How does the chemical composition of dung affect nitrous oxide and methane emissions in pasture soils?

There is an important gap in how variations in herbivore dung composition affect GHG emissions on pastures, especially due to differences in dry matter (DM) and nitrogen contents. Oversimplifications can compromise the accuracy of mitigation strategies. This study aims to address this gap by investigating how the chemical composition of dung from different species influences GHG emissions in pasture systems. The results showed that drier dung led to higher cumulative N₂O emissions. The highest emissions were observed from goat at 9.47 mg N-N₂O g⁻1 dry soil, followed by sheep at 5.95 mg N-N₂O g⁻1 dry soil, beef cattle at 5.44 mg N₂O g⁻1 dry soil, dairy cattle at 2.67 mg N₂O g⁻1 dry soil, and horse at 0.83 mg N₂O g⁻1 dry soil. It was observed that higher dung moisture content generally corresponded to increased CH₄ emissions, except for horse dung. The highest cumulative CH₄ emission was for dairy cattle dung (8.29 mg C-CH₄ g⁻1 dry soil), followed by beef cattle (3.89 mg C-CH₄ g⁻1 dry soil), sheep (2.32 mg C-CH₄ g⁻1 dry soil), goats (1.89 mg C-CH₄ g⁻1 dry soil), and horses (1.66 mg C-CH₄ g⁻1 dry soil). Principal Component Analysis illustrated that PC1, named as diet quality, explained 61.9% of the variance, was positively correlated with N₂O and negatively correlated with fiber content and C/N ratio, while PC2, named as acetrophic and hydrogenotrophic methanogenesis, explained 19.6% of the variance, linking VS to reduced CH₄ emissions. This study establishes relationships between manure chemical composition and GHG emissions, filling a fundamental knowledge gap and supporting the development of cause-and-effect models. [Display omitted] •Small ruminants emitted more N2O than large ruminants e horses.•Drier dung emitted more N2O than wetter dung.•Non-protein nitrogen did not significantly affect N₂O emissions.•An inverse relationship was observed between volatile solids and CH4.•The identified links could support causal models for improved data-driven management.