Feeding Management Strategies to Mitigate Methane and Improve Production Efficiency in Feedlot Cattle

Mitigation of greenhouse gases and decreasing nutrient excretion have become increasingly important goals for the beef cattle industry. Because feed intake is a major driver of enteric CH₄ production and nutrient excretion, feeding management systems could be important mitigation tools. Programmed feeding uses net energy equations to determine the feed required to yield a specific rate of gain, whereas restricted feeding typically involves decreasing intake relative to the expected or observed ad libitum intake. In the context of growing/finishing systems typical of those in the United States and Western Canada, experimental results with programmed and restricted feeding have often shown decreased overall feed intake and increased gain efficiency relative to ad libitum feeding, but too much restriction can negatively affect harvest weight and associated carcass quality. Slick feed bunk management is a time-based restriction that limits day-to-day variation in feed deliveries, but the effects on intake and performance are not well defined. Simulations to estimate enteric CH₄ emission and nitrogen excretion indicated that programmed feeding of a high-grain diet could appreciably decrease CH₄ emissions and nitrogen excretion compared with traditional growing programs based on high-forage diets. For feedlot finishing, programming gain for a portion of the feeding period will decrease CH₄ emission and N excretion only if cattle perform better than expected during the programmed phase or if compensatory growth occurs when cattle are transitioned to ad libitum feeding. Optimal approaches to implement programmed or restricted feeding that will yield increased efficiency should be the subject of future research in this area.

Short Communication: Prediction of Methane per Unit of Dry Matter Intake in Growing and Finishing Cattle from the Ratio of Dietary Concentrations of Starch to Neutral Detergent Fiber Alone or in Combination with Dietary Concentration of Ether Extract

Previous research demonstrated that a fixed value of 0.2433 (SE = 0.0134) Mcal of CH₄/kg of dry matter intake (DMI) could be used to predict CH₄ production with accuracy and precision on par with similar equations in the literature. Slope bias was substantially less for the fixed-coefficient equation than noted for the other DMI- or gross energy intake (GEI)-based equations, but mean bias was substantially greater, presumably reflecting the failure of the fixed-coefficient approach to account for dietary factors that affect CH₄ production. In this article, we report on the use of the dietary ratio of concentrations of starch to neutral detergent fiber (NDF) and dietary ether extract (EE) concentration to improve the accuracy and precision of the fixed-coefficient equation. The same development data set used to create the fixed-coefficient equation was used in the present study, which included 134 treatment means from 34 respiration calorimetry studies. Based on stepwise regression with dietary NDF, starch, crude protein, EE, and the starch:NDF ratio as possible dependent variables, the starch:NDF ratio and EE were the only dietary variables selected (P ≤ 0.15). The study-adjusted relationship with the starch:NDF ratio (r² = 0.673; root mean square error [RMSE] = 0.0327) was: Mcal of CH₄/kg of DMI = 0.2883 − 0.03474 × starch:NDF; whereas the relationship with a model that included both starch:NDF ratio and dietary EE (r² = 0.738; RMSE = 0.0315) was: Mcal of CH₄/kg of DMI = 0.3227 − 0.0334 × starch:NDF − 0.00868 × % EE. A previously published independent data set with 129 treatment means from 30 respiration calorimetry studies was used to evaluate these two equations, along with two additional equations in which g/d of CH₄ was predicted directly from DMI, starch:NDF ratio, and/or dietary EE. The two Mcal of CH₄/kg of DMI equations had superior fit statistics to the previously published 0.2433 Mcal of CH₄/kg of DMI equation, with a substantial decrease in mean bias and improved concordance correlation coefficients. Moreover, the Mcal of CH₄/kg of DMI equations resulted in improved fit relative to direct prediction of g/d of CH₄ from DMI, the starch:NDF ratio, and % EE. Based on these results, further evaluation of the dietary ratio of starch-to-NDF concentrations and EE concentration to predict methane production per unit DMI in beef cattle is warranted.

Predicting Metabolizable Energy from Digestible Energy for Growing and Finishing Beef Cattle and Relationships to Prediction of Methane

Reliable predictions of metabolizable energy (ME) from digestible energy (DE) are necessary to prescribe nutrient requirements of beef cattle accurately. A previously developed database that included 87 treatment means from 23 respiration calorimetry studies has been updated to evaluate the efficiency of converting DE to ME by adding 47 treatment means from 11 additional studies. Diets were fed to growing-finishing cattle under individual feeding conditions. A citation-adjusted linear regression equation was developed where dietary ME concentration (Mcal/kg of dry matter [DM]) was the dependent variable and dietary DE concentration (Mcal/kg) was the independent variable: ME = 1.0001 × DE – 0.3926; r² = 0.99, root mean square prediction error [RMSPE] = 0.04, and P < 0.01 for the intercept and slope. The slope did not differ from unity (95% CI = 0.936 to 1.065); therefore, the intercept (95% CI = −0.567 to −0.218) defines the value of ME predicted from DE. For practical use, we recommend ME = DE – 0.39. Based on the relationship between DE and ME, we calculated the citation-adjusted loss of methane, which yielded a value of 0.2433 Mcal/kg of dry matter intake (DMI; SE = 0.0134). This value was also adjusted for the effects of DMI above maintenance, yielding a citation-adjusted relationship: CH₄, Mcal/kg = 0.3344 – 0.05639 × multiple of maintenance; r² = 0.536, RMSPE = 0.0245, and P < 0.01 for the intercept and slope. Both the 0.2433 value and the result of the intake-adjusted equation can be multiplied by DMI to yield an estimate of methane production. These two approaches were evaluated using a second, independent database comprising 129 data points from 29 published studies. Four equations in the literature that used DMI or intake energy to predict methane production also were evaluated with the second database. The mean bias was substantially greater for the two new equations, but slope bias was substantially less than noted for the other DMI-based equations. Our results suggest that ME for growing and finishing cattle can be predicted from DE across a wide range of diets, cattle types, and intake levels by simply subtracting a constant from DE. Mean bias associated with our two new methane emission equations suggests that further research is needed to determine whether coefficients to predict methane from DMI could be developed for specific diet types, levels of DMI relative to body weight, or other variables that affect the emission of methane.

Accuracy of methane emissions predicted from milk mid-infrared spectra and measured by laser methane detectors in Brown Swiss dairy cows

Since heritability of CH₄ emissions in ruminants was demonstrated, various attempts to generate large individual animal CH₄ data sets have been initiated. Predicting individual CH₄ emissions based on equations using milk mid-infrared (MIR) spectra is currently considered promising as a low-cost proxy. However, the CH₄ emission predicted by MIR in individuals still has to be confirmed by measurements. In addition, it remains unclear how low CH₄ emitting cows differ in intake, digestion, and efficiency from high CH₄ emitters. In the current study, putatively low and putatively high CH₄ emitting Brown Swiss cows were selected from the entire Swiss herdbook population (176,611 cows), using an MIR-based prediction equation. Eventually, 15 low and 15 high CH₄ emitters from 29 different farms were chosen for a respiration chamber (RC) experiment in which all cows were fed the same forage-based diet. Several traits related to intake, digestion, and efficiency were quantified over 8 d, and CH₄ emission was measured in 4 open circuit RC. Daily CH₄ emissions were also estimated using data from 2 laser CH₄ detectors (LMD). The MIR-predicted CH₄ production (g/d) was quite constant in low and high emission categories, in individuals across sites (home farm, experimental station), and within equations (first available and refined versions). The variation of the MIR-predicted values was substantially lower using the refined equation. However, the predicted low and high emitting cows (n = 28) did not differ on average in daily CH₄ emissions measured either with RC or estimated using LMD, and no correlation was found between CH₄ predictions (MIR) and CH₄ emissions measured in RC. When individuals were recategorized based on CH₄ yield measured in RC, differences between categories of 10 low and 10 high CH₄ emitters were about 20%. Low CH₄ emitting cows had a higher feed intake, milk yield, and residual feed intake, but they differed only weakly in eating pattern and digesta mean retention times. Low CH₄ emitters were characterized by lower acetate and higher propionate proportions of total ruminal volatile fatty acids. We concluded that the current MIR-based CH₄ predictions are not accurate enough to be implemented in breeding programs for cows fed forage-based diets. In addition, low CH₄ emitting cows have to be characterized in more detail using mechanistic studies to clarify in more detail the properties that explain the functional differences found in comparison with other cows.