Cross-validation of predective models for deoxynivalenol in wheat at harvest

To date, several models that predict deoxynivalenol (DON) in wheat at harvest are available. This study aimed to evaluate the performance of two of such models, including a mechanistic model developed in Italy and an empirical model developed in the Netherlands. For this aim, field data collected in the period 2002-2004, 2009-2011 in Italy, and in the period 2001-2010 in the Netherlands were used. These historical data covered farm observations at 1306 wheat fields, of which 155 from the Netherland and 1151 from Italy. A subset of 10% of the Italian data, derived by random sampling from the total Italian dataset, was used to validate both the Italian and the Dutch model. Additionally, the Italian mechanistic model was validated using the total Dutch dataset. Before validation of the Dutch model, this model was recalibrated using the remaining 90% of the Italian data. Results showed that predictions of both modelling approaches (mechanistic and empirical) for independent wheat fields were in accordance. Applying a threshold for DON concentrations of 1250 ppb, the mechanistic DON predicted 90% of the samples correctly. Results for cross validation of the mechanistic DON model and the recalibrated empirical DON model, showed that 93% of the samples were correctly predicted. In general, no more than 6% of underestimates were observed.