Sclerotinia sclerotiorum disease prediction: A review and potential applications in South Africa

Keywords: Sclerotinia spp., prediction models, plant disease epidemiology, fungicide, economic optimisation


Sclerotinia sclerotiorum is a predominant plant pathogen, with host crops of agricultural and economic importance internationally. South African host crops of importance include canola, soybean and sunflower, which contribute significantly to the South African economy. This significance emphasises the importance of effective disease management strategies, including rotation with non-host crops, planting cultivars with a degree of tolerance, and using relevant cultural and chemical practices. The sporadic nature of disease outbreaks caused by Sclerotinia spp. can complicate fungicide application timing as a result of the pathogen’s interaction with the host and environment. The use of prediction modelling for diseases caused by Sclerotinia spp. can contribute to increased fungicide application efficacy and a reduction in the number of unnecessary sprays. Predictive modelling is based upon the collection and statistical analysis of multi-locality and multi-seasonal, pathogen, disease and weather data. Incorporating the complexity of disease initiation and development into such models is dependent on selecting the correct statistical tools to interpret appropriate data, which can be used to develop a model that is accurate, precise and reliable. Internationally, forecasting models for diseases caused by Sclerotinia spp. exist and are applied commercially for multiple Sclerotinia spp. on important agricultural crops. The application of these models in a South African context has been limited but provides promise for effective disease intervention technologies. This review provides a platform to raise awareness of the potential applications of plant disease epidemiology and the use of statistics and mathematical modelling in agricultural systems. Plant disease forecasts are an important part of the future for sustainable and economically viable agronomic decisions.


  • Optimisation of plant disease management through ensuring that fungicide applications coincide with disease-favourable conditions, thus targeting the disease more strategically.
  • The use of mathematical and statistical models to quantify the interactions among the host, pathogen and environment and predict future outbreaks of the disease.
  • The study of temporal and spatial interactions among the host, pathogen and environment on plant disease behaviour.
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