Epidemiology and prediction models for themanagement of rapeseed–mustard diseases: current status and future needs

Abstract

The productivity of rapeseed-mustard in India is quite low as compared to the world scenario. It is mainly due to important diseases, Alternaria blight, white rust, downy mildew, powdery mildew, and white or Sclerotinia rot. Knowledge of epidemiologyand forecasting provide the basic information to developefficient and workable plant disease control models. The various weather variableslike temperature (T), relative humidity (RH), rainfall, wind velocity, and direction, leaf wetness duration, and solar radiation influence differentparameters of infection process, and disease development. Interaction between these weather variables and disease development pave the way for the development of the prediction models. Prediction models developed for the management of important diseases of rapeseed-mustard revealed that Alternaria blight is favoured by Tmax of 20–25 °C, Tmin of 15 °C, RHmor > 90% and RHeve > 50% where as white rust influencedby > 15 °C and RH > 65% with intermittent rains. Similarly, for downy mildew, Temprange of 15–20 °C with high RH was considered optimal for its progress. Leaf wetness duration of 4–6 h at 20 °C and 6–8 h at 15 °C is essential to initiate the downy mildew infection. Stag-head due to mixed infection of downy mildew and white rust is favoured by a Temp 20 °C with high RH and reduced period of sunshine (2–6 h/day) with rainfall up to 161 mm. Powdery mildew development is favoured by Temprange of 16–28 °C, mean RH 80%), Tmax up to 25 °C and Tmin of 5–12 °C. Often prediction models developed at one location may not fit atother locations. It indicates that data needs to be generated for a longer period and the model be tested atMultilocation. The disease-forecasting models must be developed by taking into account the crop variety, the prevalence of a particular pathotype and the microclimatic factors.