Trade-offs in the externalities of pig production are not inevitable

Livestock farming, particularly pig production, significantly impacts the environment and animal welfare. While providing a substantial portion of human dietary protein and calories, it contributes significantly to greenhouse gas emissions, land use, antimicrobial resistance, and animal welfare concerns. A common assumption is that externalities of farming trade-off negatively; systems performing well in one area consistently perform poorly in others. However, empirical evidence supporting this assumption is limited and often examines externalities in isolation. This study addresses this gap by systematically quantifying four key externalities – land use, greenhouse gas (GHG) emissions, antimicrobial use (AMU), and animal welfare – across a diverse range of commercial pig farming systems in the UK and Brazil, representing a large global spectrum of commercial practices. The study aims to determine whether these externalities trade-off and to identify best- and worst-performing systems and system types. Understanding these associations is crucial for developing effective strategies to mitigate the negative impacts of pig farming.

Previous research on farming externalities has often focused on individual impacts in isolation, hindering a comprehensive understanding of their interrelationships. Studies examining co-variation among externalities have mostly involved limited sets of systems and externalities. While some studies have shown negative associations between certain externalities (e.g., freshwater use and GHGs), others have identified positive associations (e.g., GHGs and soil organic carbon costs). Few studies have been conducted in livestock systems, and even fewer have incorporated both environmental costs and animal welfare. This lack of systematic, comprehensive data makes it difficult to provide informed guidance on reducing the negative effects of farming, leading to potentially counterproductive mitigation efforts.

The study involved data collection from 74 UK and 17 Brazilian commercial breed-to-finish pig production systems. Data were gathered via farm visits, questionnaires, and animal welfare assessments conducted between 2017 and 2020. Each data point represented a breed-to-finish system potentially comprising one to three farms. Externality costs were aggregated across the entire production lifecycle (feed production, pig rearing, slaughter, processing) and expressed per kilogram of deadweight (DW). Land-use cost encompassed land required for pig rearing and feed production, considering land under tree cover as having equal biodiversity value to natural habitat. GHG cost included emissions from feed production, the pigs themselves (enteric fermentation and manure management), fuel, energy, transport, slaughter, and processing, and the opportunity cost of forgone sequestration. AMU cost was calculated from medicine records, including both total use and use of antimicrobials critically important to human health. Animal welfare cost was assessed using a novel LCA-compatible metric based on the Welfare Quality assessment system, weighing quality-of-life scores against life-years required to produce 1 kg DW. Statistical analyses, considering the non-independence of some data points due to shared breeding or rearing farms, were performed using Spearman rank correlations, Wilcoxon rank-sum tests, and Kruskal-Wallis tests with post-hoc Dunn’s analysis.

Land-use and GHG costs varied significantly across systems, with a strong positive correlation observed in UK systems. Similarly, a moderate positive correlation was found between AMU and animal welfare costs. Conversely, AMU and animal welfare exhibited moderate negative associations with GHG and land-use costs, indicating trade-offs. However, these trade-offs were not absolute. Several systems achieved low costs across all four externalities, and these high-performing systems were not exclusive to any particular farming type. While Organic systems demonstrated superior animal welfare and AMU performance, they exhibited the highest GHG emissions and land use. Notably, no label type or husbandry type consistently indicated the best- or worst-performing systems. Brazilian systems showed similar patterns although with less clear associations, potentially due to a smaller sample size. Nevertheless, some Brazilian systems performed well across all externalities. A hypothetical analysis demonstrated that meeting UK pig production entirely through Organic systems would significantly reduce AMU and improve welfare but greatly increase GHG emissions and land use compared to other system types.

The findings challenge the prevailing assumption of inevitable trade-offs among farming externalities. While trade-offs exist, they are not absolute. The study highlights the existence of systems that achieve low externality costs across all four key domains considered. This suggests that improving sustainability in pig production requires more nuanced approaches than simply changing farming types. Mitigation efforts should focus on optimizing practices within existing systems to minimize each externality. Current labelling systems do not effectively distinguish between the best- and worst-performing systems across all four externality costs, hindering informed consumer choices and regulatory decision-making. Interventions should focus on achieving measurable improvements in outcomes rather than on solely shifting to specific farming types.

This study demonstrates that trade-offs among externalities in pig production are not inevitable. High-performing systems exist across different farming types, highlighting the need for targeted improvements in practices within existing systems rather than solely focusing on system type transitions. Current labeling systems are inadequate for guiding sustainable choices and policy interventions should prioritize measurable improvements across all externality costs.

The study's conclusions may be affected by several factors. The sample size, especially for Brazilian systems and specific farm types like commercial woodland farms, limits the generalizability of the findings. Data quality, particularly regarding AMU records in Brazil, introduced some uncertainty. Land-use cost estimates did not fully account for biodiversity impacts beyond land under tree cover. Finally, the study focused on only four externalities, and future research should investigate additional environmental and social impacts.