Simulating regional winter wheat yields using input data of different spatial resolution

Abstract The success of using agro-ecosystem models for the high-resolution simulation of agricultural yields for larger areas is often hampered by a lack of input data. We investigated the effect of different spatially resolved soil and weather data used as input for the MONICA model on its ability to reproduce winter wheat yields in the Federal State of Thuringia, Germany (16,172 km 2 ). The combination of one representative soil and one weather station was insufficient to reproduce the observed mean yield of 6.66 ± 0.87 t ha −1 for the federal state. Use of a 100 m × 100 m grid of soil and relief information combined with just one representative weather station yielded a good estimator (7.01 ± 1.47 t ha −1 ). The soil and relief data grid used in combination with weather information from 14 weather stations in a nearest neighbour approach produced even better results (6.60 ± 1.37 t ha −1 ); the same grid used with 39 additional rain gauges and an interpolation algorithm that included an altitude correction of temperature data slightly overpredicted the observed mean (7.36 ± 1.17 t ha −1 ). It was concluded that the apparent success of the first two high-resolution approaches over the latter was based on two effects that cancelled each other out: the calibration of MONICA to match high-yield experimental data and the growth-defining and -limiting effect of weather data that is not representative for large parts of the region. At the county and farm level the MONICA model failed to reproduce the 1992–2010 time series of yields, which is partly explained by the fact that many growth-reducing factors were not considered in the model.