Strengthening Predictive Models for Wheat Stem Rust Infection Risk in a Changing Climate: Gaps and Opportunities

Puccinia graminis f. sp. tritici (Pgt) is a major global fungal pathogen that causes stem rust of wheat ( Triticum spp.) (the primary and principal host). As with other fungal pathogens, disease management relies on resistant cultivars, routine fungicide applications and agronomic practices tailored to limit disease spread. However, slow breeding cycles, along with the rising costs and environmental impacts of fungicides, can hinder disease control. Thus, alternative, targeted approaches to enhance the efficacy of wheat stem rust intervention strategies are urgently needed. Epidemiological forecasting offers a promising approach for better guiding intervention strategies. In this review, we evaluate the available environmental data underpinning the epidemiological models that predict wheat stem rust infection risk and disease development. We highlight the influence of light on the early stages of Pgt infection and the role of temperature during transitions throughout the Pgt life cycle. We also consider the challenges of incorporating temperature‐dependent Pgt urediniospore production and release into epidemiological models. We discuss specific data gaps that affect the precision and predictive power of these tools, particularly in the context of dynamic pathogen adaptation and climate change. These include data gaps in urediniospore viability loss, light constraints on infection risk, germination rates for contemporary Pgt isolates and the effects of host cultivar, alternate hosts, irrigation and adaptation to temperature shocks on infection risk. As these gaps close, integrating smart epidemiological models into traditional approaches to disease management holds great potential to reduce wheat stem rust outbreaks.