| Records |
| Author |
Sándor, R.; Ma, S.; Acutis, M.; Barcza, Z.; Ben, T., H.; Doro, L.; Hidy, D.; Köchy, M.; Lellei-Kovács, E.; Minet, J.; Perego, A.; Rolinski, S.; Ruget, F.; Seddaiu, G.; Wu, L.; Bellocchi, G. |
| Title |
Uncertainty in simulating biomass yield and carbon-water fluxes from Euro-Mediterranean grasslands under climate changes |
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2014 |
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LiveM |
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International Livestock Modelling and Research Colloquium, Bilbao, Spain, 2014-10-14 to 2014-10-16 |
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no |
| Call Number |
MA @ admin @ |
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2788 |
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Minet, J.; Laloy, E.; Tychon, B.; François, L. |
| Title |
Outcomes from the MACSUR grassland model inter-comparison with the model CARAIB |
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Conference Article |
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2014 |
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LiveM |
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International Livestock Modelling and Research Colloquium, Bilbao, Spain, 2014-10-14 to 2014-10-16 |
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no |
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MA @ admin @ |
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2642 |
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Minet, J.; Tychon, B.; Jacquemin, I.; François, L. |
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Can a global dynamic vegetation model be used for both grassland and crop modeling at the local scale |
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2014 |
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CropM; LiveM |
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MACSUR CropM International Symposium and Workshop: Modelling climate change impacts on crop production for food security, Oslo, Norway, 2014-02-10 to 2014-02-12 |
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no |
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MA @ admin @ |
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2641 |
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Ma, S.; Acutis, M.; Barcza, Z.; Ben, T., H.; Doro, L.; Hidy, D.; Köchy, M.; Minet, J.; Lellei-Kovács, E.; Perego, A.; Rolinski, S.; Ruget, F.; Seddaiu, G.; Wu, L.; Bellocchi, G. |
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The grassland model intercomparison of the MACSUR (Modelling European Agriculture with Climate Change for Food Security) European knowledge hub |
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Conference Article |
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2014 |
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LiveM |
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7th International Congress on Environmental Modelling and Software, 2014-06-15 to 2014-06-19 |
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MA @ admin @ |
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2618 |
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Minet, J.; Laloy, E.; Tychon, B.; François, L. |
| Title |
Bayesian inference of a dynamic vegetation model for grassland |
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2014 |
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As a part of the MACSUR task L2.4, we probabilistically calibrated the CARAIB dynamic vegetation model by Markov chain Monte Carlo (MCMC) simulation with the DREAMZS sampler. CARAIB is a mechanistic model that calculates the carbon assimilation of the vegetation as a function of the soil and climatic conditions, and can thus be used for simulating grassland production under cutting or grazing management. Bayesian model inversion was performed at 4 grassland sites across Europe: Oensingen, CH; Grillenburg, DE; Laqueuille, FR and Monte-Bodone, IT. Four daily measured variables from these sites: the Gross Primary Productivity (GPP), Net Ecosystem Exchange (NEE), Evapotranspiration (ET) and Soil Water Content (SWC) were used to sample 10 parameters related to rooting depth, stomatal conductance, specific leaf area, carbon-nitrogen ratio and water stresses. The maximized likelihood function therefore involved four objectives, whereas the applied Bayesian framework allowed for assessing the so called parameter posterior probability density function (pdf), which quantifies model parameter uncertainty caused by measurement and model errors. Sampling trials were performed using merged data from all sites (all-sites-sampling) and for each site (site-specific sampling) separately. The derived posterior parameter pdfs from the all-sites sampling and site-specific sampling runs showed differences in relation with the specificities of each site. Analysis of these distributions also revealed model sensitivity to parameters conditioned on the measured data, as well as parameter correlations. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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no |
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MA @ admin @ |
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5057 |
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