Early Warning System for Wheat Blast Disease Outbreaks by | Apr 6, 2020 Edit digital intervention Digital Intervention Title Digital Intervention Excerpt 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. Description Visual Text File Edit View Insert Format Tools Table Paragraph HashBarTimes New Roman 12pt Wheat blast (<em><u>Magnaporthe oryzae</u> Pathotype Triticum</em> (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. References: 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. <a href="https://doi.org/10.1007/s40858-017-0164-2" target="_blank" rel="noopener">https://doi.org/10.1007/s40858-017-0164-2</a>. Featured Image Types of Digital Intervention Decision support agriculture Digital advisory Digital analytics and modelling Digital disease monitoring and control Digital Financial Services Digital mechanization Farm management software Market systems Remote sensing Robotics Food system components Aggregation Consumption Distribution Policy Processing Production Food system activities Agricultural extension Agricultural subsidy provision Animal disease management Climate change Crop diseases management Crop production Farm inputs Finance Genetic resources Labor force Livestock production Natural resource management Nutrition information Packaging Post-harvest management Regulation and standards Scientific knowledge Tractor services provision Trade and market information Transport Urbanization Waste management Primary users Extension agents Farmers Financial institutions Government ministries Researchers Traders Intervention inception number of users Intervention current number of users Country/ies of implementation Afghanistan Bangladesh Bolivia Burkina Faso Benin Cameroon China Colombia Costa Rica Côte d’Ivoire El Salvador Ethiopia Ghana Grenada Guatemala Haiti Honduras India Indonesia Italy Jamaica Kenya Malaysia Mali Malawi Mexico Mongolia Mozambique Myanmar Nepal Nicaragua Niger Nigeria Pakistan Peru Philippines Rwanda Senegal Sierra Leone South Africa South Sudan Sri Lanka Tanzania Thailand Trinidad and Tobago Tunisia Turkey Uganda United States of America Vietnam Zambia Zimbabwe Region(s) of implementation Africa Asia / Pacific Europe and Central Asia Latin America and the Caribbean Middle East North America Geolocation Address We could not connect to the Google Maps autocomplete service, but you can add an address manually. Address Add new Evidence of Impact Evidence narrative Visual Text File Edit View Insert Format Tools Table Paragraph HashBarTimes New Roman 12pt 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. Intervention evidence of economic impact Increased volume of sales Increased yield Increased market access Increased access to credit Increased transaction cost Increased production cost Decreased market access Reduced transaction cost Reduced production cost Received higher product prices Decreased volume of sales I do not know Decreased yield None of the above Intervention evidence of environmental impact Increased efficiency in agro chemical use Increased resilience to climate shocks Increased access to weather information Increased access to agricultural information services in real time Decreased efficiency of agro chemical use Decreased resilience to climate shocks Decreased access to weather information I do not know Decreased access to agricultural information services in real time None of the above Intervention evidence of social impact Increased women participation Decreased women participation Increased youth participation Decreased participation of minority group Improved social capital Increased economic mobility Enhanced social inclusion I do not know Improved social equity None of the above Enhanced social well being Intervention evidence of technical impact Increased technology adoption Improved information dissemination Increased labor demand Increased the need for agricultural extension agents Better support for extension agents Reduced the need for agricultural extension agents Decreased labor demand Displacement of on-farm labor I do not know None of the above Intervention quantitative impact Increased efficiency by 0-25% Increased efficiency by 26-50% Increased efficiency by 51-75% Increased efficiency by 76-100% Decreased efficiency by 0-25% Decreased efficiency by 26-50% Decreased efficiency by 51-75% Decreased efficiency by 76-100% Additional Information Enter URL Add new Submit Intervention File Add new Contact details First Name Last Name Email
