Prediction of enteric methane emissions from cattle

Abstract

Agriculture has a key role in food production worldwide and it is a major component of the gross domestic product of several countries. Livestock production is essential for the generation of high quality protein foods and the delivery of foods in regions where animal products are the main food source. Environmental impacts of livestock production have been examined for decades, but recently emission of methane from enteric fermentation has been targeted as a substantial greenhouse gas source. The quantification of methane emissions from livestock on a global scale relies on prediction models because measurements require specialized equipment and may be expensive. The predictive ability of current methane emission models remains poor. Moreover, the availability of information on livestock production systems has increased substantially over the years enabling the development of more detailed methane prediction models. In this study, we have developed and evaluated prediction models based on a large database of enteric methane emissions from North American dairy and beef cattle. Most probable models of various complexity levels were identified using a Bayesian model selection procedure and were fitted under a hierarchical setting. Energy intake, dietary fiber and lipid proportions, animal body weight and milk fat proportion were identified as key explanatory variables for predicting emissions. Models here developed substantially outperformed models currently used in national greenhouse gas inventories. Additionally, estimates of repeatability of methane emissions were lower than the ones from the literature and multicollinearity diagnostics suggested that prediction models are stable. In this context, we propose various enteric methane prediction models which require different levels of information availability and can be readily implemented in national greenhouse gas inventories of different complexity levels. The utilization of such models may reduce errors associated with prediction of methane and allow a better examination and representation of policies regulating emissions from cattle.

An evaluation of the accuracy and precision of methane prediction equations for beef cattle fed high-forage and high-grain diets

“…IPCC (2006) Tier 2 ranked first (CI = 24). Three equations from Moraes et al (2014) outperformed the other equations; equation S-AL ranked second (CI = 39; Moraes et al, 2014), SIM S-AL ranked third (CI = 44; Moraes et al, 2014), S-GEL ranked fourth (CI = 53; Moraes et al, 2014) and N ranked fifth (CI = 56; Ellis et al, 2009).…”

“…Ricci et al (2013) equations consider GEI (MJ/day) and DMI (kg/kg DM), as well as feed type (1 or 0 according to level of concentrate > or ⩽500 g/kg DM diet) and state variables (0 = non-lactating cows; 1 = lactating cows). Moraes et al (2014) presented separate equations for heifers (H) and steers (S) with hierarchical levels based on GEI (gross energy level (GEL), MJ/day), dietary level (DL), which included GEI (MJ/day) and NDF (g/kg DM), and animal level (AL) based on BW (kg), GEI (MJ/day) and NDF (g/kg DM). For steers, equations for GEL and DL were the same (S-GEL = S-DL).…”

“…Moraes et al (2014) did not report the range of dietary forage used to develop their equations, therefore the range of NDF proportion of the diets was used instead. Conditional arguments were used to determine appropriate variable values in the Ricci et al (2013) and Moraes et al (2014) equations that consider categorical variables.…”

“…Equations IPCC (2006) Tier 2, S-AL, SIM S-AL, S-GEL (Moraes et al, 2014) and N (Ellis et al, 2009) that ranked high for the highforage data set lost accuracy and precision when they were used with the high-grain data set. Best-fit equations for the high-grain diets were I (CI = 24; Ellis et al, 2009) The principal source of error for the best-fit equations tested with the high-grain data set was ED, as was observed for high-forage diets, but the proportion of error attributed to ED was similar (mean, 87.7% v. 89.2%, respectively), error attributable to ECT was decreased by −2.2%, and conversely, ER error was increased by 1.6%.…”

“…Various prediction equations have been developed specifically for beef cattle (e.g. Ellis et al, 2009;Ricci et al, 2013;Moraes et al, 2014), but the accuracy of these equations over a range of diet compositions has not been determined using an independent data set.…”