Diet and landscape controls on greenhouse gas emissions from cattle excreta in a semi-arid environment

Abstract

Sod-seeding low productivity, or depleted, pastures with legumes, and non-bloat legumes in particular, is considered a viable method of restoring productivity to the pasture. However, the impact of the change in plant composition of the pastures on GHG emissions from the urine and dung deposited by cattle grazing the pastures is as yet unknown. Excreta were collected from beef cattle grazing a low productivity, depleted meadow bromegrass-alfalfa mixed pasture (D-MA) and D-MA pastures rejuvenated by sod-seeding with a non-bloat legume, cicer milkvetch (R-CM) or sainfoin (R-SF). The excreta were subsequently applied back to the respective pastures at locations in upper and lower slope positions. In general, plant composition of the pastures had a small but significant impact on the C and N content of the cattle excreta; however, this yielded no significant differences among treatments in either cumulative CO₂ emissions or cumulative CH₄ uptake for either the urine or dung. Yet, whereas CH₄ uptake was unaffected by the application of either urine or dung, urine applications yielded CO₂ emissions that were greater than those from the control or dung-amended treatments. Nitrous oxide emissions were significantly impacted by the chemical composition of the urine, and here we report distinct N₂O emission factors for urine and dung—with an average EF_N2O of 0.034 ± 0.024 % for dung and 0.12 ± 0.10 % for the urine from cattle that grazed the depleted and rejuvenated pastures. Our data also suggest that, for urine at least, diet can significantly impact the EF_N2O, with urine from cattle grazing the R-CM pasture yielding an EF_N2O of 0.24 ± 0.10 % and urine from confined beef cattle fed a high crude protein, total mixed ration (TMR) diet yielding an EF_N2O of 0.39 ± 0.13 %. These findings suggest that a disaggregation of emission factors based on excreta type and animal diet, while also considering temporal (seasonal) and spatial (landscape-scale) variability, can lead to improved accuracy of GHG emissions inventories.