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Schaap, B.F.; Reidsma, P.; Verhagen, J. |
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Title |
Agro Climate Calendar, a simple methodology to identify local adaptation for farm objectives |
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Year |
2015 |
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CropM |
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Climate-smart agriculture 2015. Global Science Conference. Montpellier, France, 2015-03-15 to 2015-03-18 |
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MA @ admin @ |
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2796 |
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Author |
Sanz-Cobena, A. |
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Title |
Ammonia and nitrous oxide emissions from grazing cattle in Kenya |
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Year |
2015 |
Publication |
FACCE MACSUR Reports |
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5 |
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Sp5-56 |
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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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2171 |
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Author |
Sanna, M.; Bellocchi, G.; Fumagalli, M.; Acutis, M. |
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Title |
A new method for analysing the interrelationship between performance indicators with an application to agrometeorological models |
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Journal Article |
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2015 |
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Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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73 |
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286-304 |
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model evaluation; performance indicators; stable correlation; solar-radiation; simulation-model; environmental-models; statistical-methods; crop nitrogen; validation; rice; uncertainty; calibration; software |
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The use of a variety of metrics is advocated to assess model performance but correlated metrics may convey the same information, thus leading to redundancy. Starting from this assumption, a method was developed for selecting, from among a collection of performance indicators, one or more subsets providing the same information as the entire set. The method, based on the definition of “stable correlation”, was applied to 23 performance indicators of agrometeorological models, calculated on large sets of simulated and observed data of four agronomic and meteorological variables: above-ground biomass, leaf area index, hourly air relative humidity and daily solar radiation. Two subsets were determined: {Squared Bias, Root Mean Squared Relative Error, Coefficient of Determination, Pattern Index, Modified Modelling Efficiency}, {Persistence Model Efficiency, Root Mean Squared Relative Error, Coefficient of Determination, Pattern Index}. The method needs corroboration but is statistically founded and can support the implementation of standardized evaluation tools. (C) 2015 Elsevier Ltd. All rights reserved. |
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1364-8152 |
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CropM LiveM, ftnotmacsur |
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MA @ admin @ |
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4503 |
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Sándor, R.; Ma, S.; Acutis, M.; Barcza, Z.; Ben Touhami, H.; Doro, L.; Hidy, D.; Köchy, M.; Lellei-Kovács, E.; Minet, J.; Perego, A.; Rolinski, S.; Ruget, F.; Seddaiu, G.; Wu, L.; Bellocchi, G. |
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Title |
Uncertainty in simulating biomass yield and carbon–water fluxes from grasslands under climate change |
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Journal Article |
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Year |
2015 |
Publication |
Advances in Animal Biosciences |
Abbreviated Journal |
Advances in Animal Biosciences |
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Volume |
6 |
Issue |
01 |
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49-51 |
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Keywords |
grassland productivity; carbon balance; model simulation; uncertainty; sensitivity |
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English |
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2040-4700 |
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CropM, LiveM, ft_macsur |
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MA @ admin @ |
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4651 |
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Author |
Sándor, R. |
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Title |
Sensitivity and uncertainty analysis of grassland models in Europe and Israel |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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5 |
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Sp5-55 |
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Abstract |
Grassland models are valuable tools to test hypotheses on grassland ecosystem functioning. In the frame of FACCE MACSUR LiveM, a model intercomparison was conducted using a dataset from an observational and experimental network of nine multi-year grassland sites spread across Europe (France, Italy, Germany, Switzerland, The Netherlands, and United Kingdom) and Israel, and a suite of nine models to understand grassland functioning in the region. Grassland-specific approaches were compared to approaches mainly conceived to simulate crops and plant functional types. Model evaluation against actual measurements was performed before and after model calibration. The calibrated models were used to analyze their sensitivity to independent variations of temperature, precipitation and [CO2]. The results show to which extent calibration can accommodate model discrepancies. The sensitivity of simulated gross primary production to [CO2] and temperature is an important outcome, considering the fundamental effect of rising temperature and [CO2] on the C cycling of terrestrial ecosystems in the Euro-Mediterranean region. Overall, alternative models exhibit a different sensitivity to climate change factors, with different performances over different conditions. Explained by the basic processes of each model and also induced by different calibration methods, this difference is indicative that more models can be complementary and deliver greater insights than if they were applied individually. 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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Call Number |
MA @ admin @ |
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2170 |
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