Abstract
Greenhouse gas emissions from meat and dairy production are often highly uncertain; these emissions are typically estimated using inventory-based, ‘bottom-up’ models, which contain uncertainties that are difficult to quantify. Modeled emissions estimates can be corroborated using atmospheric measurements—taken above and downwind of animal production regions—to produce ‘top-down’ emissions estimates. Top-down and bottom-up estimates of animal methane show good agreement when considering global emissions. However, in the US, where animal production is predominantly highly intensified with confined feeding operations, animal methane emissions may be 39%–90% higher than bottom-up models predict (expressed as mean differences across studies). Animal emissions may grow in the future as meat and dairy demand increases in developing countries. We examine East and Southeast Asia as a test case, where emissions from increased meat and dairy production are expected to be offset by improved efficiency from intensive methods. We adjust the share of direct emissions projected to come from intensive systems by the intensities derived from US top-down estimates. We find that region-wide emissions from meat and milk production could reach 1.52 (1.41–1.62) GtCO2eq by 2050, an amount 21% (13%–29%) higher than previously predicted. Therefore, intensification may not be as effective in mitigating emissions in developing countries as is commonly assumed.
Generated Summary
This topical review investigates the potential for underestimation of methane emissions from intensively raised animals, particularly in the context of sustainable development goals. It examines the limitations of traditional ‘bottom-up’ emissions models that are commonly used to estimate greenhouse gas emissions from meat and dairy production. The study compares these bottom-up estimates with ‘top-down’ estimates derived from atmospheric measurements to identify potential biases. The research focuses on the United States, where intensive animal production is prevalent, and extends the analysis to East and Southeast Asia as a case study to explore the implications of intensification in developing countries. The core methodology involves synthesizing existing literature on methane emissions, comparing bottom-up and top-down estimates, and adjusting emission projections to account for potential underestimations. The scope of the study includes an assessment of the factors contributing to the discrepancies in emissions estimates and an evaluation of the implications for climate change mitigation and sustainable development.
Key Findings & Statistics
- Global meat consumption is expected to increase by 50% in the coming decades (FAO 2018a).
- Animal agriculture represents 15.6% of total annual greenhouse gas (GHG) emissions globally (FAO 2017).
- In the US, animal methane emissions may be 39%-90% higher than bottom-up models predict (expressed as mean differences across studies).
- Region-wide emissions from meat and milk production in East and Southeast Asia could reach 1.52 (1.41–1.62) GtCO2eq by 2050.
- This would be 21% (13%-29%) higher than previously predicted.
- Direct animal methane emissions in the United States are 39%–90% higher than bottom-up models predict (Miller et al 2013, Wecht et al 2014, Turner et al 2015).
- A recent analysis discovered that, while three different total US inventories of methane agree within 20%, their spatial discrepancies exceed 100% across certain US states (Hristov et al 2017).
- The IPCC methodology states that Tier 1 and 2 uncertainty estimates for enteric fermentation may be on the order of 50% (Dong et al 2006).
- The Southeastern US is also an area of abundant wetland emissions (8-16 Tg CH4 yr¯¹), with close proximity to animal production areas.
- Dairy cattle and other non-dairy animals’ emissions were 45% and 69% higher, respectively, than the bottom-up model predicted (Jeong et al 2016).
- Manure emissions can be greater than models predict, sometimes by more than 300% (Owen and Silver 2015).
- If the share of direct GHG emissions projected to come from intensive systems is scaled in proportion with top-down US estimates (scaled up by 65%, the average inferred from top-down estimates, provided in table 1), future emissions per unit of meat and dairy will not decrease as much as FAO bottom-up estimates predict (figure 2(C)).
- Multiplying this emission intensity by total consumption, we find that the ESA region could reach 1.52 (1.41–1.62 total range) GtCO2eq by 2050, an amount 21% (13%-29%) higher than previously predicted (figure 2(D)).
Other Important Findings
- Greenhouse gas emissions from meat and dairy production are often highly uncertain, typically estimated using inventory-based, ‘bottom-up’ models.
- Modeled emissions estimates can be corroborated using atmospheric measurements to produce ‘top-down’ emissions estimates.
- Top-down and bottom-up estimates of animal methane show good agreement when considering global emissions.
- Intensive systems for raising chickens, pigs, and dairy cows, and facilities for fattening or ‘finishing’ mature beef cattle to reach market weight in the North America restrict animal movement.
- Intensive landless systems typically do not allow for the expression of natural behaviors, access to pasture, or the ability to graze or forage for food.
- The benefits of improved feed requirements are commonly assumed to include fewer GHG emissions: both lower indirect emissions from feed production and grazing, and fewer direct emissions from wastes like manure, urine, and belches, per unit of food produced.
- Researchers have therefore recommended intensifying existing extensive and pastoral systems to reduce GHG emissions.
- Animal-sourced food consumption is also likely to grow as these countries develop economically (Alexandratos and Bruinsma 2012), with rising population and rising income-related dietary shifts.
- The hypothesis that intensification leads to GHG emissions mitigation has not been corroborated through independent assessments.
- The discrepancies between bottom-up and top-down estimates in the US and Canada imply that models may under-predict the emissions intensity of animals raised in intensive, predominantly confined systems.
- Animal diseases have multiple adverse effects on enteric fermentation emissions.
- The current body of top-down estimates is insufficient to understand precisely why bottom-up models may underpredict animal emissions.
- If the share of direct GHG emissions projected to come from intensive systems is scaled in proportion with top-down US estimates, future emissions per unit of meat and dairy will not decrease as much as FAO bottom-up estimates predict.
- The climate impacts of growing demand for meat and dairy have implications for keeping global warming within safe limits.
- If the share of direct GHG emissions projected to come from intensive systems is scaled in proportion with top-down US estimates (scaled up by 65%, the average inferred from top-down estimates, provided in table 1), future emissions per unit of meat and dairy will not decrease as much as FAO bottom-up estimates predict (figure 2(C)).
- The case study demonstrates that widespread adaptation of intensive production methods in low- and middle-income countries may have limited benefits to GHG emissions, especially if demand continues to grow.
Limitations Noted in the Document
- Bottom-up models rely on detailed input information collected at the ground-level, which may contain large uncertainties.
- The errors in totaled emissions estimates are considerably more uncertain than any single input due to the addition and multiplication of errors within the models.
- Uncertainty envelopes in bottom-up emissions inventories may be underestimated and are difficult to compare and scrutinize.
- Bottom-up inventories uncertainties often do not reflect uncertainties arriving from model structure and parameter selection, instead reflecting uncertainties arriving from input data.
- Top-down estimates can only broadly distinguish among emission sources and may struggle with source partitioning if sources are co-located.
- Top-down atmospheric estimates rely on wind observations and models with their own errors, which are different from the errors in bottom-up models.
- The specific metabolic processes and source categories that can fully explain the discrepancy in North American emissions are still unknown.
- The study’s prospective analysis is limited by the uncertainties in the FAO data and the scaling factors derived from US top-down estimates.
- Uncertainties are not reported by FAO in their ESA regional emissions through 2050, which is a common tendency in bottom-up emissions estimates and projections.
- The assessment of indirect emissions was outside of the scope of the study.
Conclusion
The study underscores the importance of accurately accounting for methane emissions from animal agriculture to meet sustainable development goals. It demonstrates that existing ‘bottom-up’ models may underestimate these emissions, particularly in intensive production systems common in the US and potentially in other regions. The analysis suggests that relying solely on intensification strategies to reduce emissions in developing countries may be overly optimistic. The underestimation of emissions in the US highlights the need for more accurate methods, such as ‘top-down’ atmospheric measurements, to validate emissions estimates. The findings emphasize the critical need for a more comprehensive understanding of the factors contributing to these discrepancies, including the role of animal health and management practices. The study advocates for a cautious approach to the expansion of intensive animal production systems, especially in regions where demand is increasing. The research suggests that policies promoting sustainable intensification should consider the potential for overestimation of mitigation benefits and the need to address both supply and demand-side factors. The study emphasizes the importance of considering socio-environmental externalities and risks when evaluating policies that finance and incentivize intensive production. The conclusion stresses the urgency of addressing the limitations of current assessment methods and the need for further research and analysis to ensure that agricultural development strategies genuinely support both climate change mitigation and sustainable development. The study reinforces the idea that a balanced approach, addressing both production efficiency and overall consumption, is essential to achieve meaningful progress in reducing the environmental impacts of the food system.