http://researchonline.federation.edu.au/vital/access/manager/Index ${session.getAttribute("locale")} 5 http://researchonline.federation.edu.au/vital/access/manager/Repository/vital:627 Wed 07 Apr 2021 13:31:34 AEST ]]> http://researchonline.federation.edu.au/vital/access/manager/Repository/vital:183 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.]]> Wed 07 Apr 2021 13:31:02 AEST ]]>