Within-field spatial variability of greenhouse gas fluxes from an extensive and intensive sheep-grazed pasture

Greenhouse gas (GHG) fluxes from livestock grazed pasture soils are highly variable in both space and time but the quantitative importance of the factors regulating this variation remain poorly understood. Our aim was to explore this variability on contrasting extensively (low input) and intensively managed sheep-grazed ‘case-study’ pastures. We quantified (through standard and spatially-informed regressions) the statistical relationships between GHG fluxes (nitrous oxide (N2O), carbon dioxide (CO2) and methane (CH4)) and a range of soil, field and management characteristics. Fluxes of these three GHGs at two study sites were highly variable, but spatial structure (i.e. autocorrelation) was only observed in the variability of N2O fluxes across the intensive site and CO2 fluxes across the extensive site. The regression analyses identified significant GHG predictor variables for the extensive site as: NO3- (p < 0.001) and vegetation-type (p < 0.01) for N2O (R2 = 0.57; p = 0.000); NH4+ (p < 0.05), slope (p < 0.05) and elevation (p < 0.01) for CO2 (R2 = 0.34; p = 0.000); and NO3- (p < 0.01), NH4+ (p < 0.05) and soil moisture (p < 0.05) for CH4 (R2 = 0.25; p = 0.005). Significant GHG predictor variables for the intensive site were soil moisture (p < 0.01) and bulk density (p < 0.01) for N2O (R2 = 0.27; p = 0.005); soil moisture (p < 0.001) for CO2 (R2 = 0.31; p = 0.001); while none were found for CH4 (R2 = 0.10; p = 0.655). Key factors driving GHG variation were both site- and GHG-specific, with fluxes controlled by local conditions leading to differences in limiting factors (possibly even at the within-site scale). Our statistical analyses suggest a larger range of driving variables (e.g. air and soil temperature or other soil chemical properties such as total extractable N) may be required to more fully capture the observed variability in the GHG processes considered here, and that it may also be fruitful for future analyses to consider non-linear, non-stationary and interacting relationships across space- and time-scales. Adequacies of each site’s sample design also played a key interpretive role in the GHG processes, requiring further evaluation through additional sampling campaigns.