Tue, Aug 16, 2022: 1:45 PM-2:00 PM
516A
Background/Question/MethodsBackground: Nitrous oxide (N2O) emission from grazing-lands is an important source of greenhouse gases emission. However, current estimations of the magnitude of grazing-lands N2O emission remain large uncertainties at global scale.Questions: 1) the temporal and spatial variations of global grazing-lands N2O emission; 2) contribution of deposited livestock excreta associated with grazing activities and 3) the properties of managements are the major uncertainty source of the estimation of the N2O emissions.Methods: More detailed descriptions of natural processes and management were integrated into a process-based model, TRIPLEX-GHGv2.0 which was further test against field observations globally. The improved model was further applied to conduct global simulations under different management scenarios to investigate the contribution of N fertilization, manure application, livestock excreta and atmospheric N deposition. Meanwhile, additional scenarios were designed by varying fertilizer and excreta properties to evaluate possible uncertainties of estimated N2O emissions derived from N properties.
Results/ConclusionsResults: The improved model can provide reasonable estimations of N2O fluxes under varying management and environment conditions as suggested by the calibration (R2=0.97,slope=1.05,n=14) and validation (R2=0.85,slope=0.82,n=48). From 1961-1990 N2O emission significantly increased from 1.20 to 2.09TgNyr-1 while it further showed slower growing trend with the highest level as 2.26TgNyr-1 in 2000 and slightly decreased to 2.02TgNyr-1 in 2016. Europe, North America, southern Asia regions were identified as the major emission hotspots. Among different sources, chemical N fertilizer and manure applications on pasturelands was the predominant contributor whereas the effect of livestock excreta N deposited on grazing-lands was responsible for 0.46TgNyr-1 emission during the study period. Additional simulations with 20% changes in fertilizer and excreta properties suggested large uncertainties of estimated global grazing-land N2O emissions up to 41.24% variations.Conclusions: N2O emission from global grazing-lands showed a strong spatial-temporal variation pattern during study period. The grazing activity effects on N2O emission was probably overestimated by previous studies. The importance of the input N properties should be addressed for estimating N2O emission at large scale to reduce uncertainties. This study provided an effective tool to modelling grazing-lands N2O emissions and improved understanding of the magnitude, source and uncertainties of the current estimation.
Results/ConclusionsResults: The improved model can provide reasonable estimations of N2O fluxes under varying management and environment conditions as suggested by the calibration (R2=0.97,slope=1.05,n=14) and validation (R2=0.85,slope=0.82,n=48). From 1961-1990 N2O emission significantly increased from 1.20 to 2.09TgNyr-1 while it further showed slower growing trend with the highest level as 2.26TgNyr-1 in 2000 and slightly decreased to 2.02TgNyr-1 in 2016. Europe, North America, southern Asia regions were identified as the major emission hotspots. Among different sources, chemical N fertilizer and manure applications on pasturelands was the predominant contributor whereas the effect of livestock excreta N deposited on grazing-lands was responsible for 0.46TgNyr-1 emission during the study period. Additional simulations with 20% changes in fertilizer and excreta properties suggested large uncertainties of estimated global grazing-land N2O emissions up to 41.24% variations.Conclusions: N2O emission from global grazing-lands showed a strong spatial-temporal variation pattern during study period. The grazing activity effects on N2O emission was probably overestimated by previous studies. The importance of the input N properties should be addressed for estimating N2O emission at large scale to reduce uncertainties. This study provided an effective tool to modelling grazing-lands N2O emissions and improved understanding of the magnitude, source and uncertainties of the current estimation.