Data processing pipeline for managing enteric methane emissions from sniffer: Application on dairy farming systems

Accurate quantification of enteric methane (CH₄) emissions from ruminants is essential to support global strategies aimed at mitigating GHG outputs from the livestock sector. Among the available in vivo techniques, the sniffer method offers a noninvasive and cost-effective solution for large-scale monitoring, but lacks standardized procedures for data processing and validation. This study presents a pipeline for processing sniffer-derived raw data to estimate individual CH₄ and carbon dioxide (CO₂) emissions from dairy animals. The procedure was tested on 3 commercial farms in central Italy, consisting of 141 monitored animals and 47,018 milkings, each rearing a single breed: Holstein-Friesian cows, Brown Swiss cows, or Mediterranean buffaloes. The pipeline integrates measurements from sniffers with automatic milking system data and includes procedures for time alignment, data cleaning, background gas correction, and CH₄ peaks detection. Individual daily CH₄ production was estimated considering the amount of CO₂ predicted and the CH₄:CO₂ ratio method. Methane emissions averaged 416.7 ± 74.9 g/d for Holstein-Friesian and 502.1 ± 158.4 g/d for Brown Swiss cows, differing by approximately 10% from the Intergovernmental Panel on Climate Change (IPCC) Tier II model estimates. For buffaloes, average CH₄ emissions were 279.0 ± 97.5 g/d, which were consistent with the emission factor estimated by Tier II of the IPCC. The proposed pipeline provides a harmonized and replicable framework for processing sniffer data, improving the reliability and comparability of enteric CH₄ and CO₂ emission measurements across ruminant species and farming systems.