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	Author	Sándor, R.; Ehrhardt, F.; Basso, B.; Bellocchi, G.; Bhatia, A.; Brilli, L.; Migliorati, M.D.A.; Doltra, J.; Dorich, C.; Doro, L.; Fitton, N.; Giacomini, S.J.; Grace, P.; Grant, B.; Harrison, M.T.; Jones, S.; Kirschbaum, M.U.F.; Klumpp, K.; Laville, P.; Léonard, J.; Liebig, M.; Lieffering, M.; Martin, R.; McAuliffe, R.; Meier, E.; Merbold, L.; Moore, A.; Myrgiotis, V.; Newton, P.; Pattey, E.; Recous, S.; Rolinski, S.; Sharp, J.; Massad, R.S.; Smith, P.; Smith, W.; Snow, V.; Wu, L.; Zhang, Q.; Soussana, J.F.
	Title	C and N models Intercomparison – benchmark and ensemble model estimates for grassland production			Type	Journal Article
	Year	2016	Publication	Advances in Animal Biosciences	Abbreviated Journal	Advances in Animal Biosciences
	Volume	7	Issue	03	Pages	245-247
	Keywords
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2040-4700	ISBN		Medium
	Area		Expedition		Conference
	Notes	LiveM, ft_macsur			Approved	no
	Call Number	MA @ admin @			Serial	4868
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	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			Type	Conference Article
	Year	2014	Publication		Abbreviated Journal
	Volume		Issue		Pages
	Keywords	LiveM
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	International Livestock Modelling and Research Colloquium, Bilbao, Spain, 2014-10-14 to 2014-10-16
	Notes				Approved	no
	Call Number	MA @ admin @			Serial	2788
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	Author	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.
	Title	The grassland model intercomparison of the MACSUR (Modelling European Agriculture with Climate Change for Food Security) European knowledge hub			Type	Conference Article
	Year	2014	Publication		Abbreviated Journal
	Volume		Issue		Pages
	Keywords	LiveM
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference	7th International Congress on Environmental Modelling and Software, 2014-06-15 to 2014-06-19
	Notes				Approved	no
	Call Number	MA @ admin @			Serial	2618
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	Author	Sándor, R.; Barcza, Z.; Acutis, M.; Doro, L.; Hidy, D.; Köchy, M.; Minet, J.; Lellei-Kovács, E.; Ma, S.; Perego, A.; Rolinski, S.; Ruget, F.; Sanna, M.; Seddaiu, G.; Wu, L.; Bellocchi, G.
	Title	Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance			Type	Journal Article
	Year	2016	Publication	European Journal of Agronomy	Abbreviated Journal	European Journal of Agronomy
	Volume		Issue		Pages
	Keywords	Biomass; Grasslands; Modelling; Multi-model ensemble; Soil processes
	Abstract	• We simulate biomass, soil water content (SWC) and temperature (ST) in grasslands. • We compare nine models to the multi-model median (MMM) at nine sites. • With model calibration, we obtain satisfactory estimates of ST, less of SWC and biomass. • We observe discrepancies across models in the simulation of grassland processes. • We improve performance with multi-model approach. This study presents results from a major grassland model intercomparison exercise, and highlights the main challenges faced in the implementation of a multi-model ensemble prediction system in grasslands. Nine, independently developed simulation models linking climate, soil, vegetation and management to grassland biogeochemical cycles and production were compared in a simulation of soil water content (SWC) and soil temperature (ST) in the topsoil, and of biomass production. The results were assessed against SWC and ST data from five observational grassland sites representing a range of conditions – Grillenburg in Germany, Laqueuille in France with both extensive and intensive management, Monte Bondone in Italy and Oensingen in Switzerland – and against yield measurements from the same sites and other experimental grassland sites in Europe and Israel. We present a comparison of model estimates from individual models to the multi-model ensemble (represented by multi-model median: MMM). With calibration (seven out of nine models), the performances were acceptable for weekly-aggregated ST (R² > 0.7 with individual models and >0.8–0.9 with MMM), but less satisfactory with SWC (R² < 0.6 with individual models and < ∼ 0.5 with MMM) and biomass (R² < ∼0.3 with both individual models and MMM). With individual models, maximum biases of about −5 °C for ST, −0.3 m3 m−3 for SWC and 360 g DM m−2 for yield, as well as negative modelling efficiencies and some high relative root mean square errors indicate low model performance, especially for biomass. We also found substantial discrepancies across different models, indicating considerable uncertainties regarding the simulation of grassland processes. The multi-model approach allowed for improved performance, but further progress is strongly needed in the way models represent processes in managed grassland systems.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	Language	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1161-0301	ISBN		Medium
	Area	LiveM	Expedition		Conference
	Notes				Approved	no
	Call Number	MA @ admin @			Serial	4768
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	Author	Zhao, G.; Hoffmann, H.; van Bussel, L.G.J.; Enders, A.; Specka, X.; Sosa, C.; Yeluripati, J.; Tao, F.L.; Constantin, J.; Raynal, H.; Teixeira, E.; Grosz, B.; Doro, L.; Zhao, Z.G.; Nendel, C.; Kiese, R.; Eckersten, H.; Haas, E.; Vanuytrecht, E.; Wang, E.; Kuhnert, M.; Trombi, G.; Moriondo, M.; Bindi, M.; Lewan, E.; Bach, M.; Kersebaum, K.C.; Rotter, R.; Roggero, P.P.; Wallach, D.; Cammarano, D.; Asseng, S.; Krauss, G.; Siebert, S.; Gaiser, T.; Ewert, F.
	Title	Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables			Type	Journal Article
	Year	2015	Publication	Climate Research	Abbreviated Journal	Clim. Res.
	Volume	65	Issue		Pages	141-157
	Keywords	crop model; model comparison; spatial resolution; data aggregation; spatial heterogeneity; scaling; climate-change scenarios; sub-saharan africa; winter-wheat; spatial-resolution; yield response; input data; systems simulation; large-scale; soil data; part i
	Abstract	We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0936-577x	ISBN		Medium	Article
	Area		Expedition		Conference
	Notes	CropM, ft_macsur			Approved	no
	Call Number	MA @ admin @			Serial	4754
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