Grazing alters net ecosystem C fluxes and the global warming potential of a subtropical pasture

Abstract

The impact of grazing on C fluxes from pastures in subtropical and tropical regions and on the environment is uncertain, although these systems account for a substantial portion of global C storage. We investigated how cattle grazing influences net ecosystem CO and CH exchange in subtropical pastures using the eddy covariance technique. Measurements were made over several wet-dry seasonal cycles in a grazed pasture, and in an adjacent pasture during the first three years of grazer exclusion. Grazing increased soil wetness but did not affect soil temperature. By removing aboveground biomass, grazing decreased ecosystem respiration (R ) and gross primary productivity (GPP). As the decrease in R was larger than the reduction in GPP, grazing consistently increased the net CO sink strength of subtropical pastures (55, 219 and 187 more C/m in 2013, 2014, and 2015). Enteric ruminant fermentation and increased soil wetness due to grazers, increased total net ecosystem CH emissions in grazed relative to ungrazed pasture (27-80%). Unlike temperate, arid, and semiarid pastures, where differences in CH emissions between grazed and ungrazed pastures are mainly driven by enteric ruminant fermentation, our results showed that the effect of grazing on soil CH emissions can be greater than CH produced by cattle. Thus, our results suggest that the interactions between grazers and soil hydrology affecting soil CH emissions play an important role in determining the environmental impacts of this management practice in a subtropical pasture. Although grazing increased total net ecosystem CH emissions and removed aboveground biomass, it increased the net storage of C and decreased the global warming potential associated with C fluxes of pasture by increasing its net CO sink strength.

Seasonal Controls of CO2 and CH4 Dynamics in a Temporarily Flooded Subtropical Wetland

“…Previous research has shown that changes in soil hydrology can increase, decrease, or have no effect on R eco . Consistent with our results, Jimenez et al () found that increased water table decreased R eco of a tropical herbaceous wetland when soil was flooded due to low soil O 2 availability and reduced aerobic decomposition (Gomez‐Casanovas, DeLucia, Bernacchi, et al, ; Lu et al, ). Hirano et al () showed that variations in soil hydrology did not correlate with changes in R eco when factors other than hydrology (e.g., temperature and GPP as in our study) were the dominant factors controlling R eco .…”

“…Average annual precipitation (1980 to 2015) was 1,310 mm, with two thirds of total annual precipitation falling during the wet season (DayMet database; Thornton et al, ). By quantifying the biophysical controls of CO 2 and CH 4 fluxes from a subtropical wetland and the direct and indirect influence of these controls on C fluxes by combining high‐resolution greenhouse gas (GHG) data with structural equation modeling, this study complements previous work from MAERC that investigated the effect of grazing on C fluxes of subtropical pastures (Gomez‐Casanovas, DeLucia, Bernacchi, et al, ; Gomez‐Casanovas, DeLucia, Bernacchi, et al, ) and the effect of water management (Chamberlain et al, , ) on CH 4 emissions from a subtropical pasture monoculture. Other studies in the area include Chamberlain et al () that investigated the contribution of CH 4 emissions from ruminant fermentation to CH 4 fluxes in a wetland‐pasture landscape mosaic and DeLucia et al () that evaluated the impact of surrounding land use on CH 4 emissions from subtropical wetlands.…”

“…Instruments were always kept at a height higher than 1.34 times the average plant height to minimize occasions when the flux footprint extended beyond the plot's edge (Raupach, ). High‐frequency data for each scalar were processed with EddyPro v5.1.1 (LI‐COR Biosciences, Lincoln, NE, USA), and data corrections were implemented as outlined in Supplemental Information 1A (Gomez‐Casanovas, DeLucia, Bernacchi, et al, ; Gomez‐Casanovas, DeLucia, Hudiburg, et al, ). Quality control of half‐hour flux data consisted of discarding fluxes that were low‐quality, nonrepresentative, corresponded to an area outside the edges of the wetland and/or below the u*‐threshold as outlined in Supplemental Information 1A (Gomez‐Casanovas, DeLucia, Bernacchi, et al, ).…”

“…High‐frequency data for each scalar were processed with EddyPro v5.1.1 (LI‐COR Biosciences, Lincoln, NE, USA), and data corrections were implemented as outlined in Supplemental Information 1A (Gomez‐Casanovas, DeLucia, Bernacchi, et al, ; Gomez‐Casanovas, DeLucia, Hudiburg, et al, ). Quality control of half‐hour flux data consisted of discarding fluxes that were low‐quality, nonrepresentative, corresponded to an area outside the edges of the wetland and/or below the u*‐threshold as outlined in Supplemental Information 1A (Gomez‐Casanovas, DeLucia, Bernacchi, et al, ). The u*‐threshold of 0.14 in this study was estimated using the moving point test (Reichstein et al, ).…”

“…Gaps in half‐hour CH 4 and H 2 O and sensible heat fluxes were filled using a linear interpolation when gaps were short (<2.5 hr) and using the mean diurnal variation method over four adjacent days for longer gaps (Chamberlain et al, ; Dengel et al, ; Gomez‐Casanovas, DeLucia, Bernacchi, et al, ; Gomez‐Casanovas, DeLucia, Hudiburg, et al, ). Total uncertainty was derived by adding random flux uncertainty to gap‐filling uncertainty (Richardson & Hollinger, ).…”