Breed-specific enteric methane emission factor assessment in Italian dairy cattle leveraging DHI and primary ration data

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ID: 315826
2026
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Abstract
Enteric methane (CH4) emissions from dairy cattle are a major contributor to greenhouse gas footprint in the livestock sector. This study applied Intergovernmental Panel on Climate Change (IPCC) Tier 2 equations to estimate enteric methane emissions in Italian Holstein, Brown Swiss, and Red Pied herds using either default dietary assumptions or farm-specific diet information. By combining individual Dairy Herd Improvement (DHI) test-day records with primary ration data, the analysis examined how improved input data resolution affected methane estimates across breeds and animal categories under commercial farming conditions. Data were collected from 138 Italian dairy farms participating in the national Dairy Herd Improvement (DHI) program between January 2021 and December 2022, with farm rations recorded concurrently during DHI test days by trained technicians using a standardized questionnaire. Breed, emission estimation approach and their interaction were analyzed using aligned rank transform (ART) procedures with permutation-based p-values. Holstein cows exhibited greater daily methane production (MeP = 443.60 ± 5.68 g/d; p < 0.001), whereas Red Pied herds showed the greatest methane intensity (MeI = 29.99 ± 0.70 g/kg fat- and protein-corrected milk; p < 0.001). Breed and CH4 estimation approaches affected emission estimates, however their interaction was not significant, indicating consistent breed rankings across methods. Post hoc analyses revealed no significant differences in methane indices among breeds. Incorporation of farm-specific ration data impacted emission estimates, particularly for nonproductive groups such as dry cows and heifers, highlighting the importance of context-specific dietary inputs for improving the accuracy and representativeness of CH4 inventories.
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openalex_W7163176172 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Giulia Ferronato, Mesfin Mekonnen Moliso, R Mrode, Paolo Ajmone-Marsan, Riccardo Negrini
Journal italian journal of animal science
Year 2026
DOI
10.1093/jas/skag171
URL
https://doi.org/10.1093/jas/skag171
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