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

Abstract

<p><strong>Abstract.</strong> 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 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.</p>

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

“…3) was obtained by aggregating country level N 2 O emissions from manure management and manure on pasture/range/paddock. Chang et al (, SN. 4) provide estimates of human‐induced N 2 O emissions by including livestock excreta deposition (simulated internally) and manure/fertilizer application in ORCHIDEE model.…”

“…For example, total N input in the form of fertilizer/manure N in the DLEM is higher than Stehfest and Bouwman () by 3.4 Tg N (see Table S2). Similarly, using a combination of process‐based model (ORCHIDEE) and an emission factor, Chang et al () estimated human‐induced N 2 O emissions of 1.24 Tg N 2 O‐N/year during 1961–2012, which is lower than DLEM‐simulated anthropogenic N 2 O emissions (1.76 Tg N 2 O‐N/year) by 30% (Figure S8). The overestimation compared to Chang et al () is likely due to uncertainty associated with how livestock excreta N deposition and manure N application are handled within the model.…”

“…Similarly, using a combination of process‐based model (ORCHIDEE) and an emission factor, Chang et al () estimated human‐induced N 2 O emissions of 1.24 Tg N 2 O‐N/year during 1961–2012, which is lower than DLEM‐simulated anthropogenic N 2 O emissions (1.76 Tg N 2 O‐N/year) by 30% (Figure S8). The overestimation compared to Chang et al () 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.…”

“…Likewise, in some grids, the grassland productivity from ORCHIDEE for domestic animals is not able to fulfill the grass biomass use by livestock. The grassland biomass deficit led to consumption of lower grass biomass by livestock, resulting in lower excretion in the form of manure and urine from domestic animals leading to lower N 2 O emissions (Chang et al, , ). In addition, unrealistic fraction of grass and other feedstuff associated with different land cover maps for some regions may likely explain the differences in N 2 O emissions.…”

“…We have also not included other management practices such as mowing/cutting frequency in pasturelands (Chang et al, ). While some studies show that cutting frequency can stimulate N 2 O fluxes (Neftel et al, ; Rafique et al, ), others have reported reduction in N 2 O fluxes after cutting events (Chen et al, ; Kammann et al, ).…”