Effects of rumen acid load from feed and forage particle size on ruminal pH and dry matter intake in the lactating dairy cow

The objective of this study was to evaluate the effects of level of concentrate acidogenic value (AV) and forage particle size on ruminal pH and feed intake in lactating dairy cows. Two isoenergetic (net energy for lactation = 1.5 ± 0.01 Mcal/kg) and isonitrogenous (crude protein = 17.4 ± 0.1% dry matter) concentrates with either a low AV or high AV were formulated and fed in a total mixed ration with either coarsely or finely chopped corn silage and alfalfa haylage ad libitum. Four rumen-fistulated cows (114 ± 14 d in milk) were randomly assigned to 1 of the 4 treatments in a 4 × 4 Latin square with a 2 × 2 factorial treatment arrangement. Each period consisted of 3-wk (14-d treatment adaptation and 7-d data collection). Increasing the concentrate AV decreased the mean pH (from 6.07 to 5.97) and minimum pH (from 5.49 to 5.34). Cows fed high-AV diets spent a longer time below pH 5.6 (135.1 vs. 236.7 min/d; low-AV diet vs. high-AV diet, respectively) and pH 5.8 (290.0 vs. 480.6 min/d; low-AV diet vs. high-AV diet, respectively) than cows fed low-AV diets. Increasing forage particle size had no effect on the mean and minimum ruminal pH. There was an interaction between concentrate AV and forage particle size on maximum ruminal pH. Increasing forage particle size increased the maximum pH for cows fed the high-AV concentrate (6.69 vs. 6.72; low-AV diet vs. high-AV diet, respectively) and had no effect on the maximum pH for cows fed the low-AV concentrate (6.98 vs. 6.76; low-AV diet vs. high-AV diet, respectively). Increasing the concentrate AV did not affect dry matter intake but reduced neutral detergent fiber intake from 9.7 to 8.8 kg/d. Milk fat content was negatively correlated with time and area below pH 5.6 (time below, r = −0.51; area below, r = −0.56) and pH 5.8 (time below, r = −0.42; area below, r = −0.54). These results suggest that coarse forage particle size can attenuate drops in ruminal pH. However, the ameliorating effects of forage particle size on drops in ruminal pH were more apparent for high-AV diets than for low-AV diets. The AV approach combined with physically effective neutral detergent fiber would therefore improve the formulation of diets and help to mitigate subacute ruminal acidosis in dairy cows.     

Inducing subacute ruminal acidosis in lactating dairy cows

Data from experiments in which subacute ruminal acidosis (SARA) was induced in lactating dairy cows (days in milk = 154 ± 118) were evaluated to investigate the effectiveness of the induction protocol and its effect on production outcomes. For 13 cows in 3 trials, ruminal pH was measured continuously and recorded each minute; dry matter intake and milk yield were recorded daily. Milk composition data were obtained from 9 cows in 2 of these trials. The SARA induction protocol included 4 separate periods: 4 d of baseline [normal total mixed ration (TMR)], 1 d of 50% restricted feeding, 1 or 2 d of challenge feeding [addition of 3.5 or 4.6 kg of wheat-barley pellet (dry matter basis) to normal TMR], and 2 d of recovery measurements when feeding normal TMR. The SARA induction protocol lowered mean ruminal pH from 6.31 during the baseline period to 5.85 during the challenge period; pH remained below baseline level during the recovery period (6.16). Mean ruminal pH was highest (6.59) during the day of restricted feeding. Nadir ruminal pH decreased from baseline to challenge period (5.76 vs. 5.13). Hours below pH 5.6 increased from 1.10 to 8.26/d from baseline to challenge period and area below 5.6 (pH × min/d) increased from 15.0 to 190.3. Dry matter intake was not affected by SARA induction. Milk yield dropped from 35.2 kg/d during baseline to 31.7 k/d during the challenge period and did not return to baseline level during the recovery period (31.3 kg/d). No depression in milk fat percentage was observed when SARA was induced. Yield of fat was highest during the restricted feeding period (1.47 kg/d) and was lower during the recovery period than during the baseline period (1.12 vs. 1.31 kg/d). The protocol successfully induced SARA (low ruminal pH without signs of acute ruminal acidosis) on the challenge day. Milk yield was substantially reduced and did not recover within 2 d after the challenge.     

Repeated ruminal acidosis challenges in lactating dairy cows at high and low risk for developing acidosis: ruminal pH

The primary objective of this experiment was to determine whether lactating dairy cows that are at high (HR) or low (LR) risk for experiencing ruminal acidosis, because of their diet and stage of lactation, differ in their response to an acidosis challenge. A secondary objective was to determine whether the severity of acidosis changes with repeated challenges. The experiment was a completely randomized design with 2 groups (risk scenarios, HR vs. LR) and 3 periods corresponding to 3 repeated acidosis challenges. Eight lactating ruminally cannulated cows were assigned to 1 of 2 groups: HR, early lactation cows fed a 45% forage diet, or LR, midlactation cows fed a 60% forage diet. Cows were exposed to 3 acidosis challenges, each separated by 14 d. The challenge consisted of restricting total mixed rations to 50% of ad libitum intake for 24 h, followed by a 1-h meal of 4 kg of ground barley-wheat before allocating the total mixed rations. Ruminal pH was measured continuously for 9 of the 14 d each period using an indwelling system. Subacute acidosis (SARA) was described at 2 thresholds: pH <5.8 and pH <5.5. As expected, HR cows had lower ruminal pH profiles (curves) compared with LR cows: mean pH (5.81 vs. 6.21) and nadir pH (5.13 vs. 5.53). The HR cows also experienced SARA to a greater extent than LR cows during the experiment (pH <5.8, 10.6 vs. 3.5 h/d; pH <5.5, 5.9 vs. 1.6 h/d). The pH profiles of cows in both risk categories decreased with each challenge period; mean pH was 6.13, 6.03, 5.77, and nadir pH was 5.52, 5.34, and 5.14 in periods 1, 2, and 3, respectively. The challenges caused a similar decrease in pH for cows in both risk categories, but because the HR cows had a lower baseline pH, they experienced more severe SARA with each subsequent challenge. Feed restriction the day before administering the acidosis challenge caused ruminal pH to gradually increase. On the challenge day, the entire grain allotment was consumed by all cows in period 1, six cows in period 2, and only 3 cows in period 3. The pH plummeted immediately after each grain challenge. Ruminal pH remained very low during the first day after the challenge for all cows, but LR cows began their recovery more quickly than HR cows. Regardless of risk category, with each successive challenge, the pH decrease on the challenge day was more severe: nadir pH on the challenge day was 5.19, 5.07, and 4.90 and duration of SARA (pH <5.8) was 12.2, 13.4, and 15.8 h/d in periods 1, 2, and 3. This study indicates that cows become more prone to acidosis over time even though they decrease intake of the challenge grain to avoid acidosis. The severity of each subsequent bout of acidosis increases, especially for cows fed diets low in physically effective fiber and at high acidosis risk. Therefore, a bout of acidosis that occurs due to improper feed delivery or poor diet formulation can have long-term consequences on cow health and productivity.     

Effect of a low-moisture buffer block on ruminal pH in lactating dairy cattle induced with subacute ruminal acidosis

The objective of this study was to evaluate the effect of a low-moisture buffer block on ruminal pH and milk production in cows induced with subacute ruminal acidosis (SARA). Sixteen ruminally cannulated cows were randomly assigned to treatment (access to buffer blocks) or control (no buffer blocks). Ruminal pH was recorded each minute; dry matter intake (DMI), milk yield, and milk composition were measured daily. The experiment lasted 12 d and consisted of a 3-d pre-SARA period (without access to buffer blocks; d 1 to 3), after which 8 cows were given access to buffer blocks and 8 cows continued without access to buffer blocks. The next 4 d (d 4 to 7) were for evaluating the response to buffer blocks. On d 8, cows were restricted to 50% of previous DMI, and on d 9 SARA was induced (addition of 4 kg of wheat/barley pellet to pre-SARA total mixed ration (TMR). Cows were then monitored for a 3-d recovery period (d 10 to 12). The SARA challenge was successful in decreasing mean ruminal pH and time and area below pH 5.6. Intake of buffer blocks averaged 0.33 kg of DM/cow per day and was greatest on d 4 and d 8. Total DMI (TMR plus buffer block) and yields of milk and milk components were not affected by treatment. Although there was no overall effect of treatment on any of the ruminal pH variables measured, there were significant treatment by period interactions for several ruminal pH variables. Cows on the control treatment tended to experience a greater decrease in mean ruminal pH when induced with SARA than cows with access to buffer blocks (−0.55 vs. −0.20 pH units). Cows on the control treatment also experienced a greater increase in time (9.7 vs. 4.1 h/d) and area (249 vs. 83 min × pH units/d) below pH 5.6 compared with cows with access to buffer blocks. Ruminal volatile fatty acids, lactate, ethanol, and succinate concentrations during the SARA challenge did not differ between treatments. Eating behavior was not affected by treatment. Size of the first meal of the day was greater on the SARA challenge day than during the pre-SARA period (11.0 vs. 5.7 kg, as fed). Giving cows access to a buffer-containing molasses block may reduce the duration and the severity of a 1-d SARA challenge.     

Different reticuloruminal pH metrics of high-yielding dairy cattle during the transition period in relation to metabolic health,activity, and feed intake

The measurement of pH in the reticulorumen in combination with a time–pH threshold has been widely applied in research to diagnose subacute ruminal acidosis. However, other pH metrics also have biological value. In this study, 44 animals were monitored during the transition period using reticuloruminal pH boluses. Traditional and more complex pH characteristics were calculated to characterize the reticuloruminal pH profile: time pH <6, slope of a logistic cumulative pH curve (β₀), and deviations [squared error (SqEr)] from pH predictions based on a harmonic static model. In this study, we aimed to examine the associations between those pH metrics and metabolic health parameters, feed intake, and activity. Finally, to describe the reticuloruminal pH dynamically, we also constructed a dynamic linear model. The results of this model were studied in relation to feed intake. All pH parameters were mutually correlated (particularly β₀ and SqEr; mean Pearson correlation of ?0.52). pH patterns, rather than time pH <6, were associated with metabolic health and feed intake: high variation in daily pH (β₀ parameter) was reflected in higher blood concentrations of nonesterified fatty acids. Moreover, pH deviations of the harmonic model were negatively associated with feed intake and rumination behavior. This research confirms the biological importance of pH metrics focusing on pH variation and pH deviations and provides deeper insight into its associations with metabolic health status, feed intake, and activity during early lactation.     

Effect of dietary dry matter concentration on the sorting behavior of lactating dairy cows fed a total mixed ration

The objective of this study was to determine whether addition of water to a high-moisture total mixed ration reduces feed sorting by dairy cattle. Twelve lactating Holstein cows, individually fed once per day, were tested on 2 diets in a crossover design with 21-d periods. Diets had the same dietary composition and differed only in dry matter content, which was reduced by the addition of water. Treatment diets were 1) dry (57.6% DM) and 2) wet (47.9% DM). Dry matter intake (DMI) and milk production were monitored for each animal for the last 7 d of both treatment periods. For the final 3 d of each period, milk samples were taken for composition analysis and fresh feed and orts were sampled for particle size analysis. The particle size separator had 3 screens (19, 8, and 1.18 mm) and a bottom pan, resulting in 4 fractions (long, medium, short, fine). Sorting was calculated as the actual intake of each particle size fraction expressed as a percentage of the predicted intake of that fraction. Contrary to the hypothesis, cows sorted the wet diet more extensively than the dry diet. Sorting of the dry diet was limited to a tendency to refuse short particles, whereas the wet diet was sorted against long particles and for short and fine particles. Water addition reduced DMI, neutral detergent fiber intake, and starch intake of cows on the wet diet. Increased sorting on the wet diet resulted in a tendency for decreased concentration of dietary neutral detergent fiber consumed and also resulted in increased starch concentration of the diet consumed. Milk production and components were unaffected by treatment. Our results suggest that water addition to high-moisture total mixed rations, containing primarily haylage and silage forage sources, may not be an effective method to reduce sorting. Furthermore, water addition may negatively affect DMI and encourage sorting, resulting in the consumption of a ration with different nutrient composition than intended.     

Comparative dry matter intake and nutrient utilisation efficiency in lactating cattle and buffaloes

An attempt was made to assess the comparative dry matter intake and nutrient utilisation efficiency of lactating buffaloes and cows based on results obtained from experimental feeding trials conducted in India. Data on dry matter (DM) intake, total digestible nutrient (TDN) intake, digestible crude protein (DCP) intake, body weight or metabolic body size (MBS), body weight change, milk yield or 4% fat‐corrected milk (FCM) yield, milk fat percentage and roughage/concentrate ratio in the diet of lactating buffaloes and cows were collected from published reports. The data were processed and analysed to assess the comparative dry matter intake and nutrient utilisation efficiency using suitable statistical analysis models. DM intake was significantly (P < 0.01) lower in buffaloes (2.57 kg DM per 100 kg body weight or 119.2 g kg^?1 MBS) than in cattle (3.09 kg DM per 100 kg body weight or 132.0 g kg^?1 MBS). Mean gross energetic efficiency (30.53 versus 27.83%; P < 0.01), gross protein efficiency (45.48 versus 37.06%; P < 0.01), net energetic efficiency (69.16 versus 64.10%; P < 0.05) and net protein efficiency (80.15 versus 59.59%; P < 0.001) were significantly higher in buffaloes than in cattle. Lactating buffaloes consumed significantly less (P < 0.001) protein (75.69 g DCP) and less (P < 0.01) energy (695.9 g TDN) than cows (93.89 g DCP, 774.8 g TDN) for production of 1 kg of 4% fat‐corrected milk. Buffaloes had higher energy and protein utilisation efficiencies as compared with cattle at similar fat‐corrected milk production level, plane of energy and protein nutrition, body size and body weight change. Buffaloes (1.189 kg DM kg^?1 4% FCM) consumed a similar (P > 0.05) amount of feed dry matter to that of cows (1.267 kg DM kg^?1 4% FCM). However, when DM intake kg^?1 FCM (4%) was compared at constant levels of metabolic body size, fat‐corrected milk, body weight change, dietary energy concentration and green forage percentage in the diet, lactating buffaloes consumed significantly less DM kg^?1 FCM yield as compared with cattle. It was concluded that DM intake was lower in lactating buffaloes. Moreover, lactating buffaloes utilised dietary dry matter, energy and protein for milk production more efficiently than cattle. © 2003 Society of Chemical Industry     

Short communication: Effects of subacute ruminal acidosis on free-choice intake of sodium bicarbonate in lactating dairy cows

The effect of inducing subacute ruminal acidosis (SARA) on the free-choice intake of sodium bicarbonate (SB) was investigated in four midlactation Holstein cows in a switchover experiment with four 1-wk periods. The SARA was induced by replacing 25% of the ad libitum intake of total mixed ration (TMR) with pellets containing 50% ground wheat and 50% ground barley and restricting access to TMR from 0700 to 1700 h. Control consisted of feeding TMR ad libitum. Powdered SB was provided for ad libitum consumption. Rumen pH was measured continuously using indwelling pH probes. Induction of SARA reduced (P < 0.05) the average daily rumen pH from 6.08 to 5.87, increased (P < 0.05) the average duration of rumen pH below 6 from 547 min x d(-1) to 916 min x d(-1), and increased (P < 0.05) the average duration of rumen pH below 5.6 from 132 min x d(-1) to 397 min x d(-1) (P < 0.05) but did not significantly affect SB intake. Average intake of SB was 26.8 g x d(-1) during SARA and 34.5 g x d(-1) during control. These low SB intakes must not have substantially affected rumen pH. Sodium bicarbonate intake differed significantly (P < 0.05) between cows. These data indicate that cows did not select SB in order to attenuate SARA.     

Invited review: Determination of large-scale individual dry matter intake phenotypes in dairy cattle

Feed efficiency has been widely studied in many areas of dairy science and is currently seeing renewed interest in the field of breeding and genetics. A critical part of determining how efficiently an animal utilizes feed is accurately measuring individual dry matter (DM) intake. Currently, multiple methods are used to measure feed intake or determine the DM content of that feed, resulting in different levels of accuracy of measurement. Furthermore, the scale at which data need to be collected for use in genetic analyses makes some methodologies impractical. This systematic review aims to provide an overview of the current methodologies used to measure both feed intake in ruminants and DM content of feedstuffs, current methods to predict individual DM intake, and applications of large-scale intake measurements. Overall, advances in milk spectral data analysis present a promising method of estimating individual DM intake on a herd scale with further validation of prediction models. Although measurements of individual feed intake rely on the same underlying principle, the methods selected are largely dictated by the costs of capital, labor, and necessary analyses. Finally, DM methodologies were synthesized into a comprehensive protocol for use in a variety of feedstuffs.     

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)}].     

Prediction and validation of residual feed intake and dry matter intake in Danish lactating dairy cows using mid-infrared spectroscopy of milk

The present study explored the effectiveness of Fourier transform mid-infrared (FT-IR) spectral profiles as a predictor for dry matter intake (DMI) and residual feed intake (RFI). The partial least squares regression method was used to develop the prediction models. The models were validated using different external test sets, one randomly leaving out 20% of the records (validation A), the second randomly leaving out 20% of cows (validation B), and a third (for DMI prediction models) randomly leaving out one cow (validation C). The data included 1,044 records from 140 cows; 97 were Danish Holstein and 43 Danish Jersey. Results showed better accuracies for validation A compared with other validation methods. Milk yield (MY) contributed largely to DMI prediction; MY explained 59% of the variation and the validated model error root mean square error of prediction (RMSEP) was 2.24 kg. The model was improved by adding live weight (LW) as an additional predictor trait, where the accuracy R² increased from 0.59 to 0.72 and error RMSEP decreased from 2.24 to 1.83 kg. When only the milk FT-IR spectral profile was used in DMI prediction, a lower prediction ability was obtained, with R² = 0.30 and RMSEP = 2.91 kg. However, once the spectral information was added, along with MY and LW as predictors, model accuracy improved and R² increased to 0.81 and RMSEP decreased to 1.49 kg. Prediction accuracies of RFI changed throughout lactation. The RFI prediction model for the early-lactation stage was better compared with across lactation or mid- and late-lactation stages, with R² = 0.46 and RMSEP = 1.70. The most important spectral wavenumbers that contributed to DMI and RFI prediction models included fat, protein, and lactose peaks. Comparable prediction results were obtained when using infrared-predicted fat, protein, and lactose instead of full spectra, indicating that FT-IR spectral data do not add significant new information to improve DMI and RFI prediction models. Therefore, in practice, if full FT-IR spectral data are not stored, it is possible to achieve similar DMI or RFI prediction results based on standard milk control data. For DMI, the milk fat region was responsible for the major variation in milk spectra; for RFI, the major variation in milk spectra was within the milk protein region.     

Short communication: Added value of rumination time for the prediction of dry matter intake in lactating dairy cows

The objective of the current study was to quantify the change in the prediction of dry matter intake (DMI) resulting from the inclusion of rumination time (RT) in the 2001 National Research Council (NRC) DMI prediction model. Forty-one Holstein cows fed the same total mixed ration were involved in a 10-wk study. Individual DMI were measured daily. The accuracy and precision of the original NRC prediction model, based on body weight, fat-corrected milk, and week of lactation as independent variables, was compared with the accuracy and precision of the same model with RT as an additional independent variable. The RT estimate was significant in the model developed but had a low value (0.031 kg/h). Root mean square prediction errors were very similar in the 2 models (1.70 and 1.68 kg/d) as were the other indicators (R², linear bias, random error, and concordance correlation coefficient) selected to compare the models in this study. These results indicate no gain in DMI prediction precision or accuracy when RT is included in the NRC model.     

Rumen digestion and nutritional efficiency of dairy heifers limit-fed a high forage ration to four levels of dry matter intake

Eight rumen-cannulated Holstein dairy heifers [beginning body weight (BW) 340 (±5) kg and age 14.5 (±1) mo] were fed a high forage diet at 4 levels of intake. Diets were composed of grass silage, grass hay, and corn silage as the forage components and offered at 1.25, 1.50, 1.75, and 2.00% of BW to heifers in a replicated Latin square design. Diets were incubated in situ in heifers receiving all 4 levels of feed. Blood and rumen were sampled at 2-h intervals for 24 h, rumen contents were emptied, and total fecal and urine collection was made. Dietary intake increased in proportion to feed offered until dry matter intake (DMI) was 1.92% BW, after which a statistically determined plateau was evident due to greater refusals when feed was offered at 2.00% BW. In situ degradation of feed was not affected by intake level, which, combined with the greater turnover rate of rumen contents, leads to the inference that rate of passage was increased with increasing intake. Rumen pH decreased and rumen volatile fatty acid concentration and microbial protein flow to the small intestine (estimated using urinary purine derivative excretion) increased as intake increased. Manure excretion increased as DMI increased at a rate 2.54 times greater than increases in DMI; this increase was entirely due to greater excretion of wet feces because urine excretion did not change with intake level. Nitrogen digestibility decreased and N retention increased linearly as the level of feed offered increased. Efficiency of N retention was minimized when feed was offered at 1.25% BW; all levels of feed offered above this level resulted in equivalent efficiencies. From the results of this experiment it can be concluded that when dairy heifers are limit-fed a high forage diet, the efficiency of nutrient utilization is increased as intake decreases, but reducing DMI below 1.50% BW reduced efficiency.     

Active dry Saccharomyces cerevisiae can alleviate the effect of subacute ruminal acidosis in lactating dairy cows

The objective of the study was to determine the effect of active dry Saccharomyces cerevisiae (ADSC) supplementation on dry matter intake, milk yield, milk components, ruminal pH, and microbial community during a dietary regimen that leads to subacute ruminal acidosis (SARA). Sixteen multiparous, rumen-cannulated lactating Holstein cows were randomly assigned to 1 of 2 dietary treatments that included ADSC (Biomate; AB Vista, Marlborough, UK; 8 × 10¹⁰ cfu/head per day) or control. During wk 1 to 6, all cows received a high-forage (HF) diet (77:23, forage:concentrate). Cows were then abruptly switched during wk 7 to a high-grain (HG) diet (49:51, forage:concentrate) and remained on the HG until the end of wk 10. Feed intake and milk yields were recorded daily. Ruminal pH was recorded continuously using an indwelling system for 1 to 2 d per week during the pre-experimental phase, and wk 6, 7, and 10. Ruminal digesta samples were collected at the end of the experiment and analyzed for relative change in microbial communities using real-time quantitative PCR. Cows were considered to have SARA if the duration below pH 5.6 was ≥300 min/d. Ruminal pH during wk 6 (HF plateau) was not different across treatments (15 ± 46 min/d at pH <5.6). The dietary regimen successfully induced SARA during wk 7 (transition from HF to HG diet), and ruminal pH (551 ± 46 min/d at pH <5.6) was not different across treatments. However, cows receiving ADSC had an improved ruminal pH (122 ± 57 vs. 321 ± 53 min/d at pH <5.6) during wk 10 (HG plateau) compared with control. Additionally, cows receiving ADSC had a better dry matter intake (23.3 ± 0.66 vs. 21.6 ± 0.61 kg/d) and 4% fat-corrected milk yield (29.6 ± 1.2 vs. 26.5 ± 1.2 kg/d) than control cows during the HG phase (wk 8 to 10). During HG feeding, cows receiving ADSC had greater total volatile fatty acid and propionate concentrations (175 ± 7.5 vs. 154 ± 7.5 and 117 ± 6.1 vs. 94 ± 5.7 mM for ADSC and control, respectively) and lower acetate:propionate ratio (0.26 ± 0.5 vs. 0.36 ± 0.05 for ADSC and control, respectively). Microbial analyses conducted on samples collected during wk 10 showed that cows supplemented with S. cerevisiae had a 9-fold, 2-fold, 6-fold, 1.3-fold, and 8-fold increase in S. cerevisiae, Fibrobacter succinogenes, Anaerovibrio lipolytica, Ruminococcus albus, and anaerobic fungi, respectively, which suggested an increase in cellulolytic microbes within the rumen. Cows supplemented with ADSC had 2.2-fold reduction in Prevotella albensis, which is a gram-negative bacterium predominant during SARA. Prevotella spp. are suggested to be an important source of lipopolysaccharide responsible for inflammation within the rumen. Cows supplemented with ADSC had a 2.3-fold increase in Streptococcus bovis and a 12-fold reduction in Megasphaera elsdenii. The reduction in M. elsdenii may reflect lower concentration of lactic acid within the rumen for ADSC cows. In conclusion, ADSC supplementation to dairy cows was demonstrated to alleviate the condition of SARA caused by abrupt dietary changes from HF to HG, and can potentially improve rumen function, as indicated by greater numbers of cellulolytic microorganisms within the rumen.     

Water intake and dry matter intake changes as a feeding management tool and indicator of health and estrus status in dairy cows

This study investigates whether dry matter (DM) or water intake is affected by the presence of disease or estrus in dairy cows and whether water intake can serve as an accurate substitute for monitoring changes in DM intake (DMI). A combined cumulative sum (CUSUM) and Shewhart monitoring scheme is proposed to detect DMI changes and emerging disease or estrus. Daily readings from 35 inline water meters for 35 water cups in a tie-stall barn at the University of Minnesota were collected from September 2005 until June 2006. Two cows were assigned to each water cup. Individual DMI were recorded for each of the 70 cows on the study. All drug or hoof treatments administered to the cows along with breeding and calving events were also recorded and classified as 1 of the following 6 event categories: estrus, calving, mastitis, fever, hoof treatment, and other. Analysis of covariance was used to identify factors significantly changing intake. Only the first 150 d in milk (DIM) were considered in the analysis. Six event categories plus DIM, ambient temperature, relative humidity, and parity were entered as independents into the model. Calving, primiparity, and health events categorized as “other” were associated with decreased DM and water intake. Mastitis decreased DMI and fever negatively affected water intake. Both intakes increased with DIM, and water intake decreased with increase in humidity. Covariance analysis was used to investigate the relationship between DMI and water intake. In model 1, analysis was done for a pair of cows, whereas model 2 modeled DMI of the whole group of 70 cows. Water intake, ambient temperature, humidity, and DIM were entered as independents in both models and parity was entered in model 1. Polynomial models and 2-way interactions were also considered. Water intake, ambient temperature, DIM, and DIM² were kept in final models 1 and 2, and parity was kept in model 1. Final models for cow pairs and a group of 70 cows resulted in R² of 0.50 and 0.82, respectively. The proposed CUSUM-Shewhart DMI monitoring scheme successfully detected emerging disease even in the first week of lactation. Monitoring water intake can serve as an alternative to measurements of DMI for groups of cows and has the potential of predicting change in individual cow health and estrus status.     

Models for predicting dry matter intake of Holsteins during the prefresh transition period

The objectives of this study were to develop and validate a model for predicting dry matter intake (DMI) of Holsteins during the prefresh transition period. The original database (ODB) for model development was established by compiling parity, body condition score (BCS), and DMI data during the final 3 wk of gestation from 366 Holsteins fed 24 different diets that were used in eight experiments conducted at three universities. For model validation, a validation database (VDB) was established by compiling data from 333 prefresh transition Holsteins fed 25 different diets that were used in eight experiments conducted at five universities. Dry matter intake during the prefresh transition period was fitted to an exponential function: DMI(t) = a + pe(kt), where DMI(t) = DMI as a percentage of body weight (BW) at time t, a = asymptotic intercept at time--infinity, p = change in intake (kg) from the asymptotic intercept until parturition, k = rate constant influencing the shape of the curve, and t = day relative to parturition expressed as days pregnant--280. The model developed from the ODB predicted DMI of heifers in the VDB with satisfactory accuracy and precision. However, this was not true for cows, probably due to differences in BCS of cows and diets fed to cows from the two data sets. When a subset of cows was selected from each data set that had similar BCS (> 4.0) and were fed similar diets, accuracy and precision of the model predicting DMI was improved. Finally, both databases were combined to develop final models for predicting DMI of heifers and cows. Proposed models for predicting mean daily DMI of heifers and cows during the prefresh transition period were DMI(t) = 1.713-0.688e(0.344t) (R2 = 0.96) and DMI(t) = 1.979-0.756e(0.154t) (R2 = 0.97), respectively. Adjustment factors for animal and dietary factors were generated to demonstrate the plausibility of adaptive fitting of the prediction. The regression coefficients of prediction models (a, p, and k) were affected by BCS and dietary organic macronutrient concentrations.     

Invited review: Practical feeding management recommendations to mitigate the risk of subacute ruminal acidosis in dairy cattle

Rumen health is of vital importance in ensuring healthy and efficient dairy cattle production. Current feeding programs for cattle recommend concentrate-rich diets to meet the high nutritional needs of cows during lactation and enhance cost-efficiency. These diets, however, can impair rumen health. The term “subacute ruminal acidosis” (SARA) is often used as a synonym for poor rumen health. In this review, we first describe the physiological demands of cattle for dietary physically effective fiber. We also provide background information on the importance of enhancing salivary secretions and short-chain fatty acid absorption across the stratified squamous epithelium of the rumen; thus, preventing the disruption of the ruminal acid–base balance, a process that paves the way for acidification of the rumen. On-farm evaluation of dietary fiber adequacy is challenging for both nutritionists and veterinarians; therefore, this review provides practical recommendations on how to evaluate the physical effectiveness of the diet based on differences in particle size distribution, fiber content, and the type of concentrate fed, both when the latter is part of total mixed ration and when it is supplemented in partial mixed rations. Besides considering the absolute amount of physically effective fiber and starch types in the diet, we highlight the role of several feeding management factors that affect rumen health and should be considered to control and mitigate SARA. Most importantly, transitional feeding to ensure gradual adaptation of the ruminal epithelium and microbiota; monitoring and careful management of particle size distribution; controlling feed sorting, meal size, and meal frequency; and paying special attention to primiparous cows are some of the feeding management tools that can help in sustaining rumen health in high-producing dairy herds. Supplementation of feed additives including yeast products, phytogenic compounds, and buffers may help attenuate SARA, especially during stress periods when the risk of a deficiency of physically effective fiber in the diet is high, such as during early lactation. However, the usage of feed additives cannot fully compensate for suboptimal feeding management.     

Effects of replacing chopped alfalfa hay with alfalfa silage in a total mixed ration on production and rumen conditions of lactating dairy cows

The effects of replacing chopped alfalfa hay with alfalfa silage in a total mixed ration containing barley grain and corn silage on production and rumen conditions were investigated. Cows received three diets that all contained (dry matter basis) 38.5% barley grain-based energy supplement, 30.5% corn silage, 17.0% protein supplement, and 4.2% sunflower seeds. One diet contained (dry matter basis) 9.8% of chopped alfalfa hay and no alfalfa silage. One diet contained (dry matter basis) 4.9% chopped alfalfa hay and 4.9% alfalfa silage. One diet contained (dry matter basis) 9.8% of alfalfa silage and no chopped alfalfa hay. Contents of crude protein, neutral detergent fiber, acid detergent fiber, and starch, averaged across diets, were 16.7, 41.3, 21.1, and 24.4% DM, respectively, and did not differ significantly among diets. Replacing chopped alfalfa hay with alfalfa silage decreased the proportion of dietary DM passing through the 8-mm screen of the Penn State Particle Separator from 61.9 to 55.2% dry matter and significantly increased dietary physical effective NDF (peNDF) content, calculated as the NDF retained by the two screens of the Penn State Particle Separator, from 20.1 to 23.3% DM. Replacing chopped alfalfa hay with alfalfa silage also reduced dietary DM content, increased rumen pH from 6.27 to 6.47, reduced volatile fatty acid concentrations, numerically increased milk fat concentration and milk fat yield. Milk yield, milk protein concentration, dry matter intake, and rumen ammonia concentration were not affected.     

Predicting the dry matter intake of grazing dairy cows using infrared reflectance spectroscopy analysis

The objective of this study was to compare mid-infrared reflectance spectroscopy (MIRS) analysis of milk and near-infrared reflectance spectroscopy (NIRS) analysis of feces with regard to their ability to predict the dry matter intake (DMI) of lactating grazing dairy cows. A data set comprising 1,074 records of DMI from 457 cows was available for analysis. Linear regression and partial least squares regression were used to develop the equations using the following variables: (1) milk yield (MY), fat percentage, protein percentage, body weight (BW), stage of lactation (SOL), and parity (benchmark equation); (2) MIRS wavelengths; (3) MIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity; (4) NIRS wavelengths; (5) NIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity; (6) MIRS and NIRS wavelengths; and (7) MIRS wavelengths, NIRS wavelengths, MY, fat percentage, protein percentage, BW, SOL, and parity. The equations were validated both within herd using animals from similar experiments and across herds using animals from independent experiments. The accuracy of equations was greater for within-herd validation compared with across-herds validation. Across-herds validation was deemed the more suitable method to assess equations for robustness and real-world application. The benchmark equation was more accurate [coefficient of determination (R²) = 0.60; root mean squared error (RMSE) = 1.68 kg] than MIRS alone (R² = 0.30; RMSE = 2.23 kg) or NIRS alone (R² = 0.16; RMSE = 2.43 kg). The combination of the benchmark equation with MIRS (R² = 0.64; RMSE = 1.59 kg) resulted in slightly superior fitting statistics compared with the benchmark equation alone. The combination of the benchmark equation with NIRS (R² = 0.58; RMSE = 1.71 kg) did not result in a more accurate prediction equation than the benchmark equation. The combination of MIRS and NIRS wavelengths resulted in superior fitting statistics compared with either method alone (R² = 0.36; RMSE = 2.15 kg). The combination of the benchmark equation and MIRS and NIRS wavelengths resulted in the most accurate equation (R² = 0.68; RMSE = 1.52 kg). A further analysis demonstrated that Holstein-Friesian cows could predict the DMI of Jersey × Holstein-Friesian crossbred cows using both MIRS and NIRS. Similarly, the Jersey × Holstein-Friesian animals could predict the DMI of Holstein-Friesian cows using both MIRS and NIRS. The equations developed in this study have the capacity to predict DMI of grazing dairy cows. From a practicality perspective, MIRS in combination with variables in the benchmark equation is the most suitable equation because MIRS is currently used on all milk-recorded milk samples from dairy cows.