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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
agricultural and biological sciences
Combining livestock production information in a process-based vegetation model to reconstruct the history of grassland management
Biogeosciences, Volume 13, No. 12, Year 2016
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Description
Grassland management type (grazed or mown) and intensity (intensive or extensive) play a crucial role in the greenhouse gas balance and surface energy budget of this biome, both at field scale and at large spatial scale. However, global gridded historical information on grassland management intensity is not available. Combining modelled grass-biomass productivity with statistics of the grassbiomass demand by livestock, we reconstruct gridded maps of grassland management intensity from 1901 to 2012. These maps include the minimum area of managed vs. maximum area of unmanaged grasslands and the fraction of mown vs. grazed area at a resolution of 0.5° by 0.5°. The grassbiomass demand is derived from a livestock dataset for 2000, extended to cover the period 1901-2012. The grassbiomass supply (i.e. forage grass from mown grassland and biomass grazed) is simulated by the process-based model ORCHIDEE-GM driven by historical climate change, rising CO2 concentration, and changes in nitrogen fertilization. The global area of managed grassland obtained in this study increases from 6.1×106 km2 in 1901 to 12.3×106 km2 in 2000, although the expansion pathway varies between different regions. ORCHIDEE-GM also simulated augmentation in global mean productivity and herbage-use efficiency over managed grassland during the 20th century, indicating a general intensification of grassland management at global scale but with regional differences. The gridded grassland management intensity maps are model dependent because they depend on modelled productivity. Thus specific attention was given to the evaluation of modelled productivity against a series of observations from site-level net primary productivity (NPP) measurements to two global satellite products of gross primary productivity (GPP) (MODIS-GPP and SIF data). Generally, ORCHIDEE-GM captures the spatial pattern, seasonal cycle, and interannual variability of grassland productivity at global scale well and thus is appropriate for global applications presented here. © 2016 Author(s).
Authors & Co-Authors
Ciais, Philippe
France, Gif-sur-yvette
Commissariat a L'energie Atomique et Aux Energies Alternatives
Herrero, Mario
Australia, Canberra
Commonwealth Scientific and Industrial Research Organisation
Havlik, Petr
Austria, Laxenburg
International Institute for Applied Systems Analysis, Laxenburg
Campioli, Matteo
Belgium, Antwerpen
Universiteit Antwerpen
Viovy, Nicolas
France, Gif-sur-yvette
Commissariat a L'energie Atomique et Aux Energies Alternatives
Joiner, Joanna S.
United States, Greenbelt
Nasa Goddard Space Flight Center
Wang, Xuhui
France, Guyancourt
Institut Pierre-simon Laplace
China, Beijing
Peking University
Peng, Shushi
China, Beijing
Peking University
Yue, Chao
France, Gif-sur-yvette
Commissariat a L'energie Atomique et Aux Energies Alternatives
France, Saint Martin D'heres
Université Grenoble Alpes
Piao, Shilonog Long
China, Beijing
Peking University
Wang, Tao
China, Beijing
Chinese Academy of Sciences
Hauglustaine, Didier A.
France, Gif-sur-yvette
Commissariat a L'energie Atomique et Aux Energies Alternatives
Soussana, Jean François
France, Paris
Inrae
Peregon, Anna M.
France, Gif-sur-yvette
Commissariat a L'energie Atomique et Aux Energies Alternatives
Russian Federation, Novosibirsk
Siberian Branch, Russian Academy of Sciences
Statistics
Citations: 33
Authors: 14
Affiliations: 12
Identifiers
Doi:
10.5194/bg-13-3757-2016
ISSN:
17264170
Research Areas
Environmental