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| Author |
Barnes, A.; Shrestha, S.; Thomson, S.; Toma, L.; Mathews, K.; Sutherland, L.A. |
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Comparing visions for CAP reforms post 2015: Farmer intentions and farm bio-economic modelling |
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2014 |
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FACCE MACSUR Reports |
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3 |
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Sp3-2 |
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This paper illustrates the impacts of two of the potential CAP reform post 2015 scenarios using an optimising farm level model and compares results with farmers’ perception about the policy changes, captured in a farmer intentions survey. The model results suggest that beef farms suffer a loss in farm net margins under fully decoupled (up to -21%) as well as under partially decoupled scenario (up to -19%) compared to current historical single farm payments. The model also shows that farm respond by reducing the number of beef animals on farm by up to 5%. However, under a partial decoupled scenario, beef farms increase calf numbers by 15% to benefit from coupled calf payment. A survey of 1,400 beef producers with respect to their intentions toward 2020 was conducted in the Summer of 2013. A set of hypothetical payment scenarios was used to test self-reported response to a number of scenarios related to expanding and extensifying. These were compared with the modelling results and found a range of responses which could, we argue, be used for future calibration and ‘sense-checking’ of results within future modelling strategies. No Label |
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MA @ admin @ |
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2219 |
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Özkan, á¹¢.; Bonesmo, H.; Østerås, O.; Harstad, O.M. |
| Title |
Effect of Increased Somatic Cell Count and Replacement Rate on Greenhouse Gas Emissions in Norwegian Dairy Herds |
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2014 |
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FACCE MACSUR Reports |
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3 |
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Sp3-1 |
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Dairy sector contributes around 4% of global greenhouse gas (GHG) emissions, of which 2/3 and 1/3 are attributed to milk and meat production, respectively. The main GHGs released from dairy farms are methane, nitrous oxide and carbon dioxide. The increased trend in emissions has stimulated research evaluating alternative mitigation options. Much of the work to date has focused on animal breeding, dietary factors and rumen manipulation. There have been little studies assessing the impact of secondary factors such as animal health on emissions at farm level. Production losses associated with udder health are significant. Somatic cell count (SCC) is an indicator on udder health. In Norway, around 45, 60 and 70% of cows in a dairy herd at first, second and third lactation are expected to have SCC of 50,000 cells/ml and above. Another indirect factor is replacement rate. Increasing the replacement rate due to health disorders, infertility and reduced milk yield is likely to increase the total farm emissions if the milking heifer replacements are kept in the herd.In this study, the impact of elevated SCC (200,000 cells/ml and above) and replacement rate on farm GHG emissions was evaluated. HolosNor, a farm scale model adapting IPCC methodology was used to estimate net farm GHG emissions. Preliminary results indicate an increasing trend in emissions (per kg milk and meat) as the SCC increases. Results suggest that animal health should be considered as an indirect mitigation strategy; however, further studies are required to enable comparisons of different farming systems. No Label |
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MA @ admin @ |
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2218 |
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Hutchings, N.; Kipling, R. |
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Inventory of farm-scale models within LiveM |
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2014 |
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FACCE MACSUR Reports |
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3 |
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D-L3.1 |
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LiveM; |
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The aim of WP3 is to improve the assessment of the impact of climate change on livestock and grassland systems at the farm-scale. The first step in this process is to understand the current state of the art in farm-scale modelling, and the resources available within the MACSUR knowledge hub. Here, an inventory of the farm-scale models available within LiveM is presented, along with a summary of the types of model represented. Thirteen farm-scale models were identified, three of which focus on environmental aspects of farm systems (GHG emissions etc.) and ten of which focus on management strategies (productivity, economics etc.). |
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2064 |
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König, H.; Helming, K.; Ayalon, O.; Benami, E.; Palatnik, R.R. |
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Curriculum for training course on policy impact assessment |
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2014 |
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FACCE MACSUR Reports |
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3 |
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D-T4.1 |
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A one-week MACSUR training course on policy impact assessment was held in March 2014 at Haifa University in Israel. The course was organised by ZALF (Hannes König, Katharina Helming) and Haifa University (Ofira Ayalon, Edan Benami, Ruslana Palatnik), targeting at the participation of Post-Docs and PhD students associated to the MACSUR consortium. The Framework for Participatory Impact Assessment (FoPIA) was used as the main method for the course to support structuring the policy impact assessment. The Israelian MACSUR case study of the Ramat Menashe Biosphere was used the test case of assessing alternative policy options and sustainability trade-offs. No Label |
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2236 |
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Rötter, R.P.; Semenov, M.A. |
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Development of methods for the probabilistic assessment of climate change impacts on crop production |
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2014 |
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FACCE MACSUR Reports |
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3 |
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D-C4.4.1 |
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Various attempts have been made to determine the relative importance of uncertainties in climate change impact assessments stemming from climate projections and crop models, respectively, and to analyse yield outputs probabilistically. For example, in the ENSEMBLES project, probabilistic climate projections (Harris et al. 2010) have been applied in conjunction with impact response surfaces (IRS), constructed by using impact models, to estimate the future likelihood (risk) of exceeding critical thresholds of crop yield impact (see, Fronzek et al., 2011, for an explanation of the method). In this task, we aimed to further develop and operationalize these methods and testing them in different case study regions in Europe. The method combines results of a sensitivity analysis of (one or more) impact model(s) with probabilistic projections of future temperature and precipitation (Fronzek et al., 2011). Such an overlay is one way of portraying probabilistic estimates of future impacts. By further accounting for the uncertainties in crop and biophysical parameters (using perturbed parameter approaches), the outcome represents an ensemble of impact risk estimates, encapsulating both climate and crop model uncertainties. No Label |
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MA @ admin @ |
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2233 |
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