Using expert knowledge data to validate crop models on local situation data

Abstract

Cropping system models are widely used tools for simulating the growth and development of crops at field scale. However, it is often difficult to satisfy their detailed input and output data requirements for a proper evaluation of model. In this study, expert knowledge data were used as alternative source to fulfil these data requirements. The model was first calibrated for major crops of the studied area and then evaluated for the same crops by using expert knowledge data. Results showed that the model accurately simulated above-ground biomass and grain yield with a relative root mean square error (RRMSE) of 20% and 17%, respectively. On the other hand, simulated results were less satisfactory for N uptake and cumulated evapotranspiration with RRMSE of 27% and 31%, respectively. The model simulated cumulative variables more accurately than dynamic variables. The results of this study suggest that expert knowledge can be used to get data for intermediate variables rarely measured in experiments used for calibration (green leaf area index, actual evapotranspiration, rooting depth) in typical crop management conditions in the region. This approach enables a global and dynamic evaluation of cropping system models when experimental data is unavailable for large heterogeneous areas in a region.