Temporal Analysis of Northern Corn Leaf Blight (Exserohilum turcicum Pass. Leonard & Suggs) Epidemics | Temporal Analysis of Northern Corn Leaf Blight (Exserohilum turcicum Pass. Leonard & Suggs) Epidemics

anglais. Field trials were conducted in six locations in central-northern Córdoba, Argentina, using four maize hybrids with varying resistance to northern corn leaf blight (NCLB), caused by Exserohilum turcicum. Naturally occurring NCLB epidemics were evaluated. We analyzed disease severity (S%), disease progress curve (DPC), area under the disease progress curve, final severity (FS%) and apparent infection rate (r). Disease progress curves were simultaneously analyzed by fitting nonlinear epidemiological models (Gompertz and Logistic). Ballesteros and Villa María were the localities with the highest FS in susceptible hybrids (45% and 37.5%, respectively). Levels of FS were below 5% in Jesús María, Río Segundo and Freyre, and under 1% in El Tío. The highest AUDPC values were also observed in Ballesteros and Villa María (2150.1 and 1335.7, respectively). In the other locations, AUDPC values remained under 320, with statistically significant differences in all cases (p< 0.05). The resistant hybrid exhibited the lowest apparent infection rate compared to the other genotypes. Epidemic progress displayed, to varying degrees, sigmoid-shaped curves characteristic polycyclic diseases. On average, the Gompertz model best fitted disease progress data across all evaluated genotypes with an R² of 0.909 and an adjusted coefficient (R2*) of 0.849. The temporal analysis provided key epidemiological insights into the maize-NCLB pathosystem, supporting the development of effective management strategies. Highlights: Temporal analysis differentiated maize hybrids by resistance levels to NCLB. Gompertz model provided the best fit for NCLB epidemics. Final severity, AUDPC, and infection rates confirmed differences in hybrid resistance to NCLB. Understanding NCLB temporal dynamics supports the development of hybrid selection and integrated management strategies. Epidemiological tools enhance disease progress prediction and optimize NCLB control timing.

espagnol; castillan. Field trials were conducted in six locations in central-northern Córdoba, Argentina, using four maize hybrids with varying resistance to northern corn leaf blight (NCLB), caused by Exserohilum turcicum. Naturally occurring NCLB epidemics were evaluated. We analyzed disease severity (S%), disease progress curve (DPC), area under the disease progress curve, final severity (FS%) and apparent infection rate (r). Disease progress curves were simultaneously analyzed by fitting nonlinear epidemiological models (Gompertz and Logistic). Ballesteros and Villa María were the localities with the highest FS in susceptible hybrids (45% and 37.5%, respectively). Levels of FS were below 5% in Jesús María, Río Segundo and Freyre, and under 1% in El Tío. The highest AUDPC values were also observed in Ballesteros and Villa María (2150.1 and 1335.7, respectively). In the other locations, AUDPC values remained under 320, with statistically significant differences in all cases (p< 0.05). The resistant hybrid exhibited the lowest apparent infection rate compared to the other genotypes. Epidemic progress displayed, to varying degrees, sigmoid-shaped curves characteristic polycyclic diseases. On average, the Gompertz model best fitted disease progress data across all evaluated genotypes with an R² of 0.909 and an adjusted coefficient (R2*) of 0.849. The temporal analysis provided key epidemiological insights into the maize-NCLB pathosystem, supporting the development of effective management strategies. Highlights: Temporal analysis differentiated maize hybrids by resistance levels to NCLB. Gompertz model provided the best fit for NCLB epidemics. Final severity, AUDPC, and infection rates confirmed differences in hybrid resistance to NCLB. Understanding NCLB temporal dynamics supports the development of hybrid selection and integrated management strategies. Epidemiological tools enhance disease progress prediction and optimize NCLB control timing.