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Calanca, P. |
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Modelling the impacts of seasonal drought on herbage growth under climate change |
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2016 |
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FACCE MACSUR Reports |
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8 |
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SP8-3 |
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LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change |
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no |
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MA @ admin @ |
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4837 |
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Cammarano, D.; Rivington, M.; Matthews, K.; B,; Bellocchi, G. |
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Estimates of crop responses to climate change with quantified ranges of uncertainty |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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6 |
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D-C4.1.3 |
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In estimating responses of crops to future climate realisations, it is necessary to understand and differentiate between the sources of uncertainty in climate models and how these lead to errors in estimating the past climate and biases in future projections, and how these affect crop model estimates. This paper investigates the complexities in using climate model projections representing different spatial scales within climate change impacts and adaptation studies. This is illustrated by simulating spring barley with three crop models run using site-specific observed, original (50•50 km) and bias corrected downscaled (site-specific) hindcast (1960-1990) weather data from the HadRM3 Regional Climate Model (RCM). Original and bias corrected downscaled weather data were evaluated against the observed data. The comparisons made between the crop models were in the light of lessons learned from this data evaluation. Though the bias correction downscaling method improved the match between observed and hindcast data, this did not always translate into better matching of crop models estimates. At four sites the original HadRM3 data produced near identical mean simulated yield values as from the observed weather data, despite differences in the weather data, giving a situation of ‘right results for the wrong reasons’. This was likely due to compensating errors in the input weather data and non-linearity in crop models processes, making interpretation of results problematic. Overall, bias correction downscaling improved the quality of simulated outputs. Understanding how biases in climate data manifest themselves in crop models gives greater confidence in the utility of the estimates produced using downscaled future climate projections. The results indicate implications on how future projections of climate change impacts are interpreted. Fundamentally, considerable care is required in determining the impact weather data sources have in climate change impact and adaptation studies, whether from individual models or ensembles. No Label |
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2098 |
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Carter, T. |
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Scenarios and related data for MACSUR2 Timothy Carter Finnish Environment |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-11 |
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Framing scenario selection (RCP/SSP)Ongoing scenario development in FP7 IMPRESSIONSSome examples of sources of data and scenarios 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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2126 |
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Cortignani, R. |
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Common Agricultural Policy and climate variability changes: an impact assessment of the first-pillar reform on an agricultural area of Grana Padano in different climate scenarios |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-12 |
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The reform of the Common Agricultural Policy it started in 2015 with several innovative aspects. Regarding the first pillar, such aspects are especially the convergence of the basic payments, the green payments and the coupled payments. In this regard seems interesting carry out analysis about to evaluate the policy impact considering the risks and opportunities due to climate change.In this study the impact of the convergence of basic payments, the introduction of the green payments and the coupled payments has been evaluated on dairy cattle farms in the Grana Padano area. The impact has been evaluated in different climate scenarios by economic, social and environmental indicators. The methodology used is the mathematical programming and especially a model of Discrete Stochastic Programming has been used to represents farms of the FADN database.The main results show that a significant part of the farms is affected by the diversification constraint that reduces the land devoted to corn silage. Farmers could cultivate corn silage after a principal crop (e.g. ryegrass) in order to avoid the diversification constraint, however, determining a negative impact on the use of environmental resources. To consider also that in the future there is an increase of corn silage yields with long cycle.Another result to underline is that which concerns the possibility of soybean cultivation in the ecological focus areas. In fact, considering the coupled payment provided for this crop, the ecological focus areas seem to be an important source of income for the farms.Finally, the analysis shows that the convergence of the basic payment will result in a reallocation of direct payments between farms with a significant impact on farm incomes. 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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no |
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MA @ admin @ |
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2127 |
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Coucheney, E. |
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Sensitivity of crop water and N stress to soil input data in regional cropyield simulations and the implications for data aggregation effects: a case study with the COUP-model |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-13 |
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The effects of aggregating soil input data on modelling crop yields at regional scale have been explored within the MACSUR- Crop M – WP3 scaling exercise for an ensemble of crop models 1. The models were run for the North Rhine-Westphalia region in Germany with an average climate time-series (30 years) and soil data at resolution 1 km to 100 km. Aggregation effects showed substantial differences between the models 1. This could be linked to differences in model structure and concepts and to different procedures for the parameterization of soil properties. A further analysis of the sensitivity of the outputs to key soil properties, for each ‘model – method of parameterization’, could help in understanding differences observed within the model ensemble. In this study, we explored the relationship between winter wheat yields, water and N-stress indexes and simple key-soil properties, based on the COUP-model 2 simulations. Soils were grouped into classes according to selected parameters (i.e. soil depth, soil texture and soil organic content). Preliminary results show that some of those soil classes are clearly associated with high water and / or N-stress and lower yields or with high inter-annual variation of the yield. As such they represent key factors explaining the spatial pattern of the simulated yield at the different resolutions. In addition we identified differences in the fractional area of those soil classes between high and low spatial resolutions (‘inherent errors’ due to data aggregation). How this may influence soil data aggregation effects on simulated yields will be further analyzed. 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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Approved |
no |
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MA @ admin @ |
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2128 |
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