Anti-methanogenic effects of monensin in dairy and beef cattle: A meta-analysis

Monensin is a widely used feed additive with the potential to minimize methane (CH₄) emissions from cattle. Several studies have investigated the effects of monensin on CH₄, but findings have been inconsistent. The objective of the present study was to conduct meta-analyses to quantitatively summarize the effect of monensin on CH₄ production (g/d) and the percentage of dietary gross energy lost as CH₄ (Y_m) in dairy cows and beef steers. Data from 22 controlled studies were used. Heterogeneity of the monensin effects were estimated using random effect models. Due to significant heterogeneity (>68%) in both dairy and beef studies, the random effect models were then extended to mixed effect models by including fixed effects of DMI, dietary nutrient contents, monensin dose, and length of monensin treatment period. Monensin reduced Y_m from 5.97 to 5.43% and diets with greater neutral detergent fiber contents (g/kg of dry matter) tended to enhance the monensin effect on CH₄ in beef steers. When adjusted for the neutral detergent fiber effect, monensin supplementation [average 32 mg/kg of dry matter intake (DMI)] reduced CH₄ emissions from beef steers by 19 ± 4 g/d. Dietary ether extract content and DMI had a positive and a negative effect on monensin in dairy cows, respectively. When adjusted for these 2 effects in the final mixed-effect model, monensin feeding (average 21 mg/kg of DMI) was associated with a 6 ± 3 g/d reduction in CH₄ emissions in dairy cows. When analyzed across dairy and beef cattle studies, DMI or monensin dose (mg/kg of DMI) tended to decrease or increase the effect of monensin in reducing methane emissions, respectively. Methane mitigation effects of monensin in dairy cows (–12 ± 6 g/d) and beef steers (–14 ± 6 g/d) became similar when adjusted for the monensin dose differences between dairy cow and beef steer studies. When adjusted for DMI differences, monensin reduced Y_m in dairy cows (–0.23 ± 0.14) and beef steers (–0.33 ± 0.16). Monensin treatment period length did not significantly modify the monensin effects in dairy cow or beef steer studies. Overall, monensin had stronger antimethanogenic effects in beef steers than dairy cows, but the effects in dairy cows could potentially be improved by dietary composition modifications and increasing the monensin dose.     

Short communication: Prediction of intake in dairy cows under tropical conditions

A meta-analysis was conducted to develop a model for predicting dry matter intake (DMI) in dairy cows under the tropical conditions of Brazil and to assess its adequacy compared with 5 currently available DMI prediction models: Agricultural and Food Research Council (AFRC); National Research Council (NRC); Cornell Net Carbohydrate and Protein System (CNCPS; version 6); and 2 other Brazilian models. The data set was created using 457 observations (n = 1,655 cows) from 100 studies, and it was randomly divided into 2 subsets for statistical analysis. The first subset was used to develop a DMI prediction equation (60 studies; 309 treatment means) and the second subset was used to assess the adequacy of DMI predictive models (40 studies; 148 treatment means). The DMI prediction model proposed in the current study was developed using a nonlinear mixed model analysis after reparameterizing the NRC equation but including study as a random effect in the model. Body weight (mean = 540 ± 57.6 kg), 4% fat-corrected milk (mean = 21.3 ± 7.7 kg/d), and days in milk (mean = 110 ± 62 d) were used as independent variables in the model. The adequacy of the DMI prediction models was evaluated based on coefficient of determination, mean square prediction error (MSPE), root MSPE (RMSPE), and concordance correlation coefficient (CCC). The observed DMI obtained from the data set used to evaluate the prediction models averaged 17.6 ± 3.2 kg/d. The following model was proposed: DMI (kg/d) = [0.4762 (±0.0358) × 4% fat-corrected milk + 0.07219 (±0.00605) × body weight^0.75] × (1 – e^{−0.03202 (±0.00615) × [days in milk + 24.9576 (±5.909)]}). This model explained 93.0% of the variation in DMI, predicting it with the lowest mean bias (0.11 kg/d) and RMSPE (4.9% of the observed DMI) and the highest precision [correlation coefficient estimate (ρ) = 0.97] and accuracy [bias correction factor (C_b) = 0.99]. The NRC model prediction equation explained 92.0% of the variation in DMI and had the second lowest mean bias (0.42 kg/d) and RMSPE (5.8% of the observed DMI), as well as the second highest precision (ρ = 0.94) and accuracy (C_b = 0.98). The CNCPS and AFRC DMI prediction models explained 93.0 and 85.0% of the variation in DMI but underpredicted DMI by 1.8 and 1.4 kg/d, respectively. These 2 models (CNCPS and AFRC) resulted, respectively, in RMSPE of 11.3 and 10.7% of the observed DMI, with moderate to high precision (ρ = 0.81 and 0.82) and accuracy (C_b = 0.84 and 0.89). The remaining 2 models resulted in the poorest results, underpredicting DMI by 2.3 and 1.9 kg/d, with RMSPE of 22.8 and 14.9% of the observed DMI and moderate to low precision (ρ = 0.49 and 0.76) and accuracy (C_b = 0.81 and 0.86). The new model derived from the current meta-analytical approach provided the best accuracy and precision for predicting DMI in lactating dairy cows under Brazilian conditions.     

Effect of fibrolytic enzyme application to low- and high-concentrate diets on the performance of lactating dairy cattle

The objective of this study was to examine the effect of applying a fibrolytic enzyme preparation to diets with high (48% of diet dry matter, DM) or low (33% of diet DM) proportions of concentrate on production performance of lactating dairy cows. Sixty lactating Holstein cows (589 kg ± 20; 22 ± 3 d in milk) were stratified according to milk production and parity and randomly assigned to 4 treatments with a 2 × 2 factorial arrangement. Dietary treatments included the following: 1) low-concentrate diet (LC); 2) LC plus enzyme (LCE); 3) high-concentrate diet (HC); and 4) HC plus enzyme (HCE). The enzyme was sprayed at a rate of 3.4 mg of enzyme/g of DM on the total mixed ration daily and the trial lasted for 63 d. A second experiment with a 4 × 4 Latin square design used 4 ruminally fistulated cows to measure treatment effects on ruminal fermentation and in situ ruminal dry matter degradation during four 18-d periods. Enzyme application did not affect dry matter intake (DMI; 23.9 vs. 22.3 kg/d) or milk production (32.8 vs. 34.2 kg/d) but decreased estimated CH₄ production, increased total volatile fatty acid concentration (114.5 vs. 125.7 mM), apparent total tract digestibility of DM (69.8 vs. 72.6%), crude protein (CP; 69.2 vs. 73.3%), acid detergent fiber (50.4 vs. 54.8%), neutral detergent fiber (53.7 vs. 55.4%), and the efficiency of milk production (1.44 vs. 1.60 kg of milk/kg of DMI). Feeding more concentrates increased DMI (21.5 vs. 24.8 kg/d), milk yield (32.2 vs. 34.7 kg/d), milk protein yield (0.89 vs. 0.99 kg/d), and DM (69.9 vs. 72.6%), but decreased ruminal pH (6.31 vs. 6.06). Compared with cows fed HC, those fed LCE had lower DMI (20.8 vs. 25.7 kg/d) and CP intake (3.9 vs. 4.8 kg/d), greater ruminal pH (6.36 vs. 6.10), and similar milk yield (33.2 ± 1.1 kg/d). Consequently, the efficiency of milk production was greater in cows fed LCE than those fed HC (1.69 vs. 1.42 kg of milk/kg of DMI). This fibrolytic enzyme increased the digestibility of DM, CP, neutral detergent fiber, and acid detergent fiber and the efficiency of milk production by dairy cows. Enzyme application to the low-concentrate diet resulted in as much milk production as that from cows fed the untreated high-concentrate diet.     

Milk production and enteric methane emissions by dairy cows grazing fertilized perennial ryegrass pasture with or without inclusion of white clover

An experiment was undertaken to investigate the effect of white clover inclusion in grass swards (GWc) compared with grass-only (GO) swards receiving high nitrogen fertilization and subjected to frequent and tight grazing on herbage and dairy cow productivity and enteric methane (CH₄) emissions. Thirty cows were allocated to graze either a GO or GWc sward (n = 15) from April 17 to October 31, 2011. Fresh herbage [16 kg of dry matter (DM)/cow] and 1 kg of concentrate/cow were offered daily. Herbage DM intake (DMI) was estimated on 3 occasions (May, July, and September) during which 17 kg of DM/cow per day was offered (and concentrate supplementation was withdrawn). In September, an additional 5 cows were added to each sward treatment (n = 20) and individual CH₄ emissions were estimated using the sulfur hexafluoride (SF₆) technique. Annual clover proportion (±SE) in the GWc swards was 0.20 ± 0.011. Swards had similar pregrazing herbage mass (1,800 ± 96 kg of DM/ha) and herbage production (13,110 ± 80 kg of DM/ha). The GWc swards tended to have lower DM and NDF contents but greater CP content than GO swards, but only significant differences were observed in the last part of the grazing season. Cows had similar milk and milk solids yields (19.4 ± 0.59 and 1.49 ± 0.049 kg/d, respectively) and similar milk composition. Cows also had similar DMI in the 3 measurement periods (16.0 ± 0.70 kg DM/cow per d). Similar sward and animal performance was observed during the CH₄ estimation period, but GWc swards had 7.4% less NDF than GO swards. Cows had similar daily and per-unit-of-output CH₄ emissions (357.1 ± 13.6 g of CH₄/cow per day, 26.3 ± 1.14 g of CH₄/kg of milk, and 312.3 ± 11.5 g of CH₄/kg of milk solids) but cows grazing GWc swards had 11.9% lower CH₄ emissions per unit of feed intake than cows grazing GO swards due to the numerically lower CH₄ per cow per day and a tendency for the GWc cows to have greater DMI compared with the GO cows. As a conclusion, under the conditions of this study, sward clover content in the GWc swards was not sufficient to improve overall sward herbage production and quality, or dairy cow productivity. Although GWc cows had a tendency to consume more and emitted less CH₄ per unit of feed intake than GO cows, no difference was observed in daily or per-unit-of-output CH₄ emissions.     

Effect of concentrate feed level on methane emissions from grazing dairy cows

Although the effect of nutrition on enteric methane (CH₄) emissions from confined dairy cattle has been extensively examined, less information is available on factors influencing CH₄ emissions from grazing dairy cattle. In the present experiment, 40 Holstein-Friesian dairy cows (12 primiparous and 28 multiparous) were used to examine the effect of concentrate feed level (2.0, 4.0, 6.0, and 8.0 kg/cow per day; fresh basis) on enteric CH₄ emissions from cows grazing perennial ryegrass-based swards (10 cows per treatment). Methane emissions were measured on 4 occasions during the grazing period (one 4-d measurement period and three 5-d measurement periods) using the sulfur hexafluoride technique. Milk yield, liveweight, and milk composition for each cow was recorded daily during each CH₄ measurement period, whereas daily herbage dry matter intake (DMI) was estimated for each cow from performance data, using the back-calculation approach. Total DMI, milk yield, and energy-corrected milk (ECM) yield increased with increasing concentrate feed level. Within each of the 4 measurement periods, daily CH₄ production (g/d) was unaffected by concentrate level, whereas CH₄/DMI decreased with increasing concentrate feed level in period 4, and CH₄/ECM yield decreased with increasing concentrate feed level in periods 2 and 4. When emissions data were combined across all 4 measurement periods, concentrate feed level (2.0, 4.0, 6.0, and 8.0 kg/d; fresh basis) had no effect on daily CH₄ emissions (287, 273, 272, and 277 g/d, respectively), whereas CH₄/DMI (20.0, 19.3, 17.7, and 18.1 g/kg, respectively) and CH₄-E/gross energy intake (0.059, 0.057, 0.053, and 0.054, respectively) decreased with increasing concentrate feed levels. A range of prediction equations for CH₄ emissions were developed using liveweight, DMI, ECM yield, and energy intake, with the strongest relationship found between ECM yield and CH₄/ECM yield (coefficient of determination = 0.50). These results demonstrate that offering concentrates to grazing dairy cows increased milk production per cow and decreased CH₄ emissions per unit of milk produced.     

Prediction of enteric methane output from milk fatty acid concentrations and rumen fermentation parameters in dairy cows fed sunflower, flax, or canola seeds

Milk fatty acid (FA) composition has been suggested as a means of predicting enteric methane (CH₄) output in lactating dairy cattle because of the common biochemical pathways among CH₄, acetate, and butyrate in the rumen. Sixteen lactating Holstein cows were used in a Latin square design with four 28-d periods. All diets contained steam-rolled barley, a pelleted supplement, barley silage [45% of dietary dry matter (DM)] and 3.3% added fat (DM basis) from 1 of 4 sources: calcium salts of long-chain FA (palm oil; control) or crushed oilseeds from sunflower, flax, or canola. The objectives of this study were to (1) compare the effect of diets on milk FA profile; (2) model CH₄ production from milk FA composition, intake, and rumen fermentation variables; and (3) test the applicability of CH₄ prediction equations reported in previous studies. Methane (g/d) was measured in chambers (2 animals/chamber) on 3 consecutive days (d 21–23). The test variables included total DM intake (DMI, kg/d; d 21–23), forage DMI (kg/d; d 21–23), milk yield (kg/d; d 21–23), milk components (d 18–21), milk FA composition (% total FA methyl esters; d 18–21), rumen volatile FA (mol/100 mol; d 19–21), and protozoal counts (d 19–21), and were averaged by chamber and period to determine relationships between CH₄ and the test variables. Milk trans(t)10-, t11-18:1, and cis(c)9t11-18:2 were greater for sunflower seeds compared with the other diets. Forage DMI (correlation coefficient, r = 0.52; n = 32), DMI (r = 0.52; n = 32), and rumen acetate + butyrate:propionate (r = 0.72; n = 16) were positively related to CH₄ (g/d), whereas rumen propionate (r = 0.63; n = 16), milk c9-17:1 (r = 0.64; n = 32), and c11-18:1 (r = 0.64; n = 32) were negatively related to CH₄. The best regression equation (coefficient of determination = 0.90; n = 16) was CH₄ (g/d) = −910.8 (±156.7) × milk c9-17:1 + 331.2 (±88.8) × milk 16:0 iso + 0.0001 (±0.00) × total entodiniomorphs + 242.5 (±39.7). Removing rumen parameters from the model also resulted in a reasonably good estimate (coefficient of determination = 0.83; n = 32) of CH₄. Stepwise regression analysis within diets resulted in greater coefficient of determination and lower standard error values. Predictions of CH₄, using equations from previous studies for the data set from this study, resulted in a mean overestimation ranging from 19 to 61% across studies. Thus, milk FA alone may not be suitable for developing universal CH₄ prediction equations.     

Meta-analysis of relationships between enteric methane yield and milk fatty acid profile in dairy cattle

Various studies have indicated a relationship between enteric methane (CH₄) production and milk fatty acid (FA) profiles of dairy cattle. However, the number of studies investigating such a relationship is limited and the direct relationships reported are mainly obtained by variation in CH₄ production and milk FA concentration induced by dietary lipid supplements. The aim of this study was to perform a meta-analysis to quantify relationships between CH₄ yield (per unit of feed and unit of milk) and milk FA profile in dairy cattle and to develop equations to predict CH₄ yield based on milk FA profile of cows fed a wide variety of diets. Data from 8 experiments encompassing 30 different dietary treatments and 146 observations were included. Yield of CH₄ measured in these experiments was 21.5 ± 2.46 g/kg of dry matter intake (DMI) and 13.9 ± 2.30 g/kg of fat- and protein-corrected milk (FPCM). Correlation coefficients were chosen as effect size of the relationship between CH₄ yield and individual milk FA concentration (g/100 g of FA). Average true correlation coefficients were estimated by a random-effects model. Milk FA concentrations of C6:0, C8:0, C10:0, C16:0, and C16:0-iso were significantly or tended to be positively related to CH₄ yield per unit of feed. Concentrations of trans-6+7+8+9 C18:1, trans-10+11 C18:1, cis-11 C18:1, cis-12 C18:1, cis-13 C18:1, trans-16+cis-14 C18:1, and cis-9,12 C18:2 in milk fat were significantly or tended to be negatively related to CH₄ yield per unit of feed. Milk FA concentrations of C10:0, C12:0, C14:0-iso, C14:0, cis-9 C14:1, C15:0, and C16:0 were significantly or tended to be positively related to CH₄ yield per unit of milk. Concentrations of C4:0, C18:0, trans-10+11 C18:1, cis-9 C18:1, cis-11 C18:1, and cis-9,12 C18:2 in milk fat were significantly or tended to be negatively related to CH₄ yield per unit of milk. Mixed model multiple regression and a stepwise selection procedure of milk FA based on the Bayesian information criterion to predict CH₄ yield with milk FA as input (g/100 g of FA) resulted in the following prediction equations: CH₄ (g/kg of DMI) = 23.39 + 9.74 × C16:0-iso – 1.06 × trans-10+11 C18:1 – 1.75 × cis-9,12 C18:2 (R² = 0.54), and CH₄ (g/kg of FPCM) = 21.13 – 1.38 × C4:0 + 8.53 × C16:0-iso – 0.22 × cis-9 C18:1 – 0.59 × trans-10+11 C18:1 (R² = 0.47). This indicated that milk FA profile has a moderate potential for predicting CH₄ yield per unit of feed and a slightly lower potential for predicting CH₄ yield per unit of milk.     

Comparison of updates to the Molly cow model to predict methane production from dairy cows fed pasture

Molly is a deterministic, mechanistic, dynamic model representing the digestion, metabolism, and production of a dairy cow. This study compared the predictions of enteric methane production from the original version of Molly (MollyOrigin) and 2 new versions of Molly. Updated versions included new ruminal fiber digestive parameters and animal hormonal parameters (Molly84) and a revised version of digestive and ruminal parameters (Molly85), using 3 different ruminal volatile fatty acid (VFA) stoichiometry constructs to describe the VFA pattern and methane (CH₄) production (g of CH₄/d). The VFA stoichiometry constructs were the original forage and mixed-diet VFA constructs and a new VFA stoichiometry based on a more recent and larger set of data that includes lactate and valerate production, amylolytic and cellulolytic bacteria, as well as protozoal pools. The models’ outputs were challenged using data from 16 dairy cattle 26 mo old [standard error of the mean (SEM) = 1.7], 82 (SEM = 8.7) d in milk, producing 17 (SEM = 0.2) kg of milk/d, and fed fresh-cut ryegrass [dry matter intake = 12.3 (SEM = 0.3) kg of DM/d] in respiration chambers. Mean observed CH₄ production was 266 ± 5.6 SEM (g/d). Mean predicted values for CH₄ production were 287 and 258 g/d for MollyOrigin without and with the new VFA construct. Model Molly84 predicted 295 and 288 g of CH₄/d with and without the new VFA settings. Model Molly85 predicted the same CH₄ production (276 g/d) with or without the new VFA construct. The incorporation of the new VFA construct did not consistently reduce the low prediction error across the versions of Molly evaluated in the present study. The improvements in the Molly versions from MollyOrigin to Molly84 to Molly85 resulted in a decrease in mean square prediction error from 8.6 to 8.3 to 4.3% using the forage diet setting. The majority of the mean square prediction error was apportioned to random bias (e.g., 43, 65, and 70% in MollyOrigin, Molly84, and Molly85, respectively, on the forage setting, showing that with the updated versions a greater proportion of error was random). The slope bias was less than 2% in all cases. We concluded that, of the versions of Molly used for pastoral systems, Molly85 has the capability to predict CH₄ production from grass-fed dairy cows with the highest accuracy.     

Evaluation of equations to predict dry matter intake of dairy heifers

Daily pen dry matter intakes (DMI, n = 9,275) were collected over a 28-mo period at the University of Wisconsin's Integrated Dairy Research Facility. Heifers were housed in pens containing 8 Holstein or Holstein × Jersey crossbred heifers/pen. Heifer diets were formulated to energy and protein requirement twice monthly, with feed intake, dietary nutrient density, and ambient temperature recorded daily. Heifers were weighed at 60-d intervals, and mean pen body weights (BW) were estimated for each day between the weigh dates using the interval average daily gain as a regression coefficient. Prediction of heifer DMI was evaluated using the equations of NRC (2001), Quigley et al. (1986), or alternative random effects mixed models or nonlinear exponential models. The effects of breed, BW, temperature and neutral detergent fiber deviation (NDFdv) were considered as independent variables. Holstein and crossbred heifer DMI was predicted with reasonable precision [standard error (SE) < 0.86 kg/d], by the NRC (2001) or Quigley et al. (1986) equations, but heifer DMI was over- or underpredicted for heifers >500 kg, respectively. Improved heifer DMI prediction equations were achieved with exponential models. For Holsteins (SE = 0.71 kg/d), the prediction equation was: DMI (kg/d) = 15.79 × [1 - e^{(−0.00210 × BW)}] − 0.0820 × NDFdv, where NDFdv = (dietary neutral detergent fiber as a % of dry matter) - {22.07 + [0.08714 × BW] - [0.00007383 × (BW)²]}. For crossbred heifers (SE = 0.60 kg/d), the prediction equation was: DMI (kg/d) = 13.48 × [1 - e^{(−0.00271 × BW)}] - 0.0824 × NDFdv where NDFdv = (dietary neutral detergent fiber as a % of dry matter) - {23.11 + [0.07968 × BW] - [0.00006252 × (BW)²]}. Alternative exponential DMI model equations when dietary neutral detergent fiber is unknown were also developed. The Holstein DMI equation (SE = 0.73 kg/d) was: DMI (kg/d) = 15.36 × [1 - e^{(−0.00220 × BW)}], and the crossbred DMI equation (SE = 0.81 kg/d) was: DMI (kg/d) = 12.91 × [1 - e^{(−0.00295 × BW)}].     

Comparison of Molly and Karoline models to predict methane production in growing and dairy cattle

Numerous empirical and mechanistic models predicting methane (CH₄) production are available. The aim of this work was to evaluate the Molly cow model and the Nordic cow model Karoline in predicting CH₄ production in cattle using a data set consisting of 267 treatment means from 55 respiration chamber studies. The dietary and animal characteristics used for the model evaluation represent the range of diets fed to dairy and growing cattle. Feedlot diets and diets containing additives mitigating CH₄ production were not included in the data set. The relationships between observed and predicted CH₄ (pCH₄) were assessed by regression analysis using fixed and mixed model analysis. Residual analysis was conducted to evaluate which dietary factors were related to prediction errors. The fixed model analysis showed that the Molly predictions were related to the observed data (± standard error) as CH₄ (g/d) = 0.94 (±0.022) × pCH₄ (g/d) + 31 (±6.9) [root mean squared prediction error (RMSPE) = 45.0 g/d (14.9% of observed mean), concordance correlation coefficient (CCC) = 0.925]. The corresponding equation for the Karoline model was CH₄ (g/d) = CH₄ (g/d) = 0.98 (±0.019) × pCH₄ (g/d) + 7.0 (±6.0) [RMSPE = 35.0 g/d (11.6%), CCC = 0.953]. Proportions of mean squared prediction error attributable to mean and linear bias and random error were 10.6, 2.2, and 87.2% for the Molly model, and 1.3, 0.3, and 98.6% for the Karoline model, respectively. Mean and linear bias were significant for the Molly model but not for the Karoline model. With the mixed model regression analysis RMSPE adjusted for random study effects were 10.9 and 7.9% for the Molly model and the Karoline model, respectively. The residuals of CH₄ predictions were more strongly related to factors associated with CH₄ production (feeding level, digestibility, fat concentrations) with the Molly model compared with the Karoline model. Especially large mean (underprediction) and linear bias (overprediction of low digestibility diets relative to high digestibility diets) contributed to the prediction error of CH₄ yield with the Molly model. It was concluded that both models could be used for prediction of CH₄ production in cattle, but Karoline was more accurate and precise based on smaller RMSPE, mean bias, and slope bias, and greater CCC. The importance of accurate input data of key variables affecting diet digestibility is emphasized.     

Long-term effects of feeding monensin on methane production in lactating dairy cows

The objective of this study was to determine the long-term effects of feeding monensin on methane (CH₄) production in lactating dairy cows. Twenty-four lactating Holstein dairy cows (1.46 ± 0.17 parity; 620 ± 5.9 kg of live weight; 92.5 ± 2.62 d in milk) housed in a tie-stall facility were used in the study. The study was conducted as paired comparisons in a completely randomized design with repeated measurements in a color-coded, double-blind experiment. The cows were paired by parity and days in milk and allocated to 1 of 2 treatments: 1) the regular milking cow total mixed ration (TMR) with a forage-to-concentrate ratio of 60:40 (control TMR; placebo premix) vs. a medicated TMR (monensin TMR; regular TMR + 24 mg of Rumensin Premix/kg of dry matter) fed ad libitum. The animals were fed and milked twice daily (feeding at 0830 and 1300 h; milking at 0500 and 1500 h) and CH₄ production was measured prior to introducing the treatments and monthly thereafter for 6 mo using an open-circuit indirect calorimetry system. Monensin reduced CH₄ production by 7% (expressed as grams per day) and by 9% (expressed as grams per kilogram of body weight), which were sustained for 6 mo (mean, 458.7 vs. 428.7 ± 7.75 g/d and 0.738 vs. 0.675 ± 0.0141, control vs. monensin, respectively). Monensin reduced milk fat percentage by 9% (3.90 vs. 3.53 ± 0.098%, control vs. monensin, respectively) and reduced milk protein by 4% (3.37 vs. 3.23 ± 0.031%, control vs. monensin, respectively). Monensin did not affect the dry matter intake or milk yield of the cows. These results suggest that medicating a 60:40 forage-to-concentrate TMR with 24 mg of Rumensin Premix/kg of dry matter is a viable strategy for reducing CH₄ production in lactating Holstein dairy cows.     

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

There is a need to quantify methane (CH₄) emissions with alternative methods. For the past decade, milk fatty acids (MFA) could be used as proxies to predict CH₄ emissions from dairy cows because of potential common rumen biochemical pathways. However, equations have been developed based on a narrow range of diets and with limited data. The objectives of this study were to (1) construct a set of empirical models based on individual data of CH₄ emissions and MFA from a large number of lactating dairy cows fed a wide range of diets; (2) further increase the models' level of complexity (from farm to research level) with additional independent variables such as dietary chemical composition (organic matter, neutral detergent fiber, crude protein, starch, and ether extract), dairy performance (milk yield and composition), and animal characteristics (days in milk or body weight); and (3) evaluate the performance of the developed models on independent data sets including measurements from individual animals or average measurements of groups of animals. Prediction equations based only on MFA [C10:0, iso C17:0 + trans-9 C16:1,cis-11 C18:1, and trans-11,cis-15 C18:2 for CH₄ production (g/d); iso C16:0, cis-11 C18:1, trans-10 C18:1, and cis-9,cis-12 C18:2 for CH₄ yield (g/kg of dry matter intake, DMI); and iso C16:0, cis-15 C18:1, and trans-10 + trans-11 C18:1 for CH₄ intensity (g/kg of milk)] had a root mean squared error of 65.1 g/d, 2.8 g/kg of DMI, and 2.9 g/kg of milk, respectively, whereas complex equations that additionally used DMI, dietary neutral detergent fiber, ether extract, days in milk, and body weight had a lower root mean squared error of 46.6 g/d, 2.6 g/kg of DMI, and 2.7 g/kg of milk, respectively). External evaluation with individual or mean data not used for equation development led to variable results. When evaluations were performed using individual cow data from an external data set, accurate predictions of CH₄ production (g/d) were obtained using simple equations based on MFA. Better performance was observed on external evaluation with individual data for the simple equation of CH₄ production (g/d, based on MFA), whereas better performance was observed on external evaluation mean data for the simple equation of CH₄ yield (g/kg of DMI). The performance of evaluation of the models is dependent on the domain of validity of the evaluation data sets used (individual or mean).     

Short communication: Development and evaluation of predictive models of body weight for crossbred Holstein-Zebu dairy heifers

Equations to predict body weight (BW) of crossbred Holstein-Zebu dairy heifers were developed and compared with current models (Heinrichs et al. for Holsteins, United States; Reis et al. for crossbred Holstein-Zebu, Brazil). The data set was constructed from 150 measurements of BW (320 ± 107 kg) and biometric measurements such as heart girth (HG, 161 ± 19.5 cm), withers height (WH, 126 ± 11.0 cm), and hip height (HH, 132 ± 11.3 cm) of heifers from 5 commercial dairy producers in the southern Amazon region in Brazil. The data were evaluated using mixed nonlinear models with herd as a random effect. Three nonlinear equations were fitted: BW (kg) = 0.00058·HG (cm)^2.6135; BW (kg) = 0.000618·HG (cm)^2.7362; and BW (kg) = 0.000196·HH ^2.8793. An independent database was constructed to evaluate the models from 38 treatment means of 4 feeding trials: BW 258 ± 54.3 kg, HG 142.5 ± 11.8 cm, WH 113.2 ± 6.0 cm, and HH 118.7 ± 9.1 cm (mean ± SD). The evaluations were based on the relationship between observed and predicted values of BW by linear regression, root mean square prediction error (RMSPE), and concordance correlation coefficient analysis. Only the proposed model using HG accurately predicted observed BW, with bias (observed – predicted) of 4.83 kg and RMSPE of 5.41% of observed BW (87.7% of random error). The models using WH and HH failed to accurately predict observed BW, with a bias of −3.06 and 72.02 kg, and RMSPE of 9.40% of observed BW (75.2% of random error and 23.1% of systematic error) and 30.81% of observed BW (81.2% of mean bias). Additionally, the models of Heinrichs and Reis used for comparison did not predict BW accurately, with a bias of 19.32 and 29.37 kg and RMSPE of 9.08% of observed BW (68.4% of mean bias and 31.4% of random error) and 12.58% of observed BW (81.9% of mean bias). The largest concordance correlation coefficient of the proposed HG-nonlinear model (0.930), compared with the models of Heinrichs and Reis of 0.845 and 0.708, confirmed the greater accuracy and precision of the new equation to predict BW in crossbred Holstein-Zebu dairy heifers.     

Effects of grain,fructose, and histidine feeding on endotoxin and oxidative stress measures in dairy heifers

Ruminal endotoxin and plasma oxidative stress biomarker concentrations were studied in dairy heifers challenged with grain, fructose, and histidine in a partial factorial study. Holstein-Friesian heifers [n = 30; average body weight (BW) of 359.3 ± 47.3 kg] were randomly allocated to 5 treatment groups: (1) control (no grain); (2) grain [crushed triticale at 1.2% of BW dry matter intake (DMI)]; (3) grain (0.8% of BW DMI) + fructose (0.4% of BW DMI); (4) grain (1.2% of BW DMI) + histidine (6 g/head); and (5) grain (0.8% of BW DMI) + fructose (0.4% of BW DMI) + histidine (6 g/head). Rumen samples were collected by stomach tube 5, 65, 115, 165, and 215 min after diet consumption and blood samples at 5 and 215 min after consumption. Rumen fluid was analyzed for endotoxin concentrations. Plasma was analyzed for concentrations of the following oxidative stress biomarkers: reactive oxygen metabolites (dROM), biological antioxidant potential (BAP), advanced oxidation protein products, and ceruloplasmin, and activity of glutathione peroxidase. Dietary treatment had no effect on concentrations of endotoxin or oxidative stress biomarkers. We observed no interactions of treatment by time. Ruminal concentrations of endotoxin decreased during the sampling period from 1.12 × 10⁵ ± 0.06 to 0.92 × 10⁵ endotoxin units/mL ± 0.05 (5 and 215 min after diet consumption, respectively). Concentrations of dROM and the oxidative stress index (dROM/BAP × 100) increased over the sampling period, from 108.7 to 123.5 Carratelli units (Carr U), and from 4.1 to 4.8, respectively. Ceruloplasmin concentrations markedly declined 5 min after the consumption of diets, from 190 to 90 mg/L over the 215-min sampling period. Overall, a single feeding challenge for dairy cattle with grain, fructose, and histidine, and combinations thereof, may not be sufficient to induce marked changes in endotoxin or oxidative stress biomarker concentrations.     

Grape marc reduces methane emissions when fed to dairy cows

Grape marc (the skins, seeds, stalk, and stems remaining after grapes have been pressed to make wine) is currently a by-product used as a feed supplement by the dairy and beef industries. Grape marc contains condensed tannins and has high concentrations of crude fat; both these substances can reduce enteric methane (CH₄) production when fed to ruminants. This experiment examined the effects of dietary supplementation with either dried, pelleted grape marc or ensiled grape marc on yield and composition of milk, enteric CH₄ emissions, and ruminal microbiota in dairy cows. Thirty-two Holstein dairy cows in late lactation were offered 1 of 3 diets: a control (CON) diet; a diet containing dried, pelleted grape marc (DGM); and a diet containing ensiled grape marc (EGM). The diet offered to cows in the CON group contained 14.0 kg of alfalfa hay dry matter (DM)/d and 4.3 kg of concentrate mix DM/d. Diets offered to cows in the DGM and EGM groups contained 9.0 kg of alfalfa hay DM/d, 4.3 kg of concentrate mix DM/d, and 5.0 kg of dried or ensiled grape marc DM/d, respectively. These diets were offered individually to cows for 18 d. Individual cow feed intake and milk yield were measured daily and milk composition measured on 4 d/wk. Individual cow CH₄ emissions were measured by the SF₆ tracer technique on 2 d at the end of the experiment. Ruminal bacterial, archaeal, fungal, and protozoan communities were quantified on the last day of the experiment. Cows offered the CON, DGM, and EGM diets, ate 95, 98, and 96%, respectively, of the DM offered. The mean milk yield of cows fed the EGM diet was 12.8 kg/cow per day and was less than that of cows fed either the CON diet (14.6 kg/cow per day) or the DGM diet (15.4 kg/cow per day). Feeding DGM and EGM diets was associated with decreased milk fat yields, lower concentrations of saturated fatty acids, and enhanced concentrations of mono- and polyunsaturated fatty acids, in particular cis-9,trans-11 linoleic acid. The mean CH₄ emissions were 470, 375, and 389 g of CH₄/cow per day for cows fed the CON, DGM, and EGM diets, respectively. Methane yields were 26.1, 20.2, and 21.5 g of CH₄/kg of DMI for cows fed the CON, DGM, and EGM diets, respectively. The ruminal bacterial and archaeal communities were altered by dietary supplementation with grape marc, but ruminal fungal and protozoan communities were not. Decreases of approximately 20% in CH₄ emissions and CH₄ yield indicate that feeding DGM and EGM could play a role in CH₄ abatement.     

Effect of forage-to-concentrate ratio in dairy cow diets on emission of methane, carbon dioxide, and ammonia, lactation performance, and manure excretion

Holstein cows housed in a modified tie-stall barn were used to determine the effect of feeding diets with different forage-to-concentrate ratios (F:C) on performance and emission of CH₄, CO₂ and manure NH₃-N. Eight multiparous cows (means ± standard deviation): 620 ± 68 kg of body weight; 52 ± 34 d in milk and 8 primiparous cows (546 ± 38 kg of body weight; 93 ± 39 d in milk) were randomly assigned to 1 of 4 air-flow controlled chambers, constructed to fit 4 cows each. Chambers were assigned to dietary treatment sequences in a single 4 × 4 Latin square design. Dietary treatments, fed as 16.2% crude protein total mixed rations included the following F:C ratio: 47:53, 54:46, 61:39, and 68:32 [diet dry matter (DM) basis]. Forage consisted of alfalfa silage and corn silage in a 1:1 ratio. Cow performance and emission data were measured on the last 7 d and the last 4 d, respectively of each 21-d period. Air samples entering and exiting each chamber were analyzed with a photo-acoustic field gas monitor. In a companion study, fermentation pattern was studied in 8 rumen-cannulated cows. Increasing F:C ratio in the diet had no effect on DM intake (21.1 ± 1.5 kg/d), energy-corrected milk (ECM, 37.4 ± 2.2 kg/d), ECM/DM intake (1.81 ± 0.18), yield of milk fat, and manure excretion and composition; however, it increased milk fat content linearly by 7% and decreased linearly true protein, lactose, and solids-not-fat content (by 4, 1, and 2%, respectively) and yield (by 10, 6, and 6%, respectively), and milk N-to-N intake ratio. On average 93% of the N consumed by the cows in the chambers was accounted for as milk N, manure N, or emitted NH₃-N. Increasing the F:C ratio also increased ruminal pH linearly and affected concentrations of butyrate and isovalerate quadratically. Increasing the F:C ratio from 47:53 to 68:32 increased CH₄ emission from 538 to 648 g/cow per day, but had no effect on manure NH₃-N emission (14.1 ± 3.9 g/cow per day) and CO₂ emission (18,325 ± 2,241 g/cow per day). In this trial, CH₄ emission remained constant per unit of neutral detergent fiber intake (1 g of CH₄ was emitted for every 10.3 g of neutral detergent fiber consumed by the cow), but increased from 14.4 to 18.0 g/kg of ECM when the percentage of forage in the diet increased from 47 to 68%. Although the pattern of emission within a day was distinct for each gas, emissions were higher between morning feeding (0930 h) and afternoon milking (1600 h) than later in the day. Altering the level of forage within a practical range and rebalancing dietary crude protein with common feeds of the Midwest of the United States had no effects on manure NH₃-N emission but altered CH₄ emission.     

Variation in plasma concentrations of insulin-like growth factor-I in pasture-fed Holstein cows

The objective of this 5-wk study was to determine dietary effects on plasma concentrations of insulin-like growth factor-I (IGF-I), as well as milk production and milk components in pasture-fed dairy cows. Thirty-two Holstein cows 4 to 5 wk postpartum were randomly assigned to 4 dietary subgroups. Feed was provided twice daily ad libitum at 0900 and 1600 h composed of fresh-cut pasture, meadow hay, and pelleted cereal grain to achieve differing levels of DMI and ME density (LL: 16.6 kg of DMI and 174 MJ of ME; HL: 17.3 kg of DMI and 181.1 MJ of ME; LH: 15.4 kg of DMI and 183.1 MJ of ME; HH: 17.9 kg of DMI and 213.3 MJ of ME, with the first letter indicating DMI and the second ME, and with H indicating high and L indicating low, respectively). The first day cows were placed on their diets was designated d 0. Concentrations of IGF-I were measured in frozen-thawed samples of plasma using a verified ELISA. Dietary treatment had affected plasma concentrations of IGF-I by d 7 with cows on high ME diets having greater IGF-I concentrations at d 14 (83.7 vs. 45.6 ng/mL) than cows on the low ME diets. The level of DMI had less effect on plasma concentrations of IGF-I at d 14 (72.2 vs. 57.1 ng/mL). Dietary treatment effects on these concentrations had stabilized by d 21. Day-to-day variation in mean plasma concentrations of IGF-I within each dietary treatment was low during an intensive period of daily sampling for 14 d (from d 22 to 35). Within-cow day-to-day variation was also low compared with that among cows within the same dietary group and was associated with a high repeatability in the day-to-day concentration of IGF-I in individual cows. Intraclass correlation coefficients for IGF-I ranged from 0.56 (± 0.14) to 0.88 (± 0.06) with a combined (pooled) value for the 4 subgroups of 0.77 (± 0.05). The ME and DMI effects (H vs. L) at d 35 were 79.3 vs. 41.4 and 62.0 vs. 55.7 ng/mL, respectively. Although the ME and DMI differences also affected milk yield and compositional parameters, the effects were not as proportionately great as those measured for IGF-I. Altering the ME or DMI components of the pasture-based diets produced changes in plasma IGF-I concentrations that did not become stabilized for 3 wk, but were then highly repeatable for individual cows within each dietary group. Both observations have relevance to interpreting data related to plasma concentrations of IGF-I in lactating Holstein cows.     

Evaluation of nitrogen utilization and the effects of monensin in dairy cows fed brown midrib corn silage

Twenty midlactation Holstein cows (4 ruminally fistulated) averaging 101 ± 34 d in milk and weighing 674 ± 77 kg were used to compare rations with brown midrib corn silage (bm3) to rations with dual-purpose control silage (DP) on N utilization and milk production. The effect of monensin in these rations was also examined. Animals were assigned to one of five 4 × 4 Latin squares with treatments arranged in a 2 × 2 factorial. Cows were fed 1 of 4 treatments during each of the four 28-d periods. Treatments were 1) 0 mg/d monensin and bm3 corn silage, 2) 0 mg/d monensin and DP corn silage, 3) 300 mg/d monensin and bm3 corn silage, and 4) 300 mg/d monensin and DP corn silage. In vitro 30-h neutral detergent fiber (NDF) digestibility was greater for bm3 corn silage (61.0 vs. 49.1 ± 0.62). Dry matter intake (DMI) tended to be greater for cows consuming bm3 corn silage (21.3 vs. 20.2 kg/d). Neither hybrid nor monensin affected milk production, fat, or protein (37.7 kg, 3.60%, or 3.04%). Monensin tended to increase rumen pH (5.89 vs. 5.79 ± 0.07) compared with the control treatment. In addition, bm3 corn silage resulted in a significant decrease in rumen pH (5.72 vs. 5.98 ± 0.07). Supplementing monensin had no effect on molar proportions of acetate, propionate, or butyrate. In contrast, an increase was observed in branched-chain volatile fatty acids. No treatment interactions were observed for rumen pH or molar proportion of propionate but monensin decreased the molar proportion of acetate and increased the molar proportion of butyrate when cattle consumed bm3 silage. Dry matter, N, and acid detergent fiber digestibility were lower for the bm3 ration, whereas NDF digestibility was not different between treatments. There was no effect of hybrid on microbial protein synthesis (1,140 g/d) as estimated by urinary concentration of purine derivatives. Cows consuming bm3 excreted more fecal N than cows consuming DP (38.2 vs. 34.4% N intake); however, based on spot sampling, estimated urinary and manure N were not different between treatments (35.8 and 71.9% N intake). Monensin had no effect on DMI, digestibility of any nutrients, or N metabolism, and there were no hybrid by monensin interactions. Rations including bm3 corn silage tended to increase DMI but did not affect production. The reduction in the digestibility of some nutrients when cows consumed bm3 may have been caused by increased DMI and possible increased digestion in the lower gut. This increase in DMI appeared to also have negatively affected N digestibility but not NDF digestibility. This resulted in a greater amount of N excreted in feces but did not affect total mass of manure N.     

Accuracy and precision of total mixed rations fed on commercial dairy farms

Despite the significant time and effort spent formulating total mixed rations (TMR), it is evident that the ration delivered by the producer and that consumed by the cow may not accurately reflect that originally formulated. The objectives of this study were to (1) determine how TMR fed agrees with or differs from TMR formulation (accuracy), (2) determine daily variability in physical and chemical characteristics of TMR delivered (precision), and (3) investigate the relationship between daily variability in ration characteristics and group-average measures of productivity [dry matter intake (DMI), milk yield, milk components, efficiency, and feed sorting] on commercial dairy farms. Twenty-two commercial freestall herds were visited for 7 consecutive days in both summer and winter months. Fresh and refusal feed samples were collected daily to assess particle size distribution, dry matter, and chemical composition. Milk test data, including yield, fat, and protein were collected from a coinciding Dairy Herd Improvement test. Multivariable mixed-effect regression models were used to analyze associations between productivity measures and daily ration variability, measured as coefficient of variation (CV) over 7 d. The average TMR [crude protein = 16.5%, net energy for lactation (NE_L) = 1.7 Mcal/kg, nonfiber carbohydrates = 41.3%, total digestible nutrients = 73.3%, neutral detergent fiber = 31.3%, acid detergent fiber = 20.5%, Ca = 0.92%, p = 0.42%, Mg = 0.35%, K = 1.45%, Na = 0.41%] delivered exceeded TMR formulation for NE_L (+0.05 Mcal/kg), nonfiber carbohydrates (+1.2%), acid detergent fiber (+0.7%), Ca (+0.08%), P (+0.02%), Mg (+0.02%), and K (+0.04%) and underfed crude protein (−0.4%), neutral detergent fiber (−0.6%), and Na (−0.1%). Dietary measures with high day-to-day CV were average feed refusal rate (CV = 74%), percent long particles (CV = 16%), percent medium particles (CV = 7.7%), percent short particles (CV = 6.1%), percent fine particles (CV = 13%), Ca (CV = 7.7%), Mg (CV = 5.2%), and Na (CV = 10%). Every 0.5-percentage-point decrease in daily NE_L (CV = 1.2 ± 0.4%) was associated with 3.2 kg/d greater milk yield, 1.0 kg/d greater DMI, and 4.3% greater efficiency of production. Every 5-percentage-point decrease in variability in percent long particles (average percent long = 19.8 ± 6.5; CV = 16.1 ± 6.9%) in the TMR was associated with 1.2 kg/d greater milk yield and a 2.6% increase in efficiency of milk production. These results demonstrate the importance of ensuring TMR consistency to maximize DMI, production, and efficiency.     

Effects of a perennial ryegrass diet or total mixed ration diet offered to spring-calving Holstein-Friesian dairy cows on methane emissions, dry matter intake, and milk production

The objective of the present study was to compare the enteric methane (CH₄) emissions and milk production of spring-calving Holstein-Friesian cows offered either a grazed perennial ryegrass diet or a total mixed ration (TMR) diet for 10 wk in early lactation. Forty-eight spring-calving Holstein-Friesian dairy cows were randomly assigned to 1 of 2 nutritional treatments for 10 wk: 1) grass or 2) TMR. The grass group received an allocation of 17 kg of dry matter (DM) of grass per cow per day with a pre-grazing herbage mass of 1,492 kg of DM/ha. The TMR offered per cow per day was composed of maize silage (7.5 kg of DM), concentrate blend (8.6 kg of DM), grass silage (3.5 kg of DM), molasses (0.7 kg of DM), and straw (0.5 kg of DM). Daily CH₄ emissions were determined via the emissions from ruminants using a calibrated tracer technique for 5 consecutive days during wk 4 and 10 of the study. Simultaneously, herbage dry matter intake (DMI) for the grass group was estimated using the n-alkane technique, whereas DMI for the TMR group was recorded using the Griffith Elder feeding system. Cows offered TMR had higher milk yield (29.5 vs. 21.1 kg/d), solids-corrected milk yield (27.7 vs. 20.1 kg/d), fat and protein (FP) yield (2.09 vs. 1.54 kg/d), bodyweight change (0.54 kg of gain/d vs. 0.37 kg of loss/d), and body condition score change (0.36 unit gain vs. 0.33 unit loss) than did the grass group over the course of the 10-wk study. Methane emissions were higher for the TMR group than the grass group (397 vs. 251 g/cow per day). The TMR group also emitted more CH₄ per kg of FP (200 vs. 174 g/kg of FP) than did the grass group. They also emitted more CH₄ per kg of DMI (20.28 vs. 18.06 g/kg of DMI) than did the grass group. In this study, spring-calving cows, consuming a high quality perennial ryegrass diet in the spring, produced less enteric CH₄ emissions per cow, per unit of intake, and per unit of FP than did cows offered a standard TMR diet.