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Combining livestock production information in a process-based vegetation model to reconstruct the history of grassland management
Chang, JF (Chang, Jinfeng)1,2; Ciais, P (Ciais, Philippe)1; Herrero, M (Herrero, Mario)3; Havlik, P (Havlik, Petr)4; Campioli, M (Campioli, Matteo)5; Zhang, XZ (Zhang, Xianzhou)6; Bai, YF (Bai, Yongfei)7; Viovy, N (Viovy, Nicolas)1; Joiner, J (Joiner, Joanna)8; Wang, XH (Wang, Xuhui)9,10; Peng, SS (Peng, Shushi)10; Yue, C (Yue, Chao)1,11,12; Piao, SL (Piao, Shilong)10; Wang, T (Wang, Tao)13,14; Hauglustaine, DA (Hauglustaine, Didier A.)1; Soussana, JF (Soussana, Jean-Francois)15; Peregon, A (Peregon, Anna)1,16; Kosykh, N (Kosykh, Natalya)16; Mironycheva-Tokareva, N (Mironycheva-Tokareva, Nina)16; Chang, JF
Source PublicationBIOGEOSCIENCES
2016
Volume13Issue:12Pages:3757-3776
DOI10.5194/bg-13-3757-2016
AbstractGrassland 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 0.5A degrees by 0.5A degrees. The grass-biomass demand is derived from a livestock dataset for 2000, extended to cover the period 1901-2012. 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, risingaEuro-CO2 concentration, and changes in nitrogen fertilization. The global area of managed grassland obtained in this study increases from 6.1aEuro-aEuro parts per thousand x aEuro-10(6)aEuro-km(2) in 1901 to 12.3aEuro-aEuro parts per thousand x aEuro-10(6)aEuro-km(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 interannual variability of grassland productivity at global scale well and thus is appropriate for global applications presented here.
Subject Area普通生物学
WOS IDWOS:000379427700016
Language英语
Indexed BySCI
KeywordPasture Simulation-model Net Primary Production Land-cover Chlorophyll Fluorescence European Grasslands Global Database Carbon Balance Climate-change Data Set Biomass
Cooperation Status国际
Department生态
SubtypeArticle
Citation statistics
Cited Times:15[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.itpcas.ac.cn/handle/131C11/7860
Collection图书馆
Corresponding AuthorChang, JF
Affiliation1.CEA CNRS UVSQ, Lab Sci Climat & Environm, UMR8212, F-91191 Gif Sur Yvette, France
2.Sorbonne Univ UPMC, CNRS IRD MNHN, LOCEAN IPSL, 4 Pl Jussieu, F-75005 Paris, France
3.Commonwealth Sci & Ind Res Org, Agr Flagship, St Lucia, Qld 4067, Australia
4.Int Inst Appl Syst Anal, Ecosyst Serv & Management Program, A-2361 Laxenburg, Austria
5.Univ Antwerp, Dept Biol, Ctr Excellence PLECO Plant & Vegetat Ecol, B-2610 Antwerp, Belgium
6.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Lhasa Plateau Ecosyst Res Stn, Beijing 100101, Peoples R China
7.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
8.NASA Goddard Space Flight Ctr, Greenbelt, MD USA
9.Inst Pierre Simon Laplace, Lab Meteorol Dynam, F-75005 Paris, France
10.Peking Univ, Sino French Inst Earth Syst Sci, Coll Urban & Environm Sci, Beijing 100871, Peoples R China
11.CNRS, Grenoble, France
12.UJF Grenoble 1, LGGE, UMR5183, Grenoble, France
13.Chinese Acad Sci, Inst Tibetan Plateau Res, Key Lab Alpine Ecol & Biodivers, Beijing 100085, Peoples R China
14.Chinese Acad Sci, CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100085, Peoples R China
15.INRA, Ctr Siege, CODIR Coll Direct UAR0233, Paris, France
16.SB RAS, Inst Soil Sci & Agrochem, Pr Akad Lavrentyeva 8-2, Novosibirsk 630090, Russia
Recommended Citation
GB/T 7714
Chang, JF ,Ciais, P ,Herrero, M ,et al. Combining livestock production information in a process-based vegetation model to reconstruct the history of grassland management[J]. BIOGEOSCIENCES,2016,13(12):3757-3776.
APA Chang, JF .,Ciais, P .,Herrero, M .,Havlik, P .,Campioli, M .,...&Chang, JF.(2016).Combining livestock production information in a process-based vegetation model to reconstruct the history of grassland management.BIOGEOSCIENCES,13(12),3757-3776.
MLA Chang, JF ,et al."Combining livestock production information in a process-based vegetation model to reconstruct the history of grassland management".BIOGEOSCIENCES 13.12(2016):3757-3776.
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