Evaluating strategies to reduce greenhouse gas emissions from dairy production systems
Publication Date
July 24, 2025
Creator
Abstract
It is important that greenhouse gas emissions (GHGe) are limited to reduce sea level rise, extreme weather events such as flooding and protect food security (GOV, 2020). The United Kingdom (UK) government have set a goal of becoming Net Zero by 2050 to limit the country’s impact on climate change (Committee on Climate Change, 2019). In 2019, agriculture accounted for 10% of UK greenhouse gas emissions, which has led to the National Farmers union (NFU) 2040 Net Zero target (NFU, 2019), who require blueprints on how to meet their set goal.
Enteric fermentation makes up most of dairy cattle GHGe, equating to up to 71% (Gilardino et al., 2020). Published prediction equations were collated from the literature and Chapter 3 highlights the large variation in the published enteric methane prediction equations that account for dietary composition. An equation was developed based on the predictions of 32 previously published enteric equations, utilising dietary composition values from a range of diets as inputs. An equation with the dietary composition variables metabolised energy (ME) and neutral detergent fibre (NDF) was found to be the most accurate in estimating the predicted enteric methane emissions (EME) across all published equations. The devised equation resulting from Chapter 3 was used within the farm simulation modelling in the subsequent chapters.
Utilising the adapted farm simulation model, several aspects of strategies were investigated to reduce the GHGe from dairy production systems in the UK. The GHGe considered were from enteric fermentation, manure management, feed production, and maintenance of the farm, such as electricity and fuel. The total emissions were included with and without the carbon offsetting and sequestration potential of land, woodland, and hedgerows. Milk production impacts were estimated to examine the effect of actions taken to reduce GHGe on productivity and profitability, thus allowing the evaluation of the economic feasibility of the approaches. The strategies analysed included reducing replacement rates, using alternative protein diets to soyabean meal (SBM), and combining multiple management, mitigation, and sequestration strategies to assess whether a UK dairy farm could reach Net Zero by 2040.
Three replacement rates (RRs) were simulated into the model to compare their effect on total farm GHGe and milk production in Chapter 4. The lower the RR the lower the total emissions. However, milk production declined in the low RR scenario and thus, the medium RR was deemed the optimum scenario to avoid compromising productivity and profitability.
In Chapter 5, 13 diets containing different sources of dietary protein were utilised in the model to compare the effect of the alternative sources of protein to SBM on the carbon footprint of the milking herd. The carbon footprint of SBM with and without links to deforestation was compared, which highlighted a potential 7% reduction by using a SBM not associated with deforestation. The alternative proteins showed a potential 28% reduction in total GHGe for the milking herd by converting from SBM completely.
The ability for a UK dairy farm to meet the NFU 2040 Net Zero goal was examined in Chapter 6. A combination of mitigation strategies was analysed such as a medium RR highlighted in Chapter 4, feed additives to reduce EME, anaerobic digestion (AD) of manure to provide renewable energy, reducing the milking herd size, afforestation, increasing efficiency, and removing fertiliser usage. The scenario showed Net Zero is possible, but not without afforestation and carbon offsetting. The modelling showed that, although possible, given the dramatic changes required, it is unlikely that dairy farms will reach Net Zero by 2040, but 2050 is possible, if strategies are implemented in the next six years. The findings from the study provide blueprints on how the UK dairy industry can reach Net Zero and educate farmers on the potential mitigation measures available.
Item Type
ethesis
Thesis Type
PhD
Supervisors
Subjects (LC)
Associated Schools / Departments
School of Veterinary Medicine and Science
eprints ID
80191
UoN Repository URI
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Fern_Baker_PhD_Corrections_Thesis_09_12_24.pdf
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Examined. PhD thesis corrections
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