternaria leaf blotch progression and that integrating host response with weather-based predictive models enables more accurate epidemic forecasting and supports weather-informed disease management in apple orchards.</p>

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Weather dynamics drive Alternaria leaf blotch progression in apple: integration of predictive modeling and host response

  • 期刊论文

  • 发表日期：

    2026年2月11日

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  • Oshin Saini,
  • Neelam Kumari,
  • S. K. Sharma,
  • Naveen C. Sharma

摘要

Alternaria leaf blotch incited by Alternaria sp., is an emerging foliar disease that threatens apple productivity in India. Field experiments were conducted during 2022 and 2023 to evaluate the response of 11 commercial apple cultivars under natural epiphytotic conditions and to determine the role of weather factors in epidemic development. None of the cultivars expressed resistance; however, clear differences in susceptibility were observed. Super Chief and Scarlet Spur-II were consistently the most susceptible, with final severities exceeding 70%, whereas Jeromine and Auvil Early Fuji maintained the lowest severity (< 25%). The mean area under the disease progress curve (AUDPC) was similar across years (201.4 in 2022; 209.8 in 2023), though epidemics progressed slightly faster in 2023. Weather parameters strongly influenced epidemic trajectories. Minimum temperature (Tmin) and rainfall generally suppressed severity, while minimum relative humidity (RHmin) showed contrasting effects, being positively associated with disease in 2022 but negatively in 2023. Pooled analyses identified Tmin and rainfall as the most consistent suppressive factors, while regression confirmed rainfall as the dominant predictor of reduced severity. Time-series modeling demonstrated that conventional ARIMA and SARIMA captured baseline epidemic trends but failed to reflect weather-driven variability. In contrast, ARIMAX models incorporating Tmin, RHmin, and rainfall markedly improved forecasting accuracy (R² = 0.937 in 2022; 0.911 in 2023), reducing AIC values by nearly 400 units compared to ARIMA. The findings highlight that weather dynamics play a decisive role in Alternaria leaf blotch progression and that integrating host response with weather-based predictive models enables more accurate epidemic forecasting and supports weather-informed disease management in apple orchards.