An integral assessment of carbon and nitrogen emissions in dairy cattle production systems: Comparing dynamic process-based greenhouse gas emissions factors with IPCC Tier 1 and Tier 2 approaches in confinement and pasture-based systems

Abstract

The assessment of greenhouse gases (GHG) and nitrogen (N) emissions is essential for climate change mitigation. The Intergovernmental Panel on Climate Change (IPCC) provides guidelines for GHG quantification at both national and global levels. However, the IPCC Tier 1 (T1) and Tier 2 (T2) estimates, mostly used in national inventories, rely on generic emission factors (EFs) and empirical equations that are not suitable for case-specific assessments on individual farms. Thus, a more advanced Tier 3 (T3) methodology is needed to reflect the impact of key factors on emissions and reveal the effect of emission mitigation measures. Here we compare the IPCC T1 and T2 estimates to results from a cascade of process-based (PB) models referred to as T3 approach, for farm-level emissions. The results showed that the estimates from PB models differ significantly from those of the IPCC T1 and T2 estimates and allow more capability to predict variation. Moreover, PB models account for temporal changes and the underlying mechanisms responsible for GHG and N emissions. These models can be adopted for case-specific GHG assessment and project future mitigation strategies under different climate scenarios, regional contexts and on-farm management. Additional to the known applicability of PB models to estimate enteric methane and soil emissions, the present study demonstrates for the first time in Germany and Europe the effectiveness of Manure-DNDC model in simulating ammonia (NH₃) and methane (CH₄) barn emissions, highlighting the potential for using PB models in case-specific GHG and N assessments for the whole manure management chain. Overall, this study presents options for a methodology in case-specific GHG assessment that can capture the effect of climate change and mitigation measures.