A weather-based model for predicting early season inoculum build-up and spike infection by the wheat blast pathogen

Abstract

Wheat blast, caused by the fungus Magnaporthe oryzae Triticum pathotype (MoT), is a serious disease capable of causing severe losses, especially during warm and humid weather conditions. Although the pathogen attacks all aboveground parts, infection of the wheat spikes is of major concern. In this work we developed and evaluated a prediction model based on the analysis of historical epidemics and weather series in the northern Paraná state, Brazil (Apucarana, Maringá and Londrina) and available epidemiological knowledge. The disease and weather datasets (hourly scale) examined encompassed the 2001-2012 period. A specific database management application (agroDb) helped to visualize and identify patterns in weather variables during two major outbreaks (2004 and 2009). Specifically, uncommonly humid and warm weather for most locations during a 60-day period preceding wheat heading during years of major outbreaks were considered key drivers of inoculum build up and airborne spores from regional inoculum sources in the surroundings. An inoculum potential (IP) and a spore cloud (SPOR) variable were estimated from models adapted from literature to predict inoculum build-up and availability. A day favoring infection (DFI) was conditioned to rules relating temperature and relative humidity for the day derived from the epidemic analysis. Successful daily infection (INF) during a DFI was conditioned to IP > 30 and SPOR >0.4. To test the model, a wheat model simulated heading date for 10 planting dates, spaced 5 days apart, within a year, totaling 320 simulations. The model described well epidemic and non-epidemics conditions for the historical dataset, and was able to correctly predict epidemic (2015) and non-epidemic (2016) years not analyzed to build the model. An interactive risk-mapping tool that collects real-time weather data was developed for the target area to warn potential outbreaks.The system can be adapted to other regions where the disease is endemic or to asses the epidemic potential in regions where the disease is not present.

Wheat blast: from its origins in South America to its emergence as a global threat

“…The optimum weather conditions for wheat blast development include long and frequent periods of leaf wetness (24–40 h), coupled with high temperatures (25–30 °C) (Cardoso et al ., ). In Brazil, a predictive model for wheat blast outbreaks based on daily climatic data, named Sisalert ( Plant Disease Epidemic Risk Prediction System ), was developed to calculate the risk of an epidemic (available at: https://dev.sisalert.com.br/monitoramento/?page_id=14; Fernandes et al ., ; Nicolau et al ., ). In the USA, a climatic model adapted from this Brazilian model indicated that the weather conditions were favourable to wheat blast in 25% of the winter wheat cropping regions, with suitable conditions for wheat blast outbreaks in 70% of the years for Louisiana, Mississippi and Florida (Cruz et al ., ).…”

Wheat Blast in Bangladesh: The Current Situation and Future Impacts

“…Several prediction models were proposed for wheat blast ( Cardoso et al, 2008 ; Fernandes et al, 2017 ). The models used climate data such as temperature, relative humidity, rainfall, and solar radiation, and calculated values contributing to development of wheat blast (inoculum potential, spore cloud, and day favouring infection (DFI)).…”

Nitrogen and potassium sources of fertilizers may affect wheat blast and Fusarium head blight under favorable weather conditions

“…Xu (2003) working with disease forecasting system for FHB, reported that the models include the effects of weather variables on two aspects of epidemic development: i) spore production, which requires sufficient rainfall about 8─10 days prior to and during anthesis, to improve production of ascospore and conidia; ii) spore dispersal and infection, which demand enough rainfall to disperse ascospores and/or conidia, followed by prolonged periods of warm humid conditions that are conducive for infection of spikes. On the other hand, Fernandes et al (2017) reported in their study about weather-based model for predicting WB inoculum and infection that persistent rainy and warm conditions may warrant pathogen survival during spring and summer seasons in the form of conidia and mycelia on seed, wheat volunteers, plant debris, grass weed and cultivated species. Thus, the WB infection-based models have to include a combination of weather events at both, pre-and within-season, which affect inoculum build-up, spread and infection.…”