- 1College of Natural Resource and Environment, Northwest A&F Technology University, Xianyang, China (linjia.yao@nwafu.edu.cn)
- 2College of Soil and Water Conservation Science and Engineering, Northwest A&F University,(Institute of Soil and Water Conservation), Xianyang, China (yuq@nwafu.edu.cn)
Grape downy mildew, caused by the pathogen Plasmopara viticola, is one of the most devastating diseases impacting grapevine cultivation globally. Its primary infection is highly influenced by weather conditions and the presence of airborne sporangia. Effective management of this disease relies on timely preventive fungicide applications, which depend on accurate forecasting. Traditional empirical forecasting methods often lack precision, leading to costly and less effective intervention decisions. Recently, the use of spore traps for monitoring airborne spores has shown promise in enhancing plant disease forecasting accuracy.
This study aims to enhance Rossi’s primary infection model and develop a spore data assimilation method to improve the forecasting of grapevine downy mildew infections. Additionally, we examine the impact of climate change on disease occurrence risks and evaluate adaptation strategies across different grape-growing regions in China. By integrating spore trap monitoring data with the mechanistic model, our data assimilation method improved primary infection predictions and disease management strategies.
From 2022 to 2024, we conducted multisite monitoring in Nanning, Hechi, Guilin, and Yangling to analyze sporangia splash patterns and concentration changes within orchards, as well as disease index variations across regions. The collected data were used for model verification and calibration. We employed data assimilation techniques and performed a model sensitivity analysis to determine relevant parameters. The enhanced model demonstrated high sensitivity, specificity, and accuracy across major grape-growing regions in China, correctly predicting primary infection dates with a coefficient of determination (R²) of 0.85 and a root mean square error (RMSE) of 8-16%. Notably, the model accurately forecasted infection dates across multiple years and sites, with only one instance of a 7-day delay. Furthermore, the model identified optimal fungicide spraying windows, potentially reducing management costs by 10-30% compared to traditional strategies used by farmers.
Our analysis of climate change scenarios revealed significant shifts in primary infection trends, with warmer and more humid conditions projected to increase the risk and frequency of downy mildew outbreaks in several key grape-growing regions. In response, we propose adaptation strategies including the adoption of resistant grapevine varieties, modification of irrigation practices to reduce humidity around plants, and the implementation of integrated pest management (IPM) approaches that combine biological control agents with optimized fungicide application schedules.
These results indicate that assimilating real-time spore counts allows the model to effectively simulate primary infection processes, enabling timely and informed decision-making to limit disease spread. Additionally, understanding the climate change-driven shifts in infection trends facilitates the development of robust adaptation strategies to sustain grapevine cultivation under evolving environmental conditions. This approach provides grape growers with location-specific, precise, and timely information essential for developing effective disease management and adaptation strategies, thereby enhancing the sustainability and productivity of grapevine cultivation in the face of climate variability.
How to cite: Yao, L., Zhao, G., Chen, B., and Yu, Q.: Optimizing Primary Infection Forecasting and Management of Grapevine Downy Mildew with Spore Trap Data Across Chinese Vineyards, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12159, https://doi.org/10.5194/egusphere-egu25-12159, 2025.