Combining livestock production information in a process based vegetation model to reconstruct the history of grassland management
Grassland management type (grazed or mown) and intensity (intensive or extensive) play a crucial role in the GHG balance and surface energy budget of this biome, both at field scale and at large spatial scale. Yet, 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
... to 2012. These maps include the minimum area of managed vs. maximum area of un-managed grasslands, and the fraction of mown versus 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 nature of 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 CO<sub>2</sub> concentration, and changes in nitrogen fertilization. The global area of managed grassland obtained in this study is simulated to increase from 5.1 × 10<sup>6</sup> km<sup>2</sup> in 1901 to 11 × 10<sup>6</sup> km<sup>2</sup> in 2000, although the expansion pathway varies between different regions. The gridded grassland management intensity maps are model-dependent because they depend on Net Primary Productivity (NPP), which is the reason why specific attention is given to the evaluation of NPP. Namely, ORCHIDEE-GM is calibrated for C3 and C4 grass functional traits, and then evaluated against a series of observations from site-level NPP measurements to two global satellite products of Gross Primary Productivity (GPP) (MODIS-GPP and SIF data). The distribution of GPP and NPP with and without management, are evaluated against observations at different spatial and temporal scales. Generally, ORCHIDEE-GM captures the spatial pattern, seasonal cycle and interannual variability of grassland productivity at global scale well, and thus appears to be appropriate for global applications.