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Sinabell, F.; Sommer, M.; Kappert, R.; Kaul, H.P. |
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Ist Mais unentbehrlich? |
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2015 |
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Der Pflanzenarzt |
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68 |
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19-21 |
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TradeM, ft_macsur |
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MA @ admin @ |
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5015 |
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Shrestha, S.; Abdalla, M.; Hennessy, T.; Forristal, D.; Jones, M.B. |
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Title |
Irish farms under climate change – is there a regional variation on farm responses? |
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Journal Article |
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2015 |
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Journal of Agricultural Science |
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J. Agric. Sci. |
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153 |
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03 |
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385-398 |
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change impacts; elevated co2; potential impacts; maize production; united-states; winter-wheat; plant-growth; adaptation; ireland; yield |
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The current paper aims to determine regional impacts of climate change on Irish farms examining the variation in farm responses. A set of crop growth models were used to determine crop and grass yields under a baseline scenario and a future climate scenario. These crop and grass yields were used along with farm-level data taken from the Irish National Farm Survey in an optimizing farm-level (farm-level linear programming) model, which maximizes farm profits under limiting resources. A change in farm net margins under the climate change scenario compared to the baseline scenario was taken as a measure to determine the effect of climate change on farms. The growth models suggested a decrease in cereal crop yields (up to 9%) but substantial increase in yields of forage maize (up to 97%) and grass (up to 56%) in all regions. Farms in the border, midlands and south-east regions suffered, whereas farms in all other regions generally fared better under the climate change scenario used in the current study. The results suggest that there is a regional variability between farms in their responses to the climate change scenario. Although substituting concentrate feed with grass feeds is the main adaptation on all livestock farms, the extent of such substitution differs between farms in different regions. For example, large dairy farms in the south-east region adopted total substitution of concentrate feed while similar dairy farms in the south-west region opted to replace only 0.30 of concentrate feed. Farms in most of the regions benefitted from increasing stocking rate, except for sheep farms in the border and dairy farms in the south-east regions. The tillage farms in the mid-east region responded to the climate change scenario by shifting arable production to beef production on farms. |
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English |
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0021-8596 1469-5146 |
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CropM, TradeM |
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MA @ admin @ |
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4542 |
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Janssen, S. |
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Inventory of data and data sharing mechanism for model linking and scaling exercises |
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Report |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-C3.2 |
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This deliverable lays out the work as done as part of MACSUR CropM on ‘Inventory of data and data sharing mechanism for model linking and scaling exercises’. In summary not much work was done, as it was found that there was not real demand for the activity in this task. The task in itself was servicing the other work as part of MACSUR, and as the service was not in demand, it was decided to take a low profile and wait for specific requests by partners for data in relation to model linking and upscaling. No Label |
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no |
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MA @ admin @ |
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2095 |
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Author |
Korhonen, P. |
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Title |
Intercomparison of timothy models in northern countries |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-31 |
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Forage-based livestock and dairy production are the economic backbone of agriculture in many northern countries. In northern Europe and eastern Canada, forage grasses are commonly grown intensively for silage and hay as a part of crop rotation. In those regions, timothy (Phleum pratense L.) is one of the most widely grown grass species. Models that simulate the development of yield and nutritive quality have been developed for timothy, but the performance of different models has not been compared so far.In this study, we compare the performance of the models BASGRA, CATIMO, and STICS for the predictions of timothy yield at 7 sites located in Finland, Norway, Sweden, and Canada. In addition to yield, model predictions of additional variables, such as leaf area index, specific leaf area, and nutritive quality are gathered on a daily basis. Observed data will be used for two distinct calibrations: 1) Cultivar-specific and 2) ”global”, using all cultivars. The performance of the models will be tested by simulating all sites and years with both the 5 cultivar-specific parameter sets and the global parameter set.The first results of the comparison will be presented with a particular emphasis on dry matter yield predictions.The results will provide information about the uncertainties related to yield predictions of different timothy models and calibrations, the strengths and weaknesses of different modelling approaches, and the sensitivity of models to cultivar-specific parameters. 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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2146 |
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Author |
Sharif, B. |
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Title |
Inter-comparison of statistical models for projecting winter oilseed rape yield in Europe under climate change |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-61 |
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While intercomparison of process-based crop models for projections under climate change is being intensively studied at European as well as at the global scale, little effort has been made for comparing statistical models. In this study, several regression techniques (ordinary least squares, stepwise, shrinkage methods, principle components and partial least squares) were combined with different types of climate input variables (with different temporal resolution) in order to define a large range of statistical models. Each model was fitted to winter oilseed rape data collected in 689, 325 and 173 field experiments carried out in Denmark, Germany, and Czech Republic, respectively. The fitted models were then used to predict yield of winter oilseed rape in the field experiments during more than 20 years, up to 2013. Interpretability of the estimated climate variable effects and accuracy of yield predictions were both analysed. Results suggest that recent statistical methods (e.g., shrinkage methods) may have considerable capabilities to complement traditional statistical methods in yield prediction. The selection of the most influential variables was strongly influenced by the statistical method used to analyse the data. Among the most recent statistical methods, the uncertainties in projecting yield of winter oilseed rape under climate change were mainly due to residual errors and uncertainty in estimated parameter values, and not to model choice. 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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2176 |
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