Comparing AquaCrop and CropSyst models in simulating barley growth under different water and Nitrogen regimes. Does calibration year influence the performance of crop growth models?

This work investigated the performance of AquaCrop and CropSyst crop growth modelsin simulating barley growth under three water (full irrigation, 50% irrigation and rainfed)and two nitrogen (high and low) conditions with a particular attention to the influence ofcalibration year on the modelling results. Three years (2006-2008) of data from theexperimental work carried out in Southern Italy were used in this study. AquaCropmodel was based exclusively on the water productivity (WP) engine normalized forreference evapotranspiration whereas Crop Syst used both solar radiation engine andwater productivity engines for the simulation of crop growth. AquaCrop required the recalibrationof crop growth parameters under different nitrogen regimes whereasCropSyst used an internal module to adjust the impact of different nitrogen inputs. Themodels were calibrated for each of three years and then validated for two other years.The overall results pointed out that, for both models, most of crop growth parametersare conservative enough (almost constant) to be used in any of three years. Thisindicated that only one year of data could be enough for model's calibration confirmingthe robustness and validity of the approaches adopted by both models. Nevertheless,the performance of the models changed in respect to the year of calibration, from oneyear to another (i.e. different weather conditions) and for different water and nitrogenregimes. The statistical analysis based only on final biomass and yield for all threeyears has shown that the performances of AquaCrop are slightly better than that ofCropSyst independently on the year of calibration.This was particularly evident for the conditions of non-optimal water and nitrogensupply and could be attributed to several distinctive features of AquaCrop such as: i)the three-thresholds water stress function considering canopy growth, stomatal closureand canopy senescence, ii) the dynamic harvest index function linked to the crop waterstatus, and iii) the model's re-calibration for the conditions of non-optimal nutrientsupply.