Plant diseases occur in all regions of the globe where there are susceptible hosts, aggressive and virulent pathogens, and a favorable environment. We aimed to evaluate potential effects of temperature variation on the monocyclic and polycyclic processes of coffee leaf rust in susceptible Arabica coffee cultivars cultivated in Minas Gerais state, Brazil. Historical monthly mean air temperature data, from 1970–2000, was downscaled and used as the temperature reference period and maximum and minimum temperature for four future periods, 2021–2040, 2041–2060, 2061–2080, and 2081–2100 (SSP126 scenario) from Coupled Model Intercomparison Project Phase 6 (CMIP6) were used to characterize the future mean air temperature variation were used in the modeling of areas favorable to the progress of coffee leaf rust in Minas Gerais using spatial data techniques. Digital elevation model was considered for downscaling climate data. A non-linear regression model simulating the monocyclic process of coffee leaf rust was used to simulate the potential progress of the disease in susceptible cultivars under the different scenarios evaluated. In general, coffee leaf rust progress increased in susceptible cultivars located in areas with higher ground elevation, with emphasis in the south of the state, as well as in the main Arabica coffee producing regions. There was a reduction of areas favorable to rust in the north of the state due to temperature increase considering climate change scenario, however, new areas in the south of the state became more favorable to the disease. In general, in Minas Gerais state, the temperature will increase between periods from 2021 to 2040, 2041 to 2060, 2061 to 2081, and from 2081 to 2100, by 1.2 °C, 0.6 °C, 0.2 °C, and 0.1 °C, respectively.
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Author MCA declares that he has no conflict of interest. Author LS declares that she has no conflict of interest.
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de Carvalho Alves, M., Sanches, L. Potential Effects of Spatio-Temporal Temperature Variation for Monitoring Coffee Leaf Rust Progress Under CMIP6 Climate Change Scenarios. Earth Syst Environ 6, 421–436 (2022). https://doi.org/10.1007/s41748-021-00286-7
- Shared socioeconomic pathways
- CNRM-ESM2-1 model