Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the
severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides.
Disease severity and weather data were analysed using artificial neural network (ANN) models developed using
data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models
were developed using different weather summaries. Of these, ANN models with weather for the day of disease assessment
and the previous 24 h period had the highest prediction success, and models trained on data from all sites within
one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction
error was 21·9% for the Australian and 22·1% for the South American model. Of the six cross-continent ANN models
trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed
without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without
Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models,
moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as
radiation and wind on the day of disease severity assessment or the day before assessment were the most important
weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of
anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather
conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia
to serious anthracnose development.