After many decades of effective control of stem rust caused by the Puccinia graminis f.sp. tritici, (hereafter Pgt) the reported emergence of race TTKSK/Ug99 of Pgt in Uganda reignited concerns about epidemics worldwide because ∼90% of world wheat cultivars had no resistance to the new race. Since it was initially detected in Uganda in 1998, Ug99 variants have now been identified in thirteen countries in Africa and the Middle East. Stem rust has been a major problem in the past, and concern is increasing about the risk of return to Central and East Asia. Whilst control programs in North America and Europe relied on the use of resistant cultivars in combination with eradication of barberry (Berberis spp.), the alternate host required for the stem rust pathogen to complete its full lifecycle, the focus in East Asia was principally on the use of resistant wheat cultivars. Here, we investigate potential airborne transmission pathways for stem rust outbreaks in the Middle East to reach East Asia using an integrated modelling framework combining estimates of fungal spore deposition from an atmospheric dispersion model, environmental suitability for spore germination, and crop calendar information. We consider the role of mountain ranges in restricting transmission pathways, and we incorporate a representation of a generic barberry species into the lifecycle. We find viable transmission pathways to East Asia from the Middle East to the north via Central Asia and to the south via South Asia and that an initial infection in the Middle East could persist in East Asia for up to three years due to the presence of the alternate host. Our results indicate the need for further assessment of barberry species distributions in East Asia and appropriate methods for targeted surveillance and mitigation strategies should stem rust incidence increase in the Middle East region.

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.

Richardson extrapolation is a simple but powerful computational tool to enhance the accuracy of time integration methods. In the past years a few theoretical and partly practical works have been presented on this method. Detailed numerical applications of this method, however, are rarely found in the literature. Therefore, it is worth investigating whether this promising technique lives up to the expectations also in practice. In this paper we investigate the efficiency of the Richardson method in one‐dimensional numerical (reaction‐diffusion) problems.

Wheat rusts are major constraints for global wheat production, leading to substantial yield and economic losses. To mitigate rust related production risks, disease forecast models generate time and location-specific information on the emergence and spread of wheat rusts. While predictions become increasingly more accurate, information on whether model outputs can enhance the effectiveness of farmers’ disease management is scarce. In this paper, we used farm household survey data and duration analysis to assess the responsiveness of smallholder farmers to risk predictions for wheat yellow and stem rust. The study used: (i) rust pressure forecasts from the wheat rust Early Warning and Advisory System (EWAS) that was first piloted in 2015 and routinely deployed in Ethiopia since 2017; (ii) within-season data on fungicide applications from 520 farmers from Ethiopia’s major wheat production regions. Results from duration models indicated that the timing of farmers’ fungicide applications for wheat rust control was associated with increasing predicted rust pressure levels and farmer exposure to related extension services. However, farm-level fungicide application dates showed that about one fourth of all applications occurred outside critical growth stages of wheat. We discuss how the existing operational rust surveillance system can enhance farmers’ wheat rust management.