Abstract: A common format for model input variables and model output variables has been defined to be distributed to modellers participating in the model inter-comparison and improvement. The aim of common formats is to support the communication between the modellers, those providing empirical data of the experiments and those analysing the simulation results. The input format facilitates the model application in a way that each cropping-system to be modelled will be defined in the same way. Data will be delivered in EXCEL sheets with sub-tables for each block of inputs. Tables are mostly organized in a way that allows export and sequential read-in by the models. The common output format enables effective processing of results estimating model performance indicators. No Label

Abstract: Agriculture is facing increasing challenges under volatile commodity markets, on-going climate change with more frequent extreme weather events and tightened environmental constraints. Crop rotation is considered essential and may even gain more importance for sustainable farming in the context of climate change challenges while monocropping is expected to become increasingly problematic. This is, among others, because of increasing plant protection challenges due to warmer climate which is expected to result in severe droughts, heavy rainfall and waterlogging in northern latitudes more frequently. Such changes require improved soil structure and water retention, also aided by crop rotations, to avoid yield losses. Our objective is to build and apply a dynamic optimization model of farm level crop rotation on many field parcels over 30-40 years. The model takes into account various adaptation management methods such as fungicide treatment, soil improvements such as liming, and nitrogen fertilization, simultaneously with dynamic crop rotation choices. However, these management options come along with costs. Using the model, outcomes of crop growth simulation modeling can be included into economic analysis. Simulated new cultivars, suited for a longer growing season, can be defined as alternatives to current cultivars, both having specific nutrient and other input requirements such as water, labor or pesticides. The model is used in evaluating the value of future cultivars and other management practices in climate and socio-economic scenarios. The first results show that expected market prices have major impacts on the management choices, the resulting yield levels, production and income over time. No Label

Abstract: ► We compared nine crop simulation models for spring barley at seven sites in Europe. ► Applying crop models with restricted calibration leads to high uncertainties. ► Multi-crop model mean yield estimates were in good agreement with observations. ► The degree of uncertainty for simulated grain yield of barley was similar to winter wheat. ► We need more suitable data enabling us to verify different processes in the models. In this study, the performance of nine widely used and accessible crop growth simulation models (APES-ACE, CROPSYST, DAISY, DSSAT-CERES, FASSET, HERMES, MONICA, STICS and WOFOST) was compared during 44 growing seasons of spring barley (Hordeum vulgare L) at seven sites in Northern and Central Europe. The aims of this model comparison were to examine how different process-based crop models perform at multiple sites across Europe when applied with minimal information for model calibration of spring barley at field scale, whether individual models perform better than the multi-model mean, and what the uncertainty ranges are in simulated grain yields. The reasons for differences among the models and how results for barley compare to winter wheat are discussed. Regarding yield estimation, best performing based on the root mean square error (RMSE) were models HERMES, MONICA and WOFOST with lowest values of 1124, 1282 and 1325 (kg ha(-1)), respectively. Applying the index of agreement (IA), models WOFOST, DAISY and HERMES scored best having highest values (0.632, 0.631 and 0.585, respectively). Most models systematically underestimated yields, whereby CROPSYST showed the highest deviation as indicated by the mean bias error (MBE) (-1159 kg ha(-1)). While the wide range of simulated yields across all sites and years shows the high uncertainties in model estimates with only restricted calibration, mean predictions from the nine models agreed well with observations. Results of this paper also show that models that were more accurate in predicting phenology were not necessarily the ones better estimating grain yields. Total above-ground biomass estimates often did not follow the patterns of grain yield estimates and, thus, harvest indices were also different. Estimates of soil moisture dynamics varied greatly. In comparison, even though the growing cycle for winter wheat is several months longer than for spring barley, using RMSE and IA as indicators, models performed slightly, but not significantly, better in predicting wheat yields. Errors in reproducing crop phenology were similar, which in conjunction with the shorter growth cycle of barley has higher effects on accuracy in yield prediction.