Abstract: Agroecosystems are highly sensitive to climate change. To predict and describe the processes, interactions and feedbacks in the plant-soil-system a model accounting for both compartments at an appropriate level of complexity is required.To describe the processes of crop development, crop growth, water flux, heat transport, and carbon cycling three process models were coupled and adjusted to each other: the one-dimensional soil water, heat and CO2 transport model SOILCO2, the carbon turnover model RothC, and the plant growth model SUCROS. Thereby, the main focus was on the full description of the CO2 flux into the atmosphere via plant and soil processes and finally on simulating the net ecosystem exchange. Additionally, the model was modified to work at the temporal resolution between 0.5 and 24 hours.For a first model evaluation a winter wheat data set obtained within the TERENO Rur catchment (North Rhine-Westphalia, Germany) during 2009 was used. For model initialisation soil carbon fractions were available. Plant specific parameters and soil properties were taken from literature. Measured soil water contents, soil temperatures, crop measurements, autotrophic, and heterotrophic chamber-based respiration measurements were used for validation and calibration.The coupled agroecosystem model AgroC described the crop development and heat transport well. Minor adjustments had to be made for carbon cycling, and to adapt the model to site specific conditions the soil hydraulic coefficients for soil water transport had to be determined by inverse modelling.

Abstract: We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.