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Lana, M., Kersebaum, K. C., Kollas, C., Yin, X., Nendel, C., Manevski, K., et al. (2016). Effect of different levels of calibration in rotation schemes simulated in five European sites in a multi-model approach.. Berlin (Germany).
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Olesen, J. E., Sharif, B., Plauborg, F., Yin, X., Bindi, M., Doro, L., et al. (2016). Comparison of wheat models and their sensitivity towards tillage and N fertilization with different calibration approaches.. Berlin (Germany).
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Kersebaum, K. - C., Wallor, E., Ventrella, D., Cammarano, D., Choucheney, E., Ewert, F., et al. (2017). Comparison of site sensitivity of crop models using spatially variable field data from Precision Agriculture (Vol. 10).
Abstract: Site conditions and soil properties have a strong influence on impacts of climate change on crop production. Vulnerability of crop production to changing climate conditions is highly determined by the ability of the site to buffer periods of adverse climatic situations like water scarcity or excessive rainfall. Therefore, the capability of models to reflect crop responses and water and nutrient dynamics under different site conditions is essential to assess climate impact even on a regional scale. To test and improve sensitivity of models to various site properties such as soil variability and hydrological boundary conditions, spatial variable data sets from precision farming of two fields in Germany and Italy were provided to modellers. For the German 20 ha field soil and management data for 60 grid points for 3 years (2 years wheat, 1 year triticale) were provided. For the Italian field (12 ha) information for 100 grid points were available for three growing seasons of durum wheat. Modellers were asked to run their models using a) the model specific procedure to estimate soil hydraulic properties from texture using their standard procedure and use in step b) fixed values for field capacity and wilting point derived from soil taxonomy. Only the phenology and crop yield of one grid point provided for a basic calibration. In step c) information for all grid points of the first year (yield, soil water and mineral N content for Germany, yield, biomass and LAI for Italy) were provided. First results of five out of twelve participating models are compared against measured state variables analysing their site specific response and consistency across crop and soil variables. (Main text to be published in a peer-reviewed journal)
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Kersebaum, K., Kroes, J., Gobin, A., Takáč, J., Hlavinka, P., Trnka, M., et al. (2016). Assessing uncertainties of water footprints using an ensemble of crop growth models on winter wheat. Water, 8(12), 571.
Abstract: Crop productivity and water consumption form the basis to calculate the water footprint (WF) of a specific crop. Under current climate conditions, calculated evapotranspiration is related to observed crop yields to calculate WF. The assessment of WF under future climate conditions requires the simulation of crop yields adding further uncertainty. To assess the uncertainty of model based assessments of WF, an ensemble of crop models was applied to data from five field experiments across Europe. Only limited data were provided for a rough calibration, which corresponds to a typical situation for regional assessments, where data availability is limited. Up to eight models were applied for wheat. The coefficient of variation for the simulated actual evapotranspiration between models was in the range of 13%–19%, which was higher than the inter-annual variability. Simulated yields showed a higher variability between models in the range of 17%–39%. Models responded differently to elevated CO2 in a FACE (Free-Air Carbon Dioxide Enrichment) experiment, especially regarding the reduction of water consumption. The variability of calculated WF between models was in the range of 15%–49%. Yield predictions contributed more to this variance than the estimation of water consumption. Transpiration accounts on average for 51%–68% of the total actual evapotranspiration.
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Ventrella, D., Charfeddine, M., Giglio, L., & Castellini, M. (2012). Application of DSSAT models for an agronomic adaptation strategy under climate change in Southern of Italy: optimum sowing and transplanting time for winter durum wheat and tomato. Ital. J. Agron., 7(1), 16.
Abstract: Many climate change studies have been carried out in different parts of the world to assess climate change vulnerability and adaptation capacity of agricultural crops for certain environments characterized from climatic, pedological and agronomical point of view. The objective of this study was to analyse the productive response of winter durum wheat and tomato to climate change and sowing/transplanting time in one of the most productive areas of Italy (i.e. Capitanata, Puglia), using CERES-Wheat and CROPGRO cropping system models. Three climatic datasets were used: i) a single dataset (50 km x 50 km) provided by the JRC European centre for the period 1975- 2005; two datasets from HadCM3 for the IPCC A2 GHG scenario for time slices with +2°C (centred over 2030-2060) and +5°C (centred over 2070-2099), respectively. All three datasets were used to generate synthetic climate series using a weather simulator (model LARS-WG). No negative yield effects of climate change were observed for winter durum wheat with delayed sowing (from 330 to 345 DOY) increasing the average dry matter grain yield under forecasted scenarios. Instead, the warmer temperatures were primarily shown to accelerate the phenology, resulting in decreased yield for tomato under the + 5°C future climate scenario. In general, under global temperature increase by 5°C, early transplanting times could minimize the negative impact of climate change on crop productivity but the intensity of this effect was not sufficient to restore the current production levels of tomato cultivated in southern Italy.
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