Wheat blast is a devastating disease caused by the fungal pathogen Magnaporthe oryzae pathotype Triticum that has spread to both neighbouring and distant countries following its emergence in Brazil in the 1980s. Under climate change conditions, wheat blast is predicted to spread primarily in tropical regions. Here we coupled a wheat crop simulation model with a newly developed wheat blast model, to provide quantitative global estimates of wheat blast vulnerability under current and future climates. Under current climatic conditions, 6.4 million hectares of arable land is potentially vulnerable to wheat blast. A more humid and warmer climate in the future (Representative Concentration Pathway 8.5) is likely to increase the area suitable for wheat blast infection, particularly in the Southern Hemisphere, and reduce global wheat production by 69 million tons per year (13% decrease) by mid-century. Impacts of climate change could be further exacerbated and food security problems increased.

Fusarium Head Blight (FHB) is a disease of great concern in wheat (Triticum aestivum). Due to its relatively narrow susceptible phase and environmental dependence, the pathosystem is suitable for modeling. In the present work, a mechanistic model for estimating an infection index of FHB was developed. The model is process-based driven by rates, rules and coefficients for estimating the dynamics of flowering, airborne inoculum density and infection frequency. The latter is a function of temperature during an infection event (IE), which is defined based on a combination of daily records of precipitation and mean relative humidity. The daily infection index is the product of the daily proportion of susceptible tissue available, infection frequency and spore cloud density. The model was evaluated with an independent dataset of epidemics recorded in experimental plots (five years and three planting dates) at Passo Fundo, Brazil. Four models that use different factors were tested, and results showed all were able to explain variation for disease incidence and severity. A model that uses a correction factor for extending host susceptibility and daily spore cloud density to account for post-flowering infections was the most accurate explaining 93% of the variation in disease severity and 69% of disease incidence according to regression analysis.

Intercropping is extensively used to increase land productivity and agricultural benefits. In developing countries, intercropping has historically been one of the most widely used cropping systems. Crop models have been used to assess risk productivity over time and space, particularly in monocropping systems. Crop models, such as the Decision Support System for Agrotechnology Transfer (DSSAT), have been widely used to improve crop growth, development, and yield predictions; however, this model has some limitations when assessing interspecific competition in intercropping systems (e.g., it does not have a subroutine capable of running two crops simultaneously). Therefore, in this study, we developed a new approach to allow DSSAT to run two crop species in intercropping systems. A light interception algorithm and modified source code were integrated into the DSSAT to simulate the relay-strip intercropping system. The intercrop model developed in this study is the first intercrop model for DSSAT. This model is generic and can be employed to build other cereal–legume intercrop models for DSSAT Version 4.8. Regarding risk assessment of crop production, the model can evaluate long-term cereal–legume intercrop yields in low-input cropping systems. Therefore, before officially launching the new model in DSSAT, more field trials are recommended to rigorously evaluate and improve the model with data from different environments. The intercrop model developed in this study is simple, so this modeling approach can be employed to develop other cereal–noncereal intercrop models.