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

Abstract

Forecasting the severity of plant diseases is an emerging need for farmers and companies to optimize management actions and to predict crop yields. Process-based models are viable tools for this purpose, thanks to their capability to reproduce pathogen epidemiological processes as a function of the variability of agro-environmental conditions. We formalized the key phases of the life cycle of Puccinia kuenhii (W. Krüger) EJ Butler, the causal agent of orange rust on sugarcane, into a new simulation model, called ARISE (Orange Rust Intensity Index). ARISE is composed of generic models of epidemiological processes modulated by partial components of host resistance and was parameterized according to P. kuenhii hydro-thermal requirements. After calibration and evaluation with field data, ARISE was executed on sugarcane areas in Brazil, India and Australia to assess the pathogen suitability in different environments. ARISE performed well in calibration and evaluation, where it accurately matched observations of orange rust severity. It also reproduced a large spatial and temporal variability in simulated areas, confirming that the pathogen suitability is strictly dependent on warm temperatures and high relative air humidity. Further improvements will entail coupling ARISE with a sugarcane growth model to assess yield losses, while further testing the model with field data, using input weather data at a finer resolution to develop a decision support system for sugarcane growers.

Plant phenotyping relevance

サトウキビの病徴（オレンジさび病重症度）を推定するプロセスベースモデルを開発し、圃場データで較正・評価しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper's field observations of orange rust severity and the ARISE model source code are both stated to be available only on request from the corresponding author; no public repository or URL is provided. Supplementary files (ESM 1–7) are figures/documents, not datasets or code.

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