Reducing uncertainty in quantifying and reporting GHG emissions and carbon sequestration from European farming landscapes

 It has been widely reported that although IPCC methodologies appropriate for national-level accounting purposes, they lack the farm level resolution and holistic approach required for whole-farm systems analysis. The importance of evaluating greenhouse gas (GHG) emissions from crop production, animal farming and agroforestry within the whole farm setting is being realized as more important than evaluating these emissions in isolation. Thus, whole-farm systems modelling is widely used for farm-level analysis. Here we compare three whole-farm models e.g. FarmSim, Holos and IFSM to simulate the effect of management practices on GHG emissions at the whole farm level and evaluate the carbon sequestration and methane oxidation potential of afforestation as a compensation mechanism for the mitigation of farm-level GHG emissions. Ideally, we would also want information on model performance in predicting GHG emissions in future climatic scenarios. Initial results indicate that these models can accurately predict CO₂ emissions but the accuracy of these models for predicting methane (CH₄) and nitrous oxide (N₂O) emissions is quite low. We found that the most prominent drivers for GHG emissions in a whole farm setting were the enteric CH₄ from animal farming and N₂O emissions from soil management in cropland.  Thus, the low prediction accuracy for CH₄ and N₂O emissions in whole-farm models may introduce substantial errors into GHG inventories and lead to incorrect mitigation recommendations, which necessitates further fine-tuning of these models. Efforts are ongoing to integrate carbon sequestration and soil methane oxidation potential of farm-level afforestation in the whole farm models. There are indications that afforestation can be an effective mitigation strategy. The variation we found in farm system parameters, and the inherent uncertainties associated with emissions of CH₄ and N₂O can have substantial implications for reported agricultural emissions requiring uncertainty or sensitivity analysis in any modelling approach. Although there is considerable variation among the quality of farm data, boundary assumptions, the emission factors used we suggest that whole-farm systems models are an appropriate tool to develop and measure GHG mitigation strategies for the European farmed landscape.