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	Author	Ewert, F.; van Bussel, L.G.J.; Zhao, G.; Hoffmann, H.; Gaiser, T.; Specka, X.; Nendel, C.; Kersebaum, K.-C.; Sosa, C.; Lewan, E.; Yeluripati, J.; Kuhnert, M.; Tao, F.; Rötter, R.P.; Constantin, J.; Raynal, H.; Wallach, D.; Teixeira, E.; Grosz, B.; Bach, M.; Doro, L.; Roggero, P.P.; Zhao, Z.; Wang, E.; Kiese, R.; Haas, E.; Eckersten, H.; Trombi, G.; Bindi, M.; Klein, C.; Biernath, C.; Heinlein, F.; Priesack, E.; Cammarano, D.; Asseng, S.; Elliott, J.; Glotter, M.; Basso, B.; Baigorria, G.A.; Romero, C.C.; Moriondo, M.
	Title	Uncertainties in Scaling up Crop Models for Large Area Climate-change Impact Assessments			Type	Book Chapter
	Year	2015	Publication		Abbreviated Journal
	Volume		Issue		Pages	261-277
	Keywords	CropM;
	Abstract
	Address
	Corporate Author				Thesis
	Publisher	Imperial College Press	Place of Publication	London	Editor	Rosenzweig, C.; Hillel, D.
	Language		Summary Language		Original Title
	Series Editor		Series Title	Handbook of Climate Change and Agroecosystems: The Agricultural Model Intercomparison and Improvement Project (AgMIP) Integrated Crop and Economic Assessments — Joint Publication with American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America (In 2 Parts)	Abbreviated Series Title
	Series Volume	ICP Series on Climate Change Impacts, Adaptation,	Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes				Approved	no
	Call Number	MA @ admin @			Serial	2427
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	Author	Boote, K.J.; Porter, C.; Jones, J.W.; Thorburn, P.J.; Kersebaum, K.C.; Hoogenboom, G.; White, J.W.; Hatfield, J.L.
	Title	Sentinel site data for crop model improvement—definition and characterization			Type	Book Chapter
	Year	2016	Publication	Improving Modeling Tools to Assess Climate Change Effects on Crop Response	Abbreviated Journal
	Volume		Issue		Pages
	Keywords
	Abstract	Crop models are increasingly being used to assess the impacts of future climate change on production and food security. High quality, site-specific data on weather, soils, management, and cultivar are needed for those model applications. Also important is that model development, evaluation, improvement, and calibration require additional high quality, site-specific measurements on crop yield, growth, phenology, and ancillary traits. We review the evolution of minimum data set requirements for agroecosystem modeling and then describe the characteristics and ranking of sentinel site data needed for crop model improvement, calibration, and application. We in the Agricultural Model Intercomparison and Improvement Project (AgMIP), propose to rank sentinel site data sets as platinum, gold, silver, and copper, based on the degree of true site-specific measurement of weather, soils, management, crop yield, as well as the quality, comprehensiveness, quantity, accuracy, and value. For example, to be ranked platinum, the weather and soil characterization must be measured on-site, and all management inputs must be known. Dataset ranking will be lower for weather measured off-site or soil traits estimated from soil mapping. Ranking also depends on the intended purposes for data use. If the purpose is to improve a crop model for response to water or N, then additional observations are necessary, such as initial soil water, initial soil inorganic N, and plant N uptake during the growing season to be ranked platinum. Rankings are enhanced by presence of multiple treatments and sites. Examples of platinum-, gold-, and silver-quality data sets for model improvement and calibration uses are illustrated.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor	Hatfield, J.L.; Fleisher, D.
	Language		Summary Language		Original Title
	Series Editor		Series Title	Advances in Agricultural Systems Modeling	Abbreviated Series Title
	Series Volume	7	Series Issue		Edition
	ISSN		ISBN		Medium
	Area		Expedition		Conference
	Notes	CropM			Approved	no
	Call Number	MA @ admin @			Serial	4980
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	Author	Cammarano, D.; Rötter, P.; Ewert, F.; Palosuo, T.; Bindi, M.; Kersebaum, K.C.; Olesen, J.E.; Trnka, M.; van Ittersum, M.K.; Janssen, S.; Rivington, M.; Semenov, M.; Wallach, D.; Porter, J.R.; Stewart, D.; Verhagen, J.; Angulo, C.; Gaiser, T.; Nendel, C.; Martre, P.; de Wit, A.
	Title	Challenges for Agro-Ecosystem Modelling in Climate Change Risk Assessment for major European Crops and Farming systems			Type	Conference Article
	Year	2013	Publication		Abbreviated Journal
	Volume		Issue		Pages	555-564
	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		ISBN		Medium
	Area		Expedition		Conference	Impacts World 2013, International Conference on Climate Change Effects, Potsdam, Germany, 2013-05-27 to 2013-05-30
	Notes				Approved	no
	Call Number	MA @ admin @			Serial	2765
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	Author	Nendel, C.; Wieland, R.; Mirschel, W.; Specka, X.; Kersebaum, K.-C.
	Title	Die Simulation von Winterweizenerträgen in Thüringen unter Verwendung von meteorologischen Daten unterschiedlicher räumlicher Auflösung			Type	Conference Article
	Year	2013	Publication		Abbreviated Journal
	Volume		Issue		Pages
	Keywords	CropM
	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	33. GIL-Jahrestagung in Potsdam 2013 - Massendatenmanagement in der Agrar- und Ernährungswirtschaft, 2013-02-21 to 2013-02-21
	Notes				Approved	no
	Call Number	MA @ admin @			Serial	2680
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	Author	Kuhnert, M.; Yeluripati, J.; Smith, P.; Hoffmann, H.; van Oijen, M.; Constantin, J.; Coucheney, E.; Dechow, R.; Eckersten, H.; Gaiser, T.; Grosz, B.; Haas, E.; Kersebaum, K.-C.; Kiese, R.; Klatt, S.; Lewan, E.; Nendel, C.; Raynal, H.; Sosa, C.; Specka, X.; Teixeira, E.; Wang, E.; Weihermüller, L.; Zhao, G.; Zhao, Z.; Ogle, S.; Ewert, F.
	Title	Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands			Type	Journal Article
	Year	2016	Publication	European Journal of Agronomy	Abbreviated Journal	European Journal of Agronomy
	Volume	88	Issue		Pages	41-52
	Keywords	Net primary production; NPP; Scaling; Extreme events; Crop modelling; Climate Data; aggregation
	Abstract	For spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain ﬁlling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km2), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the ﬁve scales in the range of 1–100 km grid cells show changes of 0.4–7.8% and 0.0–4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9–4.2 g C m−2 d−1 and 2.7–6.1 g C m−2 d−1for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data.
	Address	2016-09-13
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language	English	Summary Language	Newsletter July	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	1161-0301	ISBN		Medium	Article
	Area		Expedition		Conference
	Notes	CropM, ft_macsur			Approved	no
	Call Number	MA @ admin @			Serial	4775
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