Biogeosciences, 13, 3757-3776, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
29 Jun 2016
Combining livestock production information in a process-based vegetation model to reconstruct the history of grassland management
Jinfeng Chang1,2, Philippe Ciais1, Mario Herrero3, Petr Havlik4, Matteo Campioli5, Xianzhou Zhang6, Yongfei Bai7, Nicolas Viovy1, Joanna Joiner8, Xuhui Wang9,10, Shushi Peng10, Chao Yue1,11, Shilong Piao10, Tao Wang12,13, Didier A. Hauglustaine1, Jean-Francois Soussana14, Anna Peregon1,15, Natalya Kosykh15, and Nina Mironycheva-Tokareva15 1Laboratoire des Sciences du Climat et de l'Environnement, UMR8212, CEA-CNRS-UVSQ, 91191 Gif-sur-Yvette, France
2Sorbonne Universités (UPMC), CNRS-IRD-MNHN, LOCEAN/IPSL, 4 place Jussieu, 75005 Paris, France
3Commonwealth Scientific and Industrial Research Organisation, Agriculture Flagship, St. Lucia, QLD 4067, Australia
4Ecosystems Services and Management Program, International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria
5Centre of Excellence PLECO (Plant and Vegetation Ecology), Department of Biology, University of Antwerp, 2610 Wilrijk, Belgium
6Lhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, 100101 Beijing, China
7State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, 100093 Beijing, China
8NASA Goddard Space Flight Center, Greenbelt, MD, USA
9Laboratoire de Météorologie Dynamique, Institute Pierre Simon Laplace, 75005 Paris, France
10Sino-French Institute of Earth System Sciences, College of Urban and Environmental Sciences, Peking University, 100871 Beijing, China
11CNRS and UJF Grenoble 1, UMR5183, Laboratoire de Glaciologie et Géophysique de l'Environnement (LGGE), Grenoble, France
12Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, 100085 Beijing, China
13CAS Center for Excellence in Tibetan Plateau Earth Sciences, Chinese Academy of Sciences, 100085 Beijing, China
14INRA, UAR0233 CODIR Collège de Direction. Centre-Siège de l'INRA, Paris, France
15Institute of Soil Science and Agrochemistry, Siberian Branch Russian Academy of Sciences (SB RAS), Pr. Akademika Lavrentyeva 8/2, 630090 Novosibirsk, Russia
Abstract. 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 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 0.5° by 0.5°. 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, 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.

Citation: Chang, J., Ciais, P., Herrero, M., Havlik, P., Campioli, M., Zhang, X., Bai, Y., Viovy, N., Joiner, J., Wang, X., Peng, S., Yue, C., Piao, S., Wang, T., Hauglustaine, D. A., Soussana, J.-F., Peregon, A., Kosykh, N., and Mironycheva-Tokareva, N.: Combining livestock production information in a process-based vegetation model to reconstruct the history of grassland management, Biogeosciences, 13, 3757-3776,, 2016.
Publications Copernicus
Short summary
We derived the global maps of grassland management intensity of 1901–2012, including the minimum area of managed grassland with fraction of mown/grazed part. These maps, to our knowledge for the first time, provide global, time-dependent information for drawing up global estimates of management impact on biomass production and yields and for global vegetation models to enable simulations of carbon stocks and GHG budgets beyond simple tuning of grassland productivities to account for management.
We derived the global maps of grassland management intensity of 1901–2012, including the minimum...