Abstract This study investigated the epidemic temporal dynamics of potato late blight epidemic and its meteorological driving mechanisms in Chongqing mountainous areas from 2020 to 2022 based on principal component analysis (PCA), Pearson correlation analysis, grey relational analysis (GRA), and lag effect models. The PCA and Pearson correlation analysis results indicated that daily mean temperature served as the core driver with a variance contribution of 58.7% and correlation coefficient r of 0.485, exhibiting a significant promoting effect at lag period 1 (β = 10.765) and an inhibitory effect at lag period 3 (β=-15.064). Additionally, GRA revealed that precipitation exhibited the highest grey relational degree (0.85), indicating its nonlinear effects on disease transmission. Model screening showed that the S-curve model (R²=0.92) was effective in characterizing the three-period epidemic pattern (latency-outbreak-stability period), among which the daily growth rate of the disease during the disease index growth period reached up to 11.05%. This study proposed an early warning system based on temperature fluctuation monitoring and recommended initiating intervention in the early stage of the disease. Our findings provide theoretical foundation for precision control over potato late blight in Southwest China’s mountainous regions.
Zhong et al. (2026) studied this question.