Dairy system,parity, and lactation stage affect enteric methane production,yield, and intensity per kilogram of milk and cheese predicted from gas chromatography fatty acids

Ruminants (and milk production) contribute to global climate change through enteric methane emissions (EME), and any attempt to reduce them is complicated by the fact that they are difficult and expensive to measure directly. In the case of dairy cows, a promising indirect method of estimating EME is to use the milk fatty acid profile as a proxy, as a relationship exists between microbial activity in the rumen and the molecules available for milk synthesis in the mammary gland. In the present study, we analyzed the detailed fatty acid profiles (through gas chromatography) of a large number of milk samples from 1,158 Brown Swiss cows reared on 85 farms with the aim of testing in the field 2 equations for estimating EME taken from a published meta-analysis. The average estimated methane yield (CH₄ emission per kg of dry matter intake, 21.34 ± 1.60 g/kg) and methane intensity (per kg of corrected milk, 14.17 ± 1.78 g/kg), and the derived methane production (CH₄ emissions per day per cow, 357 ± 109 g/d) were similar to those previously published. Using data from model cheese makings from individual cows, we also calculated estimated methane intensity per kilogram of fresh cheese (99.7 ± 16.4 g/kg) and cheese solids (207.5 ± 30.9 g/kg). Dairy system affected all EME estimates. Traditional dairy farms, and modern farms including corn silage in the TMR exhibited greater estimated methane intensities. We found very wide variability in estimated EME traits among different farms within dairy system (0.33 to 0.61 of total variance), suggesting the need to modify the farms' feeding regimens and management practices to mitigate emissions. Among the individual factors, parity order affected all estimated EME traits excepted methane yield, with an increase from first lactation to the following ones. Lactation stage exhibited more favorable estimated EME traits during early lactation, concomitant with the availability of nutrients from body tissue mobilization for mammary synthesis of milk. Our results showed a coherence between the EME traits estimated from the analysis of milk fatty acids and the expectations according to current knowledge. Further research is needed to validate the results obtained in this study in other breeds and populations, to assess the magnitude of the genetic variation and the potential of these phenotypes to be exploited in breeding programs with the aim to mitigate emissions.     

The use of Fourier-transform infrared spectroscopy to predict cheese yield and nutrient recovery or whey loss traits from unprocessed bovine milk samples

Cheese yield is an important technological trait in the dairy industry in many countries. The aim of this study was to evaluate the effectiveness of Fourier-transform infrared (FTIR) spectral analysis of fresh unprocessed milk samples for predicting cheese yield and nutrient recovery traits. A total of 1,264 model cheeses were obtained from 1,500-mL milk samples collected from individual Brown Swiss cows. Individual measurements of 7 new cheese yield-related traits were obtained from the laboratory cheese-making procedure, including the fresh cheese yield, total solid cheese yield, and the water retained in curd, all as a percentage of the processed milk, and nutrient recovery (fat, protein, total solids, and energy) in the curd as a percentage of the same nutrient contained in the milk. All individual milk samples were analyzed using a MilkoScan FT6000 over the spectral range from 5,000 to 900 wavenumber × cm⁻¹. Two spectral acquisitions were carried out for each sample and the results were averaged before data analysis. Different chemometric models were fitted and compared with the aim of improving the accuracy of the calibration equations for predicting these traits. The most accurate predictions were obtained for total solid cheese yield and fresh cheese yield, which exhibited coefficients of determination between the predicted and measured values in cross-validation (1-VR) of 0.95 and 0.83, respectively. A less favorable result was obtained for water retained in curd (1-VR = 0.65). Promising results were obtained for recovered protein (1-VR = 0.81), total solids (1-VR = 0.86), and energy (1-VR = 0.76), whereas recovered fat exhibited a low accuracy (1-VR = 0.41). As FTIR spectroscopy is a rapid, cheap, high-throughput technique that is already used to collect standard milk recording data, these FTIR calibrations for cheese yield and nutrient recovery highlight additional potential applications of the technique in the dairy industry, especially for monitoring cheese-making processes and milk payment systems. In addition, the prediction models can be used to provide breeding organizations with information on new phenotypes for cheese yield and milk nutrient recovery, potentially allowing these traits to be enhanced through selection.     

Mid-infrared prediction of bovine milk fatty acids across multiple breeds, production systems, and countries

Increasing consumer concern exists over the relationship between food composition and human health. Because of the known effects of fatty acids on human health, the development of a quick, inexpensive, and accurate method to directly quantify the fatty acid (FA) composition in milk would be valuable for milk processors to develop a payment system for milk pertinent to their customer requirements and for farmers to adapt their feeding systems and breeding strategies accordingly. The aim of this study was (1) to confirm the ability of mid-infrared spectrometry (MIR) to quantify individual FA content in milk by using an innovative procedure of sampling (i.e., samples were collected from cows belonging to different breeds, different countries, and in different production systems); (2) to compare 6 mathematical methods to develop robust calibration equations for predicting the contents of individual FA in milk; and (3) to test interest in using the FA equations developed in milk as basis to predict FA content in fat without corrections for the slope and the bias of the developed equations. In total, 517 samples selected based on their spectral variability in 3 countries (Belgium, Ireland, and United Kingdom) from various breeds, cows, and production systems were analyzed by gas chromatography (GC). The samples presenting the largest spectral variability were used to calibrate the prediction of FA by MIR. The remaining samples were used to externally validate the 28 FA equations developed. The 6 methods were (1) partial least squares regression (PLS); (2) PLS + repeatability file (REP); (3) first derivative of spectral data + PLS; (4) first derivative + REP + PLS; (5) second derivative of spectral data + PLS; and (6) second derivative + REP + PLS. Methods were compared on the basis of the cross-validation coefficient of determination (R²cv), the ratio of standard deviation of GC values to the standard error of cross-validation (RPD), and the validation coefficient of determination (R²v). The third and fourth methods had, on average, the highest R²cv, RPD, and R²v. The final equations were built using all GC and the best accuracy was observed for the infrared predictions of C4:0, C6:0, C8:0, C10:0, C12:0, C14:0, C16:0, C18:0, C18:1 trans, C18:1 cis-9, C18:1 cis, and for some groups of FA studied in milk (saturated, monounsaturated, unsaturated, short-chain, medium-chain, and long-chain FA). These equations showed R²cv greater than 0.95. With R²cv equal to 0.85, the MIR prediction of polyunsaturated FA could be used to screen the cow population. As previously published, infrared predictions of FA in fat are less accurate than those developed from FA content in milk (g/dL of milk) and no better results were obtained by using milk FA predictions if no corrections for bias and slope based on reference milk samples with known contents of FA were used. These results indicate the usefulness of equations with R²cv greater than 95% in milk payment systems and the usefulness of equations with R²cv greater than 75% for animal breeding purposes.     

Comparison between genetic parameters of cheese yield and nutrient recovery or whey loss traits measured from individual model cheese-making methods or predicted from unprocessed bovine milk samples using Fourier-transform infrared spectroscopy

Cheese yield is an important technological trait in the dairy industry. The aim of this study was to infer the genetic parameters of some cheese yield-related traits predicted using Fourier-transform infrared (FTIR) spectral analysis and compare the results with those obtained using an individual model cheese-producing procedure. A total of 1,264 model cheeses were produced using 1,500-mL milk samples collected from individual Brown Swiss cows, and individual measurements were taken for 10 traits: 3 cheese yield traits (fresh curd, curd total solids, and curd water as a percent of the weight of the processed milk), 4 milk nutrient recovery traits (fat, protein, total solids, and energy of the curd as a percent of the same nutrient in the processed milk), and 3 daily cheese production traits per cow (fresh curd, total solids, and water weight of the curd). Each unprocessed milk sample was analyzed using a MilkoScan FT6000 (Foss, Hillerød, Denmark) over the spectral range, from 5,000 to 900 wavenumber × cm⁻¹. The FTIR spectrum-based prediction models for the previously mentioned traits were developed using modified partial least-square regression. Cross-validation of the whole data set yielded coefficients of determination between the predicted and measured values in cross-validation of 0.65 to 0.95 for all traits, except for the recovery of fat (0.41). A 3-fold external validation was also used, in which the available data were partitioned into 2 subsets: a training set (one-third of the herds) and a testing set (two-thirds). The training set was used to develop calibration equations, whereas the testing subsets were used for external validation of the calibration equations and to estimate the heritabilities and genetic correlations of the measured and FTIR-predicted phenotypes. The coefficients of determination between the predicted and measured values in cross-validation results obtained from the training sets were very similar to those obtained from the whole data set, but the coefficient of determination of validation values for the external validation sets were much lower for all traits (0.30 to 0.73), and particularly for fat recovery (0.05 to 0.18), for the training sets compared with the full data set. For each testing subset, the (co)variance components for the measured and FTIR-predicted phenotypes were estimated using bivariate Bayesian analyses and linear models. The intraherd heritabilities for the predicted traits obtained from our internal cross-validation using the whole data set ranged from 0.085 for daily yield of curd solids to 0.576 for protein recovery, and were similar to those obtained from the measured traits (0.079 to 0.586, respectively). The heritabilities estimated from the testing data set used for external validation were more variable but similar (on average) to the corresponding values obtained from the whole data set. Moreover, the genetic correlations between the predicted and measured traits were high in general (0.791 to 0.996), and they were always higher than the corresponding phenotypic correlations (0.383 to 0.995), especially for the external validation subset. In conclusion, we herein report that application of the cross-validation technique to the whole data set tended to overestimate the predictive ability of FTIR spectra, give more precise phenotypic predictions than the calibrations obtained using smaller data sets, and yield genetic correlations similar to those obtained from the measured traits. Collectively, our findings indicate that FTIR predictions have the potential to be used as indicator traits for the rapid and inexpensive selection of dairy populations for improvement of cheese yield, milk nutrient recovery in curd, and daily cheese production per cow.     

Prediction of methane emission from lactating dairy cows using milk fatty acids and mid‐infrared spectroscopy

Enteric methane (CH₄) production is among the main targets of greenhouse gas mitigation practices for the dairy industry. A simple, robust and inexpensive measurement technique applicable on a large scale to estimate CH₄ emission from dairy cattle would therefore be valuable. Milk fatty acids (MFA) are related to CH₄ production because of the common biochemical pathway between CH₄ and fatty acids in the rumen. A summary of studies that investigated the predictive power of MFA composition for CH₄ emission indicated good potential, with predictive power ranging between 47% and 95%. Until recently, gas chromatography (GC) was the principal method used to determine the MFA profile, but GC is unsuitable for routine analysis. This has led to the application of mid‐infrared (MIR) spectroscopy. The major advantages of using MIR spectroscopy to predict CH₄ emission include its simplicity and potential practical application at large scale. Disadvantages include the inability to predict important MFA for CH₄ prediction, and the moderate predictive power for CH₄ emission. It may not be sufficient to predict CH₄ emission based on MIR alone. Integration with other factors, like feed intake, nutrient composition of the feed, parity, and lactation stage may improve the prediction of CH₄ emission using MIR spectra. © 2016 Society of Chemical Industry     

Effect of high-oleic-acid soybeans on production performance,milk fatty acid composition,and enteric methane emission in dairy cows

The objective of this study was to investigate the effect of 3 soybean sources differing in fatty acid profile and processing method on productivity, milk composition, digestibility, rumen fermentation, and enteric methane emission in lactating dairy cows. The soybean sources were conventional, high-linoleic-acid variety extruded soybean meal (ESBM; 8.7% ether extract with 15% oleic and 54% linoleic acids); extruded Plenish (DuPont Pioneer, Johnston, IA), high-oleic-acid variety soybean meal (EPSBM; 8.4% ether extract with 73% oleic and 8% linoleic acids); and whole, heated Plenish soybeans (WPSB; 20.2% ether extract). The study involved 15 Holstein cows in a replicated 3 × 3 Latin square design experiment with three 28-d periods. The inclusion rate of the soybean sources in the diet was (dry matter basis) 17.1, 17.1, and 7.4% for ESBM, EPSBM, and WPSB, respectively, which resulted in ether extract concentration of the diets of 3.99, 3.94, and 4.18%, respectively. Compared with ESBM, the Plenish diets tended to increase dry matter intake and decreased feed efficiency (but had no effect on energy-corrected milk feed efficiency). The Plenish diets increased milk fat concentration on average by 5.6% and tended to increase milk fat yield, compared with ESBM. The WPSB diet tended to increased milk true protein compared with the extruded soybean meal diets. Treatments had no effect on rumen fermentation and enteric methane or carbon dioxide emissions, except pH was higher for WPSB versus EPSBM. The Plenish diets decreased the prevalence of Ruminococcus and increased that of Eubacterium and Treponema in whole ruminal contents. Total-tract apparent digestibility of organic matter and crude protein were decreased by WPSB compared with ESBM and EPSBM. Compared with the other treatments, urinary N excretion was increased by EPSBM and fecal N excretion was greater for WPSB. Treatments had marked effects on milk fatty acid profile. Generally, the Plenish diets increased mono-unsaturated (mostly cis-9 18:1) and decreased polyunsaturated, total trans-, and conjugated linoleic fatty acids concentrations in milk fat. In this study, compared with conventional, high-linoleic-acid variety extruded soybean meal, the Plenish soybean diets increased milk fat concentration and tended to increase fat yield, decreased feed efficiency, and modified milk fatty acid profile in a manner expected from the greater concentration of oleic acid in Plenish soybean oil.     

Persistence of differences between dairy cows categorized as low or high methane emitters,as estimated from milk mid-infrared spectra and measured by GreenFeed

Currently, various attempts are being made to implement breeding schemes aimed at producing low methane (CH₄) emitting cows. We investigated the persistence of differences in CH₄ emission between groups of cows categorized as either low or high emitters over a 5-mo period. Two feeding regimens (pasture vs. indoors) were used. Early- to mid-lactation Holstein Friesian cows were categorized as low or high emitters (n = 10 each) retrospectively, using predictions from milk mid-infrared (MIR) spectra, before the start of the experiment. Data from MIR estimates and from measurements with the GreenFeed (GF; C-Lock Technology Inc., Rapid City, SD) system over the 5-mo experiment were combined into 7-, 14-, and 28-d periods. Feed intake, eating and ruminating behavior, and ruminal fluid traits were determined in two 7-d measurement periods in the grazing season. The CH₄ emission data were analyzed using a split-plot ANOVA, and the repeatability of each of the applied methods for determining CH₄ emission was calculated. Traits other than CH₄ emission were analyzed for differences between low and high emitters using a linear mixed model. The initial category-dependent differences in daily CH₄ production persisted over the subsequent 5 mo and across 2 feeding regimens with both methods. The repeatability analysis indicated that the biweekly milk control scheme, and even a monthly scheme as practiced on farms, might be sufficient for confirming category differences. However, the relationship between CH₄ data estimated by MIR and measured with GF for individual cows was weak (R² = 0.26). The categorization based on CH₄ production also generated differences in CH₄ emission per kilogram of milk; differentiation between cow categories was not persistent based on milk MIR spectra and GF. Compared with the high emitters, low emitters tended to show a lower acetate-to-propionate ratio in ruminal volatile fatty acids, whereas feed intake and ruminating time did not differ. Interestingly, the low emitters spent less time eating than the high emitters. In conclusion, the CH₄ estimation from analyzing the milk MIR spectra is an appropriate proxy to form and regularly control categories of cows with different CH₄ production levels. The categorization was also sufficient to secure similar and persistent differences in emission intensity when estimated by MIR spectra of the milk. Further studies are needed to determine whether MIR data from individual cows are sufficiently accurate for breeding.     

The effects of gradual replacement of barley with oats on enteric methane emissions,rumen fermentation,milk production,and energy utilization in dairy cows

This study evaluated the effects of gradual replacement of barley with oats on enteric CH₄ emissions, rumen fermentation, diet digestibility, milk production, and energy utilization in dairy cows fed a grass silage-based diet. Sixteen lactating Nordic Red dairy cows received a total mixed ration [58:42 forage:concentrate on dry matter (DM) basis]. Grass silage (Phleum pratense) was the sole forage with canola meal (10% of diet DM) as a protein supplement. The effects of gradual replacement of barley with oats on DM basis were evaluated using a replicated 4 × 4 Latin square design with 21 d periods. The grain supplements (30% of diet DM) consisted of 100% barley, 67% barley and 33% oats, 33% barley and 67% oats, and 100% oats. In addition to intake, milk production, and digestibility measurements, CH₄ emissions were measured by the GreenFeed system (C-Lock Inc.). The energy metabolism was estimated from the gas exchange measurements recorded by the GreenFeed unit. The last 10 d of each period were used for recordings of gas exchanges, feed intake and milk production. Dry matter intake, body weight, milk yield, and energy-corrected milk yield were not affected by gradual replacement of barley with oats in the diet. Increased inclusion of oats linearly decreased CH₄ emissions from 467 to 445 g/d, and CH₄ intensity from 14.7 to 14.0 g/kg energy-corrected milk. In addition, the ratio of CH₄ to CO₂ decreased with increasing inclusion of oats in the diet. Digestibility of organic matter, neutral detergent fiber, and potentially digestible neutral detergent fiber decreased linearly with increasing inclusion of oats. Increased inclusion of oats linearly increased fecal energy from 121 to 133 MJ/d, whereas urinary energy and heat production were not affected by dietary treatment. This resulted in a linear decrease in metabolizable energy intake. However, increased levels of oat in the diet did not significantly affect energy balance or efficiency of metabolizable energy utilization for lactation. This study concludes that barley could be replaced with oats in the diet of dairy cows fed a grass silage-based diet to mitigate CH₄ emissions without having any adverse effects on productivity or energy balance. However, the effect of replacing barley with oats on CH₄ emissions is dependent on the differences between barley and oats in the concentrations of indigestible neutral detergent fiber and fat.     

Effects of replacing wheat starch with glycerol on methane emissions,milk production,and feed efficiency in dairy cows fed grass silage-based diets

To lower the effect of climate change from cattle production, we should aim at decreasing their enteric methane emissions per kilogram of milk or meat. Glycerol may be absorbed through the rumen epithelium and would consequently be less available to microbes in the rumen. Glycerol could thus supply dairy cows with energy for milk production without contributing much to methane production. This study evaluated the effect of replacing wheat starch with glycerol on milk production, feed intake, and methane emissions. Twenty-two Swedish Red cows in mid lactation were used in a switch-back, change-over experiment with 3 periods of 21 d. The 2 dietary treatments consisted of a total mixed ration based on (g/kg of dry matter) grass silage (605), rapeseed meal (120), and barley (70) and either wheat starch or refined glycerol (200) fed ad libitum. The glycerol diet resulted in higher dry matter intake (21.6 vs. 20.1 kg/d) and methane emissions (482 vs. 423 g/d) compared with the diet containing wheat starch, whereas no difference was found in energy-corrected milk yield (28.4 vs. 29.7 kg/d). These results indicate that when glycerol is mixed with the feed, it is available to rumen microbes to a larger extent than initially assumed. Compared with wheat starch, adding refined glycerol (200 g/kg of dry matter) to the feed of dairy cows does not seem to have the potential to decrease enteric methane emissions.     

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.     

Method of diet delivery to dairy cows: Effects on nutrient digestion,rumen fermentation,methane emissions from enteric fermentation and stored manure,nitrogen excretion,and milk production

This study was conducted to examine the effect of method of diet delivery to dairy cows on enteric CH₄ emission, milk production, rumen fermentation, nutrient digestion, N excretion, and manure CH₄ production potential. Sixteen lactating cows were used in a crossover design (35-d period) and fed ad libitum twice daily a diet [52:48, forage:concentrate ratio; dry matter (DM) basis] provided as forages and concentrates separately (CF) or as a total mixed ration (TMR). For the CF treatment, concentrates were offered first followed by mixed forages 45 min afterward. Method of diet delivery had no effect on DM intake, but neutral detergent fiber (NDF) intake was greater when the diet was delivered as TMR as compared with CF. Apparent total-tract digestibility of DM, crude protein, and gross energy was slightly (1 percentage unit) lower when the diet was offered as TMR than when offered as CF. In contrast, NDF digestibility was greater when the cows were fed TMR versus CF. Although average daily ruminal pH was not affected by method of diet delivery, daily duration of ruminal pH <5.6 was less when the diet was delivered as TMR as compared with CF (0.9 h/d versus 3.7 h/d). Delivering the diet as TMR increased ruminal total volatile fatty acid and NH₃ concentrations, but had no effect on acetate, propionate, or branched-chain volatile fatty acid molar proportions. Yields of milk, milk fat, or milk protein, and milk production efficiency (kg of milk/kg of DM intake or g of N milk/g of N intake) were not affected by the method of diet delivery. Daily production (g/d), yield (% gross energy intake), and emission intensity (g/kg of energy-corrected milk) of enteric CH₄ averaged 420 g/d, 4.9%, and 9.6 g/kg and were not affected by diet delivery method. Fecal N output was greater when the diet was delivered as TMR versus CF, whereas urinary N excretion (g/d, % N intake) was not affected. Manure volatile solids excretion and maximal CH₄ production potential were not affected by method of diet delivery. Under the conditions of this study, delivering the diet as concentrates and forages separately versus a total mixed ration had no effect on milk production, enteric CH₄ energy losses, urinary N, or maximal manure CH₄ emission potential. However, feeding the diet as total mixed ration compared with feeding concentrates and forages separately attenuated the extent of postprandial decrease in ruminal pH, which has contributed to improving NDF digestibility.