Symposium review: Uncertainties in enteric methane inventories, measurement techniques, and prediction models

Abstract

Ruminant production systems are important contributors to anthropogenic methane (CH) emissions, but there are large uncertainties in national and global livestock CH inventories. Sources of uncertainty in enteric CH emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH emission factors. There is also significant uncertainty associated with enteric CH measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF) 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 CH 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 CH 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 CH prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes.

Volatile Fatty Acids in Ruminal Fluid Can Be Used to Predict Methane Yield of Dairy Cows

“…We speculate that these diverse fermentation pathways might alter the relationship between ruminal VFA proportions and methane production. Measurement error is present regardless of the technique used to estimate methane yield [67]. All equipment used by us was calibrated prior to use and validated after use, and our SF 6 technique has been shown to be comparable to respiration chambers [33].…”

Individual milk fatty acids are potential predictors of enteric methane emissions from dairy cows fed a wide range of diets: Approach by meta-analysis

“…Furthermore, measurement of CH 4 emissions, even when done using the gold standard methods, unavoidably includes several associated errors because these techniques need to be correctly and appropriately used to generate reliable and accurate data (Hristov et al, 2018). In addition, even when cows are fed a fixed amount of a specific diet for a period of up to 16 wk, there may be substantial changes over time in CH 4 emissions, probably associated with adaption of ruminal microbial populations (Moate et al, 2018).…”

Increasing the concentration of linolenic acid in diets fed to Jersey cows in late lactation does not affect methane production

“…The Innovation Center for the US Dairy (2014) set a goal for the US dairy industry to lower the total greenhouse gas production by 25% by 2020. Highproducing dairy cattle produce approximately 630 L of methane (CH 4 ) each day (Hristov et al, 2013), and this is affected by diet composition, feed intake, and digestibility (Hristov et al, 2018). Methane is of major interest because its effect on global warming is approximately 21 to 25 times more potent than that of carbon dioxide (CO 2 ).…”