Comparison of Methane Emission Patterns from Dairy Housings with Solid and Slatted Floors at Two Locations

Abstract

Methane (CH4) emissions from dairy husbandry are a hot topic in the context of active climate protection, where housing systems with slatted floors and slurry storage inside are in general expected to emit more than systems with solid floors. There are multiple factors, including climate conditions, that modulate the emission pattern. In this study, we investigated interrelations between CH4 emission patterns and climate conditions as well as differences between farm locations versus floor effects. We considered three data sets with 265, 264 and 275 hourly emission values from two housing systems (one slatted, one solid floor) in Switzerland and one system with solid floors in Germany. Each data set incorporated measurements in summer, winter and a transition season. The average CH4 emission was highest for the slatted floor system. For the solid floor systems, CH4 emissions at the Swiss location were around 30% higher compared to the German location. The shape of the distributions for the two solid floor systems was rather similar but very different from the distribution for the slatted floor system, which showed higher prevalence for extreme emissions. Rank correlations, which measure the degree of similarity between two rankings in terms of linear relation, were not able to detect dependencies at the selected significance level. In contrast, mutual information, which measures more general statistical dependencies in terms of shared information, revealed highly significant dependencies for almost all variable pairs. The weakest statistical relation was found between winds speed and CH4 emission, but the convection regime was found to play a key role. Clustering was consistent among the three data sets with five typical clusters related to high/low temperature and wind speed, respectively, as well as in some cases to morning and evening hours. Our analysis showed that despite the disparate and often insignificant correlation between environmental variables and CH4 emission, there is a strong relation between both, which shapes the emission pattern in many aspects much more in addition to differences in the floor type. Although a clear distinction of high and low emission condition clusters based on the selected environmental variables was not possible, trends were clearly visible. Further research with larger data sets is advisable to verify the detected trends and enable prognoses for husbandry systems under different climate conditions.

Review of Sources of Uncertainty and Techniques Used in Uncertainty Quantification and Sensitivity Analysis to Estimate Greenhouse Gas Emissions from Ruminants

“…Dispersion models' assumptions and variations in measuring methane concentrations can also contribute to uncertainties, as highlighted by Thiruvenkatachari et al [67]. Moreover, Hempel et al [70] point out uncertainties in artificial and natural tracer measurement approaches, which influence the observed differences in methane emissions between farm locations. Sampling strategies for determining indoor methane concentrations also contribute to uncertainties in emission estimates.…”

“…Additionally, the impact of assumptions in LCA and default parameters in IPCC Tier 2 methodology, emphasized by Xu et al [63] and Ndao et al [68], further contributes to the overall uncertainty in emission factor accuracy. Hempel et al [70] draw attention to the limitations of livestock population databases and the potential overestimation of emissions using default factors, underscoring the need for improved data coverage and parameter refinement.…”

Innovative Strategies for Sustainable Dairy Farming in Canada amidst Climate Change

“…This combination significantly enhances manure management, a key factor in the generation of methane and ammonia emissions [40]. The effectiveness of these systems lies in their ability to rapidly remove manure, thus limiting its exposure to air and significantly cutting down on the emission of noxious gases [41,42]. The reduction in ammonia volatilization is particularly notable, as the frequent removal of manure inhibits the enzymatic processes crucial for the transformation of urea in manure into ammonia.…”