Prediction of methane emission from beef cattle using data measured in indirect open-circuit respiration calorimeters

Abstract

The objectives of the present study were to examine relationships between methane (CH 4 ) output and animal and dietary factors, and to use these relationships to develop prediction equations for CH 4 emission from beef cattle. The dataset was obtained from 108 growing-to-finishing beef steers in five studies and CH 4 production and energy metabolism data were measured in indirect respiration calorimeter chambers. Dietary forage proportion ranged from 29.5% to 100% (dry matter (DM) basis) and forages included grass silage, fresh grass, dried grass and fodder beet. Linear and multiple regression techniques were used to examine relationships between CH 4 emission and animal and dietary variables, with the effects of experiment or forage type removed. Total CH 4 emission was positively related to live weight (LW), feeding level and intake of feed (DM and organic matter) and energy (gross energy (GE), digestible energy (DE) and metabolisable energy (ME)) (P , 0.001), while CH 4 /DM intake (DMI) was negatively related to energy digestibility and ME/GE (P , 0.05 or less). Using LW alone to predict CH 4 emission produced a poor relationship when compared to DMI and GE intake (GEI) (R 2 5 0.26 v. 0.68 and 0.70 respectively). Adding feeding level, dietary NDF concentration and CP/ME or feeding level, energy digestibility and ME/GE to support LW resulted in a R 2 of 0.66 or 0.84. The high R 2 (0.84) was similar to that obtained using DMI or GEI together with energy digestibility and ME/GE as predictors. Further inclusion of dietary forage proportion and ADF and NDF concentration to the multiple relationships using GEI as the primary predictor resulted in a R 2 of 0.87. These equations were evaluated through internal validation, by developing a range of similar new equations from two-thirds of the present data and then validating these new equations with the remaining one-third of data. The validation indicated that addition of energy digestibility and ME/GE to support LW with feeding level, DMI and GEI considerably increased the prediction accuracy. It is concluded that CH 4 emission of beef steers can be accurately predicted from LW plus feeding level, DMI or GEI together with energy digestibility and ME/GE. The dataset was also used to validate a range of prediction equations for CH 4 production of cattle published elsewhere.

Methane production and diurnal variation measured in dairy cows and predicted from fermentation pattern and nutrient or carbon flow

“…A higher R 2 might be expected as measured data for in vivo rumen digested OM converted to VFA, as well as measured molar proportions of VFA were used as an input for the calculations. However, it was well within R 2 obtained with other models predicting CH 4 production from intake parameters (Yan et al, 2009;Nielsen et al, 2013) and most likely caused by variation in the measurement of rumen VFA and OM digestion as demonstrated before in a theoretical simulation study by Bannink et al (2006). Using l CH 4 /kg DMI as the unit strongly decreased variation compared with l CH 4 /day because DMI is the major determinant for variation in CH 4 production.…”

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

“…There were similarities in dietary characteristics and intakes among the databases used by Ellis et al (2007), Yan et al (2009, Ricci et al (2013), Moraes et al (2014, heifers and steers) and our high-forage data set, as indicated by the ranges for the variables. The SD for daily nutrient intakes (DMI, NDF, ADF, starch and fat) indicate that ranges overlapped among the databases used in the various studies even though the means were not the same across the databases.…”

“…Despite commonality of data, the prediction equations from those two studies used different approaches and generally did not share common variables. Ellis et al (2009), Yan et al (2009, Moraes et al (2014) Table S2); R 2adj = adjusted coefficient of determination; r c = concordance coefficient correlation; C b = bias factor; RMSPE = root mean square prediction error; ECT% = error due to overall bias of prediction as percentage of mean square prediction error (MSPE); ER% = error due to deviation of the regression slope from unity as percentage of MSPE; ED% = random or disturbance error as percentage of MSPE; MEF = model efficiency; GEI = gross energy intake; S = steers; GEL = gross energy level; DL = dietary level; AL = animal level. Table S2); GEI = gross energy intake; S = steers; GEL = gross energy level; DL = dietary level; AL = animal level.…”

“…This comprised 9.2% of the records for Ellis et al (2007) and 17.8% for Ricci et al (2013). Data used by Ellis et al (2009), Yan et al (2009, Ricci et al (2013) were from local experiments that were not published in peer-reviewed literature.…”

“…Ellis et al (2007 and) developed equations that consider diet composition variables. Yan et al (2009) equations are based on contents of energy and DMI. 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).…”