E. Lewis

Microbiome analysis of dairy cows fed pasture or total mixed ration diets.

Understanding rumen microbial ecology is essential for the development of feed systems designed to improve livestock productivity, health and for methane mitigation strategies from cattle. Although rumen microbial communities have been studied previously, few studies have applied next-generation sequencing technologies to that ecosystem. The aim of this study was to characterize changes in microbial community structure arising from feeding dairy cows two widely used diets: pasture and total mixed ration (TMR). Bacterial, archaeal and protozoal communities were characterized by terminal restriction fragment length polymorphism of the amplified SSU rRNA gene and statistical analysis showed that bacterial and archaeal communities were significantly affected by diet, whereas no effect was observed for the protozoal community. Deep amplicon sequencing of the 16S rRNA gene revealed significant differences in the bacterial communities between the diets and between rumen solid and liquid content. At the family level, some important groups of rumen bacteria were clearly associated with specific diets, including the higher abundance of the Fibrobacteraceae in TMR solid samples and members of the propionate-producing Veillonelaceae in pasture samples. This study will be relevant to the study of rumen microbial ecology and livestock feed management.

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.

Effect of pregrazing herbage mass on methane production, dry matter intake, and milk production of grazing dairy cows during the mid-season period.

Increasing milk production from pasture while increasing grass dry matter intake (GDMI) and lowering methane (CH(4)) emissions are key objectives of low-cost dairy production systems. It was hypothesized that offering swards of low herbage mass with increased digestibility leads to increased milk output. A grazing experiment was undertaken to investigate the effects of varying levels of HM on CH(4) emissions, GDMI and milk production of grazing dairy cows during the mid-season grazing period (June to July). Prior to the experiment, 46 Holstein-Friesian dairy cows (46 d in milk) were randomly assigned to 1 of 2 treatments (n=23) in a randomized block design. The 2 treatments consisted of 2 target pregrazing HM: 1,000 kg of dry matter (DM)/ha (low herbage mass, LHM) or 2,200 kg of DM/ha (high herbage mass, HHM). The experimental period lasted 2 mo from June 1 until July 31. Within the experimental period, there were 2 measurement periods, measurement 1 (M1) and measurement 2 (M2), where CH(4) emissions, GDMI, and milk production were measured. Mean herbage mass throughout the measurement periods was 1,075 kg of DM/ha and 1,993 kg of DM/ha for the LHM and HHM treatments, respectively. Grass quality in terms of organic matter digestibility was significantly higher for the LHM treatment in M2 (+12 g/kg of DM). In M1, the effect of herbage mass on grass quality was approaching significance in favor of the LHM treatment. Herbage mass did not significantly affect milk production during the measurement periods. Cows grazing the LHM swards had increased GDMI in M1 (+1.5 kg of DM) compared with cows grazing the HHM swards; no difference in GDMI was observed in M2. Grazing HHM swards increased CH(4) production per cow per day (+42 g), per kilogram of milk yield (+3.5 g/kg of milk), per kilogram of milk solids (+47 g/kg of milk solids), and per kilogram of GDMI (+3.1 g/kg of GDMI) in M2. Cows grazing the HHM swards lost a greater proportion of their gross energy intake as CH(4) during both measurement periods (+0.9% and +1% for M1 and M2, respectively). It was concluded that grazing LHM swards would increase grass quality with a concurrent reduction in CH(4) emissions.

Comparative grazing behavior of lactating Holstein-Friesian, Jersey, and Jersey × Holstein-Friesian dairy cows and its association with intake capacity and production efficiency

The objectives of this study were to investigate differences in grazing behavior among Holstein-Friesian (HF), Jersey (JE), and Jersey x Holstein-Friesian (F(1)) cows under an intensive, seasonal, grass-based environment and to determine whether associations exist among grazing behavior, intake capacity, and production efficiency. Data from a total of 108 animals (37 HF, 34 JE, and 37F(1)) were available for analysis. Measurements included milk production, body weight (BW), intake, and grazing behavior. Breed group had a significant effect on all of the production, grass dry matter intake, and efficiency parameters investigated. No differences were observed among the breeds for grazing time, number of grazing bouts, grazing bout duration, and total number of bites. Grazing mastications were higher for the JE cows compared with the HF cows. Grass dry matter intake per bite and rate of intake per minute were higher for the HF cows compared with the JE cows. Large differences between the breeds were apparent when grazing behavior measurements were expressed per unit of BW and per unit of intake. In absolute terms, the HF cows spent more time ruminating and had more mastications during rumination than the JE cows. However, when expressed per unit of BW, ruminating time was greater for the JE cows and they tended to have more ruminating mastications compared with the HF cows. Despite these differences, ruminating time and ruminating mastications per unit of intake were similar for the 2 breeds. For the most part, the F(1) cows tended to be similar to the mid-parent mean, but results showed an increase in biting rate, lower grazing duration per bout, and a tendency to achieve a high intake per bite compared with the average of the parent breeds. The results obtained also indicate that inherent grazing and ruminating differences exist between cows varying in intake capacity and production efficiency. Cows with higher intake capacities have increased grazing time and rate of intake per unit of BW. Increased production efficiency, on the other hand, appears to be aided, in particular by improvements in mastication behavior during grazing.

Mid-infrared spectrometry of milk as a predictor of energy intake and efficiency in lactating dairy cows.

Interest is increasing in the feed intake complex of individual dairy cows, both for management and animal breeding. However, energy intake data on an individual-cow basis are not routinely available. The objective of the present study was to quantify the ability of routinely undertaken mid-infrared (MIR) spectroscopy analysis of individual cow milk samples to predict individual cow energy intake and efficiency. Feed efficiency in the present study was described by residual feed intake (RFI), which is the difference between actual energy intake and energy used (e.g., milk production, maintenance, and body tissue anabolism) or supplied from body tissue mobilization. A total of 1,535 records for energy intake, RFI, and milk MIR spectral data were available from an Irish research herd across 36 different test days from 535 lactations on 378 cows. Partial least squares regression analyses were used to relate the milk MIR spectral data to either energy intake or efficiency. The coefficient of correlation (REX) of models to predict RFI across lactation ranged from 0.48 to 0.60 in an external validation data set; the predictive ability was, however, strongest (REX=0.65) in early lactation (<60 d in milk). The inclusion of milk yield as a predictor variable improved the accuracy of predicting energy intake across lactation (REX=0.70). The correlation between measured RFI and measured energy balance across lactation was 0.85, whereas the correlation between RFI and energy balance, both predicted from the MIR spectrum, was 0.65. Milk MIR spectral data are routinely generated for individual cows throughout lactation and, therefore, the prediction equations developed in the present study can be immediately (and retrospectively where MIR spectral data have been stored) applied to predict energy intake and efficiency to aid in management and breeding decisions.

Hot topic: Innovative lactation-stage-dependent prediction of methane emissions from milk mid-infrared spectra

The main goal of this study was to develop, apply, and validate a new method to predict an indicator for CH4 eructed by dairy cows using milk mid-infrared (MIR) spectra. A novel feature of this model was the consideration of lactation stage to reflect changes in the metabolic status of the cow. A total of 446 daily CH4 measurements were obtained using the SF6 method on 142 Jersey, Holstein, and Holstein-Jersey cows. The corresponding milk samples were collected during these CH4 measurements and were analyzed using MIR spectroscopy. A first derivative was applied to the milk MIR spectra. To validate the novel calibration equation incorporating days in milk (DIM), 2 calibration processes were developed: the first was based only on CH4 measurements and milk MIR spectra (independent of lactation stage; ILS); the second included milk MIR spectra and DIM information (dependent on lactation stage; DLS) by using linear and quadratic modified Legendre polynomials. The coefficients of determination of ILS and DLS equations were 0.77 and 0.75, respectively, with standard error of calibration of 63g/d of CH4 for both calibration equations. These equations were applied to 1,674,763 milk MIR spectra from Holstein cows in the first 3 parities and between 5 and 365 DIM. The average CH4 indicators were 428, 444, and 448g/d by ILS and 444, 467, and 471g/d by DLS for cows in first, second, and third lactation, respectively. Behavior of the DLS indicator throughout the lactations was in agreement with the literature with values increasing between 0 and 100 DIM and decreasing thereafter. Conversely, the ILS indicator of CH4 emission decreased at the beginning of the lactation and increased until the end of the lactation, which differs from the literature. Therefore, the DLS indicator seems to better reflect biological processes that drive CH4 emissions than the ILS indicator. The ILS and DLS equations were applied to an independent data set, which included 59 respiration chamber measurements of CH4 obtained from animals of a different breed across a different production system. Results indicated that the DLS equation was much more robust than the ILS equation allowing development of indicators of CH4 emissions by dairy cows. Integration of DIM information into the prediction equation was found to be a good strategy to obtain biologically meaningful CH4 values from lactating cows by accounting for biological changes that occur throughout the lactation.

Gastrointestinal tract size, total-tract digestibility, and rumen microflora in different dairy cow genotypes

The superior milk production efficiency of Jersey (JE) and Jersey × Holstein-Friesian (JE × HF) cows compared with Holstein-Friesian (HF) has been widely published. The biological differences among dairy cow genotypes, which could contribute to the milk production efficiency differences, have not been as widely studied however. A series of component studies were conducted using cows sourced from a longer-term genotype comparison study (JE, JE × HF, and HF). The objectives were to (1) determine if differences exist among genotypes regarding gastrointestinal tract (GIT) weight, (2) assess and quantify whether the genotypes tested differ in their ability to digest perennial ryegrass, and (3) examine the relative abundance of specific rumen microbial populations potentially relating to feed digestibility. Over 3 yr, the GIT weight was obtained from 33 HF, 35 JE, and 27 JE × HF nonlactating cows postslaughter. During the dry period the cows were offered a perennial ryegrass silage diet at maintenance level. The unadjusted GIT weight was heavier for the HF than for JE and JE × HF. When expressed as a proportion of body weight (BW), JE and JE × HF had a heavier GIT weight than HF. In vivo digestibility was evaluated on 16 each of JE, JE × HF, and HF lactating dairy cows. Cows were individually stalled, allowing for the total collection of feces and were offered freshly cut grass twice daily. During this time, daily milk yield, BW, and dry matter intake (DMI) were greater for HF and JE × HF than for JE; milk fat and protein concentration ranked oppositely. Daily milk solids yield did not differ among the 3 genotypes. Intake capacity, expressed as DMI per BW, tended to be different among treatments, with JE having the greatest DMI per BW, HF the lowest, and JE × HF being intermediate. Production efficiency, expressed as milk solids per DMI, was higher for JE than HF and JE × HF. Digestive efficiency, expressed as digestibility of dry matter, organic matter, N, neutral detergent fiber, and acid detergent fiber, was higher for JE than HF. In grazing cows (n=15 per genotype) samples of rumen fluid, collected using a transesophageal sampling device, were analyzed to determine the relative abundance of rumen microbial populations of cellulolytic bacteria, protozoa, and fungi. These are critically important for fermentation of feed into short-chain fatty acids. A decrease was observed in the relative abundance of Ruminococcus flavefaciens in the JE rumen compared with HF and JE × HF. We can deduce from this study that the JE genotype has greater digestibility and a different rumen microbial population than HF. Jersey and JE × HF cows had a proportionally greater GIT weight than HF. These differences are likely to contribute to the production efficiency differences among genotypes previously reported.

Validation of mid-infrared spectrometry in milk for predicting body energy status in Holstein-Friesian cows

Cow energy balance is known to be associated with cow health and fertility; therefore, routine access to data on energy balance can be useful in both management and breeding decisions to improve cow performance. The objective of this study was to determine if individual cow milk mid-infrared spectra (MIR) could be useful to predict cow energy balance across contrasting production systems. Direct energy balance was calculated as the differential between energy intake and energy output in milk and maintenance (maintenance was predicted using body weight). Body energy content was calculated from (change in) body weight and body condition score. Following editing, 2,992 morning, 2,742 midday, and 2,989 evening milk MIR records from 564 lactations on 337 Scottish cows, managed in a confinement system on 1 of 2 diets, were available. An additional 844 morning and 820 evening milk spectral records from 338 lactations on 244 Irish cows offered a predominantly grazed grass diet were also available. Equations were developed to predict body energy status using the milk spectral data and milk yield as predictor variables. Several different approaches were used to test the robustness of the equations calibrated in one data set and validated in another. The analyses clearly showed that the variation in the validation data set must be represented in the calibration data set. The accuracy (i.e., square root of the coefficient of multiple determinations) of predicting, from MIR, direct energy balance, body energy content, and energy intake was 0.47 to 0.69, 0.51 to 0.56, and 0.76 to 0.80, respectively. This highlights the ability of milk MIR to predict body energy balance, energy content, and energy intake with reasonable accuracy. Very high accuracy, however, was not expected, given the likely random errors in the calculation of these energy status traits using field data.

Milk mid-infrared spectra enable prediction of lactation-stage-dependent methane emissions of dairy cattle within routine population-scale milk recording schemes

Mitigating the proportion of energy intake lost as methane could improve the sustainability and profitability of dairy production. As widespread measurement of methane emissions is precluded by current in vivo methods, the development of an easily measured proxy is desirable. An equation has been developed to predict methane from the mid-infrared (MIR) spectra of milk within routine milk-recording programs. The main goals of this study were to improve the prediction equation for methane emissions from milk MIR spectra and to illustrate its already available usefulness as a high throughput phenotypic screening tool. A total of 532 methane measurements considered as reference data (430 ± 129 g of methane/day) linked with milk MIR spectra were obtained from 165 cows using the SF6 technique. A first derivative was applied to the MIR spectra. Constant (P0), linear (P1) and quadratic (P2) modified Legendre polynomials were computed from each cows stage of lactation (days in milk), at the day of SF6 methane measurement. The calibration model was developed using a modified partial least-squares regression on first derivative MIR data points × P0, first derivative MIR data points × P1, and first derivative MIR data points × P2 as variables. The MIR-predicted methane emissions (g/day) showed a calibration coefficient of determination of 0.74, a cross-validation coefficient of determination of 0.70 and a standard error of calibration of 66 g/day. When applied to milk MIR spectra recorded in the Walloon Region of Belgium (≈2 000 000 records), this equation was useful to study lactational, annual, seasonal, and regional methane emissions. We conclude that milk MIR spectra has potential to be used to conduct high throughput screening of lactating dairy cattle for methane emissions. The data generated enable monitoring of methane emissions and production characteristics across and within herds. Milk MIR spectra could now be used for widespread screening of dairy herds in order to develop management and genetic selection tools to reduce methane emissions.

The effects of supplementing grazing dairy cows with partial mixed ration on enteric methane emissions and milk production during mid to late lactation1

This study compared the enteric CH(4) emissions and milk production of cows offered various grass-based diets during mid to late lactation. Forty-eight spring-calving Holstein-Friesian dairy cows were randomly assigned to 1 of 3 nutritional treatments for 8 wk: (1) low grass allowance (LGA) + partial mixed ration (PMR), (2) high grass allowance (HGA), or (3) LGA. The PMR group received an allocation of 13.9 kg of grass dry matter (DM)/cow per day and in addition were offered 4.1 kg of PMR DM/cow per day. The HGA group received an allocation of 19.3 kg of grass DM/cow per day and the LGA group received an allocation of 14.4 kg of grass DM/cow per day. The PMR offered was composed of 450 g of maize silage/kg of DM, 450 g of concentrate blend/kg of DM, and 100g of barley straw/kg of DM. Daily CH(4) emissions were determined using the emissions from ruminants using a calibrated tracer technique, using sulfur hexafluoride, for 5 consecutive days during 2 periods. Simultaneously, grass DM intake (DMI) was estimated using the n-alkane technique and the PMR DMI was also recorded. Cows offered PMR had higher DMI than either the HGA or LGA cows (16.5 vs. 14.9 and 13.9 kg of DM/d). The higher DMI of PMR cows increased milk production relative to HGA and LGA cows: milk yield (17.0 vs. 14.6 and 13.1 kg) and fat and protein yield (1.29 vs. 1.14 and 1.04 kg). Daily CH(4) emissions were higher for the PMR group than for the HGA and LGA groups (406 vs. 384 and 349 g/cow per day). The enteric CH(4) emissions intensity per unit of DMI, milk yield, solids-corrected milk yield, and fat and protein yield did not differ between treatments. Effects observed in the PMR treatment were due to an increase in DMI rather than to any nutritional characteristic of the PMR.