Outbreaks of wheat blast in South Asia‚Äö a region where people consume over 100 million tons of wheat each year‚Äö have an enhanced impact on food stability and income security. In 2016, wheat blast struck South Asia unexpectedly, with crop losses in Bangladesh averaging 25 to 30 percent, threatening progress in the region's food security efforts. Estimates are that blast could reduce wheat production by up to 85 million tons in Bangladesh, equivalent to $13 million in foregone farmers' profits each year when outbreak occurs.
A wheat blast simulation model that accounts for inoculum build-up and infection has been validated in Brazil (Fernandes et al., 2017). The prediction model was developed and evaluated based on the analysis of historical epidemics and weather series data in the northern Paran√° state, Brazil. Local available epidemiological knowledge was also employed in model parameterization. Importantly the model also assumes the spatially uniform presence of MoT inoculum in the environment for which simulations are run. The disease and hourly-scale weather datasets examined by Fernandes et al., (2017) for Brazil encompassed the 2001‚Äì2012 period. A specific database management application (Farman et al., 2017) developed using R Shiny was programmed to visualize and identify patterns in weather variables during two major outbreaks (2004 and 2009). Uncommonly humid and warm weather was observed for most locations in this study during a 60-day period preceding wheat heading during years of major outbreaks. These conditions were therefore considered key drivers of inoculum build-up and airborne spores from regional inoculum sources in the surroundings. The prediction blast model has four components. The first component assumes spores are present and estimates the rate of conidiophore development as a function of temperature and relative humidity (Bregaglio and Donatelli, 2015), both of which are integrated to estimate blast inoculum potential by solving equation 1 for the hourly sum of inoculum potential (IP) over the season. Where T and RH are air temperature and relative humidity, respectively. Where RH is below the threshold in Equation 1, the model does not accumulate thermal time. The model also calculates the development of a spore cloud subject to assumptions of air current uptake, atmospheric diffusion and wind shear that affect spore longevity. Survival of spores while airborne may also be affected by temperature, solar and ultraviolet radiation, in addition to relative humidity (Deacon, 2005). Spore cohorts were therefore assumed to have a half-life of three days within any seven-day window. The model also determines the number and timing of days with climatic conditions favoring blast infection using a conditional ruleset. Days favoring infection were consequently declared following spore cloud development when the daily maximum temperature exceeded 23¬∫C and temperature amplitude (calculated daily minimum temperature subtracted from daily maximum temperature) was > 13¬∫C, with mean daily RH above 70%. The model adequately described observed epidemic and non-epidemics years during and beyond the study period in Brazil. Hourly weather data for the state of Paran√°, Brazil is collected from the SIMEPAR (Technological Institute Simepar) automated weather station network. While the automated weather station network from INMET (National Institute of Meteorology) provide hourly weather data for the whole country. The short term numerical weather forecast is provided by INPE (National Institute of Spacial Research) in a grid of 15 x 15 km. In Bangladesh, observed hourly weather data is obtained from BMD (Bangladesh Meteorological Department) automated weather station network and from CIMMYT. The short term numerical weather forecast is provided by BMD in a grid of 17 x 17 km.