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Purpose <p>Multifunctionality of milk production systems requires robust allocation methods for effectively distributing life cycle environmental burdens. However, despite the attempt at standardization, some methods have been proposed and used in recent years for the dairy sector. This study aimed to analyze the influence of the allocation methods on the carbon footprint (CF) of milk production in Brazil to investigate their possible root causes and generate life cycle assessment (LCA) recommendations insights for the dairy sector.</p> Methods <p>In total, 314 heterogeneous dairy farms located in the southern, southeastern, and central-western geographical regions in the country were analyzed. The LCA sensitivity analysis considered four allocation methods: mass, economic, biological (IDF 2015), and biological (IDF (2022) A common carbon footprint approach for the dairy sector – the IDF guide to standard life cycle assessment methodology. Bulletin of the IDF N° 479). A cluster grouping was carried out to analyze the variety of the data sets and the meat-to-milk ratio (BMR).</p> Results and discussion <p>The results showed allocation percentages for milk ranging from 86 to 99% for the biological IDF 2015 and mass allocation methods, respectively. The difference between the allocation methods, in terms of environmental impacts, varied from 6% (free-stall confinement system) to 15% (grazing system) depending on the type of production system. The cluster analysis showed that most Brazilian farms had BMR of approximately 0.01 live weight/kg of fat and protein-corrected milk (FPCM).</p> Conclusion <p>Considering the particularities of dairy production systems in the different regions and aiming to reduce the uncertainties of CF results, this research concluded that the method IDF 2022 would be the most appropriate for the Brazilian context.</p>

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Biologically based net energy allocation procedures can improve carbon footprint analysis in milk production systems

  • 期刊论文

  • 发表日期：

    2025年2月19日

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  • Daiane Vitória da Silva,
  • Virginia Mendonça Lourenço Benhami,
  • Ellen De Almeida Moreira,
  • Vanessa Romário De Paula,
  • Thierry Ribeiro Tomich,
  • Diogo Aparecido Lopes Silva,
  • Luiz Gustavo Ribeiro Pereira

摘要

Purpose

Multifunctionality of milk production systems requires robust allocation methods for effectively distributing life cycle environmental burdens. However, despite the attempt at standardization, some methods have been proposed and used in recent years for the dairy sector. This study aimed to analyze the influence of the allocation methods on the carbon footprint (CF) of milk production in Brazil to investigate their possible root causes and generate life cycle assessment (LCA) recommendations insights for the dairy sector.

Methods

In total, 314 heterogeneous dairy farms located in the southern, southeastern, and central-western geographical regions in the country were analyzed. The LCA sensitivity analysis considered four allocation methods: mass, economic, biological (IDF 2015), and biological (IDF (2022) A common carbon footprint approach for the dairy sector – the IDF guide to standard life cycle assessment methodology. Bulletin of the IDF N° 479). A cluster grouping was carried out to analyze the variety of the data sets and the meat-to-milk ratio (BMR).

Results and discussion

The results showed allocation percentages for milk ranging from 86 to 99% for the biological IDF 2015 and mass allocation methods, respectively. The difference between the allocation methods, in terms of environmental impacts, varied from 6% (free-stall confinement system) to 15% (grazing system) depending on the type of production system. The cluster analysis showed that most Brazilian farms had BMR of approximately 0.01 live weight/kg of fat and protein-corrected milk (FPCM).

Conclusion

Considering the particularities of dairy production systems in the different regions and aiming to reduce the uncertainties of CF results, this research concluded that the method IDF 2022 would be the most appropriate for the Brazilian context.