A process-based model to simulate sugarcane orange rust severity from weather data in Southern Brazil

Area of Science:

• Plant pathology and agricultural meteorology.
• Development of predictive models for crop diseases.

Background:

• Accurate forecasting of plant disease severity is crucial for optimizing agricultural management and predicting crop yields.
• Process-based models are effective for simulating pathogen epidemiology under varying agro-environmental conditions.

Purpose of the Study:

• To develop and validate a simulation model, ARISE (Orange Rust Intensity Index), for forecasting sugarcane orange rust severity caused by Puccinia kuenhii.
• To assess the suitability of different environments for P. kuenhii development.

Main Methods:

• Formalized the life cycle of Puccinia kuenhii into the ARISE model, incorporating epidemiological processes and host resistance.
• Parameterized the model with P. kuenhii hydro-thermal requirements and validated it using field data from Brazil, India, and Australia.

Main Results:

• The ARISE model demonstrated good performance in calibration and evaluation, accurately matching observed orange rust severity.
• Simulations revealed significant spatial and temporal variability in pathogen suitability, strongly linked to warm temperatures and high relative humidity.

Conclusions:

• The ARISE model is a viable tool for forecasting sugarcane orange rust, highlighting the critical role of climate conditions.
• Future work includes coupling ARISE with crop growth models for yield loss assessment and developing a decision support system for growers.