Abstract
Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH4 prediction models are based on various animal or feed characteristics.
Generated Summary
This review article examines the uncertainties in enteric methane (CH4) inventories, measurement techniques, and prediction models related to livestock. It focuses on the sources of uncertainty in enteric CH4 emissions, including animal inventories, feed dry matter intake (DMI), diet composition, and CH4 emission factors. The study explores various CH4 measurement techniques, such as respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the GreenFeed system, and discusses their limitations. The research further reviews different types of models used to predict enteric CH4 emissions, ranging from simple emission factors to more complex mechanistic models. The research approach involves a critical analysis of existing literature and data, emphasizing the need for collaboration and robust data sets to improve the accuracy of CH4 emission predictions. The study is based on an intercontinental database of individual dairy cow data, emphasizing the complexities of accurately assessing and mitigating CH4 emissions from animal agriculture.
Key Findings & Statistics
- Globally, atmospheric mixing ratio of CH4 increased continuously since 2006 at a rate of 4 to 12 nmol/mol per year.
- In 2015, livestock production in the EU-28 accounted for 59% of estimated agricultural GHG emissions.
- In the United States, emissions from livestock production contributed an estimated 48% of the 2015 agricultural GHG emissions.
- The rate of increase for the populations of ruminant species declined to 7.3 million head/yr since 2006.
- The uncertainty in livestock enteric CH4 emissions in the current US EPA (2017) report are -11 and 18% (lower and upper bounds, respectively), corresponding to a 95% confidence interval.
- For CH4 emissions from manure management, the uncertainty is -18 and 20%, respectively (US EPA, 2017).
- A recent gridded inventory of livestock CH4 emissions in the continental United States reported lower and upper 95% confidence bounds of -15.6 and 16.9% (as % of the mean; enteric), -65.0 and 63.3% (manure), and -19.3 and 19.2% (total emissions), respectively (Hristov et al., 2017).
- The combined enteric and manure CH4 emissions from livestock in Texas and California in the Hristov et al. (2017) study were 36% lower and 100% greater, respectively, than estimates from EDGAR.
- The coefficient of variation (CV) for emission rate (g of CH4/d) averaged 30, 18, and 28% for RC, GF, and SF6, respectively.
- On the basis of CH4 yield; that is, grams of CH4 per kilogram of DMI, the CV for RC is reduced to 21% and is comparable to that for GF and SF6 (21 and 27%, respectively).
- The increase in atmospheric CH4 between 2002 and 2014 was 30% in the United States.
- The average isotopic signature of microbial CH4 appears to be quite distinct from that of fossil fuel CH4.
- The relationship between DMI and CH4 emissions was strong (R² = 0.92) and the intercept was close to zero when DMI range was large.
- The estimated slopes indicate a much larger incremental yield in CH4 with increasing DMI for RC than for GF and SF6 (16.12 ± 0.299, 7.53 ±0.775, and 5.87 ± 1.373 g of CH4/kg of DMI, respectively).
- The prediction error was also lower for RC than for GF or SF6.
- The relationship of DMI with CH4 emissions was demonstrated for GF in a beef data set (445 observations; DMI ranged from 3.6 to 19.1 kg/d) by Bird-Gardiner et al. (2017).
- A model designed to estimate enteric CH4 from Dutch dairy farms, Bannink et al. (2011) reported that the largest uncertainty (18%) was related to VFA stoichiometry.
- In the GLOBAL NETWORK database of individual dairy cow data (Niu et al., 2018), CH4 prediction equations with a greater number of independent variables performed best and had lower root mean squared prediction error (RMSPE) as a percentage of the mean observed value (14.7 to 19.8%).
- Less complex models requiring only DMI had predictive ability comparable to those of the more complex models (RMSPE = 15.2 to 21.4%).
Other Important Findings
- The review highlights that while DMI is the most important factor influencing CH4 production, the exact nature of this relationship remains undetermined.
- The use of constant Ym values can lead to considerable uncertainty in emission estimates, particularly in regions with diverse production systems.
- The study notes that the SF6 tracer technique can produce accurate CH4 emission data from a large group of animals when conditions are addressed.
- A key finding is that accurate prediction of DMI is crucial for predicting CH4 emissions and yield.
- The review acknowledges that the current dairy NRC (2001) model predicts DMI based on the cow’s metabolic BW, FCM yield, and stage of lactation.
- The review emphasizes the need for collaboration and robust data sets for developing prediction models.
Limitations Noted in the Document
- The study acknowledges that the global dynamics in large ruminant inventories do not fully support the suggested farmed livestock origin of the increase in atmospheric CH4 from 2006 to 2015.
- The review points out that the study does not specify uncertainty for FAOSTAT estimates, which is likely large for cattle inventories in developing countries.
- The study highlights the fact that the spatial distribution of emissions in gridded inventories likely strongly affects the conclusions of top-down approaches that use them, especially in the source attribution of emissions.
- The review notes that the findings from such analyses have to be interpreted with caution.
- The review highlights that variability in the CH4 measurement data when using the SF6 technique can be high.
- The models used to predict enteric CH4 emissions may not be appropriate due to the limitation of the framework used, such as not including random effects of animals or studies.
Conclusion
The accurate quantification of enteric methane emissions from livestock is crucial for assessing and mitigating the environmental impact of animal agriculture. This review underscores the significant uncertainties in current CH4 inventories, measurement techniques, and prediction models. Key findings emphasize the importance of DMI as the primary driver of CH4 emissions, the limitations of relying on constant emission factors, and the need for regional-specific approaches. The comparison of various measurement techniques highlights the need for standardized protocols to ensure reliable and accurate data. Additionally, the review stresses the importance of developing robust prediction models from large, collaborative data sets. The study concludes that by addressing the limitations in current methods and embracing a more nuanced, data-driven approach, it is possible to improve the accuracy of CH4 emission predictions and inform effective mitigation strategies, supporting more sustainable practices in animal agriculture. Moreover, this is to be done through more collaboration, robust data sets, and using state-of-the-art statistical techniques for model development and evaluation. The ultimate goal is to develop a multi-model ensemble to improve enteric CH4 emission prediction and determine uncertainty associated with the prediction.