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Popp, A.; Calvin, K.; Fujimori, S.; Havlik, P.; Humpenöder, F.; Stehfest, E.; Bodirsky, B.L.; Dietrich, J.P.; Doelmann, J.C.; Gusti, M.; Hasegawa, T.; Kyle, P.; Obersteiner, M.; Tabeau, A.; Takahashi, K.; Valin, H.; Waldhoff, S.; Weindl, I.; Wise, M.; Kriegler, E.; Lotze-Campen, H.; Fricko, O.; Riahi, K.; Vuuren, D.P. van |
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Title |
Land-use futures in the shared socio-economic pathways |
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Journal Article |
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Year |
2017 |
Publication |
Global Environmental Change |
Abbreviated Journal |
Glob. Environ. Change |
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Volume |
42 |
Issue |
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Pages |
331-345 |
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Keywords |
Scenarios; Land use; Emissions; Mitigation; Food prices; Integrated assessment; SSP |
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Abstract |
• Narratives for the Shared Socio-Economic Pathways (SSPs) focusing on the land sector are presented. • Integrated Assessment Models have been applied for the SSPs to assess potential future developments for land use, greenhouse gas emissions, food provision and prices. • Model results reflect the general storylines of the SSPs and indicate a broad range of potential land-use futures. • SSP-based land use pathways aim at supporting future climate research, climate impact analysis, biodiversity research and sustainability science. Abstract In the future, the land system will be facing new intersecting challenges. While food demand, especially for resource-intensive livestock based commodities, is expected to increase, the terrestrial system has large potentials for climate change mitigation through improved agricultural management, providing biomass for bioenergy, and conserving or even enhancing carbon stocks of ecosystems. However, uncertainties in future socio-economic land use drivers may result in very different land-use dynamics and consequences for land-based ecosystem services. This is the first study with a systematic interpretation of the Shared Socio-Economic Pathways (SSPs) in terms of possible land-use changes and their consequences for the agricultural system, food provision and prices as well as greenhouse gas emissions. Therefore, five alternative Integrated Assessment Models with distinctive land-use modules have been used for the translation of the SSP narratives into quantitative projections. The model results reflect the general storylines of the SSPs and indicate a broad range of potential land-use futures with global agricultural land of 4900 mio ha in 2005 decreasing by 743 mio ha until 2100 at the lower (SSP1) and increasing by 1080 mio ha (SSP3) at the upper end. Greenhouse gas emissions from land use and land use change, as a direct outcome of these diverse land-use dynamics, and agricultural production systems differ strongly across SSPs (e.g. cumulative land use change emissions between 2005 and 2100 range from −54 to 402 Gt CO2). The inclusion of land-based mitigation efforts, particularly those in the most ambitious mitigation scenarios, further broadens the range of potential land futures and can strongly affect greenhouse gas dynamics and food prices. In general, it can be concluded that low demand for agricultural commodities, rapid growth in agricultural productivity and globalized trade, all most pronounced in a SSP1 world, have the potential to enhance the extent of natural ecosystems, lead to lowest greenhouse gas emissions from the land system and decrease food prices over time. The SSP-based land use pathways presented in this paper aim at supporting future climate research and provide the basis for further regional integrated assessments, biodiversity research and climate impact analysis. |
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0959-3780 |
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TradeM, ftnotmacsur |
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MA @ admin @ |
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5006 |
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Author |
Persson, T.; Kværnø, S. |
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Title |
Impact of projected mid-21st century climate and soil extrapolation on simulated spring wheat grain yield in Southeastern Norway |
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Journal Article |
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Year |
2017 |
Publication |
Journal of Agricultural Science |
Abbreviated Journal |
J. Agric. Sci. |
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Volume |
155 |
Issue |
03 |
Pages |
361-377 |
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The effects of soil variability on regional crop yield under projected climate change are largely unknown. In Southeastern Norway, increased temperature and precipitation are projected for the mid-21st century. Crop simulation models in combination with scaling techniques can be used to determine the regional pattern of crop yield. In the present paper, the CSM-CROPSIM-CERES-Wheat model was applied to simulate regional spring wheat yield for Akershus and Østfold counties in Southeastern Norway. Prior to the simulations, parameters in the CSM-CROPSIM-CERES-Wheat model were calibrated for the spring wheat cvars Zebra, Demonstrant and Bjarne, using cultivar trial data from Southeastern Norway and site-specific weather and soil information. Weather input data for regional yield simulations represented the climate in 1961–1990 and projections of the climate in 2046–2065. The latter were based on four Global Climate Models and greenhouse gas emission scenario A1B in the IPCC 4th Assessment Report. Data on regional soil particle size distribution, water-holding characteristics and organic matter data were obtained from a database. To determine the simulated grain yield sensitivity to soil input, the number of soil profiles used to describe the soilscape in the region varied from 76 to 16, 5 and 1. The soils in the different descriptions were selected by arranging them into groups according to similarities in physical characteristics and taking the soil in each group occupying the largest area in the region to represent other soils in that group. The simulated grain yields were higher under all four projected future climate scenarios than the corresponding average yields in the baseline conditions. On average across the region, there were mostly non-significant differences in grain yield between the soil extrapolations for all cultivars and climate projections. However, for sub-regions grain yield varied by up to 20% between soil extrapolations. These results indicate how projected climate change could affect spring wheat yield given the assumed simulated conditions for a region with similar climate and soil conditions to many other cereal production regions in Northern Europe. The results also provide useful information about how soil input data could be handled in regional crop yield determinations under these conditions. |
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0021-8596 |
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CropM, LiveM, ft_macsur |
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MA @ admin @ |
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5009 |
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Park, S.K.; Sungmin, O.; Cassardo, C. |
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Soil temperature response in Korea to a changing climate using a land surface model |
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Journal Article |
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2017 |
Publication |
Asia-Pacific Journal of Atmospheric Sciences |
Abbreviated Journal |
Asia-Pacific Journal of Atmospheric Sciences |
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53 |
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4 |
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457-470 |
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Land surface process; soil temperature; climate change; soil-vegetation-atmosphere transfer (SVAT) scheme; University of TOrino model of land Process Interaction with Atmosphere (UTOPIA); REGIONAL CLIMATE; SNOW COVER; WATER-RESOURCES; SOCIOECONOMIC SCENARIOS; QUANTITATIVE-ANALYSIS; MESOSCALE MODEL; SRES EMISSIONS; FUTURE CLIMATE; CHANGE IMPACTS; SOUTH-AMERICA |
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The land surface processes play an important role in weather and climate systems through its regulation of radiation, heat, water and momentum fluxes. Soil temperature (ST) is one of the most important parameters in the land surface processes; however, there are few extensive measurements of ST with a long time series in the world. According to the CLImatology of Parameters at the Surface (CLIPS) methodology, the output of a trusted Soil-Vegetation- Atmosphere Transfer (SVAT) scheme can be utilized instead of observations to investigate the regional climate of interest. In this study, ST in South Korea is estimated in a view of future climate using the output from a trusted SVAT scheme – the University of TOrino model of land Process Interaction with Atmosphere (UTOPIA), which is driven by a regional climate model. Here characteristic changes in ST are analyzed under the IPCC A2 future climate for 2046-2055 and 2091-2100, and are compared with those under the reference climate for 1996-2005. The UTOPIA results were validated using the observed ST in the reference climate, and the model proved to produce reasonable ST in South Korea. The UTOPIA simulations indicate that ST increases due to environmental change, especially in air temperature (AT), in the future climate. The increment of ST is proportional to that of AT except for winter. In wintertime, the ST variations are different from region to region mainly due to variations in snow cover, which keeps ST from significant changes by the climate change. |
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2017-12-21 |
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English |
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1976-7633 |
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CropM, ft_macsur |
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MA @ admin @ |
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5182 |
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Özkan Gülzari, Ş.; Kipling, R. |
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Understanding the potential of existing models to characterize animal health conditions and estimate greenhouse gas emissions |
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Report |
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2017 |
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FACCE MACSUR Reports |
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10 |
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L2.2-D2 |
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The primary objective of this study was to assess the status and priorities for future development in modelling of the impacts of animal health on greenhouse gas (GHG) emissions. It also aimed to facilitate communication between experimental researchers and modellers by defining a list of parameters that are needed to model livestock health and disease, and the impact of health conditions on GHG emissions. The summary presented here provides a brief overview of ongoing work, which the L2.1/L2.2 partners, with support from the Global Research Alliance Animal Health Network (GRA AHN), is currently developing into a paper for publication in a peer reviewed journal. |
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LiveM |
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MA @ admin @ |
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4979 |
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Özkan Gülzari, Ş.; Åby, B.A.; Persson, T.; Höglind, M.; Mittenzwei, K. |
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Combining models to estimate the impacts of future climate scenarios on feed supply, greenhouse gas emissions and economic performance on dairy farms in Norway |
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Journal Article |
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2017 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
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157 |
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157-169 |
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Climate change; Dairy farming; Dry matter yield; Economics; Greenhouse gas emission; Modelling |
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• This study combines crop, livestock and economic models.
• Models interaction is through use of relevant input and output variables.
• Future climate change will result in increased grass and wheat dry matter yields.
• Changes in grass, wheat and milk yields in future reduce farm emissions intensity.
• Changes in future dry matter yields and emissions lead to increased profitability.
There is a scientific consensus that the future climate change will affect grass and crop dry matter (DM) yields. Such yield changes may entail alterations to farm management practices to fulfill the feed requirements and reduce the farm greenhouse gas (GHG) emissions from dairy farms. While a large number of studies have focused on the impacts of projected climate change on a single farm output (e.g. GHG emissions or economic performance), several attempts have been made to combine bio-economic systems models with GHG accounting frameworks. In this study, we aimed to determine the physical impacts of future climate scenarios on grass and wheat DM yields, and demonstrate the effects such changes in future feed supply may have on farm GHG emissions and decision-making processes. For this purpose, we combined four models: BASGRA and CSM-CERES-Wheat models for simulating forage grass DM and wheat DM grain yields respectively; HolosNor for estimating the farm GHG emissions; and JORDMOD for calculating the impacts of changes in the climate and management on land use and farm economics. Four locations, with varying climate and soil conditions were included in the study: south-east Norway, south-west Norway, central Norway and northern Norway. Simulations were carried out for baseline (1961–1990) and future (2046–2065) climate conditions (projections based on two global climate models and the Special Report on Emissions Scenarios (SRES) A1B GHG emission scenario), and for production conditions with and without a milk quota. The GHG emissions intensities (kilogram carbon dioxide equivalent: kgCO2e emissions per kg fat and protein corrected milk: FPCM) varied between 0.8 kg and 1.23 kg CO2e (kg FPCM)− 1, with the lowest and highest emissions found in central Norway and south-east Norway, respectively. Emission intensities were generally lower under future compared to baseline conditions due mainly to higher future milk yields and to some extent to higher crop yields. The median seasonal above-ground timothy grass yield varied between 11,000 kg and 16,000 kg DM ha− 1 and was higher in all projected future climate conditions than in the baseline. The spring wheat grain DM yields simulated for the same weather conditions within each climate projection varied between 2200 kg and 6800 kg DM ha− 1. Similarly, the farm profitability as expressed by total national land rents varied between 1900 million Norwegian krone (NOK) for median yields under baseline climate conditions up to 3900 million NOK for median yield under future projected climate conditions. |
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phase 2 |
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CropM, LiveM, TradeM, ft_macsur |
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MA @ admin @ |
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5172 |
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