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Modeling the Adequacy of Dietary Fiber in Dairy Cows Based on the Responses of Ruminal pH and Milk Fat Production to Composition of the Diet

Q. Zebeli^*, J. Dijkstra^,1, M. Tafaj^*, H. Steingass^*, B. N. Ametaj and W. Drochner^*

^* Institute of Animal Nutrition (450), University of Hohenheim, Emil-Wolff-Str. 10, D-70599 Stuttgart, Germany
Animal Nutrition Group, Wageningen University, Marijkeweg 40, 6709 PG Wageningen, the Netherlands
Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, Alberta T6G 2P5 Canada

¹ Corresponding author: zebeli{at}uni-hohenheim.de

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES List of References Used...

 
The main objective of this study was to develop practical models to assess and predict the adequacy of dietary fiber in high-yielding dairy cows. We used quantitative methods to analyze relevant research data and critically evaluate and determine the responses of ruminal pH and production performance to different variables including physical, chemical, and starch-degrading characteristics of the diet. Further, extensive data were used to model the magnitude of ruminal pH fluctuations and determine the threshold for the development of subacute ruminal acidosis (SARA). Results of this study showed that to minimize the risk of SARA, the following events should be avoided: 1) a daily mean ruminal pH lower than 6.16, and 2) a time period in which ruminal pH is <5.8 for more than 5.24 h/d. As the content of physically effective neutral detergent fiber (peNDF) or the ratio between peNDF and rumen-degradable starch from grains in the diet increased up to 31.2 ± 1.6% [dry matter (DM) basis] or 1.45 ± 0.22, respectively, so did the daily mean ruminal pH, for which a asymptotic plateau was reached at a pH of 6.20 to 6.27. This study also showed that digestibility of fiber in the total tract depends on ruminal pH and outflow rate of digesta from reticulorumen; thereby both variables explained 62% of the variation of fiber digestibility. Feeding diets with peNDF content up to 31.9 ± 1.97% (DM basis) slightly decreased DM intake and actual milk yield; however, 3.5% fat-corrected milk and milk fat yield were increased, resulting in greater milk energy efficiency. In conclusion, a level of about 30 to 33% peNDF in the diet may be considered generally optimal for minimizing the risk of SARA without impairing important production responses in high-yielding dairy cows. In terms of improvement of the accuracy to assessing dietary fiber adequacy, it is suggested that the content of peNDF required to stabilize ruminal pH and maintain milk fat content without compromising milk energy efficiency can be arranged based on grain or starch sources included in the diet, on feed intake level, and on days in milk of the cows.

Key Words: physically effective fiber • dairy cow • ruminal pH • rumen-degradable starch

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES List of References Used...

 
The challenge primarily met in dairy cow feeding is to provide an energetically high-density ration without compromising ruminal ecosystem, animal welfare, and production performances. Providing high-yielding dairy cows adequate levels of dietary fiber is critical to prevent subacute ruminal acidosis (SARA) and the resulting depressions in fiber digestion, DMI, and milk production as well as alterations in milk composition (NRC, 2001). In contrast, offering diets in excess of fiber may decrease feed intake and lower the efficiency of feed use (Yang and Beauchemin, 2006a). Thus, it is essential to find an optimum of dietary fiber that may decrease the risk of SARA without impairing important production performances in dairy cows.

However, assessment of dietary fiber adequacy in dairy cows from the current feed tables is difficult due to insufficient consideration of physical effectiveness of different feeds. For this reason, the concept of physically effective NDF (peNDF) proposed by Mertens (1997) is considered more efficient because it incorporates information on particle length and chemical NDF content of the diet. The latter 2 variables affect digesta stratification in the reticulorumen and rumination activity and, therefore, the ruminal buffering capacity and pH (Mertens, 1997; Zebeli et al., 2006a). The peNDF content of feeds or of TMR can be easily calculated even under on-farm conditions (Plaizier et al., 2004) by using the Penn State Particle Separator (Kononoff et al., 2003a).

Although different studies conducted over the last decade provide important information into various physiological effects of peNDF in high-yielding dairy cows (e.g., Yang and Beauchemin, 2006a,b; 2007a,b), the optimum concentration of peNDF in dairy cow diets is uncertain. In fact, one of the drawbacks of the peNDF concept is that it does not take into consideration the differences in ruminal fermentability of various feed-stuffs. Numerous studies (Allen, 1997; De Brabander et al., 2002; Zebeli et al., 2006b) reported that inclusion of rumen-fermentable OM or starch as a variable in the model together with dietary fiber, particle length index, or peNDF increases the accuracy of prediction of ruminal pH or milk fat content in dairy cows. The content of peNDF may interact with the content of rumen-fermentable carbohydrates and intake level and may modify the response of ruminal fermentation. The level of feed intake may determine important qualitative changes, particularly on passage and digestion kinetics in the reticulorumen (Firkins et al., 2001; Stone, 2004; Seo et al., 2006), which also may confound the response of ruminal pH to dietary peNDF.

Part of the difficulty in assessing adequacy of the dietary fiber and in determining peNDF requirements for dairy cows may also be related to the interpretation of the response of the ruminal pH and its resulting effects on fiber degradation or development of SARA. Ruminal pH in dairy cows is not constant, but fluctuates considerably in a 24-h period. In high-yielding dairy cows fed high concentrate diets at >45% of the ration (DM basis), ruminal pH generally ranges from 6.6 before morning feeding to 5.3 or 5.0 during the intensive rumen fermentation phases, with average pH typically at 6.0 or 6.1. In fact, ruminal pH can drop below these average levels for considerable periods during the feeding cycle. Because the magnitude of diurnal fluctuations of ruminal pH has not been sufficiently characterized, the time of pH measurement may be misleading. Moreover, the rumen pH that defines SARA is still a matter of individual choice, with different researchers using different pH threshold values of 5.6 (Keunen et al., 2002), 5.8 (Beauchemin et al., 2003), or 6.0 (Krehbiel et al., 1995). The length of time per day when ruminal pH is under "suboptimal" levels (Beauchemin et al., 2003; Krause et al., 2003), or the index that weights the time spent under the optimal ruminal pH by the magnitude of the deviation from this pH (Mackie and Gilchrist, 1979), seem a better determinant of fiber degradation and presence of SARA than daily mean ruminal pH, or the lowest (nadir) ruminal pH value Also, the time duration for which the ruminal pH must remain below this threshold value has not been properly defined.

The complexity of the interactions between feed intake, forage type, concentrate fed, and ruminal degradability of different feedstuffs as well as the uncertainty in defining the response of the ruminal pH make it difficult to quantitatively characterize the effects of peNDF on ruminal fermentation and prevention of SARA. These factors also hamper recommendations on optimal dietary peNDF levels for dairy cows. In this context, quantitative methods are helpful tools to synthesize knowledge and improve predictions of desired animal responses and support an optimal decision-making in formulating healthy diets for dairy cows (St-Pierre, 2001; Dijkstra et al., 2007).

The main objective of this study was to develop practical models to assess and predict the adequacy of dietary fiber in high-yielding dairy cows. Therefore, quantitative methods were used to analyze relevant research data and critically evaluate and determine the responses of ruminal pH and production performance to different variables including physical, chemical, and starch-degrading characteristics of the diet. Furthermore, extensive data were used to model the magnitude of ruminal pH fluctuations and determine the threshold for the development of SARA.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES List of References Used...

 
Responses of Ruminal pH and Production Performance to Diet Characteristics
Description of Database.
To define the responses of ruminal pH and production performance to physical and chemical characteristics of the diet, a database with data on animal performance, detailed ration components, and an evaluation of physical structure of the ration was generated. To evaluate the effects of dietary factors on production responses, data of feed intake, actual milk yield, 3.5% FCM, milk fat yield, and milk energy efficiency (MEE) were considered. The MEE was calculated by dividing milk fat yield by DMI, and this was expressed as grams of milk fat produced per kilogram of DMI. This database was compiled from 58 studies containing a total of 238 treatment means, published mainly over the last 10 yr, and conducted with high-yielding dairy cows fed TMR. (A full list of references from studies included in this database is available in Table A1 of the appendix.) A statistical description of main characteristics of the database, including cow data, diet characteristics, and response variables are listed in Table 1. All studies used lactating Holstein cows (95.5 ± 48.4 DIM; mean ± SD) weighing between 528 and 886 kg and producing 18.1 to 49.3 kg of milk/ d (33.6 kg/d of 3.5% FCM; Table 1). Milk fat content ranged from about 2.3 to 4.4%, whereas the MEE averaged 51.8 ± 7.57 g/kg of DMI. The level of DMI averaged 22.3 ± 3.28 kg/d and the percentage of forage in TMR ranged from 26.8 to 80.8% of DM (49.4 ± 10.6%). Dietary NDF was from 18.2 to 49.0% of DM (32.5 ± 7.09%) and forage NDF (FNDF) between 11.5 and 44.9% of DM (21.9 ± 5.98%). Data on total-tract NDF digestibility and outflow rate of digesta from the reticulorumen (k_p) were also included. Most of the studies included in this database recorded ruminal pH during 24 h either by using an indwelling electrode placed in the ventral rumen sac or by measuring pH from ruminal fluid in spot samples collected from the ventral sac of the rumen via rumen cannula. In some studies, ruminal pH was measured from spot fluid samples covering a time from shortly before to 12 h after morning feeding. In such cases, an average pH was calculated and considered as daily mean ruminal pH in this database. The k_p was measured using external markers (e.g., ytterbium, chromium). Overall, daily mean ruminal pH ranged from 5.30 to 6.73 (6.10 ± 0.26), NDF digestibility varied from 28.4 to 64.6%, and k_p ranged from 1.9 to 8.4 %/h. All studies were conducted in Latin square or double reversal designs and the sample size varied between 4 and 8 ruminally cannulated cows per study.

Estimation of Ruminally Degradable Starch from Grains and Forages in TMR.
Because the concept of peNDF does not account for ruminal OM fermentability of TMR, rumen degradability of starch from different grains and forages composing the TMR was estimated and included in the analysis. This study focused on the effects of starch degradation rather than OM degradation, assuming that kinetics of starch degradation in the rumen is a more sensible variable for ruminal pH than OM degradation. The latter may be confounded by degradation characteristics of soluble and particularly insoluble cell wall components, which are shown to be very variable in the feedstuffs (Sauvant et al., 2004). In addition, OM degradation was shown not to be a proper indication of starch degradation, especially in barley grains (Ramsey et al., 2001).

Rumen degradability of starch from grains was estimated separately from that of forages to investigate the weight of each source of dietary starch on rumen fermentation and milk parameters. Rumen degradability of starch from different grains, including various by-products, was considered to take into account both the different starch amounts and differences in rumen degradability of starch among grain sources (e.g., barley vs. corn) in TMR, and physical or chemical treatments of grains (e.g., ground, dry rolled, steam-rolled, cracked, flaked, dry or high-moisture corn), which may modify their ruminal degradability. However, because of insignificant content in starch, the degradability characteristics of starch from soy products or other protein-rich concentrates was ignored in this analysis. Starch from forages was included in the analysis particularly to take into account those forage sources of TMR rich in starch, such as corn, oat, or barley silage.

All studies included in this database provided detailed information about the components of concentrate mixture and forage sources of TMR. Starch content of grains and forages either was taken direct from publications or was obtained from tables compiled by Sauvant et al. (2004). The effective ruminal degradability (ERD) of the starch from grains and forages was calculated from in situ degradation parameters (a, b, and k_d) of starch for different feedstuffs compiled by Offner et al. (2003), as follows:

[1]

where a = soluble starch fractions (%); b = slowly disappearing starch fractions (%); k_d = fractional rate of disappearance of starch (%/h), and k_p = fractional passage rate of starch (%/h) from reticulorumen. The k_p was estimated using the equations of Seo et al. (2006) as shown below:

[2]

[3]

where FpBW = forage DMI as the proportion of BW (g/ kg BW), CpBW = concentrate DMI as the proportion of BW (g/kg of BW), and FDMI = forage DMI (kg).

Subsequently, the content of ruminally degradable starch of concentrate mixture (RDSG) was calculated according to the formula:

[4]

where p_i represents the proportion of dietary starch provided from grain i in the concentrate mixture, and ERD_i represents starch effective degradability for grain i, and n is the number of grains in the concentrate mixture of TMR. The formula [4] was also used to calculate the content of ruminally degradable starch from forages (RDSF) in TMR. The total content of degradable starch in the diet was calculated as the sum of RDSG and RDSF and was also included as a variable in the analysis.

Characterization of Ruminal pH Fluctuation and SARA Challenge
To characterize the magnitude of ruminal pH fluctuation (i.e., relationships between daily mean and time length of pH below 5.8, or the lowest ruminal pH) throughout the day, another database containing a total of 326 treatment means was generated from 80 studies conducted with dairy cows and steers and published from 1991 to 2007. (A full list of references included in this database is shown in Table A2 of the appendix.) This database was focused only on studies reporting ruminal pH both as daily mean and duration of time with pH <5.8. Daily mean ruminal pH of this database ranged from 5.49 to 6.72 (6.09 ± 0.27), time length of pH <5.8 ranged from 0.0 to 19 h/d (average 5.74 ± 1.94 h/d), and the lowest ruminal pH varied between 4.80 and 6.44 (Table 2).

[5]

where Y_ij = the expected outcome for the dependent variable Y (i.e., response of ruminal pH or milk parameters being modeled) observed at level j (j = 2,..., n) of the predictor variable X (dietary factor) in the study i; n is the number of treatment means in study i; ₀ = the overall intercept across all studies (fixed effect); β₁ = the overall regression coefficient of Y on X across all studies (fixed effect); X_ij = the value j of continuous variable X in study i; s_i = the random effect of the study i (i = 1,..., 58); b_i = the random effect of study i on the regression coefficient of Y on X in study i, and e_ij = the unexplained error. Thus, the random effect components of the model include s_i + b_iX_ij + e_ij, and the distributions are shown below:

which assumes that e_ij is normally distributed with a mean of 0 and constant variance, and that s_i and b_i are normally distributed, have means of 0, and is their variance-covariance matrix:

An unstructured variance-covariance structure matrix (TYPE = UN) was adopted to avoid the positive correlation between the intercepts and slopes, as suggested by St-Pierre (2001). To take the unequal variance among studies into consideration, the dependent variable was weighted by the reciprocal of its squared SE (SE of treatment means were taken directly from studies). When a dietary factor was significant (P < 0.05), its squared term was included in the model to test any quadratic relationship. In this case, the variance-covariance matrix was modeled as variance components (TYPE = VC) to assure the convergence. After a visual inspection of the data using PROC GPLOT (SAS Institute, 2003), daily mean ruminal pH response to dietary peNDF was modeled as a linear-plateau using PROC NLIN (SAS, 2003; method = Marquardt), as shown below:

[6]

where Y is daily mean ruminal pH, a is the intercept, b is the slope (of the linear portion of the model), x is dietary peNDF, and x₀ is the break-point (the point beyond which there is no significant change of Y). Because of a better fitting, the response of DMI and milk parameters to dietary peNDF was modeled using the broken-line regression technique (i.e., single- or double-breakpoint models; Robbins et al., 2006) with PROC NLIN (SAS Institute, 2003; method = Marquardt), as shown below:

[7]

where Y is the response variable, a is the general intercept, b₁, b₂, and b₃ are the slopes for the first, second, and third straight-line segment, respectively, x is dietary peNDF, and R₁ and R₂ are the first and second breakpoints, respectively. Root mean square error (RMSE) and determination coefficient (R²) were subsequently computed and used to evaluate the goodness of fit.

Further, all significant dietary factors (P < 0.05) were also tested together using the backward elimination technique, similar to the algorithm reported by Firkins et al. (2001). In this case, to limit model over-parameterization, a variance inflation factor less than 10 (which assumes no significant multicollinearity among predictor variables tested) for every continuous independent variable tested was assumed (Neter et al., 1996).

Ruminal pH Fluctuation and Threshold of SARA.
To characterize the relationship between time length of ruminal pH <5.8 and daily mean ruminal pH, data obtained from observations as summarized in Table 2 were used. The statistical rationale used was the same as model [5], only that the response variable was the time length of pH <5.8, and daily mean ruminal pH was used as a predictor variable. Random effect of study was also considered, and to take into consideration the unequal variance among studies, the dependent variable was weighted by the reciprocal of its squared SE, which can improve the overall precision and ensure homogeneity of variance for the model (St-Pierre, 2001). Relationship between the lowest pH and the daily mean ruminal pH was tested using PROC CORR (SAS Institute, 2003). After a visual inspection of the data using PROC GPLOT (SAS Institute, 2003), time length of pH <5.8 response to daily mean ruminal pH was modeled as a linear-plateau using PROC NLIN (SAS Institute, 2003; method = Marquardt), as indicated by model [6].

As previously mentioned, data summarized from 20 experiments, which were conducted to experimentally induce and characterize SARA, were used to determine cut-off points of ruminal pH (i.e., daily mean and diurnal time of pH remaining below 5.8 per day) for cows with challenged SARA conditions. The least-squares means and confidence intervals (CI; 95 and 99%) were calculated using PROC GLIMMIX (SAS Institute, 2003) with a lognormal distribution function (DIST= LOGNORMAL), which is reported as appropriate to analyze data from small samples under moderate non-normality (Douglas, 2006). Confidence intervals give a range of plausible values for the true ruminal pH characterizing SARA or normal ruminal fermentation. For estimation of parameters, the pseudo-likelihood method was used, whereas degrees of freedom were approximated using Satterthwaite’s method. To account for differences among studies, the random effect of the experiment was considered in the analysis.

Evaluation of Dietary Fiber Adequacy.
An Excel (Microsoft Office, version 2003) spreadsheet version was built from the previously mentioned model, which predicts the ruminal pH response to multiple dietary factors, to allow changes in the running parameters of this model. Subsequently, this spreadsheet was linked to RiskAMP version 2.65 (Structured Data LLC, New York, NY), a software program that works by a Monte Carlo simulation engine, to run stochastic simulations, as described by Zebeli and Drochner (2007). Monte Carlo stochastic simulations were performed to evaluate the risk of occurrence of the time length of pH <5.8 when apparently adequate levels of peNDF were offered (i.e., adequate peNDF level was previously estimated), but differing in RDSG content and DMI level. The PERT distribution was used to generate the stochastic variables characterizing the variation of ruminal pH response to dietary scenarios tested, where minimum and maximum values were included based on empirical observations reported from published studies and summarized in the database, and the most likely estimates were obtained as a product of the predictions from the model. The PERT distribution is a special case of the beta distribution that uses the parameters (i.e., minimum, maximum, and most likely) to create a smooth curve that fits well to the normal or lognormal distributions (Vose, 2000). The description of probability density function of PERT distribution is given below:

[8]

where a and b are the boundary parameters of the stochastic variable x (a < b), m is the most likely value of x (a m b), and ₁ and ₂ are parameters describing the shape of the curve.

[9]

Outputs of the analysis (i.e., the probabilities of occurrence of any duration time of pH below 5.8) are shown in the form of cumulative distribution functions (CDF). Cumulative distribution functions of the stochastic variable x [F_x(x)] is the probability that this variable, having a probability density function of f_X(x), takes on a value less than or equal to x (Vose, 2000), and is expressed as follows:

[10]

A sufficiently large number of simulation iterations were run (10,000), so that the CDF were adequately described (Isukapalli et al., 1998).

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES List of References Used...

 
Diurnal Fluctuations of Ruminal pH and SARA
The model characterizing the relationship between duration of time for ruminal pH <5.8 and daily mean ruminal pH is shown in Figure 1. The duration of time for ruminal pH <5.8 was negatively correlated to daily mean ruminal pH (R² = 0.88). The linear-plateau model revealed that the break-point of daily mean ruminal pH is reached at 6.25 ± 0.02, which corresponded to an asymptotic plateau of about 1 h/d for the time length of pH <5.8. The lowest (nadir) pH correlated positively to daily mean ruminal pH according to the following equation:

View larger version (11K): [in this window] [in a new window]   Figure 1. Model showing the relationship between time of ruminal pH remaining below 5.8 per day (Y) and daily mean ruminal pH (X): Y = 5.59 (± 0.031) + 0.0218 (± 0.002) x X, when X < 6.25 ± 0.02, (estimate ± SE), asymptotic plateau of Y = 0.97 h/d; root mean square error = 1.7 h/d, R2 = 0.88, number of experiments = 80, number of treatment means = 326.

[11]

The least squares means and CI for daily mean and time length of ruminal pH <5.8 in cows with challenged SARA or that experienced normal fermentation are given in Table 3. This analysis showed that, with 99% likelihood, the true values of daily mean pH and time length of ruminal pH <5.8 for cows with challenged SARA lie between 5.82 and 6.14, or within the range from 5.47 to 15.54 h/d, respectively (i.e., lower and upper bound of 99% CI). In contrast, with 99% likelihood, this data analysis revealed that cows may not experience SARA if daily mean ruminal pH is higher than 6.16 or the time in which ruminal pH is below 5.8 does not exceed 5.24 h/d (i.e., lower bound of 99% CI for daily mean pH and upper bound of 99% CI for time of pH <5.8 showing no SARA conditions, respectively).

View larger version (9K): [in this window] [in a new window]   Figure 2. Model showing the relationship between daily mean ruminal pH and dietary physically effective NDF (peNDF) in high-yielding dairy cows: ruminal pH = 5.59 (± 0.031) + 0.0218 (± 0.002) x peNDF, when peNDF <31.2 ± 1.60% (estimate ± SE), asymptotic plateau of pH 6.27; root mean square error = 0.14, R2 = 0.50, number of experiments = 45, number of treatment means = 187.

View larger version (9K): [in this window] [in a new window]   Figure 3. Cumulative probability distribution showing the risk of occurrence of any duration time of ruminal pH below 5.8 when simulations were performed for cows fed diets containing constant physically effective NDF (31.2% in DM), but differing in ruminally degradable starch from grains in TMR (14 and 22% in DM) and levels of DMI (20 and 25 kg of DM/d).

[12]

Response of Feed Intake and Milk Parameters
Relationship between DMI and dietary peNDF was low in this study (RSME = 3.03 kg DM/d; R² = 0.18; Figure 4). The broken-line model revealed that DMI tended to increase with increasing peNDF content up to 21.6 ± 2.92% in TMR. Further, when feeding a diet with a peNDF content ranging from 21.6 to 31.9 ± 1.97%, the model showed that DMI slightly declined (slope: –0.245 ± 0.122; Figure 4). However, when diets exceeded the latter content of peNDF, a higher depression of DMI was estimated (slope: –0.897 ± 0.349; Figure 4).

View larger version (9K): [in this window] [in a new window]   Figure 4. Model showing the relationship between DMI and dietary physically effective NDF (peNDF) in dairy cows: DMI = a + b1 x x, if x R1; DMI = a + b1 x R1 + b2 x (x – R1), if R2 x > R1; DMI = a + b1 x R1 + b2 x (R2 – R1) + b3 x (x – R2), if x > R2 (a = 22.57 ± 1.19, b1 = 0.023 ± 0.067; b2 = –0.245 ± 0.122; b3 = –0.897 ± 0.349; R1 = 21.6 ± 2.92% peNDF; R2 = 31.9 ± 1.97% peNDF; x = dietary peNDF); root mean square error = 3.03; R2 = 0.18.

View larger version (16K): [in this window] [in a new window]   Figure 5. Models showing the relationship between milk fat content (A) or milk energy efficiency (MEE; g milk fat/kg DMI; B) and dietary physically effective NDF (peNDF) in dairy cows: A, Milk fat = a + b1 x x, if x R1; milk fat = a + b1 x R1 + b2 x (x – R1), if x > R1 (a = 3.13 ± 0.162, b1 = 0.006 ± 0.011; b2 = 0.0373 ± 0.011; R1 = 20.0 ± 3.21% peNDF; x = dietary peNDF); root mean square error = 0.29; R2 = 0.37: B, MEE = a + b1 x x, if x R1; MEE = a + b1 x R1 + b2 x (x – R1), if R2 x > R1; MEE = a + b1 x R1 + b2 x (R2 – R1) + b3 x (x –R2), if x > R2 (a = 47.32 ± 3.82, b1 = 0.052 ± 0.316; b2 = 0.498 ± 0.153; b3 = –1.410 ± 0.847; R1 = 17.1 ± 5.59% peNDF; R2 = 32.4 ± 1.78% peNDF; x = dietary peNDF); root mean square error = 5.53; R2 = 0.48.

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES List of References Used...

 
Diurnal Fluctuations of Ruminal pH and SARA
Data analyzed to characterize the diurnal fluctuations of ruminal pH showed that cows spent several hours per day in which ruminal pH remained <5.8. One finding of this study was that this time length could be characterized with reasonable accuracy from the daily mean ruminal pH, whereby the latter can be estimated from nadir value of the ruminal pH. In fact, the precise time when the nadir of ruminal pH occurs could not be evaluated in this study. However, assuming that this nadir is reached at 10 to 14 h after the morning feeding in dairy cows fed high concentrate diets ad libitum (Krause and Oetzel, 2005; Tafaj et al., 2005; Yang and Beauchemin, 2007a), it is possible that by using this measurement, both daily mean ruminal pH and the time length of pH <5.8 can be assessed through the relationships established for these variables in this study.

The present study showed that a break-point for the time length of the ruminal pH <5.8 was reached when the daily mean pH value was 6.25 ± 0.02. The latter corresponded to an asymptotic plateau of 1 h for the time length of pH <5.8 (Figure 1). This time length was less than the lower bound of 99% CI (i.e., 1.62 h/d) for the duration that the pH value was <5.8, indicating minimal risk of SARA. This means that a minimal time length of 1 h/d exists when ruminal pH is <5.8, and this event is inevitable under feeding conditions in dairy cattle, as summarized in the current database. In fact, the CI for the time length that ruminal pH remains <5.8, indicating the cut-off point for SARA, was relatively wide (i.e., 5.47 to 15.54 h/d or 6.29 to 13.2 h/ d for 99 and 95% CI, respectively). On the other hand, the width of 99 and 95% CI, indicating normal fermentation in the rumen, was narrower (i.e., 1.62 to 5.24 h/ d and 1.98 to 4.49 h/d, respectively). This means that the certainty to define the pH values characterizing normal fermentation in the rumen is higher than the certainty to define the cut-off point of ruminal pH reflecting SARA conditions. Therefore, our data suggest, with 99% certainty, that to maintain a lower SARA occurrence (i.e., 1%), the time length in which the ruminal pH remains <5.8 for longer than 5.24 h/d must be avoided.

Indeed, when the time length of ruminal pH <5.8 was increased from 5.4 to 8.8 h/d in dairy cows (Gozho et al., 2007) or from 0 to 6.8 h/d in steers (Gozho et al., 2006), an acute phase response was activated. Interestingly, presence of the inflammatory response was not associated with disturbances in DMI or in milk production or its composition in dairy cows. Furthermore, in dairy cows fed to experimentally induce SARA, the DMI and milk composition were not affected even when the time length of ruminal pH <5.8 was increased from 0 to 13 h/d (Krause and Oetzel, 2005) or from 1.4 to 12.8 h/d (Krajcarski-Hunt et al., 2002), although in situ NDF digestibility significantly declined (Krajcarski-Hunt et al., 2002).

Recent research indicates alterations in ruminal microbial ecology in steers experimetally challenged to induce SARA (Goad et al., 1998; Purvis et al., 2006). In contrast, research conducted in dairy cows fed high-concentrate diets (>50% of DM) has shown that when ruminal pH remains <5.8 for 3 to 5 h/d it has no affect on the number and activity of fibrolytic bacteria attached to solid or fluid ruminal digesta (Ölschläger et al., 2006; Zebeli et al. 2007a).

Response of Ruminal pH to Dietary Factors
The greater accuracy of dietary peNDF and FNDF in predicting the response of ruminal pH compared with total dietary NDF agrees with results of previous studies. For example, Allen (1997) found a positive correlation only between FNDF and ruminal pH, but not between total dietary NDF and pH. In addition, Mertens (1997) also reported a positive curvilinear relationship between dietary peNDF and ruminal pH in dairy cows. Pitt et al. (1996), using data from sheep, beef cattle, and dairy cattle, also observed a quadratic relationship between effective NDF and ruminal pH. In the study of Pitt et al. (1996), the sheep consumed a high forage diet, which probably accounted for a higher plateau of ruminal pH in that study (approximately 6.4) compared with 6.27 observed in the present study. Because feeding diets in excess of peNDF may decrease feed intake and lower the efficiency of feed use, the determination of a break-point in dietary peNDF, beyond which no further advantages on ruminal pH response can be expected, is important particularly in terms of maximization of production responses in high-producing dairy cows.

The content of peNDF in the diet is important in maintaining a stable ruminal environment. Mertens (1997) suggested that feed particles larger than 1.18 mm (i.e., particles considered in peNDF system) are more effective in stimulating chewing activity and therefore increasing the secretion of saliva and ruminal buffering capacity compared with smaller particles, because the latter readily flow out of the rumen and provide fewer stimuli for chewing activities. However, the increase of ruminal pH due to increasing dietary peNDF is not necessarily related only to positive effects of peNDF on chewing activity (Zebeli et al., 2007b; Yang and Beauchemin, 2007a). The reason for this could be that the flow of saliva alone is unable to neutralize the increasing quantities of VFA and lactate produced after ingesting high amounts of ruminally degradable OM (Allen, 1997).

In addition, dietary peNDF also contributes to formation and maintenance of a stable, thick-packed ruminal raft in dairy cows (Zebeli et al., 2006a). The latter is important in stimulating contractions of the reticulorumen (Yang and Beauchemin, 2007a), which in turn increase the rate of passage and of absorption of VFA molecules across the rumen epithelium (Taylor and Allen, 2005). Increased VFA absorption increases buffering capacity of the rumen because absorption of VFA has been shown to be linked to secretion of HCO₃– into the rumen fluid in the ratio of 2:1 (Ash and Dobson, 1963; Gäbel et al., 1991).

The positive effects of peNDF on ruminal fermentation, and hence on attenuating the decline of ruminal pH, may also be related to effects of peNDF on meal patterns. For example, Yang and Beauchemin (2007a) reported that increasing dietary peNDF from 29 to 33% of the diet DM increased the number of meals per day from 8 to 10. In that study cows consumed less DM per meal, but increased meal frequency. The latter meal pattern was associated with decreased large diurnal fluctuations in ruminal pH, which was reflected in shorter time length of pH <5.8 and higher daily mean ruminal pH (from 10 to 1 h/d and from 6.0 to 6.5, respectively). Using a modeling approach, Pitt and Pell (1997) estimated that increased meal frequency could decrease the requirements for effective NDF to maintain a certain pH in the rumen.

Research conducted by Yang and Beauchemin (2006b) also has shown that feeding longer fiber particles shifts the site of starch digestion from rumen into the intestine, lowering the odds for development of ruminal acidosis. Indeed, our results showed positive correlation (r = 0.50; n = 51; P < 0.001) between peNDF content of the diet and the passage rate of fluid digesta out of reticulorumen (results not shown). This indicates acceleration of passage rate of ruminally degradable starch from reticulorumen with increasing peNDF content of the diet. However, in terms of animal health, milk production, and feed efficiency, the relationship between peNDF and RDSG in the diet for high-yielding dairy cows is more complicated than often assumed. Attempts to shift starch digestion from rumen to intestine to improve the efficiency of glucose metabolism have not been very effective. For example, Reynolds (2006) reviewed that although the increased starch digestion in the small intestine increases glucose supply for lactating dairy cow, this occurs at the expense of microbial protein synthesis in the rumen and with an increase in hindgut fermentation and subsequent losses of microbial protein in the feces. Thus, the best way to increase glucose supply is to have an optimal amount of ruminally degradable starch for enough propionate to be converted into glucose in the liver (Huntington, 1997), though without adversely affecting fiber digestibility and other rumen functions (Firkins et al., 2001).

The backward elimination technique showed that of the dietary factors considered, not only peNDF but also dietary RDSG and DMI level improved the prediction of ruminal pH. Allen (1997) also found that including the content or intake of rumen-degradable OM of the diet in the model together with NDF content and particle length index of forages improved predictions of ruminal pH. The present study showed that increasing dietary RDSG increased requirements for peNDF to stabilize ruminal pH. For example, in simulations for diets containing apparently adequate peNDF levels (i.e., 31.2% of DM), but differing in RDSG and DMI, the response of ruminal pH was different. Thus, the probability to maintain a time length not exceeding 5.24 h/ d in which ruminal pH remains <5.8 (a time assessed as being within the normal fermentation patterns) was 0% by feeding a diet with 31.2% peNDF and 14% RDSG, independently from DMI tested. In contrast, this probability increased by 5 and 45% when RDSG in the diet increased to 22% for a theoretical DMI of 20 and 25 kg/ d, respectively (Figure 3). It can therefore be concluded that to assess fiber requirements for high-yielding dairy cows, next to peNDF, both RDSG and DMI must be considered. In a previous study, Zebeli and Drochner (2007) assessed that to maintain the ruminal pH within the normal patterns, the requirements for peNDF in the diet may be lowered to 26 or 22%, when RDSG content in the diet is reduced to 10% with a 25 or 20 kg DMI, respectively. Similarly, Yang and Beauchemin (2006b) reported a time length of ruminal pH <5.8 shorter than 5 h/d by feeding a corn grain-based diet (54% concentrate in diet DM) with <15% RDSG (estimated according to Offner et al., 2003), with a dietary peNDF content of 26.5% and DMI of 25 kg/d. In another study, Yang and Beauchemin (2007a) reported an increased time length of ruminal pH <5.8 from 1.2 to 10.3 h/d when RDSG and DMI in the TMR diet increased from 12 to 20% (estimated according to Offner et al., 2003) and 22 to 24 kg/d, respectively, whereas peNDF content of TMR decreased from 33 to 29%. Similarly, Silveira et al. (2007) fed barley grain-based diets (60% concentrate in diet DM) differing in starch level and ruminal degradability and reported an increased time length of ruminal pH <5.8 from 3.4 to 8.2 h/d when RDSG increased from 14 to 24% (estimated according to Offner et al., 2003) and peNDF content decreased from 33.4 to 29.3%. The latter diet was associated with a significant decline in the total tract NDF digestibility from 59 to 51%, although DMI was maintained at 20 kg/d throughout the study.

Although increases in DMI are associated with proportional increases in the amount of peNDF and RDSG intakes, the backward elimination procedure showed that increasing DMI may promote fermentation rather than neutralization processes in the rumen, and hence development of SARA. However, this result should be interpreted with caution because of a possible interaction between DMI and other predictors of the model, particularly with RDSG. In fact, ruminal pH reflects the balance between acid production (i.e., VFA and lactate) in the rumen and acid removal through neutralization and absorption within the rumen (Allen, 1997). In this regard, Shaver (2002) estimated that the flow of salivary buffers remained almost constant when increasing daily intake of rumen-fermentable OM from 4 to 16 kg/d, whereas fermentation acids produced in the rumen increased linearly, which in turn may increase the risk of SARA in high-producing dairy cows. Firkins et al. (2001) and Stone (2004) concluded that high-yielding dairy cows are at increased risk of SARA due simply to an increased DMI. Oetzel (2000) also suggested limiting the DMI as a strategy to reduce the risk of SARA and improve feed efficiency in high-yielding dairy cows. However, results of our study showed that the risk of SARA could be lowered without limiting the DMI, but rather by varying the content of peNDF and RDSG in the diet. Results also suggest that a ratio of peNDF to RDSG in the diet less than 1.5 should be avoided.

The observation that rumen-degradable starch from forages did not affect ruminal pH in this study was unexpected, as forage sources like corn silage have high content of starch (18 to 35%), and its ruminal degradability is evaluated to be as high as 85% (Offner et al., 2003). However, this can be explained by the fact that the ruminal degradability of starch from corn silage is strongly affected by the variety, maturity, inoculant inclusion, and kernel processing (Johnson et al., 2002). In fact, none of these variables were accounted for in the analysis of this study as appropriate data were missing, and hence this aspect warrants further research.

Total-Tract Fiber Digestibility and Rumen Conditions
Because ruminal pH has profound effects on microbial populations, it is considered a critical factor in the normal and stable functioning of the rumen. As shown in the present study, variables of rumen conditions (i.e., ruminal pH or outflow rate of digesta from reticulorumen) have the potential to affect total fiber degradation. Similarly, Yang et al. (2002) demonstrated that decreasing daily mean ruminal pH from 6.18 to 5.78, associated with an increased outflow rate of digesta out of reticulorumen from 2.5 to 3.4 %/h, decreased NDF digestibility in the rumen and in total tract (from 40 to 37% and 52 to 42%, respectively). By increasing the outflow rate of particulate digesta out of reticulorumen from 2.7 to 4.0 %/h, which was associated with a decreased ruminal mat consistency, Zebeli et al. (2007b) showed a decreased total tract NDF digestibility from 55 to 50%. Similarly, Boddugari et al. (2001) reported that low digesta consistency in the reticulorumen due to high concentrate levels in the diet was associated with lower entrapment of small particles, increased outflow rate of solid digesta, and decreased ruminal NDF digestibility in dairy cows.

Production Responses to Diet Composition and Dietary Fiber Adequacy
This modeling approach revealed that increasing peNDF content up to about 32% slightly reduced DMI and actual milk yield; however, FCM, milk fat yield, and MEE were significantly increased. In fact, the effects of dietary peNDF on DMI in high-producing dairy cows are often controversially discussed. Because of the effects on ruminal physical fill, low peNDF content in the diet is believed to increase feed intake in dairy cows (Dado and Allen, 1995). However, diets low in peNDF and high in readily fermentable carbohydrates may result in increased fermentation intensity and particularly propionate production in the rumen (Zebeli et al., 2007a), which is reported to stimulate plasma insulin response, and this may result in a depression of DMI (Bradford and Allen, 2007) or milk fat synthesis (Reynolds, 2006).

That the content of peNDF up to 32% in the diet had low constraining effect on DMI in this study may also be explained by a variable DMI capacity of high-yielding dairy cows and by a low constraining effect of peNDF on rumen fill when diets are high in concentrates (>45% in DM basis; Allen, 2000). Feeding diets based on corn silage and ground corn with about 32% peNDF, Kononoff et al. (2003b) and Kononoff and Heinrichs (2003a) reported an average DMI of about 27 kg/d, and milk yield ranged from 41 to 48 kg/d and milk fat content averaged 3.5%. In contrast, feeding diets with about 26% peNDF and replacing corn silage with chopped alfalfa haylage, Kononoff and Heinrichs (2003b) reported a lower DMI (about 23 kg/d), milk yield (35 kg/ d), and milk fat content (3.3 to 3.4%). Increasing peNDF content from about 29 to 33% in a TMR based on alfalfa silage and steam-rolled barley, Yang and Beauchemin (2007b) reported a decreased DMI and milk yield of about 2 and 3 kg/d, respectively. In contrast, milk fat content was increased from 3.4 to 3.8% resulting in similar amounts of 4% FCM among treatments and decreased milk efficiency (i.e., FCM/DMI) from 1.40 to 1.28 with increasing peNDF content.

In fact, milk fat content is often used as an indicator for health and fiber adequacy in dairy cows. According to De Brabander et al. (2002), a reduction of milk fat content of about 0.6% in 1 wk could be considered an indicator for a fiber deficiency in the diet of dairy cows. That the response of milk fat content to dietary peNDF was somewhat lower than the response of ruminal pH in this study could be explained with a lower sensitivity of milk fat concentration to dietary fiber in cows in early lactation (Allen, 1997; Mertens, 2000), which typically are in a negative energy balance (NRC, 2001). When cows are in a negative energy balance, contribution from mobilized fatty acids increases in direct proportion to the extent of the energy deficit (Bauman and Griinari, 2001), and hence milk fat content may increase regardless of a low milk fat synthesis. In fact, milk fat content was quadratically decreased with increasing DMI in this study. In addition, there was a break-point of peNDF (i.e., 20 ± 3.2%), beyond which milk fat content became more responsive to dietary peNDF (Figure 5A). In a recent study, Mertens (2007) also found that increasing the content of dietary peNDF from 19 to 23% by adding 5% wheat straw in the diet was insufficient to alleviate milk fat depression (2.69 vs. 3.30% milk fat) in dairy cows. Other results of the current study showed that milk fat content depends also on rumen-degradable starch in the diet (Table 6). Feeding a high barley starch-based diet, Silveira et al. (2007) reported that 29% peNDF was insufficient to maintain a milk fat content of 3.4%, and this was associated with lowered daily mean ruminal pH, acetate to propionate ratio, and butyrate proportion in the rumen fluid. In contrast, when increasing the peNDF content of about 32 to 33% and reducing the content of degradable starch, milk fat content exceeded an average of 3.5%, although diets were based on barley grains (Silveira et al., 2007; Yang and Beauchemin, 2007b) and cows were in early (Yang and Beauchemin, 2007b) or mid lactation (Silveira et al., 2007). This indicates that the balance between dietary peNDF and degradable starch from grains has the capacity to affect fat concentration in the milk by dairy cows by affecting rumen fermentation and metabolism.

Inadequate balance between peNDF and RDSG levels in the diet may alter microbial populations and ruminal biohydrogenation (Weimer et al., 2007), whereby alterations in the pathways of rumen biohydrogenation results in the production of fatty acid intermediates that inhibit fatty acid synthesis in the mammary gland (Bauman and Griinari, 2003). In addition, the alteration of microbial species in the rumen may also lead to a reduction of acetate and butyrate production, hence resulting in lower lipogenic factors for de novo synthesis of fatty acids in the mammary gland. Indeed, in a recent study, AlZahal et al. (2007) reported that cows experiencing SARA showed lower de novo synthesis of fatty acids (<16C) and increased trans 18:1 fatty acids, which are considered intermediate products of ruminal biohydrogenation, and this was associated with milk fat depression.

Although milk fat content linearly increases also when diets exceeded 32% peNDF, this study showed that more than 32 ± 1.8% peNDF in the diet is not justified because this may be related to a lower DMI potential and may reduce MEE due likely to lower milk production and milk fat yield. In terms of ruminal pH and maximization of the MEE in dairy cows, results of the present modeling approach support the inclusion of a maximal content of about 30 to 33% peNDF in the TMR of lactating dairy cows. However, using the equations developed in the present study, the content of peNDF required to stabilize ruminal pH and maintain milk fat content without compromising MEE can be arranged based on grain or starch sources included in the diet, DMI, and DIM of the cows.

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES List of References Used...

 
This study showed that to minimize the risk of SARA, a daily mean ruminal pH lower than 6.16 and a time length in which ruminal pH is <5.8 is longer than 5.24 h/d should be avoided. Increasing dietary peNDF up to 31.2 ± 1.6% (DM basis) or the ratio of peNDF to RDSG up to 1.45 ± 0.22 linearly increased daily mean ruminal pH, of which the asymptotic plateau was achieved at a pH of 6.20 to 6.27. This study also showed that digestibility of NDF in the total tract depends on ruminal pH and outflow rate of digesta from reticulorumen; thereby both variables explained 62% of the variation of NDF digestibility. Feeding diets with a peNDF content from 21.6 to 31.9 ± 1.97% slightly decreased DMI and actual milk yield; however, FCM, milk fat yield, and MEE were increased. In conclusion, a level of about 30 to 33% peNDF in TMR may be considered as the general optimal level of peNDF to minimize the risk of SARA without impairing production responses in high-yielding dairy cows.

	ACKNOWLEDGEMENTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES List of References Used...

 
Authors acknowledge the support of this study by German Research Foundation (DFG, code no. DR 92/ 12-1). The assistance of D. Mansmann in compilation of the databases is acknowledged. We would like also to gratefully thank 2 anonymous reviewers for their valuable suggestions.

Received for publication August 3, 2007. Accepted for publication January 8, 2008.

	REFERENCES

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES List of References Used...

 


Allen, M. S. 1997. Relationship between fermentation acid production in the rumen and the requirement for physically effective fiber. J. Dairy Sci. 80:1447–1462.[Abstract]

Allen, M. S. 2000. Effects of diet on short-term regulation of feed intake by lactating dairy cattle. J. Dairy Sci. 83:1598–1624.[Abstract]

AlZahal, O., M. Or-Rashid, S. L. Greenwood, M. S. Douglas, and B. W. McBride. 2007. Subacute ruminal acidosis increases milk fat depression with diets supplemented with polyunsaturated fatty acids. J. Dairy Sci. 90(Suppl. 1):561. (Abstr.)

Ash, R. W., and A. Dobson. 1963. The effect of absorption on the acidity of rumen contents. J. Physiol. 169:39–61.[Free Full Text]

Bauman, D. E., and J. M. Griinari. 2001. Regulation and nutritional manipulation of milk fat: low-fat milk syndrome. Livest. Prod. Sci. 70:15–29.

Bauman, D. E., and J. M. Griinari. 2003. Nutritional regulation of milk fat synthesis. Annu. Rev. Nutr. 23:203–227.[CrossRef][Medline]

Beauchemin, K. A., W. Z. Yang, and L. M. Rode. 2003. Effects of particle size of alfalfa based-dairy cow diets on chewing activity, ruminal fermentation, and milk production. J. Dairy Sci. 86:630–643.[Abstract/Free Full Text]

Boddugari, K., R. J. Grant, R. Stock, and M. Lewis. 2001. Maximal replacement of forage and concentrate with a new wet corn milling product for lactating dairy cows. J. Dairy Sci. 84:873–884.[Abstract]

Bradford, B. J., and M. S. Allen. 2007. Depression in feed intake by a highly fermentable diet is related to plasma insulin concentration and insulin response to glucose infusion. J. Dairy Sci. 90:3838–3845.[Abstract/Free Full Text]

Dado, R. G., and M. S. Allen. 1995. Intake limitations, feeding behavior, and rumen function of cows challenged with rumen fill from dietary fiber or inert bulk. J. Dairy Sci. 78:118–133.[Abstract]

De Brabander, D. L., J. L. De Boever, J. M. Vanacker, and N. E. Geerts. 2002. Evaluation and effects of physical structure in dairy cattle nutrition. Pages 182–197 in Recent Developments and Perspectives in Bovine Medicine. M. Kaske, H. Scholz, and M. Höltershinken, ed. Proc. 22nd World Buiatrics Congress, Hannover, Germany.

Dijkstra, J., E. Kebreab, J. A. N. Mills, W. F. Pellikaan, S. Lopez, A. Bannink, and J. France. 2007. Predicting the profile of nutrients available for absorption: From nutrient requirement to animal response and environmental impact. Animal 1:99–111.[CrossRef]

Douglas, G. B. 2006. Approximate confidence interval for standard deviation of nonnormal distributions. Comput. Stat. Data Anal. 50:775–782.[CrossRef]

Firkins, J. L., M. L. Eastridge, N. R. St-Pierre, and S. M. Noftsger. 2001. Effects of grain variability and processing on starch utilization by lactating dairy cattle. J. Dairy Sci. 79(E Suppl.):E218–E238.

Gäbel, G., M. Bestmann, and H. Martens. 1991. Influences of diet, short-chain fatty acids, lactate and chloride on bicarbonate movement across the reticulorumen wall of sheep. J. Vet. Med. A 38:523–529.

Goad, D. W., C. L. Goad, and T. G. Nagaraja. 1998. Ruminal microbial and fermentative changes associated with experimentally induced subacute acidosis in steers. J. Anim. Sci. 76:234–241.[Abstract/Free Full Text]

Gozho, G. N., D. O. Krause, and J. C. Plaizier. 2006. Rumen lipopolysaccharide and inflammation during grain adaptation and subacute ruminal acidosis in steers. J. Dairy Sci. 89:4404–4413.[Abstract/Free Full Text]

Gozho, G. N., D. O. Krause, and J. C. Plaizier. 2007. Ruminal lipopolysaccharide concentration and inflammatory response during grain-induced subacute ruminal acidosis in dairy cows. J. Dairy Sci. 90:856–866.[Abstract/Free Full Text]

Huntington, G. B. 1997. Starch utilization by ruminants: From basics to the bunk. J. Anim. Sci. 75:852–867.[Abstract/Free Full Text]

Isukapalli, S. S., A. Roy, and P. G. Georgopoulos. 1998. Stochastic response surface methods (SRSMs) for uncertainty characterization and propagation application to environmental and biological systems. Risk Anal. 18:351–363.[CrossRef][Medline]

Johnson, L. M., J. H. Harrison, D. Davidson, J. L. Robutti, M. Swift, W. C. Mahanna, and K. Shinners. 2002. Corn silage management I: Effects of hybrid, maturity, and mechanical processing on chemical and physical characteristics. J. Dairy Sci. 85:833–853.[Abstract]

Keunen, J. E., J. C. Plaizier, L. Kyriazakis, T. F. Duffield, T. M. Widowski, M. I. Lindinger, and B. W. McBride. 2002. Effects of a subacute ruminal acidosis model on the diet selection of dairy cows. J. Dairy Sci. 85:3304–3313.[Abstract/Free Full Text]

Kononoff, P. J., and A. J. Heinrichs. 2003a. The effect of corn silage particle size and cottonseed hulls on cows in early lactation. J. Dairy Sci. 86:2438–2451.[Abstract/Free Full Text]

Kononoff, P. J., and A. J. Heinrichs. 2003b. The effect of reducing alfalfa haylage particle size on cows in early lactation. J. Dairy Sci. 86:1445–1457.[Abstract/Free Full Text]

Kononoff, P. J., A. J. Heinrichs, and D. A. Buckmaster. 2003a. Modification of the Penn State forage and total mixed ration particle separator and the effects of moisture content on its measurements. J. Dairy Sci. 86:1858–1863.[Abstract/Free Full Text]

Kononoff, P. J., A. J. Heinrichs, and H. A. Lehman. 2003b. The effect of corn silage particle size on eating behavior, chewing activities, and rumen fermentation in lactating dairy cows. J. Dairy Sci. 86:3343–3353.[Abstract/Free Full Text]

Krajcarski-Hunt, H., J. C. Plaizier, J.-P. Walton, R. Spratt, and B. W. McBride. 2002. Short communication: Effect of subacute ruminal acidosis on in situ fiber digestion in lactating dairy cows. J. Dairy Sci. 85:570–573.[Abstract]

Krause, K. M., D. K. Combs, and K. A. Beauchemin. 2003. Effects of increasing levels of refined cornstarch in the diet of lactating dairy cows on performance and ruminal pH. J. Dairy Sci. 86:1341–1353.[Abstract/Free Full Text]

Krause, K. M., and G. R. Oetzel. 2005. Inducing subacute ruminal acidosis in lactating dairy cows. J. Dairy Sci. 88:3633–3639.[Abstract/Free Full Text]

Krehbiel, C. R., R. A. Stock, D. H. Shain, C. J. Richards, G. A. Ham, R. A. McCoy, T. J. Klopfenstein, R. A. Britton, and R. P. Huffman. 1995. Effect of level and type of fat on subacute acidosis in cattle fed dry-rolled corn finishing diets. J. Anim. Sci. 73:2438–2446.[Abstract]

Mackie, R. I., and F. M. C. Gilchrist. 1979. Change in lactate producing and lactate utilizing bacteria in relation to pH in the rumen of sheep during stepwise adaptation to a high concentrate diet. Appl. Environ. Microbiol. 38:422–430.[Abstract/Free Full Text]

Mertens, D. R. 1997. Creating a system for meeting the fiber requirements of dairy cows. J. Dairy Sci. 80:1463–1481.[Abstract]

Mertens, D. R. 2000. Physically effective NDF and its use in dairy rations explored. Pages in Feedstuffs April 10:11–14.

Mertens, D. R. 2007. Role of effective fiber in reducing milk fat depression in lactating cows fed Rumensin. J. Dairy Sci. 90(Suppl. 1):562. (Abstr.)

National Research Council. 2001. Nutrient Requirement of Dairy Cattle. 7th rev. ed. Natl. Acad. Sci., Washington, DC.

Neter, J., M. H. Kutner, C. J. Nachtsheim, and W. Wasserman. 1996. Applied linear statistical models. 4th ed. McGraw-Hill, Boston, MA.

Oetzel, G. R. 2000. Clinical aspects of ruminal acidosis in dairy cattle. Pages 46–53 in Proc. Am. Assoc. Bovine Practitioners, Rapid City, SD.

Offner, A., A. Bach, and D. Sauvant. 2003. Quantitative review of in situ starch degradation in the rumen. Anim. Feed Sci. Technol. 106:81–93.[CrossRef]

Ölschläger, V., I. Weber, W. Vahjen, M. Tafaj, O. Simon, and W. Drochner. 2006. Effect of hay particle size and concentrate level on the main cellulolytic bacteria in the rumen of dairy cows. Proc. Soc. Nutr. Physiol. 15:147. (Abstr.)

Pitt, R. E., and A. N. Pell. 1997. Modeling ruminal pH fluctuations: Interactions between meal frequency and digestion rate. J. Dairy Sci. 80:2429–2441.[Abstract]

Pitt, R. E., J. S. Van Kessel, D. G. Fox, A. N. Pell, M. C. Barry, and P. J. Van Soest. 1996. Prediction of ruminal volatile fatty acids and pH within the net carbohydrate and protein system. J. Anim. Sci. 74:226–244.[Abstract]

Plaizier, J. C., T. Garner, T. Droppo, and T. Whiting. 2004. Nutritional practices on Manitoba dairy farms. Can. J. Anim. Sci. 84:501–509.

Purvis, H., S. Fernando, K. Rutz, F. Najar, B. Roe, and U. DeSilva. 2006. Evaluation of rumen microbial fluctuations in response to subacute rumen acidosis using 16S rDNA profiles. J. Anim. Sci. 84(Suppl. 1):86. (Abstr.)

Ramsey, P. B., G. W. Mathison, and L. A. Goonewardene. 2001. Relationships between ruminal dry matter and starch disappearance and apparent digestibility of barley grain Anim. Feed Sci. Technol. 94:155–170.[CrossRef]

Reynolds, C. K. 2006. Production and metabolic effects of site of starch digestion in dairy cattle. Anim. Feed Sci. Technol. 130:78–94.[CrossRef]

Robbins, K. R., A. M. Saxton, and L. L. Southern. 2006. Estimation of nutrient requirements using broken-line regression analysis. J. Anim. Sci. 84(E Suppl.):E155–E165.[Abstract/Free Full Text]

SAS. 2003. User’s Guide. Statistic, v. 9.1 ed. SAS Inst. Inc., Cary, NC.

Sauvant, D., J. M. Perez, and G. Tran. 2004. Tables of composition and nutritional value of feed materials: Pigs, poultry, cattle, sheep, goats, rabbits, horses, fish. Wageningen Acad. Publ., Wageningen, the Netherlands.

Seo, S., L. O. Tedeschi, C. Lanzas, C. G. Schwab, and D. G. Fox. 2006. Development and evaluation of empirical equations to predict feed passage rate in cattle Anim. Feed Sci. Technol. 128:67–83.[CrossRef]

Shaver, R. D. 2002. Rumen acidosis in dairy cattle: Bunk management considerations. Pages 75–81 in Proc. 12th Int. Symp. on Lameness in Ruminants, Orlando, FL.

Silveira, C., M. Oba, W. Z. Yang, and K. A. Beauchemin. 2007. Selection of barley grain affects ruminal fermentation, starch digestibility, and productivity of lactating dairy cows. J. Dairy Sci. 90:2860–2869.[Abstract/Free Full Text]

Stone, W. C. 2004. Nutritional approaches to minimize subacute ruminal acidosis and laminitis in dairy cattle. J. Dairy Sci. 87(E Suppl.):E12–E26.

St-Pierre, N. R. 2001. Integrating quantitative finding from multiple studies using mixed model methodology. J. Dairy Sci. 84:741–755.[Abstract]

Tafaj, M., Q. Zebeli, B. Junck, H. Steingass, and W. Drochner. 2005. Effects of particle size of a total mixed ration on in vivo ruminal fermentation patterns and inocula characteristics used for in vitro gas production. Anim. Feed Sci. Technol. 123–124:139–154.

Taylor, C. C., and M. S. Allen. 2005. Corn grain endosperm type and brown mibrid 3 corn silage: Ruminal fermentation and N partitioning in lactating cows. J. Dairy Sci. 88:1434–1442.[Abstract/Free Full Text]

Vose, D. 2000. Risk analysis: A quantitative guide. 2nd ed. Wiley, Chichester, UK.

Weimer, P. J., D. M. Stevenson, D. R. Mertens, and E. E. Thomas. 2007. Effect of monensin feeding and withdrawal on ruminal populations of individual bacterial species in cows fed high-starch diets. J. Dairy Sci. 90(Suppl. 1):175. (Abstr.)

Yang, W. Z., and K. A. Beauchemin. 2006a. Increasing the physically effective fiber content of dairy cow diets may lower efficiency of feed use. J. Dairy Sci. 89:2694–2704.[Abstract/Free Full Text]

Yang, W. Z., and K. A. Beauchemin. 2006b. Physically effective fiber: method of determination and effects on chewing, ruminal acidosis, and digestion by dairy cows. J. Dairy Sci. 89:2618–2633.[Abstract/Free Full Text]

Yang, W. Z., and K. A. Beauchemin. 2007a. Altering physically effective fiber intake through forage proportion and particle length: Chewing and ruminal pH. J. Dairy Sci. 90:2826–2838.[Abstract/Free Full Text]

Yang, W. Z., and K. A. Beauchemin. 2007b. Altering physically effective fiber intake through forage proportion and particle length: Digestion and milk production. J. Dairy Sci. 90:3410–3421.[Abstract/Free Full Text]

Yang, W. Z., K. A. Beauchemin, and L. M. Rode. 2002. Effects of particle size of alfalfa-based dairy cow diets on site and extent of digestion. J. Dairy Sci. 85:1958–1968.[Abstract/Free Full Text]

Yansari, A. T., R. Valizadeh, A. Naserian, D. A. Christensen, P. Yu, and F. E. Shahroodi. 2004. Effects of alfalfa particle size and specific gravity on chewing activity, digestibility, and performance of Holstein dairy cows. J. Dairy Sci. 87:3912–3924.[Abstract/Free Full Text]

Zebeli, Q., and W. Drochner. 2007. A simulation model to evaluate the risk of subacute ruminal acidosis in high-yielding dairy cows fed total mixed rations ad libitum. Page 160 in Proc. 13th Int. Conf. Prod. Diseases Farm Anim. Merkur Druck & Kupier-Zentrum, Leipzig, Germany. (Abstr.)

Zebeli, Q., V. Ölschläger, M. Tafaj, W. Vahjen, B. Junck, O. Simon, and W. Drochner. 2007a. Evaluation of counts of ruminal fibrolytic bacteria and enzyme activities in response to corn silage particle size in high-yielding dairy cows. J. Dairy Sci. 90(Suppl. 1):618–619. (Abstr.)

Zebeli, Q., M. Tafaj, B. Metzler, H. Steingass, and W. Drochner. 2006a. New aspects on the contribution of ruminal mat quality on digesta kinetics in reticulorumen of high-producing dairy cows. Übers. Tiernähr. 34:165–196.

Zebeli, Q., M. Tafaj, H. Steingass, B. Metzler, and W. Drochner. 2006b. Effects of physically effective fiber on digestive processes and milk fat content in early lactating dairy cows fed total mixed rations. J. Dairy Sci. 89:651–668.[Abstract/Free Full Text]

Zebeli, Q., M. Tafaj, I. Weber, J. Dijkstra, H. Steingass, and W. Drochner. 2007b. Effects of varying dietary forage particle size in two concentrate levels on chewing activity, ruminal mat characteristics and passage in dairy cows. J. Dairy Sci. 90:1929–1942.[Abstract/Free Full Text]

	List of References Used in Meta-Analysis and Listed in Appendix Tables A1, A2, and A3

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGEMENTS REFERENCES List of References Used...

 


Allen, D. M., and R. J. Grant. 2000. Interactions between forage and wet corn gluten feed as sources of fiber in diets for lactating dairy cows. J. Dairy Sci. 83:322–331.[Abstract]

AlZahal, O., E. Kebreab, J. France, M. Froetschel, and B. W. McBride. 2007a. Ruminal temperature may aid in the detection of subacute ruminal acidosis. J. Dairy Sci. 90(Suppl. 1):562. (Abstr.)

AlZahal, O., M. Or-Rashid, S. L. Greenwood, M. S. Douglas and B. W. McBride. 2007b. Subacute ruminal acidosis increases milk fat depression with diets supplemented with polyunsaturated fatty acids. J. Dairy Sci. 90(Suppl. 1):561. (Abstr.)

Bach, A., C. Iglesias, and M. Devant. 2007. Daily rumen pH pattern of loose-housed dairy cattle as affected by feeding pattern and live yeast supplementation. Anim. Feed Sci. Technol. 136:146–153.[CrossRef]

Bal, M. A., R. D. Shaver, A. G. Jirovec, K. J. Shinners, and J. G. Coors. 2000. Crop processing and chop length of corn silage: Effects on intake, digestion, and milk production by dairy cows. J. Dairy Sci. 83:1264–1273.[Abstract]

Beauchemin, K. A., L. M. Rode, and M. V. Eliason. 1997. Chewing activities and milk production of dairy cows fed alfalfa as hay, silage, or dried cubes of hay or silage. J. Dairy Sci. 80:324–333.[Abstract]

Beauchemin, K. A., L. M. Rode, M. Maekawa, D. P. Morgavi, and R. Kampen. 2000. Evaluation of a nonstarch polysaccharidase feed enzyme in dairy cow diets. J. Dairy Sci. 83:543–553.[Abstract]

Beauchemin, K. A., and W. Z. Yang. 2005. Effects of physically effective fiber on intake, chewing activity, and ruminal acidosis for dairy cows fed diets based on corn silage. J. Dairy Sci. 88:2117–2129.[Abstract/Free Full Text]

Beauchemin, K. A.,W. Z. Yang, D. P. Morgavi, G. R. Ghorbani, W. Kautz, and J. A. Z. Leedle. 2003b. Effects of bacterial direct-fed microbials and yeast on site and extent of digestion, blood chemistry, and subclinical ruminal acidosis in feedlot cattle. J. Anim. Sci. 81:1628–1640.[Abstract/Free Full Text]

Beauchemin, K. A., W. Z. Yang, and L. M. Rode. 2001. Effects of barley grain processing on the site and extent of digestion of beef feedlot finishing diets. J. Anim. Sci. 79:1925–1936.[Abstract/Free Full Text]

Beauchemin, K. A., W. Z. Yang, and L. M. Rode. 2003a. Effects of particle size of alfalfa-based dairy cow diets on chewing activity, ruminal fermentation, and milk production. J. Dairy Sci. 86:630–643.[Abstract/Free Full Text]

Bevans, D. W., K. A. Beauchemin, K. S. Schwartzkopf-Genswein, J. J. McKinnon, and T. A. McAllister. 2005. Effect of rapid or gradual grain adaptation on subacute acidosis and feed intake by feedlot cattle. J. Anim Sci. 83:1116–1132.[Abstract/Free Full Text]

Bezerra, S. E., A. C. Queiroz, F. Maldonado, J. C. Peireira, and M. F. Paulino. 2002. Efeito do perfil granulométrico das partículas dietéticas sobre parâmetros de desempenho de vacas leiteiras em lactacão. R. Bras. Zootec. 31:1511–1520.

Bhandari, S. K., K. H. Ominski, K. M. Wittenberg, and J. C. Plaizier. 2007. Effects of chop length of alfalfa and corn silage on milk production and rumen fermentation of dairy cows. J. Dairy Sci. 90:2355–2366.[Abstract/Free Full Text]

Bhandari, S. K., J. C. Plaizier, K. H. Ominski, and K. M. Wittenberg. 2006. Effects of chop length of alfalfa and oat silage on feed intake, milk production, rumen pH and feeding behaviour of dairy cows fed total mixed rations. J. Dairy Sci. 84(Suppl. 1):262 (Abstr).

Biricik, H., H. Gencoglu, B. Bozan, B. H. Gulmez, and I. I. Turkmen. 2007. The effect of dry corn gluten feed on chewing activities and rumen parameters in lactating dairy cows. Italian J. Anim. Sci. 6:61–70.

Boddugari, K., R. J. Grant, R. Stock, and M. Lewis. 2001. Maximal replacement of forage and concentrate with a new wet corn milling product for lactating dairy cows. J. Dairy Sci. 84:873–884.[Abstract]

Bowman, G. R., K. A. Beauchemin, and J. A. Shelford. 2003. Fibrolytic enzymes and parity effects on feeding behavior, salivation, and ruminal pH of lactating dairy cows. J. Dairy Sci. 86:565–575.[Abstract/Free Full Text]

Calberry, J. M., J. C. Plaizier, M. S. Einarson, and B. W. McBridge. 2003. Effects of replacing chopped alfalfa hay with alfalfa silage in a total mixed ration on production and rumen conditions of lactating dairy cows. J. Dairy Sci. 86:3611–3619.[Abstract/Free Full Text]

Chiquette, J., M. J. Allison, and M. A. Rasmussen. 2007. Efficacy of Prevotella briantii 25A and mixture of Enterococcus faecium and Saccharamyces cerevisiae to control sub-clinical acidosis in dairy cows. J. Dairy Sci. 90(Suppl. 1):340–341. (Abstr.)

Clark, P. W., and L. E. Armentano. 1997. Influence of particle size on the effectiveness of beet pulp fiber. J. Dairy Sci. 80:898–904.[Abstract]

Clark, P. W., and L. E. Armentano. 1999. Influence of particle size on the effectiveness of the fiber in corn silage. J. Dairy Sci. 82:581–588.[Abstract]

Clark, P. W., and L. E. Armentano. 2002. Influence of particle size on the effectiveness of the fiber in alfalfa silage. J. Dairy Sci. 85:3000–3007.[Abstract/Free Full Text]

Cottee, G., I. Kyriazakis, T. M. Widowski, M. I. Lindinger, J. P. Cant, T. F. Duffield, V. R. Osborne, and B. W. McBride. 2004. The effects of subacute ruminal acidosis on sodium bicarbonate-supplemented water intake for lactating dairy cows. J. Dairy Sci. 87:2248–2253.[Abstract/Free Full Text]

Couderc, J. J., D. H. Rearte, G. F. Schroeder, J. I. Ronchi, and F. J. Santini. 2006. Silage chop length and hay supplementation on milk yield, chewing activity, and ruminal digestion by dairy cows. J. Dairy Sci. 89:3599–3608.[Abstract/Free Full Text]

Cruywagen, C.W., S. J. Taylor, and M. M. Beya. 2007. The effect of buffering dairy cow diets with limestone, Acid Buf or sodium bicarbonate + limestone on production response and rumen parameters. J. Dairy Sci. 90(Suppl. 1):561. (Abstr.)

Dado R. G., and M. S. Allen. 1995. Intake limitations, feeding behavior, and rumen function of cows challenged with rumen fill from dietary fiber or inert bulk. J. Dairy Sci. 78:118–133.[Abstract]

Dann, H. M., K. W. Cotansch, M. P. Carter, C. S. Ballard, T. Eguchi, and R. J. Grant. 2007. Validation of an on farm tool (Z-Box) for determining a physical effectiveness factor using a bioassay based on chewing activity and ruminal fermentation in lactating dairy cows. J. Dairy Sci. 90(Suppl. 1):563. (Abstr.)

DeFrain, J. M., J. E. Shirley, E. C. Titgemeyer, A. F. Park, and R. T. Ethington. 2002. Impact of feeding a raw soybean hull-condensed corn steep liquor pellet on induced subacute ruminal acidosis in lactating cows. J. Dairy Sci. 85:2000–2008.[Abstract/Free Full Text]

Dohme, F., T. J. DeVries, K. Beauchemin, K. M. Krause, and K. S. Schwartzkopf-Genswein. 2007a. Susceptibility of lactating dairy cows to ruminal acidosis depends on the proportion of forage in the diet. J. Dairy Sci. 90 (Suppl. 1):560. (Abstr.)

Dohme, F., A. Scharenberg, and A. Münger. 2007b. Effects of supplements differing in ruminal starch availability on milk yield, blood metabolites and ruminal fermentation in cows grazing ryegrass/ clover pasture. Proc. Soc. Nutr. Physiol. 16:27. (Abstr.)

Douglas, M. S., O. AlZahal, S. L. Greenwood, M. Or-Rashid, and B. W. McBride. 2007. Effect of added yeast culture on lactating dairy cows with subacute ruminal acidosis. J. Dairy Sci. 90 (Suppl. 1):352. (Abstr.)[Abstract/Free Full Text]

Einarson, M. S., J. C. Plaizier, and K. M. Wittenberg. 2004. Effects of barley silage chop length on productivity and rumen conditions of lactating dairy cows fed total mixed ration. J. Dairy Sci. 87:2987–2996.[Abstract/Free Full Text]

Fairfield, A. M., J. C. Plaizier, T. F. Duffield, M. I. Lindinger, R. Bagg, P. Dick, and B. W. McBride. 2007. Effects of prepartum administration of a monensin controlled release capsule on rumen pH, feed intake, and milk production of transition dairy cows. J. Dairy Sci. 90:937–945.[Abstract/Free Full Text]

Fernandez, I., C. Martin, M. Champion, and B. Michalet–Doreau. 2004. Effect of corn hybrid and chop length of whole-plant corn silage on digestion and intake by dairy cows. J. Dairy Sci. 87:1298–1309.[Abstract/Free Full Text]

Gencoglu, H., and I. I. Turkmen. 2006. Effects of forage source on chewing and rumen fermentation in lactating dairy cows. Rev. Med. Vet. 157:463–470.

Gencoglu, H., I. I. Turkmen, G. Deniz, and H. Biricik. 2007. The effect of partial replacement of corn silage on rumen degradability, milk production and composition in lactating primiparous dairy cows. Italian J. Anim. Sci. 6:71–80.

Ghorbani, G. R., D. P. Morgavi, K. A. Beauchemin, and J. A. Z. Leedle. 2002. Effects of bacterial direct-fed microbials on ruminal fermentation, blood variables, and the microbial populations of feedlot cattle. J. Anim Sci. 80:1977–1985.[Abstract/Free Full Text]

Goad, D. W., C. L. Goad, and T. G. Nagaraja. 1998. Ruminal microbial and fermentative changes associated with experimentally induced subacute acidosis in steers. J. Anim Sci. 76:234–241.[Abstract/Free Full Text]

Gozho, G. N., D. O. Krause, and J. C. Plaizier. 2006. Rumen lipopolysaccharide and inflammation during grain adaptation and subacute ruminal acidosis in steers. J. Dairy Sci. 89:4404–4413.[Abstract/Free Full Text]

Gozho, G. N., D. O. Krause, and J. C. Plaizier. 2007. Ruminal lipopolysaccharide concentration and inflammatory response during grain-induced subacute ruminal acidosis in dairy cows. J. Dairy Sci. 90:856–866.[Abstract/Free Full Text]

Gozho, G. N., J. C. Plaizier, D. O. Krause, A. D. Kennedy, and K. M. Wittenberg. 2005. Subacute ruminal acidosis induces ruminal lipopolysaccharide endotoxin release and triggers an inflammatory response. J. Dairy Sci. 88:1399–1403.[Abstract/Free Full Text]

Graf, M. C., M. Kreuzer, and F. Dohme. 2005a. Influence of supplementing hay to grass once or three times per day on the effectiveness of the fibre as determined by changes in ruminal pH, chewing activity and milk composition of cows. Anim. Res. 54:321–335.[CrossRef]

Graf, M. C., M. Kreuzer, and F. Dohme. 2005b. Effects of supplemental hay and corn silage versus full-time grazing on ruminal pH and chewing activity of dairy cows. J. Dairy Sci. 88:711–725.[Abstract/Free Full Text]

Grant, R. J., V. F. Colenbrander, and D. R. Mertens. 1990a. Milk fat depression in dairy cows: Role of particle size of alfalfa hay. J. Dairy Sci. 73:1823–1833.[Abstract]

Grant, R. J., V. F. Colenbrander, and D. R. Mertens. 1990b. Milk fat depression in dairy cows: Role of silage particle size. J. Dairy Sci. 73:1834–1842.[Abstract]

Gulmez, B. H., and I. I. Turkmen. 2007. Effect of starch sources with different degradation rates on ruminal fermentation of lactating dairy cows. Rev. Med. Vet. 158:92–99.

Harvatine K. J., and M. S. Allen. 2006. Effects of fatty acid supplements on feed intake, and feeding and chewing behavior of lactating dairy cows. J. Dairy Sci. 89:1104–1112.[Abstract/Free Full Text]

Johnson, L. M., J. H. Harrison, D. Davidson, W. C. Mahanna, and K. Shinners. 2003. Corn silage management: Effects of hybrid, chop length, and mechanical processing on digestion and energy content. J. Dairy Sci. 86:208–231.[Abstract/Free Full Text]

Johnson, L. M., J. H. Harrison, D. Davidson, M. Swift, W. C. Mahanna, and K. Shinners. 2002. Corn silage management iii: effects of hybrid, maturity, and processing on nitrogen metabolism and ruminal fermentation. J. Dairy Sci. 85:2928–2947.[Abstract/Free Full Text]

Junck, B., M. Tafaj, Q. Zebeli, R. Funk, H. Steingass, and W. Drochner. 2004. Einfluss der Häcksellänge auf Strukturwert und Qualität der Maissilage (Influence of length of the cut on the physical evaluation and quality of corn silage). Page 96 in Proc. 116th VDLUFA Congress, Rostock, Germany. (Abstr.)

Kennelly, J. J., B. Robinson, and G. R. Khorasani. 1999. Influence of carbohydrate source and buffer on rumen fermentation characteristics, milk yield, and milk composition in early-lactation Holstein cows. J. Dairy Sci. 82:2486–2496.[Abstract]

Keunen, J. E., J. C. Plaizier, I. Kyriazakis, T. F. Duffield, T. M. Widowski, M. I. Lindinger, and B. W. McBride. 2003. Short communication: Effects of subacute ruminal acidosis on free-choice intake of sodium bicarbonate in lactating dairy cows. J. Dairy Sci. 86: 954–957.[Abstract/Free Full Text]

Keunen, J. E., J. C. Plaizier, L. Kyriazakis, T. F. Duffield, T. M. Widowski, M. I. Lindinger, and B. W. McBride. 2002. Effects of a subacute ruminal acidosis model on the diet selection of dairy cows. J. Dairy Sci. 85: 3304–3313.[Abstract/Free Full Text]

Khafipoor, E., D. O. Krause, and J. C. Plaizier. 2007a. Grain-induced subacute ruminal acidosis (SARA) stimulates translocation of lipopolysaccharide (LPS) into the blood, and increases acute phase proteins in bovine plasma and milk. J. Dairy Sci. 90 (Suppl. 1):654. (Abstr.)

Khafipoor, E., D. O. Krause, and J. C. Plaizier. 2007b. Induction of subacute ruminal acidosis (SARA) by replacing alfalfa hay with alfalfa pellets does not stimulate inflammatory response in lactating dairy cows. J. Dairy Sci. 90(Suppl. 1):654. (Abstr.)

Khezri, A., A. Nikkhah, A. Z. Shahneh, and M. H. Fooladi. 2005. The effect of reducing alfalfa hay cut length on TMR particle size distribution, rumen pH and chewing activity of cows in early lactation using Penn State Particles Separator (PSPS). Page 75 in Proc. British Soc. Anim. Sci., Midlothian, UK. (Abstr.)

Khorasani, G. R., and J. J. Kennelly. 2001. Influence of carbohydrate source and buffer on rumen fermentation characteristics, milk yield, and milk composition in late-lactation Holstein cows. J. Dairy Sci. 84:1707–1716.[Abstract]

Khorasani, G. R., E. K. Okine, and J. J. Kennelly. 2001a. Effects of substituting barley grain with corn on ruminal fermentation characteristics, milk yield, and milk composition of Holstein cows. J. Dairy Sci. 84:2760–2769.[Abstract]

Khorasani, G. R., E. K. Okine, and J. J. Kennelly. 2001b. Effects of forage source and amount of concentrate on rumen and intestinal digestion of nutrients in late-lactation cows. J. Dairy Sci. 84:1156–1165.[Abstract]

Kleinschmit, D. H., D. J. Schingoethe, A. R. Hippen, and K. F. Kalscheur. 2007. Dried distillers grains plus solubles with corn silage or alfalfa hay as the primary forage source in dairy cows. J. Dairy Sci. 90:5587–5599.[Abstract/Free Full Text]

Koenig, K. M., K. A. Beauchemin, and L. M. Rode. 2001. Effect of grain processing and silage on microbial protein synthesis and nutrient digestibility in beef cattle fed barley-based diets. J. Anim. Sci. 81:1057–1067.

Kononoff, P. J., and A. J. Heinrichs. 2003a. The effect of corn silage particle size and cottonseed hulls on cows in early lactation. J. Dairy Sci. 86:2438–2451.[Abstract/Free Full Text]

Kononoff, P. J., and A. J. Heinrichs. 2003b. The effect of reducing alfalfa haylage particle size on cows in early lactation. J. Dairy Sci. 86:1445–1457.[Abstract/Free Full Text]

Kononoff, P. J., A. J. Heinrichs, and H. A. Lehman. 2003. The effect of corn silage particle size on eating behavior, chewing activities, and rumen fermentation in lactating dairy cows. J. Dairy Sci. 86:3343–3353.[Abstract/Free Full Text]

Krajcarski-Hunt, H., J. C. Plaizier, J.-P. Walton, R. Spratt, and B. W. McBride. 2002. Short communication: Effect of subacute ruminal acidosis on in situ fiber digestion in lactating dairy cows. J. Dairy Sci. 85:570–573.[Abstract]

Krause K. M., and G. R. Oetzel. 2005. Inducing subacute ruminal acidosis in lactating dairy cows. J. Dairy Sci. 88:3633–3639.[Abstract/Free Full Text]

Krause, K. M., and D. K. Combs. 2003. Effects of forage particle size, forage source, and grain fermentability on performance and ruminal pH in midlactation cows. J. Dairy Sci. 86:1382–1397.[Abstract/Free Full Text]

Krause, K. M., D. K. Combs, and K. A. Beauchemin, 2002a. Effects of forage particle size and grain fermentability in midlactation dairy cows. II. Ruminal pH and chewing activity. J. Dairy Sci. 85:1947–1957.[Abstract/Free Full Text]

Krause, K. M., D. K. Combs, and K. A. Beauchemin. 2002b. Effects of forage particle size and grain fermentability in midlactation cows. I. Milk production and diet digestibility. J. Dairy Sci. 85:1936–1946.[Abstract/Free Full Text]

Krause, K. M., D. K. Combs, and K. A. Beauchemin. 2003. Effects of increasing levels of refined cornstarch in the diet of lactating dairy cows on performance and ruminal pH. J. Dairy Sci. 86: 1341–1353.[Abstract/Free Full Text]

Krause, M., K. A. Beauchemin, L. M. Rode, B. I. Farr, and P. Norgaard. 1998. Fibrolytic enzyme treatment of barley grain and source of forage in high-grain diets fed to growing cattle. J. Anim. Sci. 76:2912–2920.[Abstract/Free Full Text]

Krehbiel, C. R., R. A. Stock, D. H. Shain, C. J. Richards, G. A. Ham, R. A. McCoy, T. J. Klopfenstein, R. A. Britton, and R. P. Huffman. 1995. Effect of level and type of fat on subacute acidosis in cattle fed dry-rolled corn finishing diets. J. Anim. Sci. 73:2438–2446.[Abstract]

Le Liboux, S., and J. L. Peyrand. 1998. Effect of forage particle size and intake level on fermentation patterns and sites and extent of digestion in dairy cows fed mixed diets. Anim. Feed Sci. Technol. 73:131–150.[CrossRef]

Le Liboux, S., and J. L. Peyrand. 1999. Effect of forage particle size and feeding frequency on fermentation patterns and sites and extent of digestion in dairy cows fed mixed diets. Anim. Feed Sci. Technol. 76:297–319.[CrossRef]

Leonardi, C., K. J. Shinners, and L. E. Armentano. 2005. Effect of different dietary geometric mean length particle length and particle size distribution of oat silage on feeding behavior and productive performance of dairy cattle. J. Dairy Sci. 88:698–710.[Abstract/Free Full Text]

Maekawa, M., K. A. Beauchemin, and D. A. Christensen. 2002a. Effect of concentrate level and feeding management on chewing activities, saliva production, and ruminal pH of lactating dairy cows. J. Dairy Sci. 85:1165–1175.[Abstract]

Maekawa, M., K. A. Beauchemin, and D. A. Christensen. 2002b. Chewing activity, saliva production, and ruminal pH of primiparous and multiparous lactating dairy cows. J. Dairy Sci. 85:1176–1182.[Abstract]

Mertens, D. R. 2007. Role of effective fiber in reducing milk fat depression in lactating cows fed Rumensin. J. Dairy. Sci. 90(Suppl. 1):562. (Abstr.)

Mutsvangwa, T., J. P. Walton, J. C. Plaizier, T. F. Duffield, R. Bagg, P. Dick, G. Vessie, and B. W. McBride. 2002. Effects of a monensin controlled-release capsule or premix on attenuation of subacute ruminal acidosis in dairy cows. J. Dairy Sci. 85:3454–3461.[Abstract/Free Full Text]

Oba M., and M. S. Allen. 2000. Effects of brown midrib 3 mutation in corn silage on productivity of dairy cows fed two concentrations of dietary neutral detergent fiber: 1. Feeding behavior and nutrient utilization. J. Dairy Sci. 83:1333–1341.[Abstract]

Onetti, S. G., R. D. Shaver, S. J. Bertics, and R. R. Grummer. 2003. Influence of corn silage particle length on the performance of lactating dairy cows fed supplemental tallow. J. Dairy Sci. 86:2949–2957.[Abstract/Free Full Text]

Osborne, J. K., T. Mutsvangwa, O. Alzahal, T. F. Duffield, R. Bagg, P. Dick, G. Vessie, and B. W. McBride. 2004. Effects of monensin on ruminal forage degradability and total tract diet digestibility in lactating dairy cows during grain-induced subacute ruminal acidosis. J. Dairy Sci. 87:1840–1847.[Abstract/Free Full Text]

Penner, G. B., K. A. Beauchemin, and T. Mutsvangwa. 2007. Severity of ruminal acidosis in primiparous Holstein cows during the peri-parturient period. J. Dairy Sci. 90:365–375.[Abstract/Free Full Text]

Plaizier, J. C. 2004. Replacing chopped alfalfa hay with alfalfa silage in barley grain and alfalfa based total mixed rations for lactating dairy cows. J. Dairy Sci. 87:2495–2505.[Abstract/Free Full Text]

Reis R. B., and D. K. Combs. 2000a. Effects of increasing levels of grain supplementation on rumen environment and lactation performance of dairy cows grazing grass-legume pasture. J. Dairy Sci. 83:2888–2898.[Abstract]

Reis R. B., and D. K. Combs. 2000b. Effects of corn processing and supplemental hay on rumen environment and lactation performance of dairy cows grazing grass-legume pasture. J. Dairy Sci. 83:2529–2538.[Abstract]

Reis, R. B., F. San Emeterio, D. K. Combs, L. D. Satter, and H. N. Costa. 2001. Effects of corn particle size and source on performance of lactating cows fed direct-cut grass-legume forage. J. Dairy Sci. 84:429–441.[Abstract]

Robinson, P. H., M. Gill, and J. J. Kennelly. 1997. Influence of time of feeding a protein meal on ruminal fermentation and forestomach digestion in dairy cows. J. Dairy Sci. 80:1366–1373.[Abstract]

Rustomo, B., O. Alzahal, J. P. Cant, M. Z. Fan, T. F. Duffield, N. E. Odongo, and B.W. McBride. 2006b. Acidogenic value of feeds II. Effects of rumen acid load from feeds on dry matter intake, ruminal pH, fibre degradability and milk production in the lactating dairy cow. Can. J. Anim. Sci. 86:119–127.

Rustomo, B., O. AlZahal, N. E. Odongo, T. F. Duffield, and B. W. McBride. 2006a. Effects of rumen acid load from feed and forage particle size on ruminal pH and dry matter intake in the lactating dairy cow. J. Dairy Sci. 89:4758–4768.[Abstract/Free Full Text]

San Emeterio, F., R. B. Reis, W. E. Campos, and L. D. Satter. 2000. Effect of coarse or fine grinding on utilization of dry or ensiled corn by lactating dairy cows. J. Dairy Sci. 83:2839–2848.[Abstract]

Schwab, E. C., R. D., Shaver, K. J. Shinners, J. G. Lauer, and J. G. Coors. 2002. Processing and chop length in brown-midrib corn silage on intake, digestion, and milk production by dairy cows. J. Dairy Sci. 85:613–623.[Abstract]

Schwartzkopf-Genswein, K. S., K. A. Beauchemin, T. A. McAllister, D. J. Gibb, M. Streeter, and A. D. Kennedy. 2004. Effect of feed delivery fluctuations and feeding time on ruminal acidosis, growth performance, and feeding behavior of feedlot cattle. J. Anim. Sci. 82:3357–3365.[Abstract/Free Full Text]

Silveira, C., M. Oba, W. Z. Yang, and K. A. Beauchemin. 2007. Selection of barley grain affects ruminal fermentation, starch digestibility, and productivity of lactating dairy cows. J. Dairy Sci. 90:2860–2869.[Abstract/Free Full Text]

Soita, H. W., D. A. Christensen, and J. J. McKinnon. 2000. Influence of particle size on the effectiveness of the fiber in barley silage. J. Dairy Sci. 83:2295–2300.[Abstract]

Soita, H. W., M. Fehr, D. A. Christensen, and T. Mutsvangwa. 2005. Effects of corn silage particle length and forage:concentrate ratio on milk fatty acid composition in dairy cows fed supplemental flaxseed. J. Dairy Sci. 88:2813–2819.[Abstract/Free Full Text]

Tafaj, M., B. Junck, A. Maulbetsch, H. Steingass, H.-P. Piepho, and W. Drochner. 2004. Digesta characteristics of dorsal, middle and ventral rumen of cows fed with different hay qualities and concentrates levels. Arch. Anim. Nutr. 58:325–342.[CrossRef][Medline]

Tafaj, M., Q. Zebeli, A. Maulbetsch, H. Steingass, and W. Drochner. 2006. Effects of fibre concentration of diets consisting of hay and slowly degradable concentrate on the fermentation patterns and digesta particle size in the rumen of mid-lactation dairy cows. Arch. Anim. Nutr. 60:254–266.[CrossRef][Medline]

Tafaj, M., Q. Zebeli, B. Junck, H. Steingass, and W. Drochner. 2005. Effects of particle size of a total mixed ration on in vivo ruminal fermentation patterns and inocula characteristics used for in vitro gas production. Anim. Feed Sci. Technol. 123–124:139–154.

Taylor, C. C., and M. S. Allen. 2005. Corn grain endosperm type and brown midrib 3 corn silage: Ruminal fermentation and N partitioning in lactating cows. J. Dairy Sci. 88:1434–1442.[Abstract/Free Full Text]

Voelker J. A., and M. S. Allen. 2003. Pelleted beet pulp substituted for high–moisture corn: 3. effects on ruminal fermentation, pH, and microbial protein efficiency in lactating dairy cows. J. Dairy Sci. 86:3562–3570.[Abstract/Free Full Text]

Weber, I., H. Steingass, M. Tafaj, and W. Drochner. 2006. Einfluss der Kohlenhydrat-zusammensetzung von Kraftfuttermischungen in Totalen Mischrationen auf Futteraufnahme, Leistungsparameter und Fermentation im Pansen bei Milchkühen (Effects of composition of concentrates of total mixed rations on dry matter intake, performance and ruminal fermentation in dairy cows). Page 98 in Proc. 118th VDLUFA Congress, VDLUFA Verlag, Darmstadt, Germany.

Weidner, S. J., and R. J. Grant. 1994. Altered ruminal mat consistency by high percentages of soyabean hulls to lactating cows. J. Dairy Sci. 77:522–532.[Abstract]

Yang W. Z., and K. A. Beauchemin. 2007a. Altering physically effective fiber intake through forage proportion and particle length: Chewing and ruminal pH. J. Dairy Sci. 90:2826–2838.[Abstract/Free Full Text]

Yang, W. Z., and K. A. Beauchemin. 2006a. Effects of physically effective fiber on chewing activity and ruminal pH of dairy cows fed diets based on barley silage. J. Dairy Sci. 89:217–228.[Abstract/Free Full Text]

Yang, W. Z., and K. A. Beauchemin. 2007b. Altering physically effective fiber intake through forage proportion and particle length: Digestion and milk production. J. Dairy Sci. 90:3410–3421.[Abstract/Free Full Text]

Yang, W. Z., and K. A. Beauchemin. 2006b. Physically effective fiber: Method of determination and effects on chewing, ruminal acidosis, and digestion by dairy cows. J. Dairy Sci. 89:2618–2633.[Abstract/Free Full Text]

Yang, W. Z., C. Benchaar, B. N. Ametaj, A. V. Chaves, M. L. He, and T. A. McAllister. 2007. Effects of garlic and juniper berry essential oils on ruminal fermentation and on the site and extent of digestion in lactating cows. J. Dairy Sci. 90:5671–5681.[Abstract/Free Full Text]

Yang, W. Z., K. A. Beauchemin, and L. M. Rode. 2000. Effects of barley grain processing on extent of digestion and milk production of lactating cows. J. Dairy Sci. 83:554–568.[Abstract]

Yang, W. Z., K. A. Beauchemin, and L. M. Rode. 2001a. Effects of grain processing, forage to concentrate ratio, and forage particle size on rumen pH and digestion in dairy cows. J. Dairy Sci. 84:2203–2216.[Abstract]

Yang, W. Z., K. A. Beauchemin, and L. M. Rode. 2001b. Barley processing, forage:concentrate, and forage length effects on chewing and digesta passage in lactating cows. J. Dairy Sci. 84:2709–2720.[Abstract]

Yang, W. Z., K. A. Beauchemin, and L. M. Rode. 2002. Effects of particle size of alfalfa-based dairy cow diets on site and extent of digestion. J. Dairy Sci. 85:1958–1968.[Abstract/Free Full Text]

Yang, W. Z., K. A. Beauchemin, K. M. Koenig, and L. M. Rode. 1997. Comparison of hull-less barley, barley, or corn for lactating cows: effects on extent of digestion and milk production. J. Dairy Sci. 80:2475–2486.[Abstract]

Yansari, A. T., R. Valizadeh, A. Naserian, D. A. Christensen, P. Yu, and F. E. Shahroodi. 2004. Effects of alfalfa particle size and specific gravity on chewing activity, digestibility, and performance of Holstein dairy cows. J. Dairy Sci. 87:3912–3924.[Abstract/Free Full Text]

Zebeli, Q. 2006. Einfluss der Partikellänge von Totalmischrationen (TMR) auf die Digestaschichtung und Verdauungsvorgänge im Pansen hochleistender Milchkühe. PhD Dissertation, University of Hohenheim, Shaker Verlag, Aachen, Germany.

Zebeli, Q., V. Ölschläger, M. Tafaj, W. Vahjen, B. Junck, O. Simon, and W. Drochner. 2007. Evaluation of counts of ruminal fibrolytic bacteria and enzyme activities in response to corn silage particle size in high-yielding dairy cows. J. Dairy Sci. 90(Suppl. 1):618–619. (Abstr.)

Zebeli, Q., M. Tafaj, I. Weber, H. Steingass, and W. Drochner. 2008. Effects of dietary forage particle size and concentrate level on fermentation profile, in vitro degradation characteristics and concentration of liquid- or solid-associated bacterial mass in the rumen of dairy cows. Anim. Feed Sci. Technol. 140:307–345.[CrossRef]

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