# The Carbon Footprint of Milk Production on a Farm
###### Abstract
Within their framework, the CF is among the key indicators used to reveal the impact of business activities on the climate and GHG emissions [13]. For the dairy sector, this implies the need for the systematic measurement and reporting of the CF, especially in the context of cooperation with large customers such as processing plants, retail chains, and financial institutions. Over the past few years, many LCA-based studies have shown significant variability in the carbon footprint of milk production depending on the region, scale of production, and the feed, maintenance, and animal husbandry systems used. [PERSON] et al. [1] noted CF values ranging from 0.9 to 1.4 kg CO\({}_{2}\) eq kg\({}^{-1}\) FPCM in northern Europe, whereas [PERSON] et al. [14] demonstrated an average CF value at a level of 1.11 kg CO\({}_{2}\) eq kg\({}^{-1}\) FPCM in Australia. In contrast, considerably higher values were noted in small-scale systems: in India, 2.2 kg CO\({}_{2}\) eq kg\({}^{-1}\) FPCM [15]; in Ethiopia, up to 3.2 kg [16]; and in Colombia, from 1.8 to 3.9 kg, depending on the system [17]. These discrepancies result, e.g., from differences in the milk yield, feed conversion efficiency, fertiliser management and dependence on imported feed components. Feeding systems also have a major impact on emission levels. Studies have demonstrated that intensive total mixed ration (TMR) systems, although providing high productivity, are often associated with a higher CF due to the use of concentrated feed with a high land use change (LUC) index value, e.g., soybean meal [18; 19]. In contrast, systems based on, e.g., pastures achieve a lower CF value despite lower productivity, which was confirmed, among others, in Portugal (0.83 kg CO\({}_{2}\) eq kg\({}^{-1}\) of milk) [20] and New Zealand (0.75-0.81 kg, depending on the season of 2010/11-2017/18; an average of 0.78 kg) [21]. The scale of a farm also affects the CF levels. Smaller farms often have no access to the right technology to optimise resource consumption, which translates into a higher CF per product unit [3; 4]. Nevertheless, certain studies highlighted the advantages of multifunctional farms. [PERSON] et al. [15] demonstrated that after considering the additional functions, the CF of Indian farms decreased by over 20%, which suggests that the CF should be analysed in a context broader than milk production efficiency alone. Another challenge is the lack of methodological standardisation in research. The differences relate to system boundaries, functional units (e.g., raw milk vs. fat- and protein-corrected milk (FPCM)), allocation methods (e.g., economic or mass), and the global warming potential (GWP) values adopted (e.g., GWP100 vs. GWP20), which affects the interpretation of results [1; 3; 22]. The consideration of carbon (C) sequestration and other ecosystem services is also playing an increasingly important role in LCA analyses. Studies conducted in Colombia and Brazil showed that properly managed pastures and silvopastoral systems can partially offset emissions [17; 23]. In the European and Polish contexts, the significance of integrated and transparent environmental indicators is increasing. These will play a crucial role in the implementation of strategies such as the European Green Deal and the CSRD Directive. The carbon footprint is becoming part of ESG reporting and is increasingly appearing as a criterion in product labelling systems, agricultural advisory systems, and investment financing [24; 25]. Therefore, in response to these challenges, tools such as LatteGHG [26] or DairyGHG [9], which support producers in the independent assessment of the carbon footprint, are being developed. In practical terms, the CF can indicate directions for action to reduce emissions [27; 28; 29]. For example, the use of high sugar grass (HSG) varieties allows N\({}_{2}\)O emissions to be reduced by as much as 11% [27], and feed additives can reduce CH\({}_{4}\) emissions [30].