Evaluating a landscape-scale model to forecast wheat stem rust

Abstract Wheat stem rust is a fungal disease that can cause total loss of a wheat crop. Outbreaks are typically far-reaching because infectious spores can disperse aerially over thousands of kilometres. Rapid response to an outbreak requires a timely and accurate estimate of disease risk. We constructed an integrated mechanistic model of stem rust infection driven by seven-day meteorological forecasts and near-real time disease surveillance to inform mitigation at the national scale. Focusing on Ethiopia as the largest wheat producer of sub-Saharan Africa, yet where food insecurity is high, we evaluate the predictability of wheat stem rust infections during the rain-fed seasons of 2015–2022. The epidemiological model performs well at predicting disease observations two weeks ahead of time, from cross-validation root-mean-square-error values were in the range 0.25–0.35, ROC skill score 0.58–0.68, and odds ratio skill score 0.78–0.90. Results of alternative model scenarios show the benefit of assimilating recent surveys and simulating spore dispersal to achieve a good prediction. When driven by seven-day weather forecasts, the model therefore provides up to 21 days to respond to likely stem rust infections and prevent epidemic spread and subsequent crop loss. The mechanistic form and large scale of the epidemiological model has utility beyond in-season predictions, particularly with respect to scenario testing for practical interventions. The results demonstrated that long-distance dispersal has an important role in the spread of stem rust in a region of diverse agro-ecology. The forecast system presented here can be adapted to other plant pathosystems where aerial dispersal is the primary risk of disease spread.