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Combining Livestock Production Information in a Process-Based Vegetation Model to Reconstruct the History of Grassland ManagementGrassland 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 grass-biomass 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 0.5deg by 0.5deg. The grass-biomass demand is derived from a livestock dataset for 2000, extended to cover the period 19012012. The grass-biomass 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, risingCO2 concentration, and changes in nitrogen fertilization. The global area of managed grassland obtained in this study increases from 6.1 x 10(exp 6) km(exp 2) in 1901 to 12.3 x 10(exp 6) kmI(exp 2) 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 inter-annual variability of grassland productivity at global scale well and thus is appropriate for global applications presented here.
Document ID
20170003334
Acquisition Source
Goddard Space Flight Center
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Chang, Jinfeng
(Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette, France)
Ciais, Philippe
(Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette, France)
Herrero, Mario
(Commonwealth Scientific and Industrial Research Organization Saint Lucia, Australia)
Havlik, Petr
(International Inst. for Applied Systems Analysis Laxenburg, Austria)
Campioli, Matteo
(Antwerp Univ. Wilrijk, Belgium)
Zhang, Xianzhou
(Institute of Geographic Sciences and Natural Resources Research (IGSNRR) Beijing, China)
Bai, Yongfei
(Academy of Sciences Beijing, China)
Viovy, Nicolas
(Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette, France)
Joiner, Joanna
(NASA Goddard Space Flight Center Greenbelt, MD United States)
Wang, Xuhui
(Peking Univ. Beijing, China)
Peng, Shushi S.
(Peking Univ. Beijing, China)
Yue, Chao
(Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette, France)
Piao, Shilong
(Peking Univ. Beijing, China)
Wang, Tao
(Chinese Academy of Sciences Beijing, China)
Hauglustaine, Didier A.
(Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette, France)
Soussana, Jean-Francois
(French National Institute for Agricultural Research (INRA) Paris, France)
Date Acquired
April 11, 2017
Publication Date
June 29, 2016
Publication Information
Publication: Biogeosciences
Publisher: Biogeosciences
Volume: 13
Issue: 12
e-ISSN: 1726-4189
Subject Category
Geosciences (General)
Report/Patent Number
GSFC-E-DAA-TN41481
Distribution Limits
Public
Copyright
Other
Keywords
Livestock dataset
biomass supply
process-based model

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