Combining livestock production information in a process-based vegetation model to reconstruct the history of grassland management

Abstract

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 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 CO 2 concentration, and changes in nitrogen fertilization. The global area of managed grassland obtained in this study increases from 6

Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6

“…While the implementation of grazing and mowing is demonstrated at the European scale, a recent application is combining satellite-derived productivity and model simulations at the global scale to derive historical changes in grassland management (Chang et al, 2016).…”

“…With the current uncertainties in livestock density distributions (Kruska et al, 2003), management intensity and even distribution of pasture areas (Portmann et al, 2010), a global assessment of the current role of grassland management for biogeochemical cycles is not feasible. Recently, a global data set of grassland management was derived by combining regional data on livestock and its feed demand with satellite data and model simulations (Chang et al, 2016). Although this data set might be of great importance for improving managed grassland assessments, Chang et al (2016) also state that their method results in biomass production deficits in some areas caused by uncertainties in production system settings, pasture area distributions and in the mapping of regional averages to gridded data.…”

“…Recently, a global data set of grassland management was derived by combining regional data on livestock and its feed demand with satellite data and model simulations (Chang et al, 2016). Although this data set might be of great importance for improving managed grassland assessments, Chang et al (2016) also state that their method results in biomass production deficits in some areas caused by uncertainties in production system settings, pasture area distributions and in the mapping of regional averages to gridded data. Thus, further research and harmonization of these approaches are needed.…”

“…In order to reflect the actual contribution of managed grasslands to global biogeochemical fluxes, improvements of global data sets (such as Chang et al, 2016) on livestock densities and management settings are needed. The intensity of the grassland management with fertilization and irrigation could be included as well, corresponding to the intensity level that was established in Fader et al (2010).…”

Global Nitrous Oxide Emissions From Pasturelands and Rangelands: Magnitude, Spatiotemporal Patterns, and Attribution

“…Similarly, using a combination of process-based model ( Figure S8). The overestimation compared to Chang et al (2016) is likely due to uncertainty associated with how livestock excreta N deposition and manure N application are handled within the model. For instance, ORCHIDEE simulates livestock excreta deposition internally in the model as a function of livestock density and available grassland forage but then uses manure application in grasslands as input into the model.…”