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Kuhnert, M.; Yeluripati, J.; Smith, P.; Hoffmann, H.; van Oijen, M.; Zhao, G.; Constantin, J.; Raynal, H.; Coucheney, E.; Lewan, E.; Eckersten, H.; Specka, X.; Sosa, C.; Kersebaum, K.-C.; Nendel, C.; Grosz, B.; Dechow, R.; Kiese, R.; Haas, E.; Klatt, S.; Teixeira, E.; Zhao, Z.; Wang, E.; Weihermüller, L.; Gaiser, T.; Ewert, F. |
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Title |
Impact of climate aggregation over different scales on regional NPP modelling |
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2016 |
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Vienna (Austria) |
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European Geosciences Union General Assembly 2016, 2016-04-17 to 2016-04-22, Vienna |
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MA @ admin @ |
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2578 |
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Van Oijen, M. |
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Methods for risk analysis and spatial upscaling of process-based models: Experiences from projects Carbo-Extreme and GREENHOUSE |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-69 |
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In the recently finished EU-funded project Carbo-Extreme, we developed a simple probabilistic method for quantifying vulnerabilities and risks to ecosystems (http://iopscience.iop.org/1748-9326/8/1/015032). The method defines risk as expected loss due to environmental hazards, and shows how such risk can be calculated as the product of ecosystem vulnerability and hazard probability. The method was used with six different vegetation models to estimate current and future drought risks for crops, grasslands and forests across Europe (http://www.biogeosciences.net/11/6357/2014/bg-11-6357-2014.html).In the still ongoing UK-funded project GREENHOUSE, the focus is on spatial upscaling of local measurements and model predictions of greenhouse gas emissions to wider regions. As part of this work, we are comparing different model upscaling methods – ranging from naive input aggregation to geostatistics – and quantify the uncertainties associated with the upscaling. This work builds on an earlier inventory of model upscaling methods that was produced in a collaboration of CEH-Edinburgh and the University of Bonn (https://www.stat.aau.at/Tagungen/statgis/2009/StatGIS2009Van%20Oijen1.pdf). Here we show a comparison of the methods using model predictions for the border region of England and Scotland. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2184 |
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Van Oijen, M.; Cameron, D.; Levy, P.E.; Preston, R. |
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Correcting errors from spatial upscaling of nonlinear greenhouse gas flux models |
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2017 |
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Environmental Modelling & Software |
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Environmental Modelling & Software |
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94 |
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157-165 |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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4945 |
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Van Oijen, M.; Höglind, M. |
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Toward a Bayesian procedure for using process-based models in plant breeding, with application to ideotype design |
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2016 |
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Euphytica |
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Euphytica |
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207 |
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3 |
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627-643 |
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BASGRA; cold tolerance; genotype-environment interaction; plant breeding; process-based modelling; yield stability; grassland productivity; timothy regrowth; climate-change; water-deficit; forest models; late blight; leaf-area; calibration; growth; tolerance |
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Process-based grassland models (PBMs) simulate growth and development of vegetation over time. The models tend to have a large number of parameters that represent properties of the plants. To simulate different cultivars of the same species, different parameter values are required. Parameter differences may be interpreted as genetic variation for plant traits. Despite this natural connection between PBMs and plant genetics, there are only few examples of successful use of PBMs in plant breeding. Here we present a new procedure by which PBMs can help design ideotypes, i.e. virtual cultivars that optimally combine properties of existing cultivars. Ideotypes constitute selection targets for breeding. The procedure consists of four steps: (1) Bayesian calibration of model parameters using data from cultivar trials, (2) Estimating genetic variation for parameters from the combination of cultivar-specific calibrated parameter distributions, (3) Identifying parameter combinations that meet breeding objectives, (4) Translating model results to practice, i.e. interpreting parameters in terms of practical selection criteria. We show an application of the procedure to timothy (Phleum pratense L.) as grown in different regions of Norway. |
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2016-10-31 |
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English |
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0014-2336 |
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CropM, ft_macsur |
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MA @ admin @ |
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4820 |
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Virkajärvi, P.; Korhonen, P.; Bellocchi, G.; Curnel, Y.; Wu, L.; Jégo, G.; Persson, T.; Höglind, M.; Van Oijen, M.; Gustavsson, A.-M.; Kipling, R.P. |
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Title |
Modelling responses of forages to climate change with a focus on nutritive value |
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Journal Article |
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2016 |
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Advances in Animal Biosciences |
Abbreviated Journal |
Advances in Animal Biosciences |
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7 |
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03 |
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227-228 |
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2040-4700 |
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LiveM, ft_macsur |
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MA @ admin @ |
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4876 |
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