| Records |
| Author |
Kipling, R.P.; Bannink, A.; Bellocchi, G.; Dalgaard, T.; Fox, N.J.; Hutchings, N.J.; Kjeldsen, C.; Lacetera, N.; Sinabell, F.; Topp, C.F.E.; van Oijen, M.; Virkajärvi, P.; Scollan, N.D. |
| Title |
Modeling European ruminant production systems: Facing the challenges of climate change |
Type |
Journal Article |
| Year |
2016 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agricultural Systems |
| Volume |
147 |
Issue |
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Pages |
24-37 |
| Keywords |
Food security; Livestock systems; Modeling; Pastoral systems; Policy support; Ruminants |
| Abstract |
Ruminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute significantly to climate change through greenhouse gas (GHG) emissions, while intensi- fication of production has driven biodiversity and nutrient loss, and soil degradation. Modeling can offer insights into the complexity underlying the relationships between climate change, management and policy choices, food production, and the maintenance of ecosystem services. This paper 1) provides an overview of how ruminant systems modeling supports the efforts of stakeholders and policymakers to predict, mitigate and adapt to climate change and 2) provides ideas for enhancing modeling to fulfil this role. Many grassland models can predict plant growth, yield and GHG emissions from mono-specific swards, but modeling multi-species swards, grassland quality and the impact of management changes requires further development. Current livestock models provide a good basis for predicting animal production; linking these with models of animal health and disease is a prior- ity. Farm-scale modeling provides tools for policymakers to predict the emissions of GHG and other pollutants from livestock farms, and to support the management decisions of farmers from environmental and economic standpoints. Other models focus on how policy and associated management changes affect a range of economic and environmental variables at regional, national and European scales. Models at larger scales generally utilise more empirical approaches than those applied at animal, field and farm-scales and include assumptions which may not be valid under climate change conditions. It is therefore important to continue to develop more realistic representations of processes in regional and global models, using the understanding gained from finer-scale modeling. An iterative process of model development, in which lessons learnt from mechanistic models are ap- plied to develop ‘smart’ empirical modeling, may overcome the trade-off between complexity and usability. De- veloping the modeling capacity to tackle the complex challenges related to climate change, is reliant on closer links between modelers and experimental researchers, and also requires knowledge-sharing and increasing technical compatibility across modeling disciplines. Stakeholder engagement throughout the process of model development and application is vital for the creation of relevant models, and important in reducing problems re- lated to the interpretation of modeling outcomes. Enabling modeling to meet the demands of policymakers and other stakeholders under climate change will require collaboration within adequately-resourced, long-term inter-disciplinary research networks |
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0308521x |
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Review |
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LiveM, ft_macsur |
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no |
| Call Number |
MA @ admin @ |
Serial |
4734 |
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| Author |
Weindl, I.; Lotze-Campen, H.; Popp, A.; Müller, C.; Havlík, P.; Herrero, M.; Schmitz, C.; Rolinski, S. |
| Title |
Livestock in a changing climate: production system transitions as an adaptation strategy for agriculture |
Type |
Journal Article |
| Year |
2015 |
Publication |
Environmental Research Letters |
Abbreviated Journal |
Environ. Res. Lett. |
| Volume |
10 |
Issue |
9 |
Pages |
094021 |
| Keywords |
livestock; climate impacts; land use modeling; adaptation costs; production systems; greenhouse-gas emissions; global change; management implications; developing-countries; crop productivity; change mitigation; food security; model; impacts; carbon |
| Abstract |
Livestock farming is the world’s largest land use sector and utilizes around 60% of the global biomass harvest. Over the coming decades, climate change will affect the natural resource base of livestock production, especially the productivity of rangeland and feed crops. Based on a comprehensive impact modeling chain, we assess implications of different climate projections for agricultural production costs and land use change and explore the effectiveness of livestock system transitions as an adaptation strategy. Simulated climate impacts on crop yields and rangeland productivity generate adaptation costs amounting to 3% of total agricultural production costs in 2045 (i.e. 145 billion US$). Shifts in livestock production towards mixed crop-livestock systems represent a resource-and cost-efficient adaptation option, reducing agricultural adaptation costs to 0.3% of total production costs and simultaneously abating deforestation by about 76 million ha globally. The relatively positive climate impacts on grass yields compared with crop yields favor grazing systems inter alia in South Asia and North America. Incomplete transitions in production systems already have a strong adaptive and cost reducing effect: a 50% shift to mixed systems lowers agricultural adaptation costs to 0.8%. General responses of production costs to system transitions are robust across different global climate and crop models as well as regarding assumptions on CO2 fertilization, but simulated values show a large variation. In the face of these uncertainties, public policy support for transforming livestock production systems provides an important lever to improve agricultural resource management and lower adaptation costs, possibly even contributing to emission reduction. |
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1748-9326 |
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LiveM, ft_macsur |
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MA @ admin @ |
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4718 |
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Eza, U.; Shtiliyanova, A.; Borras, D.; Bellocchi, G.; Carrère, P.; Martin, R. |
| Title |
An open platform to assess vulnerabilities to climate change: An application to agricultural systems |
Type |
Journal Article |
| Year |
2015 |
Publication |
Ecological Informatics |
Abbreviated Journal |
Ecological Informatics |
| Volume |
30 |
Issue |
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Pages |
389-396 |
| Keywords |
climate change; grasslands; modeling platform; vulnerability assessment; pasture simulation-model; software component; solar-radiation; crop production; change impacts; adaptation; indicator; makers |
| Abstract |
Numerous climate futures are now available from global climate models. Translation of climate data such as precipitation and temperatures into ecologically meaningful outputs for managers and planners is the next frontier. We describe a model-based open platform to assess vulnerabilities of agricultural systems to climate change on pixel-wise data. The platform includes a simulation modeling engine and is suited to work with NetCDF format of input and output files. In a case study covering a region (Auvergne) in the Massif Central of France, the platform is configured to characterize climate (occurrence of arid conditions in historical and projected climate records), soils and human management, and is then used to assess the vulnerability to climate change of grassland productivity (downscaled to a fine scale). We demonstrate how using climate time series, and process-based simulations vulnerabilities can be defined at fine spatial scales relevant to farmers and land managers, and can be incorporated into management frameworks. (C) 2015 Elsevier B.V. All rights reserved. |
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1574-9541 |
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CropM LiveM, ft_macsur |
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no |
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MA @ admin @ |
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4708 |
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| Author |
Wallach, D. |
| Title |
Developing skills: how to train adaptive modelers |
Type |
Journal Article |
| Year |
2015 |
Publication |
Advances in Animal Biosciences |
Abbreviated Journal |
Advances in Animal Biosciences |
| Volume |
6 |
Issue |
01 |
Pages |
52-53 |
| Keywords |
capacity building; skills development; training; integrated modeling |
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2040-4700 |
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Hub, ft_macsur |
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no |
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MA @ admin @ |
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4683 |
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Martre, P.; Wallach, D.; Asseng, S.; Ewert, F.; Jones, J.W.; Rötter, R.P.; Boote, K.J.; Ruane, A.C.; Thorburn, P.J.; Cammarano, D.; Hatfield, J.L.; Rosenzweig, C.; Aggarwal, P.K.; Angulo, C.; Basso, B.; Bertuzzi, P.; Biernath, C.; Brisson, N.; Challinor, A.J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.F.; Heng, L.; Hooker, J.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Kersebaum, K.C.; Müller, C.; Kumar, S.N.; Nendel, C.; O’Leary, G.; Olesen, J.E.; Osborne, T.M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Semenov, M.A.; Shcherbak, I.; Steduto, P.; Stöckle, C.O.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; White, J.W.; Wolf, J. |
| Title |
Multimodel ensembles of wheat growth: many models are better than one |
Type |
Journal Article |
| Year |
2015 |
Publication |
Global Change Biology |
Abbreviated Journal |
Glob. Chang. Biol. |
| Volume |
21 |
Issue |
2 |
Pages |
911-925 |
| Keywords |
Climate; Climate Change; Environment; *Models, Biological; Seasons; Triticum/*growth & development; ecophysiological model; ensemble modeling; model intercomparison; process-based model; uncertainty; wheat (Triticum aestivum L.) |
| Abstract |
Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models. |
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1354-1013 |
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CropM, ftnotmacsur |
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no |
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MA @ admin @ |
Serial |
4665 |
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