DIGITAL FOOD SYSTEMS EVIDENCE CLEARING HOUSE
Food system component(s):
Food system activity/ies:
Type(s) of digital intervention:
The Early Warning System (EWS) provides alerts of disease risk available on a dashboard, via email, or text message for users. The EWS also has in-built crop models to allow researchers to explore the implications of sowing date and management on disease risks.
Wheat blast (Magnaporthe oryzae Pathotype Triticum (MoT) can be a particularly devastating disease of wheat (Figure 1). A simulation model that accounts for meteorological conditions and their effect on inoculum build-up and infection was developed in Brazil, where wheat blast has been a problem for over 30 years (Fernandes et al., 2017). This predictive model was developed and evaluated based on the analysis of historical epidemics and weather series data in the northern Paraná state in Brazil and then applied to Bangladesh. Expert national epidemiological knowledge of the meteorological conditions during which infections occurred in both countries were also employed in model parameterization.
Importantly, this model also assumes the geographically uniform presence of MoT inoculum in the environment for which simulations are run. In other words, the model assumes that inoculum is uniformly present throughout locations that it predicts for, and does not yet account for source-sink and spore dispersal mechanisms. The disease incidence and hourly-scale weather datasets examined by Fernandes et al., (2017) for Brazil encompassed the 2001–2012 period.
The EWS provides alerts of disease risk available on a dashboard, via email, or text message for users. The EWS also has in-built crop models to allow researchers to explore the implications of sowing date and management on disease risks. The EWS was developed by the International Maize and Wheat Improvement Center (CIMMYT) in partnership with EMBRAPA and the University of Passo Fundo (UPF) in Brazil, alongside a range of international and national research and extension partners, most notably the Bangladesh Maize and Wheat Research Institute (BWMRI) Bangladesh Meteorological Department (BMD), Bangladesh Department of Agricultural Extension (DAE). This effort has been supported by the U.S. Agency for International Development (USAID) funded Climate Services for Resilient Development (CSRD) in South Asia project and the USAID and Bill and Melinda Gates Foundation (BMGF) supported Cereal Systems Initiative for South Asia (CSISA). These activities are aligned with the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the CGIAR Research Program WHEAT and platform on Big Data, respectively.
Fernandes, J. M. C., Nicolau, M., Pavan, W., Hölbig, C. A., Karrei, M., de Vargas, F., Tsukahara, R. Y. (2017). A weather-based model for predicting early season inoculum build-up and spike infection by the wheat blast pathogen. Trop. Plant Pathology. https://doi.org/10.1007/s40858-017-0164-2.
Estimated number of active users:
- At inception: 20
- At time of last report: 400
Evidence of impact
The Early Warning System for Wheat Blast Disease Outbreaks was developed as a collaborative effort between CIMMYT, EMBRAPA and the University of Passo Fundo. The system was co-developed with the Bangladesh Maize and Wheat Research Institute (BWMRI) Bangladesh Meteorological Department (BMD), and the Bangladesh Department of Agricultural Extension (DAE) between 2017 and 2019. This co-development led to the endorsement of the system for institutional use in informing extension agents in Bangladesh of risks of outbreak in late 2019. The system is now endorsed by national partners in Bangladesh and is being used to deploy advice to farmers at a national scale when and where blast outbreaks are predicted. Currently, 1,100 extension agents in DAE are getting real-time disease advisories, each of which serve at least 500 farmers.