Mitigation of enteric methane emissions through improving efficiency of energy utilization and productivity in lactating dairy cows.

The data set used in the present study was obtained from 20 energy metabolism studies involving 579 lactating dairy cows (511 Holstein-Friesian, 36 Norwegian Red, and 32 Jersey-Holstein crossbreds) varying in genetic merit, lactation number, stage of lactation, and live weight. These cows were offered diets based on grass silage (n=550) or fresh grass (n=29), and their energy intake and outputs, including methane energy (CH(4)-E), were measured in indirect open-circuit respiration calorimeter chambers. The objective was to use these data to evaluate relationships between CH(4)-E output and a range of factors in animal production and energetic efficiency in lactating dairy cows under normal feeding regimens. The CH(4)-E as a proportion of milk energy output (E(l)), E(l) adjusted to zero energy balance (E(l(0))), or intakes of gross energy (GE), digestible energy (DE), or metabolizable energy (ME) was significantly related to a wide range of variables associated with milk production (E(l) and E(l(0))) and energy parameters (energy intake, metabolizability, partitioning, and utilization efficiencies). Three sets of linear relationships were developed with experimental effects removed. The CH(4)-E/GE intake (r(2)=0.50-0.62) and CH(4)-E/E(l) (r(2)=0.41-0.68) were reduced with increasing feeding level, E(l)/metabolic body weight (MBW; kg(0.75)), E(l(0))/MBW, GE intake/MBW, DE intake/MBW, and ME intake/MBW. Increasing dietary ME/DE decreased CH(4)-E/E(l) (r(2)=0.46) and CH(4)-E/GE intake (r(2)=0.72). Dietary ME concentration and ME/GE were also negatively related to CH(4)-E/GE intake (r(2)=0.47). However, increasing heat production/ME intake increased CH(4)-E as a proportion of E(l) (r(2)=0.41), E(l(0)) (r(2)=0.67) and energy intake (GE, DE, and ME; r(2)=0.62 and 0.70). These proportional CH(4)-E variables were reduced with increasing ratios of E(l)/ME intake and E(l(0))/ME intake and efficiency of ME use for lactation (r(2)=0.49-0.70). Fitting CH(4)-E/E(l) or CH(4)-E/E(l(0)) against these energetic efficiencies in quadratic rather than linear relationships significantly increased r(2) values (0.49-0.67 vs. 0.59-0.87). In conclusion, CH(4)-E as a proportion of energy intake (GE, DE, and ME) and milk production (E(l) and E(l(0))) can be reduced by increasing milk yield and energetic efficiency of milk production or by reducing energy expenditure for maintenance. The selection of dairy cows with high energy utilization efficiencies and milk productivity offers an effective approach to reducing enteric CH(4) emission rates.

Prediction of nitrogen excretion in feces and urine of beef cattle offered diets containing grass silage.

Data from 286 beef cattle, obtained in total diet digestibility assessments, were used to examine effects of dietary and animal factors on N excretion in feces and urine and to develop prediction equations for N excretion in beef cattle. The animals used were mainly from beef breeds, at various ages (from growth to finishing) and live BW (153 to 580 kg), and offered diets containing grass silage at production feeding levels. Dietary forage proportion ranged from 199 to 1,000 g/kg of DM and dietary CP concentration from 108 to 217 g/kg of DM. Linear and multiple regression techniques were used to examine relationships between the efficiency of N utilization and dietary and animal variables with the experimental effects removed. The statistical analysis indicated that N excretion was related positively (P < 0.001) to live BW and intakes of DM, N, and ME, and negatively (P < 0.001) to dietary forage proportion. The prediction equation for N excretion, developed using N intake alone, produced a large r2 (0.898) and a small SE (12.3). Addition of live BW and forage proportion as supporting predictors to this relationship only marginally increased R2 to 0.915 and reduced SE to 11.2. Nitrogen excretion was less well related to live BW (r2 = 0.771, SE = 18.5) than to N intake. Addition of N intake as a proportion of DMI or ME intake to the relationship between live BW and N excretion increased R2 to 0.824 and reduced SE to 16.2. The internal validation of these equations revealed that using N intake as the primary predictor produced a very accurate prediction of N excretion. In situations where data on N intake are not available, prediction equations based on live BW and dietary N concentration together can produce a relatively accurate assessment of N excretion. A number of mitigation strategies to reduce N excretion in feces and urine in beef cattle are discussed, including manipulation of dietary N concentration, diet quality, and level of feeding. The prediction equations and mitigation strategies developed in the current study provide an approach for beef producers to quantify N excretion against production and to develop their own mitigation strategies to reduce N excretion.

Relationships among manure nitrogen output and dietary and animal factors in lactating dairy cows.

A large data set derived from total diet digestibility assessments on lactating dairy cows (535 Holstein-Friesian and 29 Norwegian) was used to examine effects of dietary and animal factors on manure (feces and urine) nitrogen (N) output and to develop mitigation strategies and prediction equations for manure N output in lactating dairy cows. Manure N output was positively and significantly related to live weight, milk yield, dietary crude protein (CP) concentration, dry matter intake, and N intake. Reducing the dietary CP concentration or increasing the milk yield decreased manure N output per kilogram of milk yield. Prediction equations for manure N output using live weight and milk yield, either alone or combined, had relatively low R2 (0.227 to 0.474) and large standard error (70.6 to 85.6) values. Addition of dietary CP concentration to these relationships considerably increased R2 to 0.754 and reduced the standard error to 48.2. Relating manure N output to N intake produced a very high r2 (0.901) and a very low standard error (30.6). The addition of live weight and milk yield to this relationship as supporting predictors only marginally increased R2 to 0.910 and reduced the standard error to 29.3. The internal validation of these equations revealed that use of N intake as the primary predictor produced a very accurate prediction of manure N output. In situations in which data on N intake are not available, prediction equations based on dietary CP concentration, live weight, and milk yield together can produce a relatively accurate assessment of manure N output.

Effects of dairy cow genotype with two planes of nutrition on energy partitioning between milk and body tissue.

The data used in the present study were derived from a 2 (genotype) x 2 (plane of nutrition) factorial design production study using Holstein-Friesian (n = 32) and Norwegian (n = 32) first-lactation dairy cattle offered grass silage-based diets from 1 to 44 wk of lactation. The high nutrition diet had concentrate inclusions (g/kg of dry matter) of 600, 500, and 400 for lactation days of < 101, 101 to 200, and > 200, respectively, and the low nutrition diet included concentrates at 300, 200, and 100 for the same periods. Dietary metabolizable energy (ME) concentrations were measured in calorimetric chambers at lactation d 80, 160, and 240 respectively, and then applied to production data to calculate ME intake. From wk 1 to 44 of lactation, Holstein-Friesian cows had a consistently lower accumulated live weight gain and body condition score, and a consistently higher ME intake and milk energy output than Norwegian cows, irrespective of the plane of nutrition. Compared with Norwegian cows using mean data derived from the 2 planes of nutrition, Holstein-Friesian cows produced a significantly higher proportion of milk energy output over ME intake in early and mid lactation, although this increase was not significant in late lactation. In contrast, Holstein-Friesian cows partitioned a significantly lower proportion of ME intake into body tissue than Norwegian cows in early lactation, although the differences were not significant in mid or late lactation. When ME intake and energy used for maintenance, milk, and body tissue were taken into account, the efficiency of ME use for lactation was similar between the 2 genotypes offered the high or low concentrate diet during the whole lactation. It is concluded that Holstein-Friesian cows can produce more milk energy than Norwegian cows, mainly as a result of higher ME intake and because of a greater ability to partition more energy into milk and less into body tissue. The effect on energy partitioning mainly occurs in early and midlactation and is particularly evident with high concentrate diets.

Relationships between urea dilution measurements and body weight and composition of lactating dairy cows.

The objective of the present study was to investigate the potential of the urea dilution technique, coupled with live animal measures to predict the body components of dairy cattle. The study involved 104 lactating Holstein-Friesian cows offered grass silage-based diets. Urea space volume (USV) was calculated from 2 collection periods of blood samples following infusion of urea at 12 (USV12, kg) and 30 (USV30, kg) min after infusion, and then as a proportion of live weight (LW) or empty body weight (EBW). All cows were slaughtered within 2 d of the USV trials. Large ranges existed in EBW and empty body concentrations of water, crude protein (CP), lipid, ash, and gross energy (GE). The USV12 and USV30 were both positively related to LW, EBW, and empty body component weights. The r2 values for USV12 were greater than USV30. The r2 values in the relationships of EBW and empty body composition with USV, however, were smaller than those with LW. Nevertheless, the relationships were improved when both USV and LW were used as predictors, rather than using either alone. Adding milk yield and body condition score as supporting predictors to prediction equations using USV and LW data for EBW, lipid, and GE contents further improved the relationships (r2 = 0.93, 0.66, and 0.77, respectively). Internal evaluation of one-third of the present data using equations developed from two-thirds of the present data indicated that using USV, live weight, and other live animal variables as predictors, rather than using USV alone, considerably improved the prediction accuracy. It was concluded that USV can be used to predict body composition, but the relationships with USV were poorer than those with LW. The USV can only be used as a supporting variable to live weight for prediction of body components in lactating dairy cows.

Short communication: Effects of feeding level on energy concentration in grass silage-based diets offered to dairy cattle.

Twelve grass silages were offered to sheep as a sole diet at maintenance and to lactating dairy cows ad libitum as mixed silage and concentrates diets (n = 13 diets). Fecal and urinary energy outputs were measured for silages and mixed diets. Digestible energy (DE) and metabolizable energy (ME) concentrations for mixed diets with sheep at maintenance were estimated based on the silage dry matter (DM) proportion obtained in the cattle trials, the silage energy utilization values (methane energy-predicted) determined using sheep, and tabulated concentrate values. A comparison of dietary mean data (n = 13) indicated that concentrations of ME (P < 0.01) and DE (P < 0.001) in mixed diets were significantly lower for cows at production feeding level than for sheep at maintenance. The reductions were proportionately 0.015 and 0.020 with each unit increase in feeding level above maintenance, respectively. These ME and DE data were also used to evaluate the feeding level correction factors previously proposed by Van Es (1975) (ME, 0.018) and Yan et al. (2002) (ME, 0.016; DE, 0.025) using the mean square prediction error technique. The ME correction factor proposed by Yan et al. (2002) had a greater prediction accuracy than that proposed by Van Es (1975) for the prediction of ME concentration in mixed diets offered to dairy cattle at production feeding level.

Prediction of nutritive values in grass silages: I. Nutrient digestibility and energy concentrations using nutrient compositions and fermentation characteristics.

Grass silages (n = 136) were selected from commercial farms across Northern Ireland according to their pH, ammonia nitrogen, DM, and predicted ME concentration. Each silage was offered to four sheep as a sole feed at maintenance feeding level to determine nutrient digestibility and urinary energy output. Dry matter concentration was determined as alcohol-corrected toluene DM and was subsequently used as the basis for all nutrient concentrations. The objectives were to use these data to examine relationships between nutritive value and nutrient concentration or fermentation characteristics in silages and then develop prediction equations for silage nutritive values using stepwise multiple regression techniques. The silages had a large range in quality (DM = 15.5 to 41.3%, ME = 7.7 to 12.9 MJ/kg of DM, pH = 3.5 to 5.5) and a relatively even distribution over the range. There was a positive relationship (P < 0.001) between silage GE and DE or ME concentration. Digestible OM in total DM (DOMD); ME/GE; and digestibility of DM, OM, and GE were positively related (P < 0.05) to CP, soluble CP, ether extract, lactic acid concentration, and lactic acid/ total VFA, whereas they were negatively related (P < 0.05) to ADF, NDF, lignin, individual VFA concentration, pH, and ammonia N/total N. Concentrations of DE and ME and digestibility of CP and NDF had similar relationships with those variables, although some relationships were not significant. Three sets of multiple prediction equations for DE and ME concentration; ME/ GE; DOMD; and digestibility of DM, OM, GE, CP, and NDF were therefore developed using three sets of predictors. The first set included GE, CP, soluble N/total N, DM, ash, NDF, lignin, lactic acid/total VFA, and ammonia N/total N; the second set excluded soluble N/ total N and lignin because they are not typically measured; the third set further excluded the fermentation data. The R2 values generally decreased with exclusion of predictors. The second and third sets of equations, except for NDF digestibility, were validated using the mean-square-prediction-error model and an independent grass silage data set published since 1977 (n = 17 [DM digestibility] to 28 [DOMD and OM digestibility]). The validation indicated that the equations developed in the present experiment could accurately predict DE and ME concentrations and DE/GE and ME/GE in grass silages.

Prediction of nutritive values in grass silages: II. Degradability of nitrogen and dry matter using digestibility, chemical composition, and fermentation data.

One hundred thirty-six perennial rye-grass silages with wide variations in quality were evaluated for N and DM degradability in three beef steers offered grass silage of medium quality ad libitum. The silages were incubated in the rumen of each animal in triplicate for 0, 6, 12, 24, 48, and 72 h, respectively. The disappearance rates of N or DM were used to calculate the readily soluble fraction ("a" value), potentially degradable fraction ("b" value), and the fractional degradation rate of"b" ("c" value). The effective degradability (P) of N or DM was then estimated assuming a ruminal outflow rate of 0.02, 0.05, or 0.08/h (P0.02, P0.05, or P0.08). The objective was to use these data to develop prediction equations for N and DM degradability in grass silages. There were considerable variations in "a," "b," and "c" values and the P0.02, P0.05, or P0.08 of N and DM (e.g., the P0.02 of N ranged from 75.0 to 93.4% and the P0.02 of DM from 51.5 to 82.5%). The P0.02, P0.05, or P0.08 of N and DM were negatively related (P < 0.001) to ADF, NDF, and lignin concentrations but positively related (P < 0.001) to protein fractions (CP, soluble CP, and true protein concentrations) and digestibility of DM, OM, GE, CP, and NDF and digestible OM in the total DM (measured with sheep). The N and DM degradability data were also positively related to silage lactic acid concentration, but the relationships between DM degradability data and pH, ammonia N/total N, and VFA concentration in silages were negative (P < 0.05). Several sets of prediction equations (linear and multiple) were thus developed for N and DM degradability using CP or NDF concentration, "a" value or digestibility data as primary predictors, together with or without other nutrient concentration and silage fermentation variables. All these relationships were highly significant (P < 0.001), and each predictor had a significant effect on the relationship (P < 0.05). The R2 values in multiple regression for N and DM degradability were generally over 0.70 and higher than in linear regression equations. Four equations were also developed to convert N and DM degradability at a given ruminal outflow rate, predicted using the above-mentioned equations, to their counterparts at any ruminal outflow rate (0.02 to 0.10/h), respectively.

Alternative approaches to predicting methane emissions from dairy cows.

Previous attempts to apply statistical models, which correlate nutrient intake with methane production, have been of limited value where predictions are obtained for nutrient intakes and diet types outside those used in model construction. Dynamic mechanistic models have proved more suitable for extrapolation, but they remain computationally expensive and are not applied easily in practical situations. The first objective of this research focused on employing conventional techniques to generate statistical models of methane production appropriate to United Kingdom dairy systems. The second objective was to evaluate these models and a model published previously using both United Kingdom and North American data sets. Thirdly, nonlinear models were considered as alternatives to the conventional linear regressions. The United Kingdom calorimetry data used to construct the linear models also were used to develop the three nonlinear alternatives that were all of modified Mitscherlich (monomolecular) form. Of the linear models tested, an equation from the literature proved most reliable across the full range of evaluation data (root mean square prediction error = 21.3%). However, the Mitscherlich models demonstrated the greatest degree of adaptability across diet types and intake level. The most successful model for simulating the independent data was a modified Mitscherlich equation with the steepness parameter set to represent dietary starch-to-ADF ratio (root mean square prediction error = 20.6%). However, when such data were unavailable, simpler Mitscherlich forms relating dry matter or metabolizable energy intake to methane production remained better alternatives relative to their linear counterparts.

Alternatives to linear analysis of energy balance data from lactating dairy cows.

The current energy requirements system used in the United Kingdom for lactating dairy cows utilizes key parameters such as metabolizable energy intake (MEI) at maintenance (MEm), the efficiency of utilization of MEI for 1) maintenance, 2) milk production (kl), 3) growth (kg), and the efficiency of utilization of body stores for milk production (kt). Traditionally, these have been determined using linear regression methods to analyze energy balance data from calorimetry experiments. Many studies have highlighted a number of concerns over current energy feeding systems particularly in relation to these key parameters, and the linear models used for analyzing. Therefore, a database containing 652 dairy cow observations was assembled from calorimetry studies in the United Kingdom. Five functions for analyzing energy balance data were considered: straight line, two diminishing returns functions, (the Mitscherlich and the rectangular hyperbola), and two sigmoidal functions (the logistic and the Gompertz). Meta-analysis of the data was conducted to estimate kg and kt. Values of 0.83 to 0.86 and 0.66 to 0.69 were obtained for kg and kt using all the functions (with standard errors of 0.028 and 0.027), respectively, which were considerably different from previous reports of 0.60 to 0.75 for kg and 0.82 to 0.84 for kt. Using the estimated values of kg and kt, the data were corrected to allow for body tissue changes. Based on the definition of kl as the derivative of the ratio of milk energy derived from MEI to MEI directed towards milk production, MEm and kl were determined. Meta-analysis of the pooled data showed that the average kl ranged from 0.50 to 0.58 and MEm ranged between 0.34 and 0.64 MJ/kg of BW0.75 per day. Although the constrained Mitscherlich fitted the data as good as the straight line, more observations at high energy intakes (above 2.4 MJ/kg of BW0.75 per day) are required to determine conclusively whether milk energy is related to MEI linearly or not.

Evaluation of different energy feeding systems with production data from lactating dairy cows offered grass silage-based diets.

A set of data from 838 lactating dairy cows, drawn from 12 long-term feeding studies (at least 8 wk/period), was used to evaluate the energy feeding systems for dairy cows currently adopted in Australia, France, The Netherlands, the United Kingdom, and the United States. The animals were offered mixed diets of concentrates, forage [grass silages (n = 33) and corn silages (n = 5)] ad libitum. Data used in the present evaluation were either measured [dry matter (DM) intake, milk production and live weight], measured/estimated [dietary metabolizable energy (ME) concentration] or estimated [milk energy output and live weight change (LWC)]. The mean-square prediction error (MSPE) was used for the evaluation. Total ME intake, milk yields, and LWC varied from 91 to 338 MJ/d, 7.7 to 48.9, and -1.23 to 1.73 kg/d, respectively. Australian and French systems predicted total energy requirement and milk yield relatively well, while British, Dutch and American systems underpredicted total energy requirement by proportionately 0.06, 0.04, and 0.03, respectively; and overpredicted milk yield by 0.09, 0.06, and 0.04. The Agricultural and Food Research Council (AFRC) each produced a relatively larger error of the bias (predicted - actual data) over the total MSPE for ME requirement and milk yield and a relatively smaller error of random than other systems. However, an addition of proportionately 0.05 to the total predicted ME requirement of AFRC, as suggested in this system and currently used in the UK, indicated the prediction accuracy of ME requirement and milk yield is similar to Australian and French systems. Nevertheless, all the systems had a poor prediction of LWC. For each system, the total prediction error (total MSPE) was mainly derived from the line (slope; 0.49 to 0.64 of total MSPE), while less derived from the random (0.20 to 0.48 of total MSPE), indicating a large variation between the predicted and actual LWC existed among individual cows. The residual plots of the residual differences in LWC against predicted LWC revealed that the prediction error was greater with increasing LWC. It is concluded that Australian and French systems have a better prediction of total energy requirement and milk yield than other systems, and LWC is an inappropriate indicator of energy balance in lactating dairy cows.

The influence of body condition on the fasting energy metabolism of nonpregnant, nonlactating dairy cows.

The objective of this experiment was to investigate the effect of cow body condition score on fasting heat production. Twelve nonpregnant, nonlactating Holstein-Friesian cows were selected from within the dairy herd at the Agricultural Research Institute of Northern Ireland. Six of these animals (group A) had condition scores > or = 4.5, and the remainder (group B) had condition scores <2. All cows were offered dried grass pellets at estimated maintenance energy level (0.58 MJ of metabolizable energy/kg(0.75)) for a minimum of 21 d. The diet also supplied 2.5 times the metabolizable protein requirement for maintenance. Following this, each cow underwent a 5-d fast in open circuit respiration calorimeters during which fasting heat production (FHP) was measured. On completion of measurement, group A was fed to reduce condition score (CS) below 2, while group B was fed to raise each individual condition score above 4.5. When the appropriate condition scores were achieved, dried grass pellets were again offered at maintenance for a minimum of 21 d, and fasting heat production was measured. It was observed that fasting heat production (MJ/kg(0.75)) was significantly higher for cows with low body condition (<2; ultrasonic fat depth < or = 2.9 mm) compared with cows displaying high body condition (> or = 4.5; ultrasonic fat depth > or = 8.2 mm). A linear relationship between condition score and fasting heat production (MJ/kg(0.75)) was defined by regression analysis as; FHP (MJ/kg(0.75)) = 0.501(SE 0.0121) - 0.030CS (SE 0.0035).