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.

Abstract: Crop models describe the growth and development of a crop interacting with its surrounding agro-environmental conditions (soil, climate and the close conditions of the plant). However, the implementation of such models remains difficult because of the high number of explanatory variables and parameters. It often happens that important discrepancies appear between measured and simulated values. This article aims to highlight the different sources of uncertainty related to the use of crop models, as well as the actual methods that allow a compensation for or, at least, a consideration of these sources of error during analysis of the model results. This article presents a literature review, which firstly synthesises the general mathematical structure of crop models. The main criteria for evaluating crop models are then described. Finally, several methods used for improving models are given. Parameter estimation methods, including frequentist and Bayesian approaches, are presented and data assimilation methods are reviewed.

Abstract: Cropping systems are affected by climate change because of the strong relationship between crop development, growth, yield, CO2 atmospheric concentration and climate conditions. The increasing temperatures and the reduction of available water resources may result in negative impacts on the agricultural activity in Mediterranean environments than other areas. In this study the CERES-Wheat and CROPGRO-Tomato models were used to assess the effects of climate change on winter wheat (Triticum durum L.) and processing tomato (Lycopersicon aesculentum Mill.) in one of most productive areas of Italy, located in the northern part of the Puglia region. In particular we have compared three different General Circulation Models (HadCM3, CCSM3, ECHAM5) subjected to a statistical downscaling under two future IPCC scenarios (B1 and A2). The analysis was carried out at regional scale repeating the simulations for seven homogeneous area characterizing the spatial variability of the region. In the second part of the study, considering only HadCM3 data set, climate change impact on long-term sequences of the two crops combined in three crop rotations were evaluated in terms of yield performances and soil fertility as indicated by the soil organic content of carbon and nitrogen. The comparison between GCMs showed no significant differences for winter durum wheat yield, while noticeable differences were found for yield and irrigation requirements of tomato. Under future scenarios, the production levels were reduced for tomato, whereas positive yield effects were observed for winter durum wheat. For winter durum wheat the simulation indicated that two- and three-year rotations, including one year of tomato cultivation, improved the cereal yield and this positive effect maintained its validity also in future scenarios. For both crops higher requirements of water and nitrogen were predicted under future scenarios. This result coupled with the decrease of yield caused negative reduction of water use efficiency and nitrogen use efficiency for tomato cultivation.