Tropical Plant Pathology

, Volume 42, Issue 3, pp 230–237 | Cite as

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

  • José Maurício Cunha Fernandes
  • Márcio Nicolau
  • Willingthon Pavan
  • Carlos Amaral Hölbig
  • Maurício Karrei
  • Felipe de Vargas
  • Jorge Luis Boeira Bavaresco
  • Alexandre Tagliari Lazzaretti
  • Rodrigo Y. Tsukahara
Original Article

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.

Keywords

Magnaporthe oryzae Triticum Disease forecasting Warning system 

Notes

Acknowledgements

The authors are thankful to the editor and to anonymous reviewers for their comments and suggestions. This work was partially supported by Agriculture and Food Research Initiative Competitive Grant no. 2013-68004-20378 from the United States Department of Agriculture National Institute of Food and Agriculture (USDA- NIFA) and by the CNPq Grant no. 444047/2014-0.

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Copyright information

© Sociedade Brasileira de Fitopatologia 2017

Authors and Affiliations

  • José Maurício Cunha Fernandes
    • 1
    • 2
  • Márcio Nicolau
    • 1
  • Willingthon Pavan
    • 2
  • Carlos Amaral Hölbig
    • 2
  • Maurício Karrei
    • 2
  • Felipe de Vargas
    • 2
  • Jorge Luis Boeira Bavaresco
    • 3
  • Alexandre Tagliari Lazzaretti
    • 3
  • Rodrigo Y. Tsukahara
    • 4
  1. 1.Embrapa TrigoPasso FundoBrazil
  2. 2.ICEG/UPFPasso FundoBrazil
  3. 3.Instituto Federal Sul-Rio-grandense (IFSUL)Passo FundoBrazil
  4. 4.Fundação ABCCastroBrazil

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