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Trnka, M.; Kersebaum, K.; Christian,; Olesen, J.E. |
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Title |
Description of the compiled experimental data available in the MACSUR CropM database |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-C2.1 |
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The input data necessary for crop model simulations and data for their calibration/validation (and thus requirements for observations and measurements in suitable experiments) have been collected through out the project together with data for additional analysis of abiotic factors influencing yields. A list of possible dataset was collated in the first year of project however very few of the existing datasets were found usable for the crop model simulation as they fell short of the requirements defined in the part 2.3. However database has been populated as planned with the results of the ongoing MACSUR studies and will serve in the same way for the MACSUR 2 duration. No Label |
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MA @ admin @ |
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2090 |
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Ewert, F.; Rötter, R.P.; Bindi, M.; Webber, H.; Trnka, M.; Kersebaum, K.; Christian,; Olesen, J.E.; Van Ittersum, M.K.; Janssen, S.; Rivington, M.; Semenov, M.A.; Wallach, D.; Porter, J.R.; Stewart, D.; Verhagen, J.; Gaiser, T.; Palosuo, T.; Tao, F.; Nendel, C.; Roggero, P.P.; Bartošová, L.; Asseng, S. |
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Crop modelling for integrated assessment of risk to food production from climate change |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-C0.3 |
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The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches. No Label |
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MA @ admin @ |
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2089 |
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Ruiz-Ramos, M.; Trnka, M. |
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Riesgos asociados a los eventos extremos meteorológicos para la producción de trigo en Europa |
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2015 |
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Tierras |
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224 C6 - |
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92-98 |
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CropM |
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MA @ admin @ |
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2077 |
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Salo, T.J.; Palosuo, T.; Kersebaum, K.C.; Nendel, C.; Angulo, C.; Ewert, F.; Bindi, M.; Calanca, P.; Klein, T.; Moriondo, M.; Ferrise, R.; Olesen, J.E.; Patil, R.H.; Ruget, F.; Takáč, J.; Hlavinka, P.; Trnka, M.; Rötter, R.P. |
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Comparing the performance of 11 crop simulation models in predicting yield response to nitrogen fertilization |
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2016 |
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Journal of Agricultural Science |
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J. Agric. Sci. |
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154 |
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7 |
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1218-1240 |
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northern growing conditions; climate-change impacts; spring barley; systems simulation; farming systems; soil properties; winter-wheat; dynamics; growth; management |
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Eleven widely used crop simulation models (APSIM, CERES, CROPSYST, COUP, DAISY, EPIC, FASSET, HERMES, MONICA, STICS and WOFOST) were tested using spring barley (Hordeum vulgare L.) data set under varying nitrogen (N) fertilizer rates from three experimental years in the boreal climate of Jokioinen, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area index (LAI) and yield observations. The models were then tested against new data for 2009 and their performance was assessed and compared with both the two calibration years and the test year. For the calibration period, root mean square error between measurements and simulated grain dry matter yields ranged from 170 to 870 kg/ha. During the test year 2009, most models failed to accurately reproduce the observed low yield without N fertilizer as well as the steep yield response to N applications. The multi-model predictions were closer to observations than most single-model predictions, but multi-model mean could not correct systematic errors in model simulations. Variation in soil N mineralization and LAI development due to differences in weather not captured by the models most likely was the main reason for their unsatisfactory performance. This suggests the need for model improvement in soil N mineralization as a function of soil temperature and moisture. Furthermore, specific weather event impacts such as low temperatures after emergence in 2009, tending to enhance tillering, and a high precipitation event just before harvest in 2008, causing possible yield penalties, were not captured by any of the models compared in the current study. |
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0021-8596 1469-5146 |
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CropM, ft_macsur |
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MA @ admin @ |
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4713 |
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Hlavinka, P.; Kersebaum, K.C.; Dubrovský, M.; Fischer, M.; Pohanková, E.; Balek, J.; Žalud, Z.; Trnka, M. |
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Title |
Water balance, drought stress and yields for rainfed field crop rotations under present and future conditions in the Czech Republic |
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Journal Article |
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2015 |
Publication |
Climate Research |
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Clim. Res. |
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65 |
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175-192 |
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crop growth model; evapotranspiration; soil; climate change; climate-change scenarios; spring barley; wheat production; winter-wheat; model; impacts; europe; uncertainties; simulation; strategies |
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Continuous crop rotation modeling is a prospective trend that, compared to 1-crop or discrete year-by-year calculations, can provide more accurate results that are closer to real conditions. The goal of this study was to compare the water balance and yields estimated by the HERMES crop rotation model for present and future climatic conditions in the Czech Republic. Three locations were selected, representing important agricultural regions with different climatic conditions. Crop rotation (spring barley, silage maize, winter wheat, winter rape) was simulated from 1981-2080. The 1981-2010 period was covered by measured meteorological data, while 2011-2080 was represented by a transient synthetic weather series from the weather generator M& Rfi. The data were based on 5 circulation models, representing an ensemble of 18 CMIP3 global circulation models, to preserve much of the uncertainty of the original ensemble. Two types of crop management were compared, and the influences of soil quality, increasing atmospheric CO2 and adaptation measures (i. e. sowing date changes) were also considered. Results suggest that under a ‘dry’ scenario (such as GFCM21), C-3 crops in drier regions will be devastated for a significant number of seasons. Negative impacts are likely even on premium-quality soils regardless of flexible sowing dates and accounting for increasing CO2 concentrations. Moreover, in dry conditions, the use of crop rotations with catch crops may have negative impacts, exacerbating the soil water deficit for subsequent crops. This approach is a promising method for determining how various management strategies and crop rotations can affect yields as well as water, carbon and nitrogen cycling. |
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0936-577x 1616-1572 |
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CropM, ft_macsur |
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MA @ admin @ |
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4663 |
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