Dataset Information

Response of ecosystem CO2 fluxes to grazing intensities - a five-year experiment in the Hulunber meadow steppe of China.

ABSTRACT: Grazing is the primary land use in the Hulunber meadow steppe. However, the quantitative effects of grazing on ecosystem carbon dioxide (CO2) fluxes in this zone remain unclear. A controlled experiment was conducted from 2010 to 2014 to study the effects of six stocking rates on CO2 flux, and the results showed that there were significant differences in CO2 fluxes by year, treatment, and month. The effects of light and intermediate grazing remained relatively constant with grazing year, whereas the effects of heavy grazing increased substantially with grazing duration. CO2 flux significantly decreased with increasing grazing intensity and duration, and it was significantly positively correlated with rainfall, soil moisture (SM), the carbon to nitrogen ratio (C/N ratio), soil available phosphorus (SAP), soil NH4+-N, soil NO3-N, aboveground biomass (AGB), coverage, height, and litter and negatively correlated with air temperature, total soil N (TN) and microbial biomass N (MBN). A correspondence analysis showed that the main factors influencing changes in CO2 emissions under grazing were AGB, height, coverage, SM, NH4+-N and NO3-N. Increased rainfall and reduced grazing resulted in greater CO2 emissions. Our study provides important information to improve our understanding of the role of livestock grazing in GHG emissions.

SUBMITTER: Yan RR

PROVIDER: S-EPMC5573324 | biostudies-other | 2017 Aug

REPOSITORIES: biostudies-other

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Response of ecosystem CO2 fluxes to grazing intensities - a five-year experiment in the Hulunber meadow steppe of China.

Yan R R RR Tang H J HJ Lv S H SH Jin D Y DY Xin X P XP Chen B R BR Zhang B H BH Yan Y C YC Wang X X Murray Philip J PJ Yang G X GX Xu L J LJ Li L H LH Zhao S S

Scientific reports 20170825 1

Grazing is the primary land use in the Hulunber meadow steppe. However, the quantitative effects of grazing on ecosystem carbon dioxide (CO2) fluxes in this zone remain unclear. A controlled experiment was conducted from 2010 to 2014 to study the effects of six stocking rates on CO2 flux, and the results showed that there were significant differences in CO2 fluxes by year, treatment, and month. The effects of light and intermediate grazing remained relatively con ...[more]

PMID: 28842675

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Project description:Forests play a crucial role in the global carbon (C) cycle by storing and sequestering a substantial amount of C in the terrestrial biosphere. Due to temporal dynamics in climate and vegetation activity, there are significant regional variations in carbon dioxide (CO2) fluxes between the biosphere and atmosphere in forests that are affecting the global C cycle. Current forest CO2 flux dynamics are controlled by instantaneous climate, soil, and vegetation conditions, which carry legacy effects from disturbances and extreme climate events. Our level of understanding from the legacies of these processes on net CO2 fluxes is still limited due to their complexities and their long-term effects. Here, we combined remote sensing, climate, and eddy-covariance flux data to study net ecosystem CO2 exchange (NEE) at 185 forest sites globally. Instead of commonly used non-dynamic statistical methods, we employed a type of recurrent neural network (RNN), called Long Short-Term Memory network (LSTM) that captures information from the vegetation and climate's temporal dynamics. The resulting data-driven model integrates interannual and seasonal variations of climate and vegetation by using Landsat and climate data at each site. The presented LSTM algorithm was able to effectively describe the overall seasonal variability (Nash-Sutcliffe efficiency, NSE = 0.66) and across-site (NSE = 0.42) variations in NEE, while it had less success in predicting specific seasonal and interannual anomalies (NSE = 0.07). This analysis demonstrated that an LSTM approach with embedded climate and vegetation memory effects outperformed a non-dynamic statistical model (i.e. Random Forest) for estimating NEE. Additionally, it is shown that the vegetation mean seasonal cycle embeds most of the information content to realistically explain the spatial and seasonal variations in NEE. These findings show the relevance of capturing memory effects from both climate and vegetation in quantifying spatio-temporal variations in forest NEE.

Dataset Information

Response of ecosystem CO2 fluxes to grazing intensities - a five-year experiment in the Hulunber meadow steppe of China.

Publications

Response of ecosystem CO<sub>2</sub> fluxes to grazing intensities - a five-year experiment in the Hulunber meadow steppe of China.

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