FORAGE PARTICLE SIZE AND RATION SORTING IN LACTATING DAIRY …

The Pennsylvania State University

The Graduate School

College of Agricultural Sciences

FORAGE PARTICLE SIZE AND RATION SORTING

IN LACTATING DAIRY COWS

A Dissertation in

Animal Science

by

Daryl D. Maulfair

2011 Daryl D. Maulfair

Submitted in Partial Fulfillment

of the Requirements

for the Degree of

Doctor of Philosophy

August 2011

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The dissertation of Daryl D. Maulfair was reviewed and approved* by the following:

Arlyn J. Heinrichs

Professor of Dairy and Animal Science

Dissertation Advisor

Chair of Committee

Chad D. Dechow

Associate Professor of Dairy Cattle Genetics

Kevin J. Harvatine

Assistant Professor of Nutritional Physiology

Robert J. Van Saun

Professor of Veterinary Science

Gabriella A. Varga

Distinguished Professor of Animal Science

Terry D. Etherton

Distinguished Professor of Animal Nutrition

Head of the Department of Dairy and Animal Science

*Signatures are on file in the Graduate School

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Abstract

Three studies were conducted on early to late lactation Holstein dairy cows to examine

the effects of forage particle size (FPS) and ration sorting on chewing behavior, ruminal

fermentation, and milk yield and components. The objective of the first experiment was to study

effects of replacing alfalfa haylage with dry chopped alfalfa hay in the ration on sorting activity

and to determine effects on ruminal fermentation, milk production, or milk composition. In

addition, a second objective of this study was to compare results of the PSPS and RTPS for the

same TMR samples and to determine effects of separation method on particle size distribution.

Ration FPS was varied by replacing alfalfa silage with dry chopped alfalfa hay. The levels of hay

used were 5, 10, 20, and 40% of forage DM. The results of this study showed that sorting

occurred in all rations, but there was only minimal difference in the type or degree of sorting

between treatments and only during the first 4 h after feeding. Sorting activity was highest at the

beginning of the d and by 24 h after feeding the diets consumed by the cows were not

significantly different from the offered diets. There were no negative effects of including dry

chopped alfalfa hay in rations up to 23.5% of ration DM on DM intake, milk yield, and rumen

fermentation. Small decreases in milk fat and protein content were found to occur with increasing

dry hay inclusion. Data from the Penn State and Ro-Tap particle separators were compared, when

separating the same TMR samples, and it was determined that data obtained from these 2

methods of particle separation are not directly comparable and that method of particle separation

should be considered when interpreting experimental results.

The second experiment’s objective was to study the interactions between FPS and

ruminally fermentable carbohydrates (RFC) for ration sorting, ruminal fermentation, chewing

activity, and milk yield and components. This study varied FPS and RFC by feeding 2 lengths of

corn silage and 2 grind sizes of corn grain. The results showed that altering RFC had greater

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influence on milk production parameters than FPS; increasing RFC increased milk yield and

protein content and decreased milk fat content. Ruminal fermentation was not affected by either

FPS or RFC. Ration sorting occurred on all diets as evidenced by the changes in starch, NDF, and

particle size composition of the refusals throughout the d and also by selection indices. Diets

containing long FPS were sorted to a greater degree than diets containing short FPS, but there

was no interaction between FPS and RFC for ration sorting. There was an interaction between

FPS and RFC for DMI; DMI decreased with increasing FPS when the diet included low RFC and

did change when the diet included high RFC and DMI increased with RFC for the long diets and

did not change with RFC on the short diets. Finally, it was determined that approximately 5% of

fecal particles were greater than 6.7 mm and that this may be a more accurate estimate of the

critical particle size for rumen escape in modern lactating dairy cows.

The objective of the final experiment was to induce a bout of SARA in lactating dairy

cows that had ad libitum access to 2 distinct diets that varied in FPS and starch fermentability and

to determine how SARA affects TMR selection in dairy cows. One diet consisted of long corn

silage and dry cracked corn and the other diet consisted of short corn silage and dry fine ground

corn. When offered these 2 diets simultaneously cows consumed 18.1% of their total daily intake

as long FPS and low RFC diet. However, after a bout of subacute ruminal acidosis, cows

increased their intake of the longer ration to 38.3% of total daily intake. The following d long

ration intake moderated to 28.0% and 2 d after the acidosis bout intakes were back to normal at

18.6%. These results indicate that cows are able to alter their diet preference for higher physically

effective fiber and slower starch fermentability during a bout of subacute ruminal acidosis, and

that they can effectively recover from this type of SARA within 72 h when appropriate diets are

available.

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Table of Contents

List of Figures .......................................................................................................................... ix

List of Tables ........................................................................................................................... xi

Acknowledgements .................................................................................................................. xiv

Chapter 1 Introduction ............................................................................................................. 1

Chapter 2 Literature Review .................................................................................................... 3

Ruminal Acidosis ............................................................................................................. 3 Fiber Requirements of Dairy Cattle ................................................................................. 5 Forage Particle Size in the Cow ....................................................................................... 8 Ration Sorting .................................................................................................................. 11 Critical Particle Size for Rumen Escape .......................................................................... 16 The Various Particle Sieving Methods ............................................................................ 18

Penn State Particle Separator ................................................................................... 19 American Society of Agricultural and Biological Engineers’ Particle Separator .... 20 Ro-Tap Particle Separator ........................................................................................ 21 Z-Box Particle Separator .......................................................................................... 23 Wet Sieving .............................................................................................................. 24 The Best Separating Method .................................................................................... 25

Forage Particle Size and Starch Fermentability Interaction ............................................. 26 Ruminal Acidosis and Diet Selection .............................................................................. 29 Conclusions ...................................................................................................................... 36 References ........................................................................................................................ 37

Chapter 3 Eating Behavior, Ruminal Fermentation, and Milk Production in Lactating

Dairy Cows Fed Rations That Varied in Dry Alfalfa Hay and Alfalfa Silage Content ... 45

Abstract ............................................................................................................................ 45 Introduction ...................................................................................................................... 46 Materials and Methods ..................................................................................................... 48

Diets, Cows, and Experimental Design .................................................................... 48 Feed, Refusal, and Particle Size Analysis ................................................................ 48 Chewing Activity ..................................................................................................... 49 Rumen Sampling ...................................................................................................... 50 Milk Production ....................................................................................................... 50 Statistical Analyses .................................................................................................. 50

Results and Discussion ..................................................................................................... 52 Chemical Composition and Particle Size Distribution ............................................. 52 Ration Sorting .......................................................................................................... 53 Intake of DM and Particle Fractions ........................................................................ 55 Chewing Activity ..................................................................................................... 56 Rumen Characteristics .............................................................................................. 56 Milk Production and Composition ........................................................................... 57

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Penn State Versus Ro-Tap Particle Separator .......................................................... 57 Conclusions ...................................................................................................................... 58 Acknowledgements .......................................................................................................... 59 References ........................................................................................................................ 59

Chapter 4 Effects of Varying Forage Particle Size and Fermentable Carbohydrates on

Feed Sorting, Ruminal Fermentation, and Milk and Component Yields of Dairy

Cows ................................................................................................................................ 75

Abstract ............................................................................................................................ 75 Introduction ...................................................................................................................... 76 Material and Methods ...................................................................................................... 77

Diets, Cows, and Experimental Design .................................................................... 77 Chewing Activity ..................................................................................................... 79 Rumen Parameters.................................................................................................... 79 Feed, Refusal, and Particle Size Analysis ................................................................ 80 Milk Production ....................................................................................................... 82 Fecal Sampling ......................................................................................................... 82 Statistical Analyses .................................................................................................. 83

Results and Discussion ..................................................................................................... 84 Chemical Composition and Particle Size Distribution of Diets ............................... 84 Chewing Behavior .................................................................................................... 86 Ruminal Characteristics ........................................................................................... 87 Intakes, Refusals, and Ration Sorting ...................................................................... 88 Milk Yield and Composition .................................................................................... 91 Fecal Particle Size .................................................................................................... 91

Conclusions ...................................................................................................................... 92 Acknowledgements .......................................................................................................... 93 References ........................................................................................................................ 93

Chapter 5 Effect of Subacute Ruminal Acidosis on Total Mixed Ration Preference in

Lactating Dairy Cows ...................................................................................................... 111


Diets, Cows, and Experimental Design .................................................................... 114 Rumen Sampling ...................................................................................................... 116 Feed, Refusal, and Particle Size Analysis ................................................................ 117 Statistical Analyses .................................................................................................. 118

Results and Discussion ..................................................................................................... 119 Chemical Composition and Particle Size Distribution of Diets ............................... 119 Rumen Characteristics .............................................................................................. 121 TMR Preference, Dry Matter Intake, and Refusals .................................................. 122 Ration Sorting .......................................................................................................... 124

Conclusions ...................................................................................................................... 125 Acknowledgements .......................................................................................................... 125 References ........................................................................................................................ 126

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Chapter 6 Conclusions ............................................................................................................. 137

Appendix A Technical Note: Evaluation of Procedures for Analyzing Ration Sorting and

Rumen Digesta Particle Size in Dairy Cows .................................................................... 140

Abstract ............................................................................................................................ 140 Acknowledgements .......................................................................................................... 146 References ........................................................................................................................ 146

Appendix B Effect of Feed Sorting on Chewing Behavior, Production, and Rumen

Fermentation in Lactating Dairy Cows ............................................................................ 151


Diets, Cows, and Experimental Design .................................................................... 153 Chewing Activity ..................................................................................................... 155 Rumen Sampling ...................................................................................................... 158 Feed, Refusal, and Particle Size Analysis ................................................................ 159 Milk Production ....................................................................................................... 160 Statistical Analyses .................................................................................................. 160

Results and Discussion ..................................................................................................... 161 Chemical Composition and Particle Size Distribution ............................................. 161 Ration Sorting .......................................................................................................... 162 Intake of DM, NDF, Starch, and Particle Fractions ................................................. 164 Chewing Activity ..................................................................................................... 165 Rumen Characteristics .............................................................................................. 166 Milk Production and Composition ........................................................................... 167

Conclusions ...................................................................................................................... 168 Acknowledgments ............................................................................................................ 168 References ........................................................................................................................ 169

Appendix C Effect of Varying TMR Particle Size on Rumen Digesta and Fecal Particle

Size and Digestibility in Lactating Dairy Cows ............................................................... 185


Diets, Cows, and Experimental Design .................................................................... 187 Rumen Sampling ...................................................................................................... 188 Fecal Sampling ......................................................................................................... 189 Digestibility .............................................................................................................. 190 Statistical Analyses .................................................................................................. 191

Results and Discussion ..................................................................................................... 191 Chemical Composition and Particle Size Distribution ............................................. 191 Rumen Particle Size ................................................................................................. 192 Fecal Particle Size and Composition ........................................................................ 193 Intakes, Fecal Output, and Digestibility ................................................................... 195

Conclusions ...................................................................................................................... 197

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Acknowledgments ............................................................................................................ 197 References ........................................................................................................................ 197

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List of Figures

Figure 2-1. Effect of the ratio between physically effective NDF (peNDF1.18) to

ruminally degradable starch from grains (RDSG) in the diet on daily mean ruminal

pH ..................................................................................................................................... 44

Figure 3-1. Effect of feeding increasing levels of dry chopped alfalfa hay (5, 10, 20, and

40% of forage DM) on refusal particle size distribution for 19.0 (A), 8.0 (B), 1.18

mm (C) sieves, and pan (D). ............................................................................................ 70


40% of forage DM) on cumulative percent of diet daily intake at various times after

feeding. ............................................................................................................................. 71


40% of forage DM) on rumen pH over time. ................................................................... 72


40% of forage DM) on rumen NH3 over time. ................................................................. 73

Figure 3-5. Particle size distributions of TMR samples separated with the Penn State

(PSPS) and Ro-Tap particle separators divided into particle fractions; > 19.0, > 8.0,

> 1.18 mm. ....................................................................................................................... 74

Figure 4-1. Effect of feeding TMR varying in forage particle size (FPS) and ruminally

fermentable carbohydrates (RFC) on starch concentration at 0 and 24 h after

feeding1 ............................................................................................................................ 107


fermentable carbohydrates (RFC) on NDF concentration at 0 and 24 h after feeding1 ... 108


fermentable carbohydrates (RFC) on TMR particle fractions > 26.9 mm (A), > 1.65

mm (B), and pan (C) at 0, 8, 16, and 24 h after feeding1 ................................................. 110

Figure 5-1. Effect of rumen challenge while offering 2 free choice TMR containing long

forage and slowly fermentable starch or short forage and rapidly fermentable starch

on rumen pH over time for baseline, feed restriction, rumen challenge, and recovery

d. ....................................................................................................................................... 134



on preference for TMR with long forage (expressed as a percentage of total daily

intake)............................................................................................................................... 135



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on cumulative percent of diet daily intake at various times after feeding for baseline

and rumen challenge d. .................................................................................................... 136

Figure B-1. Effect of feeding TMR of increasing particle size on refusal geometric mean

particle size. ..................................................................................................................... 179

Figure B-2. Effect of feeding TMR of increasing particle size on refusal particle

distribution as a percentage of original diet. Selected data shown; 26.9-mm sieve (A)

and pan (B). ...................................................................................................................... 180

Figure B-3. Effect of feeding TMR of increasing particle size on refusal NDF (A) and

starch (B) concentration. .................................................................................................. 181

Figure B-4. Effect of feeding TMR of increasing particle size on cumulative particle size

selection index. Selected data shown; 26.9-mm sieve (A) and pan (B). .......................... 182

Figure B-5. Effect of feeding TMR of increasing particle size on cumulative NDF (A)

and starch (B) selection indices. ....................................................................................... 183

Figure B-6. Effect of feeding TMR of increasing particle size on cumulative geometric

mean length (Xgm) selection index. .................................................................................. 184

Figure C-1. Mean rumen digesta particles of all treatments retained on 1.18-, 0.6-, 0.15-

mm screens, soluble fraction, and soluble DM to retained DM ratio throughout the d. .. 205

Figure C-2. Effect of feeding Short (A), Medium (B), Long (C), and Extra Long (D)

TMR on rumen digesta particles retained on 9.5-, 6.7-, and 3.35-mm screens

throughout the d. .............................................................................................................. 207

Figure C-3. Effect of feeding TMR of increasing particle size on fecal NDF (A),

indigestible NDF (B), and starch (C) concentration throughout the d. ............................ 209

Figure C-4. Effect of feeding TMR of increasing particle size on fecal geometric mean

particle length (calculated using data from all particle fractions) throughout the d. ........ 210

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List of Tables

Table 2-1. Physical effectiveness factors (pef) for NDF in feeds of each physical form

classification based on total chewing activity in relation to that elicited by long grass

hay. ................................................................................................................................... 43

Table 3-1. Chemical compositions and particle size distributions determined for corn

silage, alfalfa haylage, and dry chopped alfalfa hay ........................................................ 62

Table 3-2. Ingredients, chemical compositions, and particle size distributions for TMR

with increasing levels of dry chopped alfalfa hay (5, 10, 20, and 40% of forage DM) ... 63

Table 3-3. Effect of feeding increasing levels of dry chopped alfalfa hay (5, 10, 20, and

40% of forage DM) on DMI, feed efficiency, and milk production and components ..... 64


40% of forage DM) on intake of 4 particle size fractions (> 19.0, > 8.0, > 1.18, and <

1.18 mm) .......................................................................................................................... 65


40% of forage DM) on chewing behavior ........................................................................ 66


40% of forage DM) on rumen fermentation .................................................................... 67

Table 3-7. Particle size distributions of TMR containing 5, 10, 20, and 40% of forage

DM as dry chopped alfalfa hay in samples taken at feeding (0 h) and 24 h after

feeding and separated with the Penn State and Ro-Tap particle separators ..................... 68

Table 4-1. Chemical compositions and particle size distributions determined with the

ASABE particle separator for alfalfa haylage and long and short corn silage ................. 96

Table 4-2. Chemical compositions, particle size distributions, and rates of disappearance

determined via in situ incubation for dry cracked and dry fine ground corn ................... 97

Table 4-3. Chemical composition and particle size distributions determined with the

ASABE particle separator for TMR varying in forage particle size (FPS) and

ruminally fermentable carbohydrates (RFC)1 .................................................................. 98

Table 4-4. Effect of feeding TMR varying in forage particle size (FPS) and ruminally

fermentable carbohydrates (RFC) on chewing behavior1 ................................................ 99


fermentable carbohydrates (RFC) on cumulative selection indices1 for various

particle fractions2 ............................................................................................................. 101

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fermentable carbohydrates (RFC) on interval selection indices1 for various particle

fractions2 .......................................................................................................................... 102


fermentable carbohydrates (RFC) on daily DM, NDF, starch, and particle fraction

intake1 ............................................................................................................................... 103


fermentable carbohydrates (RFC) on milk yield and components1 ................................. 104


fermentable carbohydrates (RFC) on daily weighted mean1 fecal particle size and

DM content2 ..................................................................................................................... 105


fermentable carbohydrates (RFC) on daily weighted mean1 ruminal digesta particle

size distribution and DM content2 .................................................................................... 106

Table 5-1. Chemical compositions and particle size distributions determined with the

ASABE particle separator for alfalfa haylage and long and short corn silage ................. 128


determined via in situ incubation for dry cracked corn, dry fine ground corn, and

ground wheat .................................................................................................................... 129

Table 5-3. Chemical composition and particle size distributions determined with the

ASABE particle separator for TMR containing long forage and slowly fermentable

starch (LC) or short forage and rapidly fermentable starch (SF) ..................................... 130

Table 5-4. Effect of rumen challenge while offering 2 free choice TMR containing long


on rumen pH and VFA for baseline and rumen challenge d ............................................ 131

Table 5-5. Effect of rumen challenge while offering 2 free choice TMR containing long

forage and slowly fermentable starch (LC) or short forage and rapidly fermentable

starch (SF) on DMI and refusals for baseline, feed restriction, rumen challenge, and

recovery d ......................................................................................................................... 132

Table 5-6. Effect of offering 2 free choice TMR containing long forage and slowly

fermentable starch (LC) or short forage and rapidly fermentable starch (SF) on mean

selection indices1 of baseline, rumen challenge, and recovery d (4 d) ............................. 133

Table A-1. Percentage of uneaten TMR particles (DM basis) retained on sieves at 8-h

intervals after feeding when sampled by 2 different procedures...................................... 148

Table A-2. Geometric mean particle length of uneaten TMR and sorting index of the

consumed diet1 obtained with 2 different sampling procedures ....................................... 149

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Table A-3. Percentage of rumen digesta particles (DM basis) retained on sieves after wet

sieving when digesta samples were prepared with or without being squeezed through

cheesecloth ....................................................................................................................... 150

Table B-1. Chemical composition and particle size distributions determined with the

ASABE particle separator for corn silage, alfalfa haylage, and short (S), medium

(M), long (L), or extra long (XL) grass hay ..................................................................... 172

Table B-2. Chemical composition and particle size distributions determined with the

ASABE particle separator for TMR containing short (S), medium (M), long (L), or

extra long (XL) grass hay ................................................................................................. 173

Table B-3. Effect of feeding TMR containing short (S), medium (M), long (L), or extra

long (XL) grass hay on DM, NDF, and starch intake at various times after feeding

and total consumption (measured 24 h after feeding) of various sized particles ............. 174

Table B-4. Observed meal characteristics for diets containing short (S), medium (M).

long (L), or extra long (XL) grass hay ............................................................................. 175


long (XL) grass hay on chewing behavior as determined by observed meal criteria1 ..... 176


long (XL) grass hay on rumen fermentation .................................................................... 177


long (XL) grass hay on milk production and components1 .............................................. 178

Table C-1. Chemical composition and particle size distributions determined with the

ASABE particle separator for corn silage, alfalfa haylage, and short (S), medium

(M), long (L), or extra long (XL) grass hay ..................................................................... 200

Table C-2. Chemical composition and particle size distributions determined with the

ASABE particle separator for TMR containing short (S), medium (M), long (L), or

extra long (XL) grass hay ................................................................................................. 201

Table C-3. Effect of feeding TMR containing short (S), medium (M), long (L), or extra

long (XL) grass hay on daily weighted means of fecal NDF, indigestible NDF

(INDF), starch, ash, DM and Xgm .................................................................................... 202


long (XL) grass hay on daily weighted mean fecal particle size distribution. ................. 203


long (XL) grass hay on DMI, indigestible NDF intake (INDFI), fecal output and

apparent digestibilities of DM, NDF, and starch ............................................................. 204

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Acknowledgements

I wish to first thank my advisor Dr. Jud Heinrichs for giving me the great opportunity to

attend graduate school and pursue a Ph.D. studying dairy cattle nutrition at one of the greatest

institutions in the world. He allowed me the freedom to take my research in a direction of my

choosing and was always able to offer sound advice. I hope my future endeavors will bring great

respect to his program. Next, I would like to thank my committee members Drs. Chad Dechow,

Kevin Harvatine, Robert Van Saun, and Gabriella Varga for their excellent advice, expertise,

suggestions, and constructive criticisms; also for taking the time to read my lengthy dissertation,

it is very much improved because of them.

I also wish to thank everyone in the Heinrichs’ lab for all of their help and support. Our

lab technician, Maria Long, saved me from spending even more hours in the lab and was always

able to offer me assistance no matter the procedure. I am also very thankful to the many

undergraduates that helped me during my tenure in graduate school: Blair, Catherine, Hilary,

Kolby, Laraya, Meghan, Pam, and Peter. Much of my research involved very laborious and

tedious tasks, such as measuring hay particles by hand with a ruler or particle separating samples

for months on end; these students completed all of their work with enthusiasm and dedication. I

want to thank all of the graduate students whose tenure overlapped with mine for their friendship

and assistance, but especially to: Dr. Geoff Zanton whose help and advice on anything related to

statistics and experimental design was immeasurable, Dr. Gustavo Lascano for showing me the

ropes when I first started graduate school, and to Javier Suarez for always happily volunteering

anytime I needed an extra hand. I am greatly indebted to Coleen Jones for her excellent editing

skills which made me look like a much better writer than I am. I also wish to thank Kyle Heyler,

who despite working in a different lab, helped me numerous times during studies and answered

many questions; perhaps most importantly for helping me watch football games from the front

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row of Beaver Stadium. I am also grateful to the dairy farm personnel who went out of their way

to assist me during my studies, especially Boyd, Dante, Mark, Nadine, Randy, and Travis.

I owe a lot of my success to my girlfriend, Suzie Reding, for being supportive of me in

everything that I do. Suzie was always willing to help me with my experiments at hours of the

day when few others were willing to help. She also happily made lunches and brought me meals

when I was “living” at the dairy barns. Finally spending time with Suzie made graduate school

more bearable by taking my mind of my research and studies even if only for a moment.

Lastly and most importantly I wish to thank my family. My parents, Dale and Pattie

Maulfair, provided me with the best upbringing that is possible; living on a dairy farm. What I

learned from my father has directly made me the person I am today. On a daily basis he taught

me, by example, the importance of hard work, determination, and honor. I hope to someday be as

good a father as he is. My mother has always provided me with the love that only a mother can.

My siblings, Jennie and David, were always supportive of my endeavors and their commitment to

the family farm made leaving it a little easier.

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The cow is the foster mother of the human race. From the day of

the ancient Hindoo to this time have the thoughts of men turned

to this kindly and beneficent creature as one of the chief

sustaining forces of human life.

–William Dempster Hoard

Chapter 1

Introduction

Forage particle size, relative to the dairy industry, is a very important but also very

complex topic. Dairy cows, being ruminants, require adequate fiber for proper rumen function.

Fiber is required by the ruminant to maintain a healthy ruminal environment that allows ruminal

microorganisms to flourish, which is necessary to achieve optimal digestion and feed efficiency.

However, cattle not only have a chemical fiber requirement but also a physical fiber requirement.

Cows need physical fiber to maintain the ruminal mat, stimulate chewing, and buffer the rumen.

Longer particle size can decrease dry matter and energy intake and lead to sorting, a condition

where cattle do not eat the ration as mixed, but rather eat certain parts of the ration and refuse

others. It is thought that ration sorting can lead to subacute ruminal acidosis, a condition of

abnormally low ruminal pH (< 5.5), because dairy cows generally sort against longer particles

and for shorter particles. Not much is known about how cows decide what feed particles to sort

for and against and also what factors influence and change feed sorting preference. This sorting

behavior leads to a decrease in fiber intake and an increase in starch intake, as generally fiber and

starch are positively and negatively associated, respectively, with particle length. It is well known

that low ruminal pH has many detrimental effects on not only the rumen, but the whole animal.

Acidosis can lead to decreased intake, digestion, and milk fat content and can cause diarrhea and

lameness in addition to many other conditions. In addition, forage particle size must be short

enough to allow proper fermentation during storage. Shorter particles are necessary to allow for

adequate packing of the silage which limits oxygen during storage thus preventing improper

fermentation and molding. These conflicting factors make it difficult to describe the optimum

particle size distribution for forages to be fed to dairy cattle.

2

Another important consideration regarding forage particle size is the method used to

measure particle size distribution. Many systems currently exist to measure particle size and even

more methods exist to use particle size data to calculate physically effective fiber in rations.

However, since there is not a standard method for the dairy industry or dairy researchers several

different systems are currently being used and their data are used interchangeably, though their

results may not be comparable. Many of the systems attempting to estimate physically effective

fiber are based upon the theory that there is a critical size threshold for particles leaving the

rumen and that particles above this threshold are effective because they stimulate chewing to

promote their particle size reduction and rumen escape. However, the research that the current

critical particle size is based on is aged, and the feeding systems that were used when it was

conducted were very different than the feeding systems being used in modern dairy production.

Dairy cattle nutrition has changed dramatically, even in the last 30 years. In order to take

advantage of the great genetic gains available in current dairy breeds a ration that is much higher

in energy must be fed. Common ways to increase energy intake are to decrease the forage to

concentrate ratio, which increases the energy density of the diet, and to decrease forage particle

size, which increases dry matter intake. These factors make cows more susceptible to acidosis and

studying forage particle size will help allow dairy nutritionists to push to the limits of energy

intake while maintaining ruminal health.

This dissertation will attempt to answer some of these questions that currently exist in the

area of forage particle size in lactating dairy cows and perhaps ask some new questions that will

encourage further research into this exciting field.

Chapter 2

Literature Review

Ruminal Acidosis

The ruminant animal is unique in the animal kingdom because to achieve optimum feed

intake and efficiency its ruminal environment must be maintained within certain physiological

limits. These limits are required to be maintained to provide a favorable symbiotic relationship

between ruminant host and ruminal microorganisms. The ruminant should provide the

microorganisms an environment of limited oxygen, relatively neutral pH, constant temperature,

relatively continuous influx of water and organic matter, constant removal or neutralization of

waste products and indigestible matter, and mean retention time greater than microbial generation

time (Van Soest, 1994). The feeding systems necessary in modern dairy cattle production have

made it increasingly difficult to provide a ruminal environment that stays within all of these

narrow constraints. The enormous energy requirements of high producing dairy cattle require

dairy farmers to feed cattle rations of increasing dry matter intakes (DMI) and levels of

concentrate feeds. One of the problems associated with this type of feeding system is an increased

susceptibility to ruminal acidosis.

Ruminal acidosis is a condition where ruminal pH falls below a certain physiological

range of which there are 2 distinct types. The first, more severe, condition is referred to as acute

ruminal acidosis and it is generally defined as such when ruminal pH drop below 5.0; the second,

less severe, condition is referred to as subacute ruminal acidosis (SARA) and it is generally

defined as such when ruminal pH falls in the range of 5.0 to 5.5 (Krause and Oetzel, 2006). The

decreased ruminal pH that causes acute acidosis is thought to be mainly caused by an increase in

4

ruminal lactate, while the decreased ruminal pH that causes SARA is thought to be mainly caused

by an accumulation of volatile fatty acids (VFA) (Harmon et al., 1985; Britton and Stock, 1986;

Oetzel et al., 1999). Clinical signs of acute acidosis include anorexia, abdominal pain,

tachycardia, tachypnea, diarrhea, lethargy, staggering, recumbency, and death (Krause and

Oetzel, 2006). Clinical signs of SARA can be delayed for weeks or months after the bout of

depressed ruminal pH. There are many negative side effects associated with SARA, including:

decreased DMI (Britton and Stock, 1986; Nocek, 1997), decreased milk production and milk fat

content (Nocek, 1997), lameness (Nocek, 1997; NRC, 2001; Stone, 2004), decreased feed

efficiency (Huntington, 1993; Nocek, 1997), rumenitis (Brent, 1976), and liver abscesses (Brent,

1976; Britton and Stock, 1986).

While acute acidosis is a more severe condition, the incidence of SARA is much higher

in dairy cattle and thus has a greater economic impact. A study that evaluated 14 Wisconsin dairy

herds and tested 154 cows determined that 20.1% of lactating cows had SARA when tested using

rumenocentesis (Oetzel et al., 1999). In a case study of a 500-cow dairy in central New York

state, Stone (1999) estimated that SARA could cost up to $475 per cow per year in lost

production and components only. Therefore, SARA should be heavily focused on for research

and prevention. Stone (2004) suggested that there are 4 types of dairy cattle that are at high risk

of developing SARA, they are: transition cows, cows with high DMI, cows that experience high

variability in ration composition and meal patterns, and cows fed poorly formulated rations. This

is closely related to the suggestion of Krause and Oetzel (2006) that there are 3 major causes of

SARA in dairy herds: excessive intake of rapidly fermentable carbohydrates, inadequate ruminal

adaptation to a highly fermentable diet, and inadequate ruminal buffering caused by inadequate

dietary fiber or inadequate physical fiber. Dairy cattle can consume excessive amounts of

fermentable carbohydrates in 2 ways, through high levels of concentrate in the ration or moderate

levels of concentrates at high DMI (Krause and Oetzel, 2006). The ruminant should be adapted

5

slowly to ration changes, especially when going from high forage to low forage diets, to allow the

ruminal microorganism profile to adapt (Van Soest, 1994) and ruminal papillae to lengthen

(NRC, 2001). The many aspects of dietary and physical fiber will be discussed in greater detail

below.

Fiber Requirements of Dairy Cattle

The National Research Council (NRC; 2001) recommended a minimum NDF level of

25% of ration DM with a forage NDF level of 19% of ration DM for lactating dairy cows. The

NRC based its recommendations on NDF as it is the fiber measure that best separates structural

from nonstructural carbohydrates and is comprised of most of the compounds that are considered

fiber (NRC, 2001). Forage NDF is included in these recommendations because NDF from

nonforage sources is estimated to be about 50% as effective at maintaining chewing activity, milk

fat content, and ruminal pH; therefore for every 1 percentage unit decrease in forage NDF, total

NFD content should be increased by 2 percentage units (NRC, 2001). The NRC (2001) stated that

their recommendations are based on cows fed: a TMR, alfalfa or corn silage as the predominant

forage, forage with adequate particle size, and dry ground corn as the predominant starch source.

These recommendations are therefore limited to rather specific conditions due to the limited data

available and because adequate particle size is not defined in a measurable manner. In addition,

NDF minimum levels should be increased if corn is replaced by a more readily fermentable starch

source (grain starch fermentability: oats > wheat > barley > corn > milo; Herrera-Saldana et al.,

1990) or if finely chopped forage is fed. The minimum level of NDF required by dairy cows is a

product of cow and ruminal health (NRC, 2001). Forages are the major supplier of NDF in rations

and their slower fermentation and physical characteristics are essential for maintaining ruminal

health. The decreased digestibility of forage helps to maintain an optimal ruminal environment by

6

diluting the effects of large amounts of VFA produced by NFC fermentation. Fiber (NDF) with

adequate length is thought to increase chewing in cattle, which increases salivary secretion of

NaHCO3 and buffers the ruminal digesta (Allen, 1997; Nocek, 1997; Krause et al., 2002b).

Saliva production and its ability to buffer the rumen is very important to high producing

dairy cows. Large amounts of saliva enter the rumen of lactating dairy cows, approximately 98 to

190 L/d (Bailey, 1961a). The primary buffering compounds in saliva are HCO3- and HPO4

2-

(Bailey and Balch, 1961; Bailey, 1961b). These compounds will associate with free H+ ions in the

rumen and decrease pH. HCO3- and HPO4

2- are very strong buffers at higher pH, but when pH

drops too low (approximately 5.5) VFA become the primary buffering system in the rumen

(Counotte et al., 1979). Bailey (1961a) found that saliva secretion during eating was 2 to 4 times

higher than when at rest. Beauchemin et al. (2008) showed that rate (g/min) of salivation stayed

constant during eating; however, changes in the rate of eating affected the amount of saliva

secreted per unit of DMI when cows were fed barley silage, alfalfa silage, long-stemmed alfalfa

hay, or barley straw, these results agree with the previous research of Bailey (1961a). Particle

size, DM, and NDF content of forages are factors affecting rate of eating and time spent eating;

chewing rate was decreased and thus saliva secreted per unit of DMI increased when ration

particle size, DM, and NDF were increased (Bailey, 1961a; Beauchemin et al., 2008).

Chewing was probably first suggested as a means of estimating a feed’s effectiveness at

maintaining ruminal health by Balch (1971). Sudweeks et al. (1981) continued this work with

their roughage value index system and since then several methods have been suggested to

estimate the effectiveness of fiber. Most methods relate a feed’s effectiveness to its ability to

stimulate chewing activity in the cow. Mertens (1997) first defined the concept of effective NDF

(eNDF) as the sum total ability of a feed to replace forage or roughage in a ration so that the

percentage of fat in milk produced by cows eating the ration is effectively maintained. While

maintaining or improving milk fat is a major impetus for trying to define fiber requirements of

7

dairy cattle there are many factors that influence milk fat, some not related to diet, making the

eNDF concept broad and hard to measure. For instance, milk fat is heavily affected by stage of

lactation and eNDF would not be able to account for those differences (Allen, 1997).

Another interrelated term was also introduced by Mertens (1997) to describe a slightly

different characteristic of forage. Physically effective NDF (peNDF) is defined as the physical

characteristics of fiber (primarily particle size) that influence chewing activity and the biphasic

nature of ruminal contents (Mertens, 1997). This measure combines the physical and chemical

properties of a feedstuff to predict chewing and is a product of a feed’s physical effectiveness

factor (pef) and its NDF content. Physically effective NDF differs from other measures of

effective fiber (Balch, 1971; Sudweeks et al., 1981) in that it is based on the relative effectiveness

of NDF to promote chewing rather than being expressed as min of chewing per kg of DMI

(Mertens, 1997). This eliminates animal variation from being attributed to a feed’s effectiveness

because chewing per unit of feed varies with animal size, breed, and intake (Mertens, 1997). The

more specific concept of peNDF is easier to measure than eNDF since peNDF is only concerned

with the effect of a feed on chewing and the ruminal mat, which are mostly influenced by particle

size and NDF content; although fragility and specific gravity probably have a small influence on

peNDF as well. Mertens (1997) developed a pef system to calculate peNDF that ranges from 0

(feed has no effectiveness in promoting chewing) to 1 (feed has maximum effectiveness in

promoting chewing); a hypothetical long grass hay with 100% NDF was defined as having a pef

of 1 and an estimated 240 min of chewing per kg of DM or NDF for nonlactating cows eating 0.4

to 2.0 times maintenance. A pef table (Table 1-1) was created that classified various feedstuffs by

types and physical forms and assigned each feedstuff a pef value that could be multiplied by the

NDF content of a corresponding feedstuff to achieve its peNDF. This peNDF method not only

includes NDF content and particle size but differences in NDF composition, specific gravity, and

fragility would be partially accounted for by classifying different feedstuffs separately. However,

8

Mertens (1997) also developed a laboratory assessment of peNDF where feeds are separated via

dry vertical shaking and the proportion of the samples retained above a 1.18-mm sieve (1.65-mm

sieve diagonal) are multiplied by sample NDF content. This method is based on 3 assumptions:

NDF is uniformly distributed over all particles, chewing activity is equal for all particles retained

on a 1.18-mm sieve, and fragility is not different among sources of NDF (Mertens, 1997). The

first assumption can be eliminated if the portion of samples retained on a 1.18-mm sieve is

directly analyzed for NDF. Measurement of peNDF has become widely used in dairy cattle

nutrition and research, but it is often measured differently from Mertens’ (1997) procedure. Many

instead use the Penn State particle separator (PSPS) and more discussion of this area will follow.

Another problem is that the NRC (2001) failed to publish a requirement for peNDF because of a

stated lack of a standard, validated method to measure effective fiber of feeds or to establish

requirements for effective fiber. A weakness of using the latter peNDF method is that NDF

fractions are not chemically identical for all forages. NDF composition (the ratio of

hemicellulose:cellulose:lignin) of forage varies wildly (Van Soest et al., 1991) and is affected by

species, maturity, and storage method. This is probably part of the reason for the many

contradictions in the literature about effects of peNDF on intake, milk production, milk fat

content, and chewing behavior. Using the pef system developed by Mertens (1997) that includes

values that differ with type of feedstuff would partially correct for differences across NDF

compositions and may improve the correlation between peNDF and chewing in the literature.

Forage Particle Size in the Cow

Adequate forage particle size (FPS) is necessary to maintain cow and ruminal health

through buffering ruminal pH, but varying FPS also has many other effects. Many of these effects

are inconsistent in the literature due to the many interactions that can occur between diet and cow.

9

For instance, it is generally accepted that as FPS increases DMI will decrease due to increased gut

fill. Kononoff and Heinrichs (2003b), Leonardi et al. (2005b), and Maulfair et al. (2010) all

determined that DMI decreased with increasing FPS; major diet ingredients in these studies were:

alfalfa haylage and ground corn; oat silage, corn silage, and ground corn; and corn silage, alfalfa

haylage, and ground corn respectively. These results are contrary to Yang et al. (2001b), Krause

et al. (2002a), Kononoff and Heinrichs (2003a), Beauchemin and Yang (2005), and Yang and

Beauchemin (2007) because they showed no effect of FPS on DMI when feeding: barley silage,

alfalfa silage, alfalfa hay, and steam-rolled barley grain, alfalfa silage with either high moisture or

dry cracked corn, corn silage and ground corn, corn silage and steam-rolled barley grain, and

alfalfa silage and steam-rolled barley grain, respectively. Additionally, Allen (2000) reported that

only 3 of 20 comparisons, in 13 articles reviewed, where the same source of forage (hay or silage)

was chopped to 2 or more lengths reported a significant effect of forage particle length on DMI.

Finally, Krause and Combs (2003) found that when feeding rations of alfalfa silage and corn

silage of increasing FPS with either dry cracked shelled corn or high-moisture corn DMI actually

increased. A possible reason for this discrepancy is that although longer FPS can increase the

filling effect of NDF, longer forages also can lead to increased saliva secretion, which may

counteract the filling effect by increasing flow out of the rumen (Allen, 2000). Indeed, Froetschel

(1995) showed that infusing saliva in the abomasum of steers led to a linear increase (2.3 to 8.3%

higher) in reticular contractions and a linear decrease (7.8 to 13.5% lower) in ruminal contents.

The author indicated that the infusion rate of 1.5 L/h for 3 h was within physiological range.

There is also some inconsistency in the literature regarding effect of FPS on DM

digestibility (DMD). Kononoff and Heinrichs (2003a) and Yang and Beauchemin (2005) reported

that increasing ration particle size increased DMD when feeding diets of corn silage with ground

corn and steam rolled barley grain, respectively. On the contrary, Kononoff and Heinrichs

(2003b) and Maulfair et al. (2011) observed that increasing ration particle size decreased DMD

10

when rations of alfalfa silage with ground corn and corn silage, alfalfa haylage, and ground corn

were fed. In addition, there are several studies that reported no effect of ration particle size on

DMD: Krause et al. (2002a) feeding alfalfa silage with either high moisture or dry cracked corn;

Yang and Beauchemin (2006a) feeding barley silage with steam rolled barley grain; and Yang

and Beauchemin (2007) feeding alfalfa silage with steam rolled barley grain. Clearly the

influence of FPS on DMD interacts with other aspects of diet or management.

The effect of FPS on digestibility does not become any clearer when digestibilities of

NDF (NDFD) and starch (StarchD) are added to the analysis. Comparing some of the previously

cited studies, several reported no differences in DMD, NDFD, or StarchD (Yang and

Beauchemin, 2006a; 2007) while another study reported no differences in DMD and NDFD but

StarchD decreased (Krause et al., 2002a) with increasing ration particle size. In addition, Yang

and Beauchemin (2005) reported an increase in DMD and NDFD with no change in StarchD, but

Kononoff and Heinrichs (2003a) did not see a change in NDFD with an increase in DMD

(StarchD was not determined in this study) when ration particle size was increased. Finally,

Maulfair et al. (2011) reported a decrease in DMD with no change in NDFD and StarchD. These

differing results are likely caused by interactions between forage type, forage to concentrate ratio

(F:C), and starch fermentability with FPS. None of the experiments with steam-rolled barley

grain as the main starch source had any effect of ration particle size on StarchD when fed with

multiple forage types (alfalfa, barley, and corn silage) (Yang and Beauchemin, 2005; 2006a;

2007). Two studies using corn grain as the main starch source measured StarchD; Maulfair et al.

(2011) found that feeding ground corn with corn silage and alfalfa haylage resulted in no change

in StarchD while Krause et al. (2002a) determined that StarchD decreased with increasing ration

particle size when feeding high-moisture shelled corn and dry cracked shell corn with alfalfa

silage. Therefore, it seems that barley grain digestibility is independent of FPS while corn grain

digestibility is variable. Forage source did not have consistent results for NDFD with differing

11

ration particle size either. Studies feeding an alfalfa silage-based ration had both no effect of

ration particle size on NDFD (Krause et al., 2002a; Yang and Beauchemin, 2007) and a decrease

in NDFD with increasing ration particle size (Kononoff and Heinrichs, 2003b). Corn silage-based

rations were also inconsistent, with 1 study having an increase in NDFD with increasing ration

particle size (Yang and Beauchemin, 2005) and 2 studies that had no effect of ration particle size

on NDFD (Kononoff and Heinrichs, 2003a; Maulfair et al., 2011). The interactions occurring in

these studies between FPS and digestibility are certainly complex and much more research is

needed to elucidate these effects.

Ration Sorting

It has been estimated that the majority (51.1%) of U.S. dairy farms have adopted TMR as

a means of feeding lactating cows; additionally, the percentage of dairies with rolling herd

averages over 20,000 lb/cow and dairies with over 500 cows estimated to use TMR are 70.7 and

94.1%, respectively (USDA, 2007). The TMR was developed to provide cows a uniform and

consistent diet throughout the d, which is beneficial to the ruminal environment. However, dairy

cows have been shown to selectively consume or sort their rations when fed a TMR. Cows

generally sort against long particles and for finer particles in their ration (Leonardi and

Armentano, 2003; Leonardi et al., 2005a; Maulfair et al., 2010). This behavior can create

problems because not only are cows reducing the particle size of their consumed diet but also

reducing their NDF intake, because generally longer particles of TMR are composed mainly of

forages and contain a higher proportion of NDF than the rest of the ration (Leonardi and

Armentano, 2003).

In the field and in research up until recently, the presence of sorting was usually

determined by comparing the particle size distribution of TMR at feeding to its particle

12

distribution at the end of the d. While these distributions are still reported in the literature, sorting

activity is now more commonly described using a selection index. Leonardi and Armentano

(2003) described a selection index as the actual intake of each fraction (Yi) expressed as a

percentage of the expected intake. Expected intake of Yi equals as-fed intake multiplied by the as-

fed fraction of Yi in the TMR. The resulting values will fall into 3 categories; sorting for (>

100%), sorting against (< 100%), and no sorting (= 100%) for each particle fraction. Sorting can

also be described with this same technique using DM instead of as-fed and results are similar

(Leonardi et al., 2005a). A potential problem for dealing with sorting in research or the field is the

fact that variability of sorting between cows can be very substantial, especially with the longest

fraction (Leonardi and Armentano, 2003; Leonardi et al., 2005a).

Several factors have been identified that influence sorting behavior in lactating dairy

cows. Increasing the proportion of dry hay in the ration, from 20 to 40% of ration DM, has been

shown to increase sorting (Leonardi and Armentano, 2003). However, this effect is likely caused

by the large change in ration DM (69.3 to 89.9 %). Leonardi et al. (2005a) showed that when

feeding a mixed hay (80% alfalfa and 20% grass) based diet with alfalfa silage an increase in

ration DM, from 64.4 to 80.8%, increased sorting against long particles and for short particles.

Ration DM in these studies however is much higher than silage-based rations typically found on

modern dairy farms. In contrast, Miller-Cushon and DeVries (2009) and Felton and DeVries

(2010) recently completed studies that looked at effects of ration DM on sorting with diet DM

within the normal range and composed of corn silage, alfalfa haylage, and high-moisture corn.

Both of these studies found that decreasing ration DM by adding water during or right after

mixing actually increased ration sorting when changing from 57.6 to 47.9% DM and from 56.3 to

50.8 and 44.1% DM for Miller-Cushon and DeVries (2009) and Felton and DeVries (2010),

respectively. However, both of these experiments were completed during the summer months and

heating was noticed by Felton and DeVries (2010) in the lower DM diets. Therefore the authors

13

concluded that increased ration sorting was due to diet instability and spoilage and that adding

water to diets with < 60% DM may not decrease sorting and depending on environmental

conditions may actually increase sorting.

Feeding rations of greater particle size have also been shown to increase sorting.

Leonardi and Armentano (2003) reported that feeding longer alfalfa hay versus chopped alfalfa

hay increased sorting of rations (against long particles and for fine particles); but intake of long

particles still increased because of their higher proportion in the diet. Also the authors determined

that, surprisingly, there seems to be no difference in sorting between high quality (34.5% NDF)

and low quality (44.5% NDF) hay of the same particle size (Leonardi and Armentano, 2003).

Other studies that showed increased sorting against long particles and for short particles with

increasing FPS are Kononoff et al. (2003b) and Kononoff and Heinrichs (2003a) both feeding

corn silage and ground corn. Bhandari et al. (2008) reported that when feeding rolled barley,

ration sorting increased with increasing alfalfa silage particle size, but decreased with increasing

oat silage particle size.

DeVries et al. (2007) determined that when increasing the F:C in a ration containing

grass silage, corn silage, and concentrate mash (from approximately 51:49 to 62:38), cows

decreased sorting against long particles and for short particles. The authors suggested that an

increased proportion of concentrate made it more available and easier for cows to sort for it. This

study also showed that it takes dairy cows only 1 d to adjust their TMR sorting behavior when

changing from a high forage diet to a low forage diet (DeVries et al., 2007).

Leonardi and Armentano (2007) compared feed sorting in tie- versus free-stall barns

when feeding a ration that contained forages of 30.2% alfalfa hay, 20.2% corn silage, and 10.3%

wheat straw. They found that when housed in a tie-stall barn cows consumed 73.2% of their

expected intake of the longest particles, but cows housed in a free-stall consumed only 63.3%,

therefore cows in free-stalls exhibited a greater degree of sorting. The authors suggested the

14

reason for the difference is because sorting in a tie-stall is limited by the fact that refusals become

coarser over time and the cow becomes forced to eat the long particles whereas cows in free-stalls

can move to a bunk location that has not been extensively sorted. Additionally, it was discovered

that sorting against particles was increased with increasing feed refusal percentage. Leonardi and

Armentano (2007) suggested that ration sorting estimates based on individually fed cows likely

underestimate feed sorting that would occur in free-stalls.

Hosseinkhani et al. (2008) studied the effect of feed bunk competition in close-up dry

cows on feeding behavior. Cows were fed a ration containing alfalfa hay, corn silage, and

concentrate mash and were assigned to 1 of 2 treatments that either had 1 cow per bin

(noncompetitive) or 2 cows per bin (competitive). It was determined that cows on both treatments

sort against particles > 19.0 mm and for particles retained on an 1.18-mm sieve and pan;

additionally there was no effect of competition level on feed sorting (Hosseinkhani et al., 2008).

Cows in the competition group did have much higher feed intake than cows in the noncompetitive

group. The influence of feeding frequency on ration sorting was studied by DeVries et al. (2005)

by feeding once, twice, and 4 times per d. Feed sorting occurred in all rations as evidenced by

increasing levels of refusal NDF throughout the d. It was determined that increasing feeding

frequency from once/d to twice/d decreased sorting activity but increasing from twice/d to 4

times/d had no effect on ration sorting in these cows.

Recently Maulfair et al. (2010) studied the effect of increasing dry hay particle size in a

corn silage and alfalfa hay based ration. They reported large differences in TMR refusal

composition (particle size distribution, NDF, and starch) compared to the ration fed as a result of

high ration sorting activity; and difference between fed TMR and orts increased with increased

ration particle size. However, actual intake of these components after 24 h was similar for all

rations, and as a result milk production, milk components, and ruminal characteristics were

similar among the rations (Maulfair et al., 2010). Therefore, cows were essentially receiving

15

different rations throughout the d, but the final daily outcome was not different. However, while

the rations used in this study varied greatly in particle size, they were relatively high in forage and

NDF (59 and 34% of ration DM respectively) and low on rapidly fermentable carbohydrates

(starch was 27% of ration DM) and therefore unlikely to cause much stress on the ruminal

environment. The authors suggested that when measuring sorting activity in lactating dairy cattle

it is important to not only consider composition of the orts (which comprise only a small

percentage of daily intake) but also actual intakes of various ration components. Another

interesting component of this study was that although the diets fed varied greatly in geometric

mean particle size, the consumed geometric mean particle size was very similar across treatments

(Maulfair et al., 2010). Cows on the shortest diet ate a ration similar in particle size to what was

offered, and cows on all other rations ate a shorter ration than what was offered. Maulfair et al.

(2010) suggested perhaps cows were sorting to achieve a desired mean particle size and a ration

with the proper particle size may be able to limit or eliminate ration sorting by lactating cows.

The results of Maulfair et al. (2010) are generally in agreement with the literature about

the effect of sorting on ruminal fermentation and milk production and components. Unfortunately

many of the studies with objectives specifically looking at sorting do not report ruminal

fermentation and milk data (Leonardi and Armentano, 2003; DeVries et al., 2005; DeVries et al.,

2007; Hosseinkhani et al., 2008). Of the studies that experienced significant ration sorting and

also reported milk data (Kononoff and Heinrichs, 2003a; Kononoff et al., 2003b; Leonardi et al.,

2005a; Leonardi and Armentano, 2007; Bhandari et al., 2008; Miller-Cushon and DeVries, 2009;

Felton and DeVries, 2010; Maulfair et al., 2010) there were no differences in milk production,

milk fat and protein yields, or milk fat and protein concentrations except for the following

instances: increased fat % (P = 0.08) and decreased protein % (P = 0.04) and yield (P = 0.03)

with increased sorting (Kononoff and Heinrichs, 2003a); quadratically increased fat % (P = 0.03)

and yield (P = 0.03) with increased sorting (Kononoff et al., 2003b); decreased fat % with

16

increased sorting (P = 0.09; Leonardi et al., 2005a); increased fat yield with increased sorting (P

= 0.09, only for oat silage; Bhandari et al., 2008); and decreased protein % with increased sorting

(P = 0.07; Felton and DeVries, 2010). Of the studies that experienced significant ration sorting

and also reported ruminal fermentation data (Kononoff and Heinrichs, 2003a; Kononoff et al.,

2003b; Leonardi et al., 2005a; Bhandari et al., 2008; Maulfair et al., 2010) there were no

differences in average: ruminal pH, total VFA concentrations, or NH3 concentrations except for

the following instances: linearly decreased total VFA concentration with increased sorting (P =

0.07; Kononoff et al., 2003b) and quadratically increased ruminal pH with increased sorting (P =

0.07; Maulfair et al., 2010). Therefore the dairy nutrition industry’s general consensus that ration

sorting causes decreases in milk production and components and ruminal health is not supported

by the literature.

Critical Particle Size for Rumen Escape

The sieve size 1.18 mm has been widely used as the size at which feed particles retained

on or above are considered physically effective for dairy cows. This number originated from

research of Evans et al. (1973) and Poppi et al. (1980; 1981), where resistance of particles leaving

the rumen of cattle and sheep was measured. It was determined that 1.18 mm was a threshold

particle size (not mean) for both cattle and sheep for greatly increased resistance to particles

leaving the rumen and < 5% of fecal particles are generally retained on a 1.18-mm sieve. It

should be noted that a wet sieving technique was used in these studies to measure particle size;

differences between results of various particle separators will be discussed later.

Some researchers have suggested that the critical particle size for rumen escape in dairy

cattle may be larger than 1.18 mm. Yang et al. (2001a) discovered that when feeding cows diets

composed of alfalfa silage, barley silage, alfalfa hay, and steam rolled barley their fecal mean

17

particle length averaged 1.86 mm and that 24.8% of fecal particles were retained above a 1.18-

mm sieve and 3.1% of particles were above a 3.35-mm sieve. There was no effect of FPS on fecal

particle size. Oshita et al. (2004) completed a study with 4 different diets; long alfalfa hay,

chopped alfalfa round bale silage, long orchard grass hay, and chopped corn silage and measured

fecal particle size; their percentage of fecal particles retained on a 1.0-mm sieve were: 28.0, 25.2,

12.6, and 26.2% respectively. Other studies that reported larger fecal mean particle size than

traditionally expected are Kononoff and Heinrichs (2003a; 2003b) where fecal mean particle size

averaged 1.13 and 1.03 mm respectively; the rations fed were composed mainly of ground corn

with corn silage and alfalfa haylage respectively. The authors also reported that the proportion of

fecal particles > 1.18 mm was 48 and 46% of DM respectively and that FPS did not have an

effect on fecal mean particle size in either study.

Maulfair et al. (2011) fed 4 diets of increasing FPS (achieved via grass hay chop length)

and calculated the geometric mean particle size (Xgm) of feces 2 ways; 1. including only particles

retained on the smallest sieve and above and 2. including all sample DM by calculating the

amount of soluble DM lost during the sieving process. The retained Xgm procedure (using only

particles retained on ≥ 0.15-mm screens) did not result in any differences among rations and

retained Xgm of all rations averaged 1.13 mm. The total Xgm procedure (using all particle

fractions) had much lower values than retained Xgm and also had a significant linear contrast for

fecal Xgm to decrease with increasing TMR particle size, decreasing from 0.33 to 0.31 mm for the

shortest to the longest ration respectively. The fecal particle distribution resulted in approximately

16% of particles > 3.35 mm and 37% > 1.18 mm as a proportion of DM retained on the 0.15-mm

sieve. The distribution had approximately 7% of particles > 3.35 mm and 17% > 1.18 mm as a

proportion of total sample DM. These results, and the results of the previously cited studies, are

much higher than those observed by Poppi et al. (1981; 1985) where < 5% of particles were >

1.18 mm as a proportion of total sample DM in mature steers fed exclusively forage. The reasons

18

for the 3- to 4-fold increase in particles > 1.18 mm escaping the rumen are probably due to large

differences in DMI and passage rate of high producing dairy cows compared to steers being fed a

maintenance diet. It is clear, based upon all of this data, that 1.18 mm is not the critical threshold

for rumen escape in modern lactating dairy cows; however more research is needed to determine

the exact size and what factors can lead to variance in this critical size.

The Various Particle Sieving Methods

Several studies have used the particles retained on the 1.18-mm sieve of the PSPS to

determine peNDF of TMR (Yang and Beauchemin, 2006b; Yang and Beauchemin, 2007;

Bhandari et al., 2008). Also, studies have been conducted that used the 8-mm screen of the PSPS

to determine peNDF (Calberry et al., 2003; Plaizier, 2004; Yang and Beauchemin, 2005; DeVries

et al., 2007). However, the PSPS uses a very different particle separating technique from the one

specified by Mertens’ (1997) peNDF procedure. In addition, it should also be noted that when

using the 1.18-mm sieve in the PSPS to measure peNDF there may be no significant differences

in peNDF of TMR found, even though there are significant differences in particle size

distribution (Yang and Beauchemin, 2006b; Yang and Beauchemin, 2007; Bhandari et al., 2008)

and even cow response (Yang and Beauchemin, 2006b). This shows a lack of sensitivity when

using this technique to measure peNDF, likely caused because when forage chop lengths are

varied, most of the differences in particle distribution of the TMR are in particles above this

sieve. There currently seems to be no standard TMR and forage particle separating technique for

determining peNDF and many problems can be created by interchanging peNDF values

determined by different sieving methods. Several of the most popular particle separating methods

will be discussed in more detail.

19

Penn State Particle Separator

The PSPS is probably considered to be the standard particle separating technique in the

dairy cattle industry. The PSPS is a manually operated particle separator that separates as-fed

forage and TMR samples via horizontal shaking. Lammers et al. (1996) first developed the PSPS

as an easy to use, practical, on-farm tool to mimic Standard S424 of the American Society of

Agricultural and Biological Engineers (ASABE), which is the standard method of determining

particle size distribution of chopped forages. The first PSPS consisted of 3 particle fractions; >

19.0, > 8.0, and < 8.0 mm. The PSPS was later improved upon by Kononoff et al. (2003a) by

adding an 1.18-mm screen to allow for a more accurate characterization of TMR and forages that

have a large portion of particles < 8.0 mm. The top 2 screens have circular holes and the screen

depth is varied (12.2 and 6.4 mm for the top and middle screens respectively) to provide a 3-

dimensional barrier to prevent particles larger than the hole sizes from falling through (Lammers

et al., 1996). The bottom sieve is composed of a stainless steel wire cloth that has a nominal

screen size of 1.18 mm and a diagonal screen size of 1.67 mm (Kononoff et al., 2003a).

Recommended sample size for the PSPS is 1.4 L or ¼ of the ASABE standard sample size since

the PSPS has approximately ¼ of the surface area of the ASABE separator (Lammers et al.,

1996). The recommended shaking procedure is (on a flat surface) shake the separator horizontally

5 times (at 1.1 Hz with a stroke length of 17 cm; (Kononoff et al., 2003a), then rotate the

separator ¼ turn and repeat; a total of 8 sets of 5 shakes should be completed for a total of 40

shakes in 2 full turns (Lammers et al., 1996). Lammers et al. (1996) determined that there was no

difference in the results of the PSPS and the ASABE separator in predicting fractions of particles

< 19.0 and < 8.0 mm in 21 of the 36 statistical tests. Advantages of the PSPS are its: portability,

low cost ($300; Nasco, Fort Atkinson, WI), ease of use, quick results, use of as-fed samples, and

good repeatability. It is because of these reasons that it has become popular with dairy farmers

20

and nutritionists worldwide. The PSPS can be easily used in a field or barn whenever it is needed

without the need for time consuming drying of samples. Some disadvantages of the PSPS are it:

determines fewer particle fractions than other methods and uses manual operation. Anytime a

procedure requires manual manipulation it introduces a certain amount of human error; however,

the ability to rest the PSPS on a smooth, steady surface does a good job of limiting human error.

Other disadvantages of the PSPS were reported by Kononoff et al. (2003a), they determined that

moisture content of samples and shaking frequency affected particle size distribution and mean

particle size. Small losses of moisture caused only minor changes in particle size distributions

while complete drying caused large differences, by increasing the amount of particles passing

through each sieve (Kononoff et al., 2003a). Therefore it is important to standardize the shaking

procedure and consider the effects of moisture when utilizing the PSPS.

American Society of Agricultural and Biological Engineers’ Particle Separator

The ASABE or “Wisconsin” separator is the standard method for determination of

particle size distribution of chopped forages (S424.1; ASABE, 2007). It is a very large (> 225 kg)

particle separator that is mechanically operated and utilizes a horizontal shaking motion. The

ASABE separator consists of a pan and 5 square-hole screens with sizes of 19.0, 12.7, 6.3, 3.96,

and 1.17 mm when measured nominally or 26.9, 18.0, 8.98, 5.61, and 1.65 mm when measured

diagonally, which are all in frames of 565 × 406 × 63.5 mm (length × width × depth; ASABE,

2007). All of the screens are made of aluminum of varying thickness, increasing with increasing

screen size, except the smallest screen, which is wire mesh. Thicknesses of the screens are from

top to bottom: 12.7, 9.6, 4.8, 3.1, and 0.64 mm (American Society of Agricultural and Biological

Engineers., 2007). The recommended procedure is to use a sample size of 9 to 10 L of

uncompressed forage, but samples as small as 2 to 3 L can be used if extra care is taken to

21

recover the particles from the screens, and to operate the shaker for 2 min (ASABE, 2007).

Several advantages of this separator are it: is mechanically operated, has a moderate number of

particle fractions, uses as-fed samples, has screens with more surface area (longer and wider) than

PSPS. These advantages help to: reduce human error, more accurately describe particle

distribution, eliminate the need for sample drying, and allow for better separation of extremely

long particles respectively. Maulfair et al. (2010) found that when using rations of extremely long

particle size the PSPS did not adequately separate the particles. The extremely long hay particles

would bind together and not allow any particles to fall through the top screen when shaken with

the PSPS. The larger screens and more vigorous shaking of the ASABE separator allowed enough

movement of the longest particles for the smaller particles to fall through the screens (Maulfair et

al., 2010). This situation would not be realized very often though as these diets were very

extreme. The disadvantage of this separator is that it is the least portable of all separators; it is

very heavy, takes of a lot of space (102 × 64 × 145 cm; length × width × height), and requires

electricity to operate. It is also likely very expensive as they must be custom manufactured.

Results of the ASABE particle separator are also susceptible to variation with sample moisture

content (ASABE, 2007). Disadvantages of this particle separator strictly limit its use to research.

Ro-Tap Particle Separator

The Ro-Tap particle separator (RTPS; W.S. Tyler, Mentor, OH) uses a very interesting

technique for separating particles. A dried sample is placed on a series of stacked sieves (same

sieves used in wet sieving) placed on the machine, which horizontally shakes them while

simultaneously a metal arm repeatedly taps the top of the sieve stack (holds 8 to 16 depending on

sieve height) to incorporate a vertical shaking element as well. This shaking system could

probably be considered obsolete, except it was used for much of the research of Mertens. Mertens

22

(1997) developed the concept of peNDF and used the RTPS for development of the laboratory

assessment of peNDF, where the particles retained on a 1.18-mm after shaking are multiplied by

the sample NDF content. Mertens’ (2005) RTPS procedure specifies a sample size of 0.6 L, sieve

sizes of 19.0, 13.2, 9.5, 6.7, 4.75, 3.35, 2.36, 1.18, 0.60, and 0.30 mm, and a 10 min operation

time. A major factor that creates a difference between the RTPS and other methods is that vertical

shaking tends to separate particles by their minimum cross-sectional dimension (usually width in

forage particles) whereas horizontal shaking tends to separate particles by their length (Mertens,

1997; Mertens, 2005); this difference is amplified by the fact that the RTPS uses wire screens that

have a minimum screen thickness versus the large thicknesses of the PSPS and ASABE separator

screens. Since the RTPS utilizes vertical shaking and dried samples it produces results that can be

very different from conventional techniques (PSPS and ASABE separator) that use horizontal

shaking and as-fed samples. Which shaking technique is optimal may depend on the samples

being separated and the hypothesis that is being answered, for instance, separating particles based

on their smallest diameter may be more similar to how particles attempt to leave the rumen.

Further discussion on the differences between the RTPS and the PSPS can be found in Chapter 3.

The other divergence of the RTPS from most conventional techniques is that samples are dried

before they are separated. Drying forage samples makes particles become smaller and more

fragile, making them more likely to break during the separating process; both of these factors can

artificially decrease the resulting particle size distributions (Kononoff et al., 2003a). Drying

samples also makes this technique more time consuming as samples are usually dried for at least

24 h (Mertens, 2005). Other disadvantages of the RTPS are: not very portable, expensive ($2,300

– 2,500 plus sieves; Thermo Fisher Scientific, Waltham, MA), requires electricity, and is

extremely loud to operate. Some advantages of the RTPS are that it is mechanically operated,

many screens can be used (up to 8 or 16 depending on sieve height), and the screen sizes can be

customized for intended use. The characteristics of the RTPS again relegate its use to research.

23

Z-Box Particle Separator

The Z-Box particle separator was recently developed at the William H. Miner

Agricultural Research Institute (Chazy, NY) and was specifically designed to measure pef of as-

fed forage and TMR samples. The Z-Box was also designed to be highly correlated with the

proportion of particles retained above a 1.18-mm sieve when separated via the RTPS. Research

and development of this separator involved testing various screen sizes (1.14, 2.38, 3.18, 4.76,

and 9.53 mm), shaking motions (horizontal and vertical), and sample sizes (50 and 100 g)

(Cotanch and Grant, 2006). Samples of corn silage, hay crop silage, and TMR that varied in pef

were separated using the various combinations and the results were compared to the RTPS.

Cotanch and Grant (2006) determined that vertical shaking of 50-g samples correlated best with

the RTPS particle fraction > 1.18 mm and that the best screen size varied with the type of samples

sieved. They suggested that a 3.18-mm screen should be used for corn silage and TMR and a

4.76-mm screen should be used for hay crop silage. The Z-Box is a handheld plastic box (21 × 21

× 11 cm, length × width × height) that has a removable screen. Cotanch and Grant (2006)

recommended the following procedure for Z-Box use: place 50-g sample in box and record

weight, insert appropriate sieve, invert box and vigorously shake vertically for 50 shakes (rotating

box ¼ turn every 10 shakes), invert box and remove lid and sieve to weigh. Even though Cotanch

and Grant (2006) reported low variability both within and between technicians, field observations

have proved the opposite. It appears that because of the large requirement for human

manipulation the Z-Box does not have very good repeatability. The Z-Box does have the

advantages of portability, low cost ($250; William H. Miner Agricultural Research Institute,

Chazy, NY), and ease of use (except for having to change screens); however, these factors are

overshadowed by its lack of repeatability.

24

Wet Sieving

There are 2 types of wet sieving reported in the literature. The first type consisted of a

series of stacked sieves being completely submersed in a vat of water and moving vertically in the

water for a period of time. This type of wet sieving was used by Poppi et al. (1980; 1981; 1985)

when 1.18 mm was first suggested as the critical particle size for particles leaving the rumen of

cattle and sheep. This type of sieving seemingly has not been used for several decades and would

likely be considered obsolete. The other method of wet sieving is the type of procedure used by

Beauchemin et al. (1997) and improved upon by Maulfair and Heinrichs (2010). In this procedure

a series of stacked sieves of decreasing size have water sprayed onto the top screen and in the

middle of the sieve stack. While the water is being sprayed onto the samples in the sieve stack,

the entire stack is vibrated via vertical oscillation. The bottom pan in the sieve stack is drained to

allow water and soluble matter to flow out. Soluble DM (DM that passes through the smallest

sieve) can be determined by calculating the DM lost during the sieving process (Maulfair and

Heinrichs, 2010). Six different sieve sizes can be used at 1 time (up to 12 if half-size sieves are

used) and the sizes can be customized to suit the intended uses of the separating. This technique

lends itself very well to separating samples that have high moisture contents (rumen digesta and

fecal samples) because these samples will not separate well using other techniques without drying

and drying can change the physical properties of samples. Wet sieving is valuable for research

because it most accurately mimics conditions in the rumen as particles pass through the omasal

canal. Particles in the rumen are completely water saturated and suspended in fluid when they

pass though the omasal canal, and this is the only particle separating method that closely

resembles this action. However there are many disadvantages to using this method. This

procedure is very time consuming; even with the modifications to increase processing time made

by Maulfair and Heinrichs (2010) at least 30 min are required to process a single sample. Wet

25

sieving equipment is expensive ($2,900 – 3,500 plus sieves; Thermo Fisher Scientific, Waltham,

MA), not easily portable, and needs running water and electricity to operate. The characteristics

of this method make it very valuable for research but impractical for field use.

The Best Separating Method

Clearly there is no single separator that is best for all uses. The type of sample being used

and the hypothesis being questioned influence which particle separator to use. Wet sieving is

most likely the best technique when studying particles passing out of the rumen, because rumen

digesta and fecal samples can be separated without changing their physical conformation. The

separating action of wet sieving also more closely mimics the actions that occur in the rumen;

separating on smallest diameter while suspended in fluid. The particle separator that best

measures ration peNDF is not as easy to define. Since peNDF is described as the ability of a feed

to stimulate chewing and maintain the rumen mat (Mertens, 1997); the best separator should be

the one that best correlates to chewing activity. An as-fed sample may correlate better to chewing

because that is the form it is in when presented to the cow. Horizontal separating may correlate

better to chewing because it separates on longest diameter (Mertens, 1997; Mertens, 2005) and

the cow likely chews until the longest diameter of forage particles are below a certain size.

Additionally, repeatability of the separator is extremely important and portability, ease of use, and

cost must also be considered if the separator is to be accepted for field use. Therefore, the best

particle separator for estimating peNDF may be the PSPS, but more research is needed to find the

sieve size or combination of sieve sizes that will best correlate to chewing activity or rumen pH.

26

Forage Particle Size and Starch Fermentability Interaction

Few studies have specifically studied the interaction of FPS and ruminally fermentable

carbohydrates (RFC) by altering both simultaneously. Yang et al. (2001b) fed rations that varied

extent of grain processing, F:C, and FPS. These factors were altered by feeding: coarse and flat

steam-rolled barley grain, F:C of 35:65 and 55:45, and long and short barley silage, alfalfa silage,

and alfalfa hay respectively. Yang et al. (2001b) determined that DMI increased with increasing

RFC and was not affected by FPS. Average ruminal pH was decreased with increasing RFC and

again not affected by FPS. Finally, milk yield, milk fat content, and milk protein content were

increased, decreased, and increased, respectively, by increasing RFC; while they were not

affected, tended to increase, and tended to increase with increasing FPS. The authors concluded

that RFC was the most influential factor affecting milk production while FPS had minimal impact

(Yang et al., 2001b). It is important to note that in this study the variation in FPS was not great.

The percent of DM retained above the PSPS 19.0-mm sieve for long and short barley silage, long

and short alfalfa silage, and long and short alfalfa hay were: 5.6, 0.4, 3.9, 0.3, 20.6, and 0.0%,

respectively; therefore even the long hay crop silages were below the current recommendation of

10 to 20% retained on the 19.0-mm sieve when determined with the PSPS (Heinrichs and

Kononoff, 2002). The only interaction involving FPS was with F:C for milk fat content (P =

0.06). It was found that when increasing FPS, milk fat content had a higher increase for the high

forage diet compared to the low forage diet, likely because the peNDF intake increased to a

greater extent for the high forage diets (Yang et al., 2001b). Yang et al. (2001b) suggested that

ruminal pH and SARA cannot be predicted directly using only the physical characteristics of the

diet, as RFC appears to have greater impact on ruminal pH than FPS.

Krause et al. (2002a; 2002b) also examined the interactions of FPS and RFC and fed

rations that varied FPS with short and long alfalfa silage and varied RFC with dry cracked shelled

27

corn and high-moisture corn. It was determined that increasing RFC decreased DMI, while FPS

had no effect (Krause et al., 2002a). Krause et al. (2002a) reported that the interaction between

FPS and RFC was significant for NDF, ADF, and starch intake; increasing FPS with high RFC

decreased NDF and ADF intake and increased starch intake, but increasing FPS with low RFC

increased NDF and ADF intake and decreased starch intake. Mean ruminal pH decreased with

increasing RFC (5.99 to 5.85) and increased with increasing FPS (5.81 to 6.02), and no

interaction between FPS and RFC for mean ruminal pH was present (Krause et al., 2002b).

Increasing RFC tended to increase milk yield but had no affect on milk fat or protein content,

while increasing FPS had no affect on milk yield, fat, or protein content (Krause et al., 2002a).

An interaction between FPS and RFC also occurred (P = 0.06) for milk yield, as milk yield

tended to increase with FPS with high RFC and tended to decrease with low RFC (Krause et al.,

2002a). The authors suggested that this interaction might be an affect of the shift toward a lower

fiber and higher starch intake when FPS was increased with high RFC allowing higher energy

intake, whereas the opposite occurred with low RFC. This situation probably also caused the

trend towards an interaction (P = 0.09) occurring for milk protein content, as higher energy intake

could lead to increased microbial synthesis and result in higher milk protein content (Krause et

al., 2002a). Interestingly, it was determined that increasing RFC, by replacing dry cracked shelled

corn with high-moisture corn, tended to increase (P = 0.08) ruminating min/d and increased (P =

0.03) ruminating min/kg of NDF intake. Krause et al. (2002b) suggested that since alfalfa silage

should be the only diet component that could stimulate rumination, the increase in ruminating

activity is a result of an adaptive response by the animals to increased RFC to attenuate low

ruminal pH via increased saliva secretion. Also this finding indicates that physical effectiveness

of forages is affected by other dietary components such as corn grain moisture and fermentability

(Krause et al., 2002b). The authors of this study concluded that diets low in effective fiber and

28

high in RFC can be fed to midlactation cows without causing negative effects on cow

productivity (Krause et al., 2002a).

Finally, the interactions between FPS and RFC were also studied by Krause and Combs

(2003). In this study RFC, FPS and F:C were varied by feeding; dry cracked shelled corn or

ground high-moisture corn, short or long alfalfa silage, and alfalfa silage as the only forage or a

50:50 mixture of alfalfa and corn silage respectively. It was determined that DMI decreased with

increasing RFC and increased with increasing FPS. Mean ruminal pH decreased with increasing

RFC and was not affected by FPS, and it should be noted that mean ruminal pH was below 6.0

for all treatments, probably due to the low NDF (24.3 to 26.4%) and high starch (28.4 to 38.7%)

contents of the diets (Krause and Combs, 2003). There were significant interactions between FPS

and RFC for time below pH 5.8 (h/d) and area below pH 5.8 (h × pH units/d); because as FPS

increased time and area below pH 5.8 decreased with high RFC but increased with low RFC, but

the authors were unable to explain the reasons for these interactions (Krause and Combs, 2003).

Finally, milk yield was not affected by RFC but tended to decrease with increasing FPS, milk fat

content was decreased with increasing RFC and increased with FPS, and milk protein content was

decreased with increasing RFC and not affected by FPS. Krause and Combs (2003) concluded

that because of interactions that occurred between FPS and RFC for ruminal fermentation

variables, the effects of FPS and RFC are not always additive which complicates the inclusion of

these factors in dairy ration formulation and evaluation programs.

It is clear from the results of these studies that RFC generally has a greater influence on

DMI, milk yield and components, and ruminal fermentation than FPS. Also interactions between

FPS and RFC for milk production and ruminal fermentation regularly occur making balancing

rations for these components much more difficult. None of these studies that varied both FPS and

RFC in order to study their interactions also measured or reported on the effects of these variables

on ration sorting or diet selection; so the interactions of FPS and RFC on ration sorting is not

29

known. A recent review of 45 studies including 134 different experimental diets examined the

influence peNDF and ruminally degradable starch from grains on rumen pH and also determined

that ruminally degradable starch had a higher impact on ruminal fermentation than peNDF

(Zebeli et al., 2010). Zebeli et al. (2010) also determined that the ratio of peNDF to ruminally

degradable starch from grains should be no lower than 1.45 to maintain mean ruminal pH above

6.2 (Figure 2-1).

Ruminal Acidosis and Diet Selection

The optimal foraging theory of feed selection put forth by Krebs and McCleery (1984)

states that animals will select the feed that offers them the greatest potential energy intake rate

when given a choice. However, Forbes and Kyriazakis (1995) stated that the ruminant animal is

faced with the dilemma of choosing a nutrient dense feed, which allows for it to grow and reach

puberty as quickly as possible, or choosing a fibrous feed and supporting a stable and healthy

ruminal environment. The work of Cooper et al. (1996) also suggests that ruminants make diet

choices that are contrary to the optimal foraging theory by selecting feeds that do not maximize

their energy intake rate. Instead they put forth the hypothesis that 1 objective of diet selection in

ruminants is to sustain high levels of feed intake by keeping ruminal conditions within certain

physiological limits (Cooper et al., 1996).

In their study 42 sheep were divided into seven 6 × 6 Latin squares and were offered a

combination of diet choices to study the effects of physical form, carbohydrate source, and

NaHCO3 inclusion rate on feed selection. The feed choice combinations included: low energy

density (LED) feeds, long alfalfa hay and alfalfa pellets; high energy density (HED) feeds, barley

pellets and sugar beet pulp/barley pellets each with varying NaHCO3 inclusion rates of 0, 1, 2,

4% (wt/wt). When fed either long or pelleted alfalfa singly, sheep consumed higher amounts of

30

alfalfa pellets probably due to an increased rate of passage through the rumen. Of the HED feeds

offered singly, sheep consumed more sugar beet pulp/barley pellets than barley pellets. The

reason for increased consumption of the sugar beet pulp/ barley pellets is most likely due to its

greater buffering capacity which helps maintain higher rumen pH levels as opposed to barley

pellets which decrease rumen pH and thus feed intake (Cooper et al., 1996). Sheep tended to

select a diet that was supplemented with NaHCO3 when given a choice; however, there was not a

dose dependent response. The 2 most likely reasons for this are that either the NaHCO3 inclusion

rates were too similar and the sheep were not able to differentiate among them or that the highest

level of NaHCO3 inclusion was associated with negative effects on the rumen through increased

rumen osmolality (Cooper et al., 1996). When offered the choice between long alfalfa hay and

alfalfa pellets, sheep consumed a higher proportion of the pellets. However, the sheep did not eat

alfalfa pellets exclusively and chose to consume substantial amounts of long alfalfa hay in order

to maintain their rumen health (Cooper et al., 1996). Finally, when offered the choice between

either LED feeds and a HED feed, sheep ate a higher proportion of the LED feed when the other

feed choice was barley pellets compared to sugar beet pulp/ barley pellets. Cooper et al. (1996)

suggested that sheep consumed more LED feed when offered the more highly fermentable barley

pellets to minimize the adverse affects on the rumen associated with consuming this type of feed.

Also when offered both a LED feed with either HED feed, sheep ate a higher proportion of the

LED feed when it was offered as alfalfa pellets, this could be due to the fact that the pellets offer

a higher intake rate or that less long hay is needed to be consumed by the sheep in order to

maintain certain ruminal conditions (Cooper et al., 1996). Surely there are many factors that

influence diet and feed selection in ruminants, but this study shows that a substantial factor is the

maintenance of a healthy rumen environment.

Castle et al. (1979) completed a study that showed that dairy cattle also do not always

follow the optimal foraging theory of Krebs and McCleery (1984). In this study, 3 grass silages of

31

different particle lengths were fed simultaneously to 3 pregnant Ayrshire heifers. This study was

3 wk long, at the beginning of each wk the position of silages were changed randomly, and each

silage was fed to achieve a 20% refusal rate. Silage intake preferences were measured on the last

4 d of each wk. The heifers consumed 15.9, 31.9, and 52.2% of total DMI as long, medium, and

short silages respectively (Castle et al., 1979). One confounding factor in this study was that the 3

silages were chopped differently at harvest and stored separately. The variation of particle sizes

altered silage densities and caused differences in silage fermentation; the long silage had the

highest pH and butyrate concentration with the lowest lactate concentration (Castle et al., 1979).

These factors most likely increased aversion to the long silage and they should be considered

when interpreting the results. These heifers clearly showed a preference to consume substantial

amounts of longer forages at the expense of maximum feed intake, agreeing with the theory put

forth by Cooper et al. (1996).

Four models of feed selection in the ruminant have been proposed by Provenza (1995):

euphagia, hedyphagia, body morphophysiology and size, and learning through foraging

consequences. The euphagia model is described as an animal’s innate ability to taste and smell

specific nutrients and toxins in feed, which would allow the animal to simultaneously select

nutritious feeds while avoiding those that are harmful (Provenza, 1995). The hedyphagia feed

selection model states that animals will select feeds that are immediately “pleasing” to olfactory,

gustatory, and tactile senses and avoid feeds that are not (Provenza, 1995). This model relies on

the assumption that nutritious compounds taste good and harmful compounds taste bad. The third

model, body morphophysiology and size, is based on the fact that ruminant species differ in their

ability to ingest forages with different physical and chemical characteristics because of varying

morphological and physiological adaptations (Provenza, 1995). Finally, the learning through

foraging consequences model involves feedback mechanisms that allow animals to select

nutritious and healthy diets when faced with feeds that vary in nutrients and toxins (Provenza,

32

1995). This model assumes that diet selection is based upon positive and negative consequences

experienced by the animal through ingestion of diverse feeds and includes prominent aspects of

the other 3 models. This model relies on neural interactions and feedback mechanisms among

taste, smell, and the gastrointestinal tract. These feed selection models may help explain how

ruminal acidosis can influence diet selection.

In a study by Phy and Provenza (1998b) lambs were fed a meal of rolled barley and then

offered a choice of flavored (onion or oregano) rabbit pellets that either contained NaHCO3 and

lasalocid or NaCl. The authors determined that after a grain meal lambs preferred rabbit pellets

that contained NaHCO3 and lasalocid over pellets that contained NaCl. The second part of this

study examined the effects of feeding different levels of wheat on the intake of a solution

containing NaHCO3, NaCl, or pure water. First, Phy and Provenza (1998b) determined that lambs

increased their consumption of a NaHCO3 solution when wheat intake was increased but water

intake was not affected by wheat intake. In addition, it was determined that lambs increased their

intake of a NaHCO3 solution (186%) to a greater degree than a NaCl solution (140%) when wheat

intake was increased. All of these results show that lambs prefer feeds and solutions (NaHCO3

and lasalocid) that attenuate acidosis after a grain meal to maintain ruminal health (Phy and

Provenza, 1998b). However even though clinical acidosis was reduced in the high wheat

treatment groups that provided NaHCO3 compared to NaCl or water (9 versus 26) there was still a

substantial number of lambs that had access to NaHCO3 that showed signs of clinical acidosis

(16%), indicating that lambs cannot completely eliminate acidosis problems through feed

selection.

Another study by Phy and Provenza (1998a) examined what effect eating a meal of

rapidly fermentable feed had on the preference for rapidly fermentable feed later on and whether

NaHCO3 and lasalocid influenced this preference change. Lambs fed a lower amount (400 g) of

rolled barley for a meal exhibited equal preference for rolled barley and alfalfa pellets (52 and

33

48% of total intake respectively) during the next 4 h. However, when a higher amount (1,200 g)

of rolled barley was fed the lambs increased their preference for alfalfa pellets over rolled barley

(71 and 29% of total intake respectively) during this same time (Phy and Provenza, 1998a). Even

though the lambs being fed large amounts of fermentable carbohydrates seemed to adjust their

diet preference to maintain rumen health, they were unsuccessful, as all animals in that treatment

exhibited signs of clinical acidosis (diarrhea). In the second part of this study, it was determined

that lambs had higher intakes when barley was mixed with NaHCO3 and lasalocid than when

barley was offered with NaHCO3 only, lasalocid only, or no additives (Phy and Provenza, 1998a).

These results show that lambs will increase their intake of rapidly fermentable carbohydrates

when it is offered with an additive that helps attenuate acidosis.

There are only a couple of studies that have examined effects of ruminal acidosis on feed

or diet selection in dairy cattle. In one such study, Keunen et al. (2002) studied effects of SARA

on the preference of long alfalfa hay versus alfalfa pellets. Four cows were fed a ration that had

25% of ad libitum DMI of TMR replaced with wheat/barley pellets (50% ground wheat and 50%

ground barley) for 2 wk (separated by 1 control wk). To determine feed preference cows were

offered long alfalfa hay and alfalfa pellets 2 times per d for 30 min each. The preference ratio

(amount of hay consumed/ amount of hay + pellets consumed) during the SARA wk was higher

than the control wk (0.85 versus 0.60; Keunen et al., 2002). These results suggest that dairy cows

will change their feed preference during a bout of SARA to attempt to maintain rumen health.

However, because of the design of this experiment, the feed preferences of these cows were only

measured for 1 h per d.

DeVries et al. (2008) completed another study that examined feed selection and SARA in

dairy cattle. This study again used a rumen challenge model to induce SARA, but the challenge

consisted of 1 d of feed restriction to 50% of ad libitum intake followed the next morning by 4 kg

of ground barley/wheat and then ad libitum access to TMR for the remainder of the d. This model

34

was repeated to produce 2 periods. The ration sorting activity of 2 groups of cows, early lactation

cows fed a low forage diet and mid-lactation cows fed a high forage diet [high risk (HR) and low

risk (LR) to acidosis respectively] were then compared to their sorting activity prior to the rumen

challenge. The rumen challenge successfully induced SARA and decreased daily mean rumen pH

in HR cows from 5.88 to 5.56 and from 6.25 to 5.88 in LR cows (Dohme et al., 2008). Before the

rumen challenge, cows of both groups would sort against long particles (> 19.0mm) and fine

particles (< 1.18mm) and sort for medium particles (> 8.0 mm), HR cows sorted against short

particles (> 1.18 mm) while LR cows sorted for them (DeVries et al., 2008). In addition HR cows

sorted their rations to a greater degree than LR cows. After the rumen challenge, cows in both

groups changed their sorting behavior; HR cows generally increased their sorting for medium

particles and against short and fine particles, and exhibited no change in sorting long particles,

while LR cows exhibited variable responses with sorting activity changing with d and period

(DeVries et al., 2008). DeVries et al. (2008) therefore suggested that dairy cattle will alter their

ration sorting behavior during a bout of SARA in order to maintain ruminal health. However

these results are very difficult to interpret, first because of the many interactions that occur. There

was a significant d × group effect for long particles, d × period effects for all particle fractions,

and d × group × period effects for long, short, and fine particle fractions. In addition, the TMR

fed in this study utilized pelleted grain which was contained in the medium particle fraction;

therefore the composition of this particle fraction was vastly different than most other studies

where the medium fraction is composed mainly of forages. Increasing sorting for the medium

particle fraction may not help attenuate acidosis because it contains both forages (fiber) and grain

pellets (highly fermentable carbohydrates).

There is evidence that a cyclical pattern of intake can occur when ruminants eat grain;

when high levels of VFA from starch digestion are produced they can cause malaise (feeling of

general discomfort) that decreases intake (Huber, 1976; Provenza, 1995). And while ruminants

35

prefer nutritionally dense foods like grains, they will decrease their intake of grains and increase

their intake of other feeds when they ingest too much grain (Britton and Stock, 1986; Ortega-

Reyes et al., 1992). In fact, animals experiencing malaise will increase the variety of feeds

consumed in order to help attenuate their discomfort (Provenza et al., 1994). Feedback

mechanisms that alert the ruminant of positive or negative consequences resulting from eating

certain feeds are not fully understood. Feedback is possibly related to changes in rumen content

composition or blood plasma variables (Keunen et al., 2002). Huber (1976) stated that since

hydrogen ion receptors have not been demonstrated to exist in rumen mucosa, there are 3 possible

mechanisms for rumen stasis following ruminal digesta acidification: involvement of hydrogen

ion receptors elsewhere in the gastrointestinal tract, central inhibition by absorbed acid, and

inhibition by absorbed amines or toxins. Provenza et al. (1994) studied mechanisms that allow for

postingestive feedback to influence feeding behavior by examining the effects of feeding

antiemetic drugs on feed aversions in sheep. Antiemetics are drugs that are effective against

vomiting and nausea; the antiemetics used in this study were diphenhydramine hydrochloride,

metoclopramide monohydrochloride, and crystalline dexamethasone, which were dosed as a

mixture to increase their effectiveness (Provenza et al., 1994). There were 4 treatments in this

study, antiemetics plus LiCl (A+L), antiemetics only (A), LiCl only (L), and a control (C).

Lithium chloride was included in this study because it is known to induce a malaise that is similar

to that caused by excessive ingestion of many compounds (Provenza, 1995). These 4 treatments

were applied to 3 different feeds separately in 3 different experiments; feeds were oat grain,

wheat grain, and milo. In all 3 cases the feed intakes of the 2 treatments that included antiemetics

were higher than the 2 that did not (A+L > L and A > C), also the feed intakes of the treatments

that included LiCl were lower than the treatments that did not (A > A+L and C > L) (Provenza et

al., 1994). The authors suggested that the reason A consistently had higher intakes than C was

because the amount of highly fermentable carbohydrates ingested from grains was able to cause

36

mild malaise without the addition of LiCl. Based on these results Provenza et al. (1994)

concluded that LiCl and grain overload stimulate the emetic system which induces internal

malaise and therefore reduce feed intake.

There is substantial evidence in the literature that feed and diet selection is a very

complex mechanism and that ruminants have some sort of feedback mechanism(s) in order to

maintain ruminal health. Ruminants prefer feeds that maintain macronutrient balance, diminish

toxicosis, and attenuate acidosis over feeds that strictly provide high energy intake (Phy and

Provenza, 1998b). The overriding principle of ruminant diet selection is probably best summed

up by Kyriazakis et al. (1999) who stated that diet selection should be considered within a

framework of feeding behavior that views both feed intake and diet selection as an outcome of the

animal’s internal state and knowledge of the feeding environment.

Conclusions

A thorough review of the FPS literature leads to the following conclusions: that the

general consensus among dairy industry professionals and researchers that ration sorting in

lactating dairy cows negatively impacts milk production and components and ruminal

fermentation is not supported by the results of the majority of studies reporting ration sorting; that

the effects of FPS on DMI, ruminal fermentation, digestibility of DM, NDF, and starch, and milk

production and components is very variable and inconsistent; and that RFC has a larger influence

on the former variables than FPS and that using a measure that combines FPS and RFC would

provide a more accurate and consistent way to predict the affects of diet on animal performance.

37

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Zebeli, Q., D. Mansmann, H. Steingass, and B. N. Ametaj. 2010. Balancing diets for physically

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43

Table 2-1. Physical effectiveness factors (pef) for NDF in feeds of each physical form

classification based on total chewing activity in relation to that elicited by long grass hay.

From Mertens, D. R. 1997. Creating a system for meeting the fiber requirements of dairy cows. J.

Dairy Sci. 80:1463–1481.

44

Figure 2-1. Effect of the ratio between physically effective NDF (peNDF1.18) to ruminally

degradable starch from grains (RDSG) in the diet on daily mean ruminal pH

Ruminal pH = 5.53 + 0.449*peNDF:RDSG ratio, if peNDF:RDSG ratio < 1.45 ± 0.22,

asymptotic plateau of pH = 6.20; root mean square error = 0.15; R2 = 0.41, P < 0.001 (variables

were plotted based on a meta-analysis conducted from 45 studies with a total of 134 different

experimental diets). From Zebeli, Q., D. Mansmann, H. Steingass, and B. N. Ametaj. 2010.

Balancing diets for physically effective fibre and ruminally degradable starch: A key to lower the

risk of sub-acute rumen acidosis and improve productivity of dairy cattle. Livest. Sci. 127:1–1.

Chapter 3

Eating Behavior, Ruminal Fermentation, and Milk Production in Lactating

Dairy Cows Fed Rations That Varied in Dry Alfalfa Hay and Alfalfa Silage

Content

Abstract

The objective of this experiment was to evaluate effects of various inclusion levels of dry

chopped alfalfa hay and alfalfa silage in lactating dairy cow rations on eating behavior, rumen

fermentation, milk yield and components. A second objective of this study was to compare results

of the Penn State and Ro-Tap particle separators for the same TMR samples and to determine

effects of separation method on particle size distribution. Eight multiparous Holstein cows (79 ±

11 d in milk initially; 647 ± 36 kg body weight) were randomly assigned to a replicated 4 × 4

Latin square design. During each of the 4 periods, cows were fed 1 of 4 diets that were

chemically similar but varied in dry chopped alfalfa hay level. Forage dry matter (DM) content of

each ration consisted of 50% corn silage and 5, 10, 20, or 40% dry chopped alfalfa hay. The

remaining forage DM content was alfalfa silage (45, 40, 30, and 10% respectively). It was

determined that there were minimum effects on sorting early in the d and no effects 4 h after

feeding and later with increasing alfalfa hay content. Dry alfalfa hay was included in rations up to

23.5% of ration DM with no negative effects on DM intake, milk yield, and rumen fermentation.

Small decreases in milk fat and protein content occurred with increasing dry hay inclusion.

Despite changes in total mixed ration refusal particle size distribution throughout the d, by 24 h

after feeding no significant ration sorting occurred when measured either by selection indices or

actual consumption of various particle size fractions (> 19.0, > 8.0, > 1.18 mm, and pan). Data

from the Penn State and Ro-Tap particle separators produced different particle size distributions

46

from the same sample. This indicates that data obtained from these 2 methods of particle

separation are not directly comparable and that the method of particle separation should be

considered when interpreting experimental results.

Key Words: chewing, particle size, rumination, sorting

Introduction

Ration sorting has generally been considered a concern for lactating dairy cow health and

feeding. It is believed that ration sorting can lead to SARA because cows usually sort against

longer particles and for shorter particles (Leonardi and Armentano, 2003; Kononoff et al., 2003b;

DeVries et al., 2007). This type of sorting behavior could lead to decreased NDF intake and

physical effectiveness of the diet while starch intake is increased. A decrease in effective fiber

can be especially detrimental to high producing dairy cows being fed energy dense rations that

rely on longer fiber to increase chewing and saliva secretion to help buffer their rumen (Nocek,

1997; Allen, 1997; Krause et al., 2002). However, Maulfair et al. (2010) determined that drastic

ration sorting, when determined by changes in TMR refusal particle size distributions, can occur

in diets without any negative effects on milk production, milk components, and rumen

fermentation under certain feeding conditions. The authors suggested that the actual consumption

of particle size fractions, NDF, and starch should be considered when measuring ration sorting.

Therefore there is a need to study ration sorting in greater detail to understand what factors

interact to cause negative effects in the cow and develop methods to limit these effects.

A main component of forage particle size research is the particle separating equipment.

The Penn State particle separator (PSPS) was developed as an inexpensive and easy to use device

to characterize particle size distribution of TMR and forages in the field (Lammers et al., 1996;

Kononoff et al., 2003a). The PSPS has been increasingly used in research to describe particle size

47

distribution of treatment diets and for estimation of physically effective NDF (peNDF) by using

the proportion of samples’ particles retained above the 1.18-mm sieve multiplied by their NDF

content. The PSPS uses as-fed samples and a horizontal shaking motion to separate the particles.

This is in contrast to the Ro-Tap particle separator (RTPS) which uses dried samples and

vigorous vertical shaking to separate particles. The RTPS is important to forage particle size

research because Mertens (1997) used it to develop the laboratory assessment of peNDF, where

particles retained on a 1.18-mm sieve after shaking are multiplied by the sample NDF content.

One major factor that creates a difference between the PSPS and the RTPS is that vertical shaking

tends to separate particles by their minimum cross-sectional dimension, whereas horizontal

shaking tends to separate particles by their length (Mertens, 1997; Mertens, 2005). Another factor

that could cause different results between these 2 separators are sample drying. Drying can cause

particles to shrink and increase their fragility causing them to break; both of which will decrease

particle size distributions (Kononoff et al., 2003a). Therefore it is important to understand how

the data from these 2 methods of particle separation differ so that their results may be interpreted

accurately.

The objective of this experiment was to study effects of replacing alfalfa haylage with dry

chopped alfalfa hay in the ration on sorting activity and to determine effects on ruminal

fermentation, milk production, or milk composition. In addition, a second objective of this study

was to compare results of the PSPS and RTPS for the same TMR samples and to determine

effects of separation method on particle size distribution.

48

Materials and Methods

Diets, Cows, and Experimental Design

Cows used in this experiment were cared for and maintained according to a protocol

approved by The Pennsylvania State University Institutional Animal Care and Use Committee.

Eight (4 rumen cannulated) lactating, multiparous, Holstein cows (79 ± 11 DIM initially; 647 ±

36 kg BW) were randomly assigned to a replicated 4 × 4 Latin square design. There were 4

periods of 21 d, 13 d of adaptation and 8 d of sample collection. Cows were fed 1 of 4 rations

each period that were chemically similar and varied only in concentration of chopped alfalfa hay

(replacing alfalfa haylage). Dry alfalfa inclusion rates were 5, 10, 20, or 40% of forage DM,

representing 2.9, 5.8, 11.7, and 23.5% of total ration DM. Ration ingredients, other than dry

chopped alfalfa hay and alfalfa silage, remained similar for all diets except the 40% hay diet. This

diet had a decreased amount of canola meal and 0.5% of urea added to maintain similar levels of

rumen degradable protein among all rations. Cows were housed in individual tie-stalls in a

mechanically ventilated barn and milked twice/d at 0700 and 1900 h. They were fed once/d at

0730 h for ad libitum consumption and a 10% refusal rate to allow maximum opportunity to sort.

Feed was pushed up 3 times/d at 1230, 1730, and 2400 h. Rations were balanced to meet or

exceed NRC (2001) requirements for cows producing 38.5 kg of milk/d containing 3.75% fat and

3.07% true protein assuming a DMI of 23.9 kg/d and water was available for ad libitum

consumption.

Feed, Refusal, and Particle Size Analysis

Offered TMR and refusals were weighed daily for the duration of the study. On d 20 and

21 of each period feed bunk contents were weighed and sampled at 0, 2, 4, 8, 12, 16, and 24 h

49

after feeding to determine particle size distribution and DM content of remaining feed. Particle

size distributions of samples were determined with the PSPS according to Kononoff et al.

(2003a). Samples were then dried in a forced air oven at 55°C for 48 h to determine DM content.

Samples of each TMR and forage were collected on d 16 and 19 of each period, composited by

period and analyzed by Cumberland Valley Analytical Services, Inc. (Hagerstown, MD) for CP

(AOAC, 2000), ADF (AOAC, 2000), NDF (Van Soest et al., 1991), ash (AOAC, 2000), NFC

(Van Soest et al., 1991), and NEL (NRC, 2001). There were 2 procedures used to calculate

peNDF: peNDF8.0 = % of particles > 8.0 mm × NDF of whole sample (top 2 sieves of PSPS) and

peNDF1.18 = % of particles > 1.18 mm × NDF of whole sample (top 3 sieves of PSPS; Kononoff

et al., 2003a). The RTPS was used to separate 95 dried TMR samples comprised of the 4

treatment diets and 0 and 24 h time points to compare to the results of the PSPS. Approximately

0.6 L of dried sample were placed on the top of the sieve stack, which contained sieves of: 9.5,

8.0, 6.7, 4.75, 3.35, 2.36, 1.70, 1.18, 0.60, and 0.15 mm. The RTPS was run for 10 min and

particles retained on each sieve were then weighed to determine the proportion of sample DM

retained on each sieve.

Chewing Activity

On d 14 to 18 of each period, eating and rumination behavior were recorded using

Institute of Grassland and Environmental Research Behavior Recorders and Graze Jaw

Movement Analysis Software (Ultra Sound Advice, London, UK) as described by Rutter (1997;

2000). Chewing was measured for all cows for two 24-h periods including while cows were being

milked. These recorders analyze jaw movements of cattle, and the software determines eating or

ruminating chews based on amplitude and frequency of jaw movements. This procedure was

validated for use with cows housed in tie-stalls by Kononoff et al. (2002).

50

Rumen Sampling

Rumen sampling was conducted on d 15 of each period at 0.0, 1.5, 3.5, 5.5, 8.5, 11.5,

14.5, 18, 21.5, and 24.5 h after feeding (Kononoff et al., 2003b). Samples were taken from dorsal,

ventral, cranial, caudal, and medial areas of the rumen, mixed thoroughly, and then filtered

through 4 layers of cheesecloth. Rumen liquid pH was immediately determined using a handheld

pH meter (phTestr 10 BNC, Oakton, Vernon Hills, IL). Approximately 15 mL of filtered liquid

was placed into bottles containing 3 mL of 25% metaphosphoric acid and 3 mL of 0.6% 2-

ethylbutyric acid (internal standard) and stored at -20C. After thawing, samples were centrifuged

3 times at 4000 g for 30 min at 4C to obtain a clear supernatant and were analyzed for NH3

using a phenol-hypochlorite assay (Broderick and Kang, 1980) and VFA concentration using gas

chromatography (Yang and Varga, 1989).

Milk Production

Milk production was recorded daily and milk samples were taken on d 20 and 21 (4

consecutive milkings). Samples were collected and preserved using 2-bromo-2-nitropropane-1,3

diol. Milk samples were analyzed for fat, true protein, lactose, MUN, and SCC by the Dairy One

milk testing laboratory (State College, PA) using infrared spectrophotometry (Foss 605B Milk-

Scan; Foss Electric, Hillerod, Denmark).

Statistical Analyses

Statistical analyses were conducted using PROC MIXED of SAS (Version 9.2, SAS

Institute, Cary, NC). Dependent variables were analyzed as a 4 4 Latin square design. All

51

denominator degrees of freedom for F-tests were calculated according to Kenward and Roger

(1997) and repeated measurements for rumen samples were analyzed using first-order

autoregressive covariance structure (Littell et al., 1998), as well as terms for time and interaction

of treatment by time. Because of unequally spaced rumen sampling, weighted mean daily rumen

pH, NH3, and VFA concentrations were determined by calculating the area under the response

curve according to the trapezoidal rule (Shipley and Clark, 1972). A selection index based on

refusals was calculated for each of the 4 particle size fractions at 2, 4, 8, 12, 16, and 24 h after

feeding. This index was calculated as the actual intake of each fraction (Yi) expressed as a

percentage of the expected intake. Expected intake of Yi equals intake multiplied by the fraction

of Yi in the TMR fed (Leonardi and Armentano, 2003). Values > 1.0 indicate cows were sorting

for the particle fraction and values < 1.0 indicate cows were sorting against the particle fraction.

Sorting indices were calculated using both the expected intake since time point 0 h (cumulative)

and the expected intake since the previous time point (interval). The 95% confidence limits were

used to determine if selection index was significantly different from 1.0. Chewing behavior and

meal criterion was analyzed using the procedure of Maulfair et al. (2010). The data used for

calculating the sieve size in the PSPS that is equivalent to the 1.18-mm sieve in the RTPS were

natural log transformed to correct for abnormal distribution and improve model fit, which

included terms for separator, ration, period, d, time, sieve size, sieve size2, separator by sieve size,

and separator by sieve size2 along with a random effect of cow. All data are presented as least

squares means and treatment effects are considered significant when P ≤

0.05 and a trend when

0.05 < P ≤ 0.10. Means separation tests were conducted using the protected least significant

differences (PDIFF) procedure, with significance at P ≤ 0.05.

52

Results and Discussion

Chemical Composition and Particle Size Distribution

The chemical compositions and particle size distributions of the forages included in the

rations of this study are shown in Table 3-1. Alfalfa silage was replaced with dry chopped alfalfa

hay in this study and there were differences in their chemical and physical properties. Dry hay

naturally had much higher DM content than silage, 90.4 and 40.0% respectively. This difference

led to the treatment TMR differing in DM content as well, but to a lesser degree. Dry chopped

alfalfa hay was lower in CP, higher in ADF and NDF, and approximately equal in NFC compared

to alfalfa silage. A much greater proportion of particles were retained on the 19.0 mm sieve and

the pan for alfalfa hay than silage (4.4 and 4.3 times more for 19.0 mm sieve and pan

respectively). Approximately 88% of alfalfa silage particles were retained on the middle 2

screens, compared to approximately 43% of hay particles. Since alfalfa hay had higher fiber

levels but fewer particles greater than 8.0 and 1.18 mm than alfalfa silage, peNDF values between

the 2 forages remained similar. Hay had higher peNDF values but was only 17 and 13% higher

than silage for peNDF8.0 and peNDF1.18 respectively. Ingredients, chemical compositions, and

particle size distributions of the treatment diets used in this study are shown in Table 3-2. Ration

DM numerically increased with increasing hay inclusion and was significantly higher in the 40%

hay diet as a result of higher DM of hay versus silage, though there was only a 7.1% maximum

variation among the diets. Crude protein, NDF, and NFC were not different among diets and

averaged 17.9, 34.5, and 36.6% of DM respectively. Forage NDF was increased slightly in the

40% hay ration because of the increased NDF content of the hay over the silage. The peNDF

values of treatment diets showed mixed results; peNDF1.18 was not different among rations and

averaged 29.6% of DM, while peNDF8.0 decreased slightly with increasing alfalfa hay inclusion

53

(from 15.1 to 13.7% of DM). Particle size distributions of the treatments were varied; both the

19.0 mm and pan particle fractions generally increased with increasing dry hay inclusion, while

the 8.0 mm fraction decreased and the 1.18 mm fraction remained similar among rations.

Ration Sorting

Refusal particle size distributions for each treatment over the course of 1 d are displayed

in Figure 3-1 as the changes in each particle fraction (> 19.0, > 8.0, > 1.18 mm, and pan) over 24

h. The treatments exhibited similar patterns over time in each of the 4 particle fractions. The

particles retained on the 19.0 mm sieve decreased after feeding for 3.5 h and then remained stable

for the rest of the d; the 40% hay diet had significantly higher values than the other diets for the

entire d. All treatments showed a gradual increase in particles retained on the 8.0 mm sieve; the

40% hay was significantly lower than the other treatments and increased at a greater rate. The

particles retained on the 1.18 mm screen increased slightly throughout the d and were similar

among treatments. Finally, particles retained in the pan decreased in all rations over 24 h; the

40% hay diet started with a higher proportion of these particles but it decreased at a faster rate

and was approximately equal to the other rations by 16 h after feeding.

Cumulative selection indices for each treatment throughout the d were calculated, and it

was determined that at 2 h after feeding, cows in the 40% hay treatment were sorting against

particles retained on the 19.0-mm sieve while cows on all other treatments were sorting for these

particles. By 4 h after feeding cows on all treatments were sorting for these larger particles and

for the remainder of the d did not significantly sort for or against this particle fraction. Particles

retained on the 8.0-mm sieve were sorted against by cows on the 20% hay treatment and were not

sorted for or against by the other treatments at h 2. For the remainder of the d all treatments were

similar and not different from 1.0. The 1.18-mm particle fraction was sorted against by cows on

54

the 10% diet at 2 h and on the 5% diet at 4 h; there was no sorting for the other treatments and

times. Finally, particles < 1.18 mm were sorted for by cows in the 20% hay treatment at h 2 and

were not sorted for or against for the other treatments and times. The selection indices standard

errors were quite large due to sorting variation between cows, which made finding significant

differences between treatments and from 1.0 difficult. Large variation in ration sorting between

animals was also reported by Leonardi and Armentano (2003) and Leonardi et al. (2005),

especially in the longer particle fractions. At the end of the d (24 h after feeding) average

selection indices for each particle fraction were very close to 1.0: 1.04, 0.98, 0.99, and 1.04 for

the 19.0, 8.0, 1.18-mm sieves and pan respectively. This indicates that animals consumed rations

that were very close in particle distribution to their offered TMR. These results are in agreement

with Maulfair et al. (2010) where it was determined that despite large changes in refusal particle

size distribution, by the end of the d ration sorting was not significant when measured via

selection indices. In this study ration DM content increased with increasing dry hay inclusion but

it had minimal effects on ration sorting behavior during the first 4 h of the d and did not affect

sorting later in the d. Leonardi et al. (2005) determined that increasing ration DM increased

sorting activity of lactating cows; however, their ration DM increased from 64.4 to 80.8%. Diets

in the current study had much less variation (difference between treatments was 7.1 versus 16.4

percentage units) and all had DM contents lower than the lowest DM diet in Leonardi et al.

(2005). Two recent studies (Miller-Cushon and DeVries, 2009; Felton and DeVries, 2010)

determined that increasing ration DM content actually decreased sorting in lactating cows. The

DM content of diets used in these studies ranged from 47.9 to 57.6% and 44.1 to 56.3% for

Miller-Cushon and DeVries (2009) and Felton and DeVries (2010) respectively. Miller-Cushon

and DeVries (2009) added water to the mixer during diet preparation at a rate of 20% of the diet

(DM basis). Felton and DeVries (2010) first mixed the TMR for 10 min and then transferred the

diets to a feed cart where water was added at rates of 20 and 44% of the diet (DM basis). The

55

authors suggested that summer temperatures experienced during these studies caused heating of

the rations that had added water, which contributed to increased sorting behavior. Ration DM

contents of diets used in the current study fell in the range of DM where increased sorting was

seen with decreasing ration DM; however, the current study took place in November through

February and no additional water was added to the diets, potentially increasing stability compared

to diets in the cited studies.

Intake of DM and Particle Fractions

Dry matter intake was not different among treatments and averaged 27.8 kg/d (Table 3-

3). Feed efficiency was also similar among diets and averaged 1.41 kg 3.5% FCM/kg DMI.

Figure 3-2 shows the cumulative percentage of total daily intake for each ration. There were no

differences among treatments and since the cows were fed only once/d, DMI was heavily skewed

toward feeding time with the treatments averaging 21.9, 30.5, 49.0, and 70.3% of their daily

intake consumed by 2, 4, 8, and 12 h after feeding respectively.

Another measure of ration sorting activity, that was determined to be more accurate than

refusal particle size distribution (Maulfair et al., 2010), is individual consumption of each particle

size fraction. Table 3-4 shows the kg of each particle fraction (> 19.0, > 8.0, > 1.18 mm, and pan)

consumed by various time points (2, 4, 8, 12, 16, and 24 h after feeding) throughout the d. These

data show that at 24 h after feeding consumption of particles retained on the 19.0-mm sieve and

the pan increased with increasing alfalfa hay content. Also, particles retained on the 8.0-mm sieve

decreased with increasing hay inclusion while consumption of particles retained on the 1.18-mm

sieve were similar across treatments. Trends seen in particle fraction intake are the same trends

seen in particle size distributions of the offered TMR; where particles on the 19.0-mm sieve and

pan increased, particles on the 8.0-mm sieve decreased and particles on the 1.18-mm sieve did not

56

change with increasing dry hay content (Table 3-2).The intake of particle fractions matching

changes in particle size distributions of the offered TMR reinforces the conclusion that ration

sorting was not significant at the end of the d.

Chewing Activity

Table 3-5 shows ruminating, eating, and total chewing behavior of the cows in this study.

No significant treatment effects on ruminating, eating, and total chewing min/d were found and

they averaged 475.4 (7.9 h), 430.9 (7.2 h), and 896.3 (14.9 h) min/d respectively. However there

was a tendency for eating time to differ (P = 0.09) as eating time for the 40% diet was higher than

for the 10% diet. The reason that larger differences between the treatments for ruminating and

eating were not seen is probably because while particles > 19.0 mm increased by 4.9 percentage

units with increasing alfalfa hay content, this change was associated with a 10.9 percentage unit

decrease in the 8.0-mm particle fraction and a 4.4 percentage unit increase in particles < 1.18 mm.

These changes in ration particle size distribution may have effectively canceled each other out,

causing chewing behavior to be similar across treatments.

Rumen Characteristics

There were no differences in rumen pH among treatments; average daily weighted mean,

minimum, and maximum pH was 6.29, 5.87, and 7.00 for all treatments (Table 3-6). Figure 3-3

shows rumen pH for each treatment over the course of 1 d. Rumen pH for all treatments

decreased immediately after feeding to a nadir between 11.5 and 18.0 h after feeding and then

increased to pre-prandial levels. Rumen NH3 concentration was also not different among

treatments for daily weighted mean (averaged 12.1 mg/dL) and minimum; however, maximum

57

daily NH3 concentration showed a trend to increase with increasing dry hay inclusion (Table 3-6).

This result is the effect of the inclusion of urea in the 40% hay diet. Figure 3-4 shows rumen NH3

concentrations for each treatment over the course of 1 d. In all treatments, rumen NH3

concentrations increased sharply immediately after feeding and peaked 1.5 h later; NH3 then

gradually decreased for the remainder of the d to pre-prandial levels. Finally, daily weighted

mean concentrations of acetate, butyrate, valerate, and isobutyrate were not different among

rations. There was some minor variation in propionate and isovalerate concentrations among

rations that are not practically significant (Table 3-6).

Milk Production and Composition

Milk production averaged 38.7 kg/d and did not differ among treatments (Table 3-3). Fat,

protein, and lactose yields were also not different among treatments; however, fat and protein

concentration decreased slightly with increasing dry hay inclusion (3.65 to 3.46% and 3.04 to

2.98% for fat and protein respectively). Finally, MUN concentrations were elevated for the 40%

hay treatment, possibly a product of higher maximum rumen NH3 concentrations (Table 3-6)

caused by this treatment being the only one supplemented with urea.

Penn State Versus Ro-Tap Particle Separator

Particle size distributions of 95 TMR samples from this study separated with both PSPS

and RTPS are shown in Table 3-7. Both methods of particle separation produced results that

showed significant differences for diet and time effects; however, the actual proportions of

particles retained on the various sieves differ dramatically. The large differences between these 2

methods of particle separation can easily been seen in Figure 3-5. The RTPS retained 66.8% of

58

particles above the 1.18-mm sieve compared to 88.2% for the PSPS (a 32% increase). This large

difference is of importance because generally when calculating peNDF the NDF content of the

sample is multiplied by this value. Assuming a sample NDF content of 35%, there would be an

increase of 7.5 percentage units in the calculated value of peNDF when going from the RTPS to

PSPS (23.4 to 30.9% peNDF respectively). This difference could lead to diet formulations that

over estimate peNDF and lead to problems associated with lack of fiber. There was an even

greater difference in particles retained above an 8.0-mm sieve; the PSPS retained almost 9 times

more particles above this threshold than the RTPS. Using natural log transformed data it was

determined that for all samples tested, 76.8% (71.9, 82.1; 95% confidence limits) of particles

were retained above a 1.18-mm screen in the RTPS. The equivalent screen size in the PSPS that

would achieve 76.8% (69.6, 84.8; 95% confidence limits) of particles oversized for these samples

was determined to be 5.78 mm. It is clear from these results that the RTPS allows particles to pass

through much smaller diameter sieves than the PSPS, and results determined via these separators

are not comparable. Since these results are only based on 4 different TMR that were similar in

composition and particle size distribution, more study of these methods should be conducted

comparing a greater variety of TMR and forages of various types in order to determine the

correlation of results from these 2 systems.

Conclusions

Increasing inclusion of dry chopped alfalfa hay from 10 to 40% of forage DM in a corn

silage and alfalfa haylage based TMR had minimal effects on sorting during the first 4 h after

feeding and did not change sorting activity of cows later in the d. By 24 h after feeding ration

sorting was insignificant when measured via either sorting indices or actual consumption of

various particles size fractions, despite changes in TMR refusal particle size distributions over the

59

course of the d. It was determined that lactating cows can be fed TMR containing dry chopped

alfalfa hay levels up to 23.5% of ration DM without any negative effects on rumen fermentation

and milk production. It was also determined that the PSPS and the RTPS produced very different

particle size distributions for the same sample, and therefore results from these 2 methods of

particle separation should not be used interchangeably. However, more research should be

conducted on a greater variety of TMR and also unmixed forages to achieve a more accurate

comparison between these 2 methods.

Acknowledgements

This research was supported in part by agricultural research funds administered by The

Pennsylvania Department of Agriculture.

References



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AOAC, Arlington, VA.

Broderick, G. A., and J. H. Kang. 1980. Automated simultaneous determination of ammonia and

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Felton, C. A., and T. J. DeVries. 2010. Effect of water addition to a total mixed ration on feed

temperature, feed intake, sorting behavior, and milk production of dairy cows. J. Dairy Sci.

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Gaines, W. L. 1928. The energy basis of measuring milk yield in dairy cows. Illinois Agr. Expt.

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Kenward, M. G., and J. H. Roger. 1997. Small sample inference for fixed effects from restricted

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the physical attributes of corn silage. Pages 211–220 in Proceedings of the Four-State Dairy

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74:3583–3597.

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62

Table 3-1. Chemical compositions and particle size distributions determined for corn silage,

alfalfa haylage, and dry chopped alfalfa hay

Item Corn Silage Alfalfa Haylage Alfalfa Hay

Composition, % of DM

DM, % 31.9 40.0 90.4

CP, % 9.2 20.9 14.2

ADF, % 29.0 33.2 41.0

NDF, % 47.7 38.5 49.6

peNDF8.01, % 32.4 24.0 28.1

peNDF1.182, % 47.1 37.2 42.1

Ash, % 4.2 11.9 7.9

NFC, % 36.4 26.5 27.6

NEL, Mcal/kg 1.51 1.43 1.22

Particle size, as-fed % retained

19.0 mm 2.4 8.7 38.6

8.0 mm 65.4 53.4 17.9

1.18 mm 31.0 34.4 28.4

Pan 1.2 3.5 15.1 1Physically effective NDF8.0 = % of particles > 8.0 mm × NDF of whole sample; top 2 sieves of

Penn State particle separator (Kononoff et al., 2003a).

2Physically effective NDF1.18 = % of particles > 1.18 mm × NDF of whole sample; top 3 sieves of


63

Table 3-2. Ingredients, chemical compositions, and particle size distributions for TMR with

increasing levels of dry chopped alfalfa hay (5, 10, 20, and 40% of forage DM)

Item 5 10 20 40 SEM P-value

Ingredients, % of DM

Corn silage 29.2 29.2 29.2 29.4 – –

Alfalfa haylage 26.3 23.4 17.5 5.9 – –

Alfalfa hay 2.9 5.8 11.7 23.5 – –

Ground corn 22.7 22.7 22.7 22.9 – –

Canola meal 6.6 6.6 6.6 5.7 – –

Roasted soybeans 5.9 5.9 5.9 6.0 – –

Mineral/ vitamin mix 3.8 3.8 3.8 3.8 – –

Heat-treated soybean meal 2.5 2.5 2.5 2.5 – –

Urea – – – 0.5 – –


DM, % 49.1b 49.7

b 51.6

b 56.2

a 1.40 < 0.01

CP 18.1 18.1 17.5 17.7 0.41 0.16

ADF 23.2 23.7 23.7 24.3 1.44 0.92

NDF 34.6 34.2 34.1 35.2 1.64 0.86

Forage NDF 25.5b 25.8

b 26.5

b 27.9

a 0.82 < 0.01

peNDF8.01 15.1

a 13.6

b 14.5

ab 13.7

b 1.09 0.13

peNDF1.182 30.5 28.5 29.5 29.8 1.65 0.48

Ash 7.88 8.15 8.10 7.60 0.39 0.71

NFC 36.1 36.3 37.2 36.7 1.65 0.90

NEL, Mcal/kg 1.64 1.63 1.63 1.63 0.02 0.94


19.0 mm 4.6b 3.9

b 6.0

b 9.5

a 1.28 < 0.01

8.0 mm 39.2a 38.2

a 35.1

a 28.1

b 1.60 < 0.01

1.18 mm 43.4b 44.5

ab 44.9

ab 45.3

a 0.66 0.20

Pan 12.8b 13.4

b 14.0

b 17.2

a 1.24 0.02

a–bMeans within a row with different superscripts differ (P ≤ 0.05).





64

Table 3-3. Effect of feeding increasing levels of dry chopped alfalfa hay (5, 10, 20, and 40% of

forage DM) on DMI, feed efficiency, and milk production and components


DMI, kg/d 28.2 27.8 27.5 27.8 1.15 0.54

Milk yield, kg/d 38.7 38.4 39.2 38.6 1.58 0.96

3.5% FCM, kg/d1 39.7 38.9 39.1 38.4 1.74 0.86

Feed efficiency2 1.41 1.41 1.43 1.38 0.05 0.85

Fat, % 3.65a 3.60

ab 3.53

ab 3.46

b 0.14 0.08

Fat, kg/d 1.42 1.38 1.37 1.34 0.07 0.63

Protein, % 3.04a 3.03

ab 3.01

ab 2.98

b 0.08 0.15

Protein, kg/d 1.17 1.15 1.17 1.15 0.04 0.95

Lactose, % 4.73 4.74 4.72 4.75 0.07 0.92

Lactose, kg/d 1.83 1.82 1.86 1.83 0.08 0.97

MUN, mg/dL 16.0b 16.4

b 15.8

b 17.9

a 0.68 < 0.01

SCC, 1,000 cells/mL 39.0 48.3 36.8 29.6 19.7 0.77 a–b

Means within a row with different superscripts differ (P ≤ 0.05).

13.5% FCM = 0.432 (milk kg) + 16.23 (fat kg); (Gaines, 1928).

2Feed efficiency = 3.5% FCM / DMI

65


forage DM) on intake of 4 particle size fractions (> 19.0, > 8.0, > 1.18, and < 1.18 mm)

Intake, kg 5 10 20 40 SEM P-value

19.0 mm

2 h 0.64 0.27 0.86 0.97 0.26 0.27

4 h 0.67b 0.55

b 1.10

ab 1.46

a 0.26 0.08

8 h 0.82b 0.68

b 1.29

ab 1.67

a 0.26 0.05

12 h 0.98b 0.91

b 1.54

ab 2.26

a 0.26 < 0.01

16 h 1.13b 0.98

b 1.69

b 2.49

a 0.26 < 0.01

24 h 1.28b 1.12

b 1.81

b 2.74

a 0.26 < 0.01

8.0 mm

2 h 2.02 2.06 1.54 0.83 0.63 0.39

4 h 3.13 2.84 2.76 1.82 0.63 0.39

8 h 4.94 5.06 4.33 3.46 0.63 0.18

12 h 7.30a 7.06

a 6.68

a 5.04

b 0.63 0.03

16 h 8.49a 8.36

a 7.94

a 6.04

b 0.63 0.02

24 h 10.78a 10.57

a 9.56

a 7.46

b 0.63 < 0.01

1.18 mm

2 h 1.68 1.90 2.27 2.15 0.56 0.78

4 h 2.54 3.14 3.57 3.13 0.56 0.43

8 h 5.13 5.69 5.65 6.02 0.56 0.55

12 h 7.92 8.28 8.79 8.39 0.56 0.57

16 h 9.24 9.52 10.27 10.17 0.56 0.28

24 h 11.91 12.25 12.41 12.31 0.56 0.86

Pan

2 h 0.39b 0.66

b 0.66

b 1.26

a 0.22 0.04

4 h 0.70b 0.95

ab 1.13

ab 1.44

a 0.22 0.10

8 h 1.63b 1.77

b 1.83

b 2.60

a 0.22 < 0.01

12 h 2.71b 2.77

b 3.10

ab 3.65

a 0.22 0.01

16 h 3.07b 3.11

b 3.50

b 4.26

a 0.22 < 0.01

24 h 3.63b 3.77

b 4.03

b 4.90

a 0.22 < 0.01


66


forage DM) on chewing behavior

Item, min/d 5 10 20 40 SEM P-value

Ruminating 476.9 477.1 470.1 477.4 17.3 0.92

Eating 431.7ab

395.3b 414.1

ab 482.5

a 26.3 0.09

Total chewing 908.6ab

872.4b 884.3

ab 960.0

a 33.8 0.16


67


forage DM) on rumen fermentation


Rumen pH

Weighted mean1 6.36 6.23 6.25 6.30 0.05 0.25

Minimum 5.97 5.76 5.89 5.84 0.11 0.59

Maximum 6.93 7.05 6.96 7.06 0.08 0.29

NH3, mg/dL

Weighted mean 10.7 13.5 11.6 12.6 1.18 0.31

Minimum 3.88 5.81 5.76 5.15 0.91 0.43

Maximum 20.8b 25.8

ab 22.7

ab 28.3

a 2.43 0.10

VFA weighted mean, µM/mL

Acetate 78.2 80.9 80.4 80.7 1.85 0.61

Propionate 25.6a 27.5

b 26.8

ab 26.6

ab 0.59 0.21

Butyrate 14.2 14.4 15.3 14.8 0.52 0.22

Valerate 2.91 3.02 2.88 2.80 0.09 0.44

Isovalerate 2.36ab

2.59a 2.28

ab 2.16

b 0.16 0.21

Isobutyrate 1.77 1.88 2.13 1.65 0.17 0.27 a–b


1Weighted averages determined by calculating

the area under the response curve according to the

trapezoidal rule (Shipley and Clark, 1972).

68

Table 3-7. Particle size distributions of TMR containing 5, 10, 20, and 40% of forage DM as dry chopped alfalfa hay in samples taken at

feeding (0 h) and 24 h after feeding and separated with the Penn State and Ro-Tap particle separators

Percentage of

DM retained

5 10 20 40 P-value

0 h 24 h 0 h 24 h 0 h 24 h 0 h 24 h SEM Diet Time

Penn State particle separator

19.0 mm 3.9 3.2 3.0 3.0 6.8 3.2 9.4 6.3 0.98 < 0.01 < 0.01

8.0 mm 40.6 42.1 38.8 44.5 33.7 39.4 28.0 35.3 1.59 < 0.01 < 0.01

1.18 mm 43.1 45.4 44.6 44.4 45.0 48.4 45.0 48.8 1.37 0.05 < 0.01

Pan 12.4 9.0 13.0 7.8 14.4 9.0 17.9 9.7 0.79 < 0.01 < 0.01

Ro-Tap particle separator

9.5 mm 1.2 2.1 1.1 2.6 1.4 2.8 2.8 3.9 2.31 < 0.01 < 0.01

8.0 mm 1.2 1.7 1.5 2.2 1.5 2.8 1.9 2.8 0.26 < 0.01 < 0.01

6.7 mm 1.6 2.5 1.9 2.9 1.9 3.1 2.0 2.9 0.27 0.22 < 0.01

4.75 mm 6.4 7.1 7.0 7.5 6.4 8.3 6.7 8.3 0.43 0.25 < 0.01

3.35 mm 11.5 12.4 11.8 12.3 10.7 13.2 10.4 12.3 0.42 0.20 < 0.01

2.36 mm 14.9 15.0 14.3 15.0 13.6 15.0 12.8 13.9 0.35 < 0.01 < 0.01

1.70 mm 13.8 14.2 13.7 14.0 12.9 13.7 12.3 12.8 0.28 < 0.01 < 0.01

1.18 mm 12.6 13.3 12.6 13.2 13.0 12.5 11.9 11.8 0.32 < 0.01 0.40

0.60 mm 17.1 17.0 17.1 16.9 17.9 16.0 17.5 16.2 0.66 0.99 0.04

0.15 mm 16.9 12.8 16.1 11.7 17.7 11.1 18.4 13.3 0.92 0.10 < 0.01

Pan 2.9 1.8 2.9 1.6 3.0 1.5 3.3 1.8 0.15 0.18 < 0.01

69

A

B

70

C

D

Figure 3-1. Effect of feeding increasing levels of dry chopped alfalfa hay (5, 10, 20, and 40% of

forage DM) on refusal particle size distribution for 19.0 (A), 8.0 (B), 1.18 mm (C) sieves, and pan

(D).

71


forage DM) on cumulative percent of diet daily intake at various times after feeding.


forage DM) on rumen pH over time.

73


forage DM) on rumen NH3 over time.

74

Figure 3-5. Particle size distributions of TMR samples separated with the Penn State (PSPS) and

Ro-Tap particle separators divided into particle fractions; > 19.0, > 8.0, > 1.18 mm.

Chapter 4

Effects of Varying Forage Particle Size and Fermentable Carbohydrates on

Feed Sorting, Ruminal Fermentation, and Milk and Component Yields of

Dairy Cows

Abstract

Ration sorting is thought to negatively affect ruminal fermentation and yield of milk and

components. However, the influence of ruminally degradable starch on ration sorting has not

been studied. Therefore the objective of this experiment was to study the interactions between

forage particle size (FPS) and ruminally fermentable carbohydrates (RFC) for ration sorting,

ruminal fermentation, chewing activity, and milk yield and components. In this study 12 (8

ruminally cannulated) multiparous, lactating Holstein cows were fed TMR that varied in FPS and

RFC. Two lengths of corn silage were used to alter FPS and 2 grind sizes of corn grain were used

to alter RFC. It was determined that increasing RFC increased ruminating time and did not affect

eating time, while increasing FPS increased eating time and did not affect ruminating time.

Ruminal fermentation did not differ by altering either FPS or RFC. However, increasing FPS

tended to increase mean and maximum ruminal pH and increasing RFC tended to decrease

minimum ruminal pH. Particle size distribution and NDF content of refusals increased over time

while starch content decreased; indicating that cows were sorting against physically effective

NDF and for RFC. Selection indices determined that virtually no interactions occurred between

FPS and RFC and that despite significant sorting throughout the d, by 24 h after feeding cows had

consumed a ration very similar to what was offered. This theory was reinforced by particle

fraction intakes that very closely resembled the proportions of particle fractions in the offered

TMR. An interaction between FPS and RFC was seen for DMI, as DMI decreased with

76

increasing FPS when the diet included low RFC and did not change when the diet included high

RFC and DMI increased with RFC for long diets and did not change with RFC on short diets.

Increasing RFC was found to increase milk yield, milk protein content and yield, and lactose

content and yield but decrease milk fat content. Increasing FPS did not have as great an impact on

milk production as RFC. This study found that there was no significant interaction between FPS

and RFC for ration sorting although there was an interaction between FPS and RFC for DMI.

RFC had a greater influence on milk yield and components than FPS, but neither affected ruminal

fermentation.

Key Words: forage particle size, ruminally fermentable starch, sorting

Introduction

Ration sorting is thought to increase cows’ susceptibility to SARA. Cows will generally

sort for finer particles and against longer particles in their rations, which effectively decreases

their fiber intake while increasing their starch intake as fiber and starch are positively and

negatively associated, respectively, with longer particles in dairy cow rations (Leonardi and

Armentano, 2003; Leonardi et al., 2005). However, Maulfair et al. (2010) showed drastic

increases in refusal particle size distribution and NDF content and decreases in starch content

throughout the d, the classical determinants of ration sorting, yet found no negative effects on

ruminal fermentation and milk production when cows were fed a ration that contained about 34

and 27% of ration DM as NDF and starch content respectively.

Ruminally fermentable carbohydrates (RFC) may influence the effective fiber

requirement of dairy cows. Yang et al. (2001) suggested that ruminal pH and SARA cannot be

predicted using only physical characteristics of rations, because RFC has a greater influence on

ruminal pH than forage particle size (FPS). Krause et al. (2002b) determined that the physical

77

effectiveness of forages is affected by other dietary components such as corn grain moisture and

fermentability. Finally, Krause and Combs (2003) found that significant interactions between FPS

and RFC existed for ruminal fermentation and milk production, which indicates that effects of

FPS and RFC are not always additive and complicates the formulation of dairy rations. None of

these studies measured or determined ration sorting when studying the interaction between FPS

and RFC; FPS had been shown to have major influence on ration sorting (Leonardi and

Armentano, 2003; Kononoff and Heinrichs, 2003; Kononoff et al., 2003b), but effects of RFC on

ration sorting have not been studied. Therefore the objective of this experiment was to study the

interactions between FPS and RFC for ration sorting, ruminal fermentation, chewing activity, and

milk yield and components.

Material and Methods


Cows used in this research were cared for and maintained according to a protocol


Twelve lactating (8 ruminally cannulated), multiparous, Holstein cows averaging 115 ± 49 DIM,

weighing 662 ± 64 kg, and with parity of 3.08 ± 0.79 (mean ± SD) were studied. The

experimental design consisted of 3 replicated, balanced 4 × 4 Latin squares with treatments

arranged in a 2 × 2 factorial design; 2 squares were composed of ruminally cannulated cows.

Cows were assigned to squares by parity and randomly assigned to 1 of 4 treatments. Treatments

were designed to study the effects of 2 lengths of FPS and 2 levels of RFC. Treatment diets

varied in FPS by feeding either long (LCS) or short corn silage (SCS) and RFC were varied by

feeding either dry cracked corn (CC) or dry fine ground corn (FC). The 4 treatment diets then

78

consisted of LCS + CC (LC), LCS + FC (LF), SCS + CC (SC), and SCS + FC (SF). Except for

altering corn silage and grain particle size, the 4 treatment diets contained identical ingredients

and proportions. Diet ingredients and their percentage of ration DM were: corn silage (42.6), dry

ground corn (22.2), alfalfa haylage (15.4), canola meal (9.4), roasted split soybeans (7.1),

mineral/vitamin mix (2.5), salt (0.4), and Optigen (Alltech, Nicholasville, KY; 0.4). The study

consisted of 4 21-d periods consisting of 14 d of adaptation followed by a 7-d collection period.

Corn silage hybrid was Pioneer 34M78 (Pioneer Hi-Bred International, Inc., Johnston,

IA) that was planted on 4/19/2010 and harvested on 8/30/2010. Corn silage was harvested with a

John Deere 6750 forage harvester (John Deere, Moline, IL) equipped with a kernel processor set

at approximately 6.35 mm. The harvester cutterhead used 16 knives (maximum capacity is 48

knives) with the length-of-cut transmission at its highest setting to produce a theoretical length of

cut of 47.1 mm. After harvesting, corn silage was ensiled in an Ag-Bag (Ag-Bag, St. Nazianz,

WI) and allowed to ferment for 62 d before beginning the study. Corn silage that was removed

from the Ag-Bag and mixed into TMR without further processing was considered LCS. A cut-

and-throw type, single row, forage harvester that was modified to operate on a trailer and be fed

manually with a 25 horsepower V-Twin small gas engine was used to reduce the particle size of

corn silage to produce SCS. Corn silage was rechopped twice through the custom forage chopper

on a daily basis to minimize the chemical variance between LCS and SCS. Dry corn was ground

through a Roskamp roller mill (California Pellet Mill Co., Crawfordsville, IN) to produce the CC

used in this study. This corn was then ground further with a Case International 1250 grinder-

mixer (Case IH, Racine, WI) using a 3.18 mm screen to produce FC. Diets were mixed separately

using an I. H. Rissler model 1050 TMR mixer (E. Rissler Mfg. LLC, New Enterprise, PA).

Animals were housed in individual stalls in a mechanically ventilated barn, milked

twice/d at 0500 and 1700 h, and fed once/d at approximately 0800 h for ad libitum consumption.

Feed refusals were weighed daily and the amount of TMR fed was adjusted daily to maintain a

79

10% refusal rate. Feeding once/d at a 10% refusal rate was designed to allow cattle to have

increased opportunity to sort rations. Feed was pushed up 3 times/d at 1230, 1730, and 2400 h.

Rations were balanced to meet or exceed NRC (2001) requirements for cows producing 52.2 kg

of milk/d containing 3.75% fat and 3.07% true protein assuming a DMI of 29.5 kg/d and water

was available for ad libitum consumption.

Chewing Activity

Eating and rumination behavior were recorded on d 15 to 21 of each period, using



2000). Chewing was measured for all 12 cows for 2 24-h periods including while cows were

being milked. These recorders analyze jaw movements of cattle, and the software determines

eating or ruminating chews based on the amplitude and frequency of jaw movements. This

procedure was validated for use with cows housed in tie-stalls by Kononoff et al. (2002).

Rumen Parameters

Ruminal contents were collected from dorsal, ventral, cranial, caudal, and medial areas of

the rumens of all 8 ruminally cannulated cows on d 20 of each period at 0.0, 1.5, 3.5, 5.5, 8.5,

11.5, 14.5, 18.0, 21.5, and 24.5 h after feeding (Kononoff et al., 2003b). At each ruminal

sampling collected digesta was mixed thoroughly and then separated into 2 equal subsamples.

One digesta subsample was strained through 2 layers of cheesecloth. Rumen fluid pH was

immediately determined using a handheld pH meter (HI 98121, HANNA Instruments Inc.,

Woonsocket, RI). Strained ruminal fluid (15 mL) was placed into bottles containing 3 mL of 25%

80

metaphosphoric acid and 3 mL of 0.6% 2-ethylbutyric acid (internal standard) and stored at

approximately -20C. After thawing, samples were centrifuged 3 times at 4000 g for 30 min at

4C to obtain a clear supernatant and were analyzed for VFA concentration using gas

chromatography (Yang and Varga, 1989). The second ruminal digesta subsample was utilized for

particle size distribution and DM analysis using the procedure of Maulfair et al. (2011) except

that samples were not processed in duplicate; particle fractions determined were soluble, > 0.15,

> 0.6, > 1.18, > 3.35, > 6.7, and > 9.5 mm. Soluble fraction of samples were calculated as the DM

lost during sieving and drying. Data were analyzed using each particle fraction as a percentage of

DM retained on ≥ 0.15-mm screen (retained) and also as the percentage of DM of the entire

sample sieved (total).

Finally, rumens of the cannulated cows were completely emptied on d 21 of each period

at 5 h after feeding. The weight and volume of ruminal digesta was then recorded, and digesta

was sampled for DM analysis. Digesta was then immediately returned to the rumen of each cow.


Feed bunk contents for each animal were weighed and sampled on d 18 and 19 of each

period at 0, 8, 16, and 24 h after feeding for DM and particle size analysis. All samples were

sieved in the American Society of Agriculture and Biological Engineers (ASABE) forage particle

separator, which can determine 6 particle fractions (> 26.9, > 18.0, > 8.98, > 5.61, > 1.65, and <

1.65 mm; screen diagonals; ASABE, 2007). Whole samples were then placed in a forced air oven

at 65°C for 48 h to determine DM content. Samples of forages, ground corn, and TMR were

taken on d 18 and 9 of each period, composited by period and analyzed by Cumberland Valley

Analytical Services, Inc. (Hagerstown, MD) for CP (AOAC, 2000), ADF (AOAC, 2000), NDF

(Van Soest et al., 1991), ash (AOAC, 2000), NFC (Van Soest et al., 1991), and NEL (NRC,

81

2001). Starch and NDF contents of forages, ground corn, and TMR (at 0 and 24 h after feeding)

were determined by drying in a forced-air oven at 65°C for 48 h and grinding (0.5- and 1.0-mm

screen for starch and NDF respectively; Wiley Mill, Arthur H. Thomas Co. Inc., Swedesboro,

NJ). Starch was then analyzed via the procedure reported by Zanton and Heinrichs (2009) and

NDF was analyzed using heat-stable α-amylase and Na2SO3 according to Van Soest (1991).

Particle size distributions of forages and TMR were determined via sieving with the ASABE

forage particle separator (ASABE, 2007). To determine particle size distributions of ground corn,

samples were placed on a series of stacked sieves (sizes 0.15, 0.425, 0.60, 0.85, 1.18, 1.70, 2.36,

3.35, 4.75, and 6.7 mm; VWR, Arlington Heights, IL) contained in a Retsch AS 200 Control

sieve shaker (Retsch, Haan, Germany) and were sieved for 10 min at 2.5 mm amplitude. Particles

retained on each sieve were then weighed to determine their proportion of total sample DM.

There were 2 procedures used to calculate physically effective NDF (peNDF): peNDF8.0 = % of

particles > 8.98 mm × NDF of whole sample (similar to top 2 sieves of the Penn State particle

separator) and peNDF1.18 = % of particles > 1.65 mm × NDF of whole sample (similar to top 3

sieves of the Penn State particle separator; Kononoff et al., 2003a). Corn grain fermentability was

determined via in situ bags incubated in quadruplicate in the rumen of 2 lactating cows (each cow

incubated 2 bags of each sample for each time point) for 0.5, 1, 2, 4, 6, 8, 12, 16, 24, and 48 h.

Approximately 7 g of samples were sealed in nylon bags (10 × 20 cm, 50 μm pore size; ANKOM,

Macedon, NY) attached to a string that was anchored to the rumen cannulae and weighted to

locate the bags centrally in the rumen. After removal from the rumen, bags were rinsed in cold

water by hand until water was almost clear. Bags were then dried in a forced-air oven at 65°C for

48 h and then weighed to determine remaining DM.

82

Milk Production

Milk production was recorded daily and milk samples were taken on d 20 and 21 (4

consecutive milkings). Samples were collected and preserved using 2-bromo-2-nitropropane-1,3

diol. Milk samples were analyzed for fat, true protein, lactose, MUN, and SCC by the Dairy One

milk testing laboratory (State College, PA) using infrared spectrophotometry (Foss 605B Milk-

Scan; Foss Electric, Hillerod, Denmark).

Fecal Sampling

Fecal samples were taken from all 12 cows at the same time points as rumen sampling (d

20 at 0.0, 1.5, 3.5, 5.5, 8.5, 11.5, 14.5, 18, 21.5, and 24.5 h after feeding) via grab samples from

the rectum. Samples were stored at -20C until later determination of DM and particle size

distribution. Particle size of samples was determined using the same wet sieving technique used

for rumen digesta, with the exception of eliminating the top screen (9.5 mm). Geometric mean

particle length (Xgm) and standard deviation of particle length (Sgm) were calculated according to

ASABE (2007) procedure. Xgm was calculated using 2 procedures; the first, retained Xgm

(XgmRet), only considered particles retained on the 0.15-mm screen or larger, the second

procedure, total Xgm (XgmTot), considered all particle fractions including the soluble fraction that

passed through the bottom screen (0.15 mm). Mean particle length of the soluble fraction was

assumed to be 0.106 mm, which is half of the diagonal screen diameter (0.212 mm) of the bottom

screen; this is the assumption that ASABE (2007) uses for mean length of particles on the pan.

Subsamples were also placed in a forced air oven at 65°C for 48 h to determine DM content.

83


Statistical analysis was conducted using PROC MIXED of SAS (Version 9.2, SAS

Institute, Cary, NC). Dependent variables were analyzed as a 4 4 Latin square design. All


(1997) and repeated measurements for ruminal pH, VFA, and NH3 concentrations and ground

corn DM disappearance were analyzed using the first order autoregressive covariance structure

(Littell et al., 1998) as well as terms for time and interaction of treatment by time. Because of

unequally spaced rumen and fecal sampling, the weighted mean daily ruminal pH, VFA, and NH3

concentrations and ruminal digesta and fecal particle size distribution were determined by

calculating the area under the response curve according to the trapezoidal

rule (Shipley and Clark,

1972). Area under the curve for the SARA thresholds of 5.8 and 5.5 were also calculated using

the trapezoidal rule (Shipley and Clark, 1972). A selection index based on refusals was calculated

for each of the 6 particle size fractions at 8, 16, and 24 h after feeding. This index was calculated

as the actual intake of each fraction (Yi to pan) expressed as a percentage of the expected intake.

Expected intake of Yi equals intake multiplied by the fraction of Yi in the fed TMR (Leonardi and

Armentano, 2003). Sorting indices were calculated using both the expected intake since time

point 0 (cumulative) and the expected intake since the previous time point (interval). Values > 1.0

indicate cows were sorting for the particle fraction and values < 1.0 indicate cows were sorting

against the particle fraction. The 95% confidence limits were used to determine if a selection

index was significantly different from 1.0. Chewing behavior and meal criteria was analyzed

using the procedure of Maulfair et al. (2010). All data are presented as least squares means and

treatment effects are considered significant when P ≤ 0.05 and a trend when 0.05 < P ≤ 0.15.

Means separation tests were conducted using the protected least significant differences (PDIFF)

84

procedure with significance at P ≤ 0.05 and are reported when P-value of FPS × RFC interaction

≤ 0.15.


Chemical Composition and Particle Size Distribution of Diets

Particle size distributions and chemical compositions of forages used in this study are

shown in Table 4-1. There was a large difference in particle size distribution between LCS and

SCS. When separated with the ASABE particle separator, LCS had many more particles retained

on 26.9 and 18.0 mm screens, equal particles on the 8.98 mm screen, and many fewer particles on

5.61 and 1.65 mm screens and the pan than SCS. The approximate equivalency of Penn State

particle separator fractions to the ASABE screens are: top (26.9 + 18.0 mm), middle (8.98 mm),

lower (5.61 + 1.65 mm), and pan (pan). The particle size distribution of alfalfa haylage was

similar to SCS. Chemical compositions of the corn silages were similar and not practically

different despite being statistically different for DM and NEL. Sampling error may be responsible

for the small differences seen between LCS and SCS since they were taken from the same bag

each d as a single batch, with part being re-chopped as the only difference. The peNDF measures

were, as expected, very different between corn silages, but there was a much greater difference

for peNDF8.0 than for peNDF1.18. The LCS was 1.81 and 1.15 times greater than SCS for

peNDF8.0 and peNDF1.18 respectively. The particle size distribution of the corn silage before

bagging was analyzed by taking 5 samples evenly spaced over the length of the bag. It was

determined that the process of bagging and ensiling corn silage altered its particle size

distribution; before ensiling, the proportions retained on each sieve were 30.5 ± 0.73, 24.7 ± 0.70,

24.0 ± 1.05, 9.0 ± 0.10, 8.5 ± 0.44, and 3.4 ± 0.39% (mean ± SEM) for the 26.9, 18.0, 8.98, 5.61,

85

1.65 mm sieves, and pan respectively. All particle fractions of the fresh forage were different

from ensiled LCS (P < 0.05) except for the pan which tended to be different (P = 0.06).

The particle size distributions, chemical compositions, and rates of disappearance for

corn grains used in this study are shown in Table 4-2. The particle size distributions of CC and

FC were different at all 11 particle fractions. The greatest differences occurred at screen sizes

2.36 mm and larger, where CC had 67.4% and FC had 5.6% of particles retained, and at screen

sizes 1.18 mm and smaller, where CC had 18.4% and FC had 78.2% of particles retained. The

chemical compositions of CC and FC were similar and not practically different despite being

statistically different in DM and CP content. The rates of disappearance of CC and FC were

different at every time point except 48 h (P-value = 0.15). The greatest differences between CC

and FC were in the first 2 h of incubation, where FC had about 2.1 times more DM disappearance

than CC. The disappearance of FC continued to be greater than CC at each time point (except 48

h), but differences between them decreased with increasing incubation time. These data should be

interpreted with caution as the impact of eating and rumination on ground corn was not a factor in

this analysis, and it is reasonable to assume that chewing would have a larger impact on CC

because of its greater potential for further particle size reduction.

Particle size distributions and chemical compositions of treatment TMR are shown in

Table 4-3. Varying FPS and RFC altered the particle size distribution of diets. The 2 largest

fractions were increased with increasing FPS while the 4 other fractions were affected by both

FPS and RFC. Increasing FPS increased particles retained on the 8.98-mm sieve and decreased

particles retained on the 5.61-, 1.65-mm sieves, and pan. Increasing RFC decreased particles

retained on the 8.98- and 5.61-mm sieves and increased particles on the 1.65-mm sieve and pan.

Chemical compositions of TMR were similar and not practically different. The CP, NDF, forage

NDF, and starch content of TMR were approximately 16.4, 31.9, 21.4, and 31.0% of DM

respectively. The peNDF measures were affected by both FPS and RFC effects; increasing FPS

86

and decreasing RFC increased both peNDF measures. The greatest variation occurred with

peNDF8.0, where LC was 2.20 times higher than SF (14.1 versus 6.4%). The LC diet was only

1.33 times higher than SF for peNDF1.18 (27.9 versus 21.0%).

Chewing Behavior

Ruminating min/d was shown to increase with RFC but was not affected by FPS, this

increase was much larger for diets containing short FPS (Table 4-4). The ability of RFC to

increase ruminating time may be counterintuitive, but this result was also seen by Krause et al.

(2002b). The authors determined that increasing RFC, by replacing dry cracked shelled corn with

high-moisture corn in an alfalfa silage diet, tended to increase (P = 0.08) ruminating min/d and

increased (P = 0.03) ruminating min/kg of NDF intake. Krause et al. (2002b) suggested that since

forage should be the only diet component that could stimulate rumination, the increase in

ruminating activity is a result of an adaptive response by the animals to increased RFC to

attenuate low ruminal pH via increased saliva secretion. Daily ruminating times varied from 5.9

to 6.7 h/d across treatments. Eating min/d, in contrast to ruminating min/d, were not affected by

RFC but increased with FPS. The effect of increased eating time with longer FPS is well known

in the literature (Bailey, 1961; Beauchemin et al., 2008). Finally, total chewing time/d was not

significantly affected by FPS or RFC, but an increase in either tended to increase total chewing

min/d. These results conflict with those reported by Krause et al. (2002b) who found that

increasing FPS increased both eating and ruminating min/d and that increasing RFC decreased

eating min/d while increasing rumination min/d. These differences in the results might be related

to how RFC was increased in the 2 studies; in the current study it was increased by decreasing

grind size of dry corn grain whereas Krause et al. (2002b) increased RFC by replacing dry

cracked corn with high-moisture corn. However, Krause and Combs (2003) found that increasing

87

FPS increased both eating and ruminating time and that RFC did not affect eating or ruminating

and this study altered RFC the same way as Krause et al. (2002b).

Ruminal Characteristics

Daily weighted mean, minimum, and maximum ruminal pH did not differ by varying

FPS or RFC; though there was a trend for weighted mean and maximum pH to increase with FPS

and for minimum pH to decrease with increasing RFC (Table 4-5). Increasing FPS likely affected

ruminal pH through the increased eating time it caused, which has been shown to increase saliva

secretion (Bailey, 1961; Beauchemin et al., 2008) and increase ruminal buffering. The increased

rumination time caused by increasing RFC did not have the same positive effect of increased

eating time on ruminal pH, as ruminal pH actually tended to decrease with increasing RFC. This

likely occurred because either increased saliva secretion was unable to compensate for increased

RFC or increasing eating time was more effective at elevating saliva secretion than increasing

ruminating time. These results conflict with several studies that showed that increasing RFC

decreased mean ruminal pH (Yang et al., 2001; Krause et al., 2002b; Krause and Combs, 2003).

Perhaps the methods of increasing RFC in these studies (replacing dry cracked with high-

moisture corn or replacing coarsely rolled with flatly rolled barley grain) were more effective

than the one used in the current study (replacing dry cracked with fine ground corn). Two of these

same studies found that FPS had no effect on mean ruminal pH (Yang et al., 2001; Krause and

Combs, 2003) and 1 found that ruminal pH increased with FPS (Krause et al., 2002b).

Concentrations of NH3 and lactate did not differ by altering FPS and RFC, though there tended to

be an interaction of FPS and RFC for weighted mean NH3 concentration because NH3 increased

with RFC for the short diets but decreased with increasing RFC for the long diets. Weighted

mean concentrations of VFA were also not different when changing FPS or RFC, although there

88

were several trends. Increasing FPS tended to decrease acetate, butyrate, and isobutyrate while

increasing RFC tended to increase valerate and decrease the acetate to propionate ratio. Also FPS

and RFC tended to interact for propionate as it increased with RFC for short FPS diets but did not

change for long FPS diets. Finally, ruminal digesta weight and volume were not affected by FPS

or RFC.

Intakes, Refusals, and Ration Sorting

The TMR refusals were analyzed for NDF and starch at 0 and 24 h after feeding and for

particle size distribution at 0, 8, 16, and 24 h after feeding. The TMR concentrations of starch and

NDF are shown in Figures 4-1 and 4-2 respectively. Starch concentration was lower 24 h after

feeding especially for diets that contained long FPS; while NDF concentrations tended to be

increased at 24 h after feeding. This indicates that cows were generally sorting for concentrates

and against fiber in all treatments. This theory is reinforced by the particle size distribution of

TMR over time (Figure 4-3). Particles retained on the 26.9- and 18.0-mm (data not shown) sieve

showed very similar patterns over the course of the d for each diet; these particles increased in

diets containing LCS with time after feeding and did not change in diets containing SCS. The

particles retained on the 8.98- and 5.61-mm sieves generally did not change over time for any

diets (data not shown), and the amount of particles retained on these sieves relative to each diet

remained constant as well. Particles retained on the 1.65-mm sieve decreased in diets containing

LCS and did not change in diets containing SCS, while particles retained on the pan decreased in

all diets over a 24 h period. These results show that cattle were effectively altering TMR

composition (chemical and physical) through sorting, and that cows were able to sort to a higher

degree on the long FPS diets.

89

Ration sorting was also evaluated via selection indices at 8, 16, and 24 h after feeding.

Cumulative selection indices are shown in Table 4-6 and represent actual consumption of each

particle fraction at various time points compared to estimated consumption if cows consumed all

particles in the proportion offered in the original TMR. These results indicate that ration sorting

was affected by FPS for the 26.9-mm particle fraction and by RFC for the 8.98- and 5.61-mm

particle fractions. Generally greater sorting activity was seen at earlier time points and longer

particle fractions; no selection index was significantly different from 1.0 at 24 h after feeding for

the 3 shortest particle fractions. When significantly affecting selection indices, increasing both

FPS and RFC increased sorting activity, but the changes and degrees of sorting were relatively

small. Selection indices were also analyzed on an interval basis, which compared actual

consumption of each particle fraction at various time points to estimated consumption if cows

consumed all particles in the proportion found in TMR at the previous time point (Table 4-7). The

interval method allows for a clearer view of how sorting changed throughout the d. For example,

cows on LC were sorting against particles retained on the 1.65-mm sieve during the first 8 h of

the d, but then sorted for these particles during the last 16 h of the d. Using this method, FPS was

much more likely to affect ration sorting than RFC (7 versus 1 significant particle fraction by

time point effects). As the d progressed, cows on all treatments increased their sorting against

particles retained on the 18.0-mm sieve and generally the 8.98-mm sieve. Cows being fed diets

that contained LCS increased their sorting for particles retained on the 5.61-, 1.65-mm sieve, and

pan as time after feeding increased. Clearly ration sorting was occurring in all treatments and at

various times after feeding, but not to as great a degree as found in other studies (Maulfair et al.,

2010).

There was a significant interaction between FPS and RFC for DMI (Table 4-8). DMI

decreased with increasing FPS when the diet included low RFC and did change when the diet

included high RFC; DMI increased with RFC for the long diets and did not change with RFC on

90

the short diets. Effects of FPS and RFC on DMI have been variable in the literature: DMI

increased with increasing RFC and was not affected by FPS in Yang et al. (2001); DMI decreased

with increasing RFC and was not affected by FPS in Krause et al. (2002a); and DMI decreased

with increasing RFC and increased with increasing FPS in Krause and Combs (2003). Total NDF

intake was not affected by FPS or RFC but starch intake was affected by both. Daily starch intake

followed the same pattern of interaction that was found with DMI, and these differences were

probably a result of DMI variations. Daily intakes of each particle fraction are also shown in

Table 4-8. Intakes of all particle fractions, except 8.98 mm, were affected by FPS, and RFC

affected intakes of particle fractions 8.98 mm and smaller. Increasing FPS increased intake of

particles retained on the 26.9- and 18.0-mm sieves and decreased intake of particles retained on

the 5.61- and 1.65-mm sieves, and pan. Increasing RFC decreased intake of particles retained on

the 8.98- and 5.61-mm sieves and increased intake of particles retained in the 1.65-mm sieve and

pan. These differences in particle fraction intakes are representative of the differences in their

proportions in the offered TMR, indicating that ration sorting was not sufficient to cause

differences between the consumed and offered rations.

The percentage of total daily intake consumed by 8 and 16 h after feeding was

determined and is also shown in Table 4-8. Cows on the longer FPS treatments had consumed a

greater percentage of their daily intakes at both 8 and 16 h after feeding compared to cows fed

shorter FPS; 62.5 versus 54.6% and 92.1 versus 86.6% of total daily intake for long and short

FPS treatments at 8 and 16 h after feeding respectively. This is somewhat surprising as it has been

shown that increasing FPS decreases eating rate (Bailey, 1961; Beauchemin et al., 2008). Finally,

feed refusal rate was not different among treatments and was successfully managed to a rate of

10.1%.

91

Milk Yield and Composition

Milk yield and composition data are reported in Table 4-9. Milk yield was not influenced

by FPS but was increased with RFC and averaged 43.5 kg/d across treatments. Though increasing

RFC increased milk yield it also decreased milk fat content (from 3.54 to 3.35% on average), and

therefore 3.5% FCM only tended to be higher with higher RFC levels. Increasing RFC also

increased milk protein concentration and yield and lactose concentration and yield. This is likely

an effect of increasing available energy in the rumen, which can increase microbial protein

synthesis and propionate production. These changes in milk yield and composition were not

preceded by changes in ruminal fermentation as measured in this study. A possible explanation

for this discrepancy is that only the concentrations of ruminal compounds were measured, and

actual production and absorption of VFA and NH3 are not known. Increasing FPS decreased milk

protein concentration and increased MUN levels; this could be due to insufficient available

energy for ruminal microorganisms to allow then to effectively utilize available NH3. Feed

efficiency increased with increasing FPS as a result of longer FPS decreasing DMI while

maintaining 3.5% FCM.

Fecal Particle Size

Interestingly RFC has greater influence on fecal particle size distribution than FPS (Table

4-10). Corn silage length had no effect on any particle fractions and Xgm when calculated using

the retained method, though it did have effects on fecal particle size when calculated using the

total method. Increasing RFC influenced virtually every particle fraction and Xgm for both

methods of calculation. Fecal particle size was decreased with increasing RFC. The reason for

this is not clear although it is possible that increased ruminating time/d allowed for a greater

92

reduction in digesta particle size via mastication. The XgmRet for low and high RFC diets

averaged 1.49 and 1.15 mm, respectively. The XgmRet of LF and SF were very close to the results

of Maulfair et al. (2011) where the average XgmRet across all treatments was 1.13 mm. Increasing

RFC also decreased XgmTot from 0.40 to 0.34 mm for low and high RFC diets, respectively.

Again XgmTot of LF and SF agreed with Maulfair et al. (2011) who reported and average of 0.32

mm XgmTot across all treatments. The current study also determined that up to 6.6 and 3.5% of

fecal particles were > 6.7 mm when measures via the retained and total methods, respectively.

Particles > 3.35 mm (retained) averaged 26.5 and 16.2% for low and high RFC diets respectively,

the latter of which again agreed with Maulfair et al. (2011) where 15.7% of fecal particles were >

3.35 mm (retained). These results agree with the suggestion of Maulfair et al. (2011) that the

critical particle size for rumen escape is larger than the commonly held 1.18 mm. This study

further suggests that since approximately 5% of fecal particles were retained on a 6.7-mm sieve,

this size may be a more accurate estimate of the particle size threshold for increased resistance to

ruminal escape.

Conclusions

It was determined that increasing RFC increased ruminating time and increasing FPS

increased eating time. Ruminal fermentation was not affected by either FPS or RFC, though

increasing FPS tended to increase mean and maximum ruminal pH and increasing RFC tended to

decrease minimum ruminal pH. Refusal particle size distribution and NDF and starch content

were observed to change over the course of the d and indicated that cows were sorting against

peNDF and for RFC. Analysis of selection indices revealed virtually no interaction between FPS

and RFC occurred and despite significant sorting throughout the d, by 24 h after feeding cows

had consumed a ration very similar to what was offered. This view of sorting was reinforced by

93

particle fraction intakes that very closely resembled the proportions of particle fractions in the

offered TMR. An interaction between FPS and RFC was seen for DMI, as DMI decreased with

increasing FPS when the diet included low RFC and did change when the diet included high RFC

and DMI increased with RFC for the long diets and did not change with RFC on the short diets.

Increasing RFC was found to increase milk yield, milk protein content and yield, and lactose

content and yield but decrease milk fat content. Increasing FPS did not have as great an impact on

milk production as RFC. This study therefore concludes that: there was not significant interaction

between FPS and RFC for ration sorting although both affected it separately; RFC had greater

influence on milk yield and components than FPS; neither FPS of RFC affected ruminal

fermentation; and there was an interaction between FPS and RFC for DMI. Finally, it was

determined that approximately 5% of fecal particles were greater than 6.7 mm and that this may

be a more accurate estimate of the critical particle size for rumen escape in modern lactating dairy

cows.

Acknowledgements

Sincere appreciation is extended to Growmark FS, LLC (Sangerfield, NY) for generously

allowing the use of their modified forage harvester for the duration of this trial. This research was

supported in part by agricultural research funds administered by The Pennsylvania Department of

Agriculture.

References



94



Bailey, C. B. 1961. Saliva secretion and its relation to feeding in cattle. Br. J. Nutr. 15:443–451.



Gaines, W. L. 1928. The energy basis of measuring milk yield in dairy cows. Illinois

Agr.Expt.Sta. Bull. 308.



Kononoff, P. J., and A. J. Heinrichs. 2003. The effect of corn silage particle size and cottonseed







Sci. 86:3343–3353.



85:1801–1803.

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.



85:1936–1946.



85:1947–1957.



Leonardi, C., F. Giannico, and L. E. Armentano. 2005. Effect of water addition on selective




95

Maulfair, D. D., M. Fustini, and A. J. Heinrichs. 2011. Effect of varying total mixed ration

particle size on rumen digesta and fecal particle size and digestibility in lactating dairy cows. J.

Dairy Sci. 94:3527–3536.



93:4791–4803.








New York, NY.



74:3583–3597.



Yang, W. Z., K. A. Beauchemin, and L. M. Rode. 2001. Effects of grain processing, forage to

concentrate ratio, and forage particle size on rumen pH and digestion by dairy cows. J. Dairy Sci.

84:2203–2216.

Zanton, G. I., and A. J. Heinrichs. 2009. Digestion and nitrogen utilization in dairy heifers limit-

fed a low or high forage ration at four levels of nitrogen intake1. J. Dairy Sci. 92:2078–2094.

96

Table 4-1. Chemical compositions and particle size distributions determined with the ASABE

particle separator for alfalfa haylage and long and short corn silage

Alfalfa Corn Silage

Item Haylage Long Short SEM1 P-value

1

Particle size, as-fed % retained2

26.9 mm 1.9 13.2 0.9 0.64 < 0.01

18.0 mm 5.7 31.0 13.4 0.60 < 0.01

8.98 mm 25.2 28.5 28.7 1.30 0.90

5.61 mm 23.8 13.5 22.1 2.48 0.08

1.65 mm 32.2 11.7 25.8 1.47 < 0.01

Pan 11.1 2.1 9.0 2.34 0.08


DM 47.7 39.3 40.6 1.18 < 0.01

CP 18.8 8.7 8.5 0.07 0.19

ADF 36.0 20.7 19.0 0.61 0.09

NDF 45.8 34.8 32.6 0.85 0.12

peNDF8.03

15.1 25.4 14.0 0.86 < 0.01

peNDF1.184

40.8 34.1 29.6 1.24 < 0.01

Ash 10.3 3.2 3.3 0.12 0.59

NFC 24.0 50.1 52.8 0.92 0.12

Starch 0.67 40.7 41.2 1.88 0.85

NEL, Mcal/kg 1.36 1.73 1.79 0.01 0.02 1Associated with corn silages.

2Approximate equivalency to Penn State particle separator: top sieve (26.9 + 18.0 mm), middle

sieve (8.98 mm), lower sieve (5.61 + 1.65 mm), and pan (pan).

3Physically effective NDF8.0 = % of particles > 8.98 mm × NDF of whole sample (similar to top 2

sieves of Penn State particle separator; Kononoff et al., 2003a).

4Physically effective NDF1.18 = % of particles > 1.65 mm × NDF of whole sample (similar to top

3 sieves of Penn State particle separator; Kononoff et al., 2003a).

97


determined via in situ incubation for dry cracked and dry fine ground corn

Item Cracked Fine Ground SEM P-value


6.70 mm 2.2 0.0 0.18 < 0.01

4.75 mm 10.0 0.1 1.38 < 0.01

3.35 mm 29.7 0.3 1.51 < 0.01

2.36 mm 25.5 5.2 1.29 < 0.01

1.70 mm 14.2 16.2 1.22 0.05

1.18 mm 6.5 17.7 0.54 < 0.01

0.85 mm 3.2 13.3 0.29 < 0.01

0.60 mm 2.7 11.0 0.33 < 0.01

0.425 mm 2.0 11.4 0.80 < 0.01

0.15 mm 3.0 22.3 0.78 < 0.01

Pan 1.0 2.5 0.22 0.01


DM 90.3 88.3 0.36 0.03

CP 8.8 9.4 0.16 0.04

ADF 5.1 4.4 0.38 0.27

NDF 11.3 11.1 0.52 0.77

Ash 1.4 1.6 0.12 0.31

NFC 75.1 74.7 0.94 0.75

NEL, Mcal/kg 1.96 1.96 0.00 1.00

Rate of disappearance1, %

0.5 h 17.7 36.1 2.96 < 0.01

1.0 h 18.4 38.3 2.96 < 0.01

2.0 h 19.0 42.0 2.96 < 0.01

4.0 h 27.6 48.8 2.96 < 0.01

6.0 h 34.2 58.2 2.96 < 0.01

8.0 h 41.9 65.8 2.96 < 0.01

12.0 h 56.1 76.9 2.96 < 0.01

16.0 h 59.1 83.8 2.96 < 0.01

24.0 h 76.4 92.0 2.96 < 0.01

48.0 h 90.7 96.2 2.96 0.15 1Nylon bags were incubated in quadruplicate in the rumen of 2 lactating cows (each cow

incubated 2 bags of each sample for each time point).

98

Table 4-3. Chemical composition and particle size distributions determined with the ASABE

particle separator for TMR varying in forage particle size (FPS) and ruminally fermentable

carbohydrates (RFC)1

Treatment P-value

Item LC LF SC SF SEM FPS RFC FPS ×

RFC


26.9 mm 5.5 4.0 0.7 0.6 0.91 < 0.01 0.35 0.41

18.0 mm 17.6 15.6 4.5 4.4 1.08 < 0.01 0.37 0.40

8.98 mm 20.3 17.9 18.7 16.1 0.77 < 0.01 < 0.01 0.82

5.61 mm 20.1 14.4 23.2 16.4 0.77 < 0.01 < 0.01 0.39

1.65 mm 21.3 24.5 29.4 31.2 0.99 < 0.01 0.02 0.48

Pan 15.2 23.7 23.6 31.2 1.11 < 0.01 < 0.01 0.59


DM, % 51.9 53.7 53.9 53.8 1.42 0.23 0.31 0.26

CP 16.5 16.8 16.1 16.3 0.33 0.18 0.42 0.94

ADF 21.1 20.2 20.0 19.3 0.41 0.02 0.04 0.83

NDF 33.0 32.0 32.0 30.6 0.61 0.02 0.02 0.64

Forage NDF 21.9 21.9 20.9 20.9 0.52 < 0.01 1.00 1.00

peNDF8.03 14.1 12.0 7.6 6.4 0.84 < 0.01 0.05 0.54

peNDF1.184 27.9 24.4 24.4 21.0 0.76 < 0.01 < 0.01 0.97

Ash 6.5 6.2 6.0 6.0 0.18 0.07 0.54 0.45

NFC 40.8 42.0 42.9 44.2 0.37 < 0.01 < 0.01 0.83

Starch 29.2b 30.7

ab 32.8

a 31.3

ab 1.05 0.02 0.95 0.07

NEL, Mcal/kg 1.68 1.68 1.70 1.70 0.01 0.01 1.00 1.00 a–b


1LC = long corn silage and dry cracked corn, LF = long corn silage and dry fine ground corn, SC

= short corn silage and dry cracked corn, SF = short corn silage and dry fine ground corn.







99


fermentable carbohydrates (RFC) on chewing behavior1

Treatment

P-value

Item, min/d LC LF SC SF SEM FPS RFC FPS ×

RFC

Ruminating 373.0 389.0 354.7 400.2 18.0 0.80 0.03 0.30

Eating 200.2 206.6 178.0 174.8 11.7 0.01 0.88 0.65

Total chewing 573.2 595.6 532.7 575.1 23.4 0.14 0.11 0.62 1LC = long corn silage and dry cracked corn, LF = long corn silage and dry fine ground corn, SC


100


fermentable carbohydrates (RFC) on rumen fermentation1

Treatment

P-value


RFC

Rumen pH

Weighted mean2 5.92 5.87 5.85 5.82 0.08 0.15 0.43 0.77

Minimum 5.41 5.36 5.42 5.31 0.06 0.69 0.13 0.47

Maximum 6.68 6.77 6.62 6.60 0.12 0.11 0.66 0.47

AUC3 < 5.8 193.1 207.3 146.1 206.9 47.0 0.44 0.26 0.45

AUC < 5.5 37.4 35.3 15.4 40.8 15.7 0.56 0.44 0.33

NH3, mg/dL

Weighted mean 11.3 9.4 9.5 10.2 1.00 0.62 0.51 0.13

Minimum 3.2 3.7 3.5 3.8 0.89 0.78 0.59 0.97

Maximum 22.4 21.6 20.0 20.4 1.51 0.18 0.86 0.62

Lactate, µM/mL

Weighted mean 0.76 0.74 0.71 0.74 0.05 0.51 0.92 0.57

Minimum 0.42 0.44 0.42 0.41 0.03 0.61 0.97 0.64

Maximum 1.90 2.26 2.01 2.35 0.45 0.81 0.43 0.98

VFA weighted mean, µM/mL

Acetate 89.1 87.1 90.4 89.6 1.20 0.13 0.25 0.64

Propionate 41.2 41.2 39.3 43.8 3.19 0.82 0.19 0.15

Butyrate 15.4 15.5 16.6 15.8 0.54 0.10 0.43 0.30

Valerate 2.8 3.1 2.9 3.0 0.24 0.70 0.09 0.39

Isovalerate 2.8 2.6 2.8 2.8 0.17 0.50 0.45 0.66

Isobutyrate 1.4 1.4 1.5 1.5 0.04 0.10 0.42 0.28

A:P 2.32 2.25 2.41 2.14 0.18 0.92 0.10 0.32

Rumen digesta

Volume, L 128.8 133.3 126.3 131.7 8.03 0.67 0.32 0.93

DM Weight, kg 21.3 21.0 21.2 20.2 1.29 0.64 0.51 0.76 1LC = long corn silage and dry cracked corn, LF = long corn silage and dry fine ground corn, SC


2Weighted averages determined by calculating



3AUC = Area under curve, pH units × min/d (area below pH threshold (5.5 or 5.8) and above pH

profiles of cows).

101


fermentable carbohydrates (RFC) on cumulative selection indices1 for various particle fractions

2

Treatment

P-value

Item LC LF SC SF SEM FPS RFC FPS x RFC

26.9 mm

8 h 0.90 1.02 1.08 0.93 0.09 0.60 0.86 0.12

16 h *0.89 *0.85 1.01 0.95 0.03 < 0.01 0.16 0.91

24 h *0.92 *0.90 1.00 0.95 0.03 0.02 0.21 0.65

18.0 mm

8 h 0.97 1.00 1.06 0.94 0.05 0.73 0.39 0.10

16 h 0.97 *0.96 0.98 0.99 0.02 0.15 0.93 0.40

24 h 0.97 *0.96 0.98 *0.95 0.02 0.89 0.25 0.72

8.98 mm

8 h 0.99 0.99 1.02 0.99 0.02 0.38 0.43 0.60

16 h 0.99 *0.98 1.00 *0.98 0.01 0.47 0.02 0.47

24 h *0.99 *0.99 1.00 *0.99 0.00 0.48 < 0.01 0.88

5.61 mm

8 h 1.00 0.97 1.01 0.99 0.02 0.54 0.13 0.86

16 h 1.00 0.99 1.00 *0.98 0.01 0.26 0.05 0.30

24 h 1.00 1.00 1.00 1.00 0.00 0.26 0.31 0.53

1.65 mm

8 h 1.00 0.97 1.02 1.02 0.02 0.07 0.43 0.48

16 h 0.99 1.01 1.00 1.00 0.01 0.85 0.28 0.35

24 h 1.00 1.01 1.00 1.00 0.01 0.51 0.22 0.30

Pan

8 h 0.99 0.96 *0.93 0.98 0.04 0.59 0.76 0.22

16 h 0.98 1.02 0.99 1.02 0.02 0.82 0.16 0.58

24 h 0.98 1.02 1.00 1.02 0.02 0.71 0.15 0.42

*Sorting index is significantly different from 1.00 based on a 95% confidence limit.

1Values = 1.00 indicate no sorting, values < 1.00 indicate sorting against, and values > 1.00

indicate sorting for.


= short corn silage and dry cracked corn, SF = short corn silage and dry fine ground corn;

approximate equivalency to Penn State particle separator: top sieve (26.9 + 18.0 mm), middle


102


fermentable carbohydrates (RFC) on interval selection indices1 for various particle fractions

2

Treatment

P-value

Item LC LF SC SF SEM FPS RFC FPS x RFC

26.9 mm

8 h 0.98 1.02 1.08 0.93 0.09 0.93 0.55 0.29

16 h *0.69 *0.58 0.91 0.93 0.07 < 0.01 0.49 0.35

24 h 0.81 0.80 0.95 1.03 0.11 0.09 0.78 0.67

18.0 mm

8 h 1.02 1.00 1.06 0.94 0.05 0.83 0.17 0.31

16 h *0.91b *0.85

b *0.92

b 1.04

a 0.04 < 0.01 0.34 0.01

24 h *0.89 *0.84 *0.85 *0.83 0.05 0.67 0.48 0.74

8.98 mm

8 h 0.99 0.99 1.02 0.99 0.02 0.36 0.43 0.57

16 h 0.97 *0.95 0.98 *0.96 0.01 0.47 0.16 0.82

24 h 0.98 *0.95 *0.93 0.96 0.02 0.31 0.87 0.22

5.61 mm

8 h 0.95 0.97 1.01 0.99 0.03 0.17 0.97 0.39

16 h *1.03 1.01 0.99 *0.96 0.01 < 0.01 < 0.01 0.66

24 h *1.05 *1.06 1.02 1.03 0.26 0.16 0.64 0.86

1.65 mm

8 h *0.92 0.97 1.02 1.02 0.03 0.03 0.44 0.41

16 h *1.04 *1.06 0.99 0.97 0.02 < 0.01 0.97 0.28

24 h *1.10 *1.12 1.02 0.97 0.04 < 0.01 0.71 0.36

Pan

8 h 0.93 0.96 0.93 0.98 0.04 0.75 0.30 0.78

16 h *1.14 *1.13 *1.05 *1.06 0.02 < 0.01 0.87 0.56

24 h *1.15 *1.15 1.04 1.09 0.06 0.14 0.61 0.72 a–b


*Sorting index is significantly different from 1.00 based on a 95% confidence limit.




= short corn silage and dry cracked corn, SF = short corn silage and dry fine ground corn;

approximate equivalency to Penn State particle separator: top sieve (26.9 + 18.0 mm), middle


103


fermentable carbohydrates (RFC) on daily DM, NDF, starch, and particle fraction intake1

Treatment P-value

Item, kg LC LF SC SF SEM FPS RFC PFS × RFC

DMI 27.9b 30.9

a 31.2

a 31.6

a 1.08 < 0.01 < 0.01 < 0.01

NDF 10.0 10.4 10.2 9.9 0.33 0.42 0.98 0.07

Starch 8.2b 9.6

a 10.2

a 9.8

a 0.36 < 0.01 0.06 < 0.01

Particle fractions2

26.9 mm 1.43 1.14 0.21 0.14 0.13 < 0.01 0.16 0.36

18.0 mm 4.73 4.61 1.36 1.31 0.18 < 0.01 0.59 0.85

8.98 mm 5.63ab

5.42b 5.77

a 4.95

c 0.20 0.16 < 0.01 0.01

5.61 mm 5.67b 4.42

d 7.20

a 5.10

c 0.19 < 0.01 < 0.01 < 0.01

1.65 mm 6.10d 7.63

c 9.11

b 9.76

a 0.29 < 0.01 < 0.01 0.04

Pan 4.45 7.49 7.30 9.93 0.29 < 0.01 < 0.01 0.43

Cumulative % of daily intake

8 h 63.6 61.4 54.0 55.1 2.14 < 0.01 0.71 0.32

16 h 92.9 91.2 86.8 86.3 1.33 < 0.01 0.31 0.57

Refusal, % 9.9 9.8 10.6 9.9 0.39 0.21 0.23 0.39 a–d






104


fermentable carbohydrates (RFC) on milk yield and components1

Treatment

P-value


RFC

Milk yield, kg/d 41.7 44.9 42.2 45.1 1.69 0.66 < 0.01 0.85

3.5% FCM, kg/d2 41.7 43.6 42.6 43.9 1.92 0.53 0.10 0.76

Feed efficiency3 1.50

a 1.41

ab 1.37

b 1.40

b 0.06 0.05 0.36 0.09

Fat, % 3.52 3.32 3.56 3.37 0.16 0.40 < 0.01 0.90

Fat, kg/d 1.46 1.49 1.50 1.50 0.09 0.48 0.67 0.73

Protein, % 3.14 3.18 3.17 3.20 0.06 0.05 0.04 0.88

Protein, kg/d 1.31 1.42 1.34 1.44 0.06 0.38 < 0.01 0.82

Lactose, % 4.75b 4.83

a 4.78

ab 4.79

ab 0.06 0.87 0.02 0.07

Lactose, kg/d 1.98 2.17 2.03 2.18 0.10 0.63 < 0.01 0.68

MUN, mg/dL 10.6 11.1 10.2 10.0 0.68 < 0.01 0.58 0.23

SCC, 1,000 cells/mL 800a 43

c 242

bc 504

ab 274 0.76 0.11 < 0.01

a–cMeans within a row with different superscripts differ (P ≤ 0.05).



23.5% FCM = 0.432 (milk kg) + 16.23 (fat kg); (Gaines, 1928).

3Feed efficiency = 3.5% FCM / DMI.

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fermentable carbohydrates (RFC) on daily weighted mean1 fecal particle size and DM content

2

Treatment

P-value

Item, % of DM LC LF SC SF SEM FPS RFC

FPS x

RFC

DM 18.6 17.3 17.3 16.5 0.31 < 0.01 < 0.01 0.35

Retained DM3

6.7 mm 5.8a 4.0

b 6.6

a 3.3

b 0.53 0.95 < 0.01 0.14

3.35 mm 20.5 13.2 19.7 11.9 1.04 0.11 < 0.01 0.68

1.18 mm 17.3b 17.6

ab 17.0

b 18.7

a 0.53 0.35 0.03 0.12

0.60 mm 13.0 15.8 13.1 16.3 0.27 0.26 < 0.01 0.41

0.15 mm 43.4 49.4 43.5 49.9 1.45 0.68 < 0.01 0.84

Xgm5, mm 1.48 1.17 1.49 1.12 0.06 0.68 < 0.01 0.50

Sgm6, mm 1.36 1.30 1.36 1.29 0.01 0.26 < 0.01 0.24

Total DM4

6.7 mm 2.8a 1.9

b 3.5

a 1.6

b 0.29 0.49 < 0.01 0.06

3.35 mm 9.8 6.3 10.2 5.8 0.55 0.77 < 0.01 0.21

1.18 mm 8.3 8.4 8.7 9.2 0.29 0.02 0.16 0.48

0.60 mm 6.2 7.5 6.6 7.9 0.14 < 0.01 < 0.01 0.91

0.15 mm 20.5 23.3 21.8 24.1 0.74 0.01 < 0.01 0.55

Soluble 52.5a 52.5

a 49.3

b 51.4

a 0.69 < 0.01 0.03 0.04

Xgm, mm 0.38b 0.34

c 0.42

a 0.34

c 0.01 0.04 < 0.01 0.05

Sgm, mm 1.67 1.54 1.70 1.52 0.02 0.87 < 0.01 0.19 a–c


1Weighted means determined by calculating

area under the response curve according to the




3Retained DM = Parameters determined from sample retained on sieve stack.

4Total DM = Parameters determined from total sample including soluble fraction.

5Xgm = geometric mean particle length determined by ASABE (2007).

6Sgm = particle length standard deviation determined by ASABE (2007).

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fermentable carbohydrates (RFC) on daily weighted mean1 ruminal digesta particle size

distribution and DM content2

Treatment

P-value

Item, % of DM LC LF SC SF SEM FPS RFC

FPS x

RFC

DM 18.5 18.6 19.4 18.7 0.38 0.17 0.35 0.25

Retained DM3

9.5 mm 21.3 21.3 16.1 16.2 0.86 < 0.01 0.92 0.98

6.7 mm 7.3 6.7 7.2 6.7 0.30 0.94 0.08 0.72

3.35 mm 17.5 14.2 19.8 16.5 0.63 < 0.01 < 0.01 0.96

1.18 mm 18.1 18.4 20.1 19.8 0.48 < 0.01 0.99 0.37

0.60 mm 11.8 13.0 12.3 13.3 0.27 0.06 < 0.01 0.66

0.15 mm 24.0 26.5 24.5 25.9 0.95 0.94 < 0.01 0.26

Total DM4

9.5 mm 13.9 13.8 10.5 10.4 0.58 < 0.01 0.83 0.97

6.7 mm 4.8 4.3 4.7 4.3 0.19 0.73 0.02 0.82

3.35 mm 11.3 9.1 12.9 10.5 0.40 < 0.01 < 0.01 0.75

1.18 mm 11.7 11.9 13.1 12.7 0.31 < 0.01 0.53 0.20

0.60 mm 7.7 8.4 8.0 8.6 0.18 0.11 < 0.01 0.51

0.15 mm 15.6 17.1 15.9 16.6 0.64 0.68 < 0.01 0.25

Soluble 34.9 35.4 34.9 35.5 0.68 0.96 0.44 0.88 a–c







3Retained DM = Parameters determined from sample retained on sieve stack.

4Total DM = Parameters determined from total sample including soluble fraction.

107


fermentable carbohydrates (RFC) on starch concentration at 0 and 24 h after feeding1



*time effect P ≤ 0.05; overall time effect P < 0.01.

108


fermentable carbohydrates (RFC) on NDF concentration at 0 and 24 h after feeding1


= short corn silage and dry cracked corn, SF = short corn silage and dry fine ground corn; overall

time effect P = 0.15.

109

A

B

110

C


fermentable carbohydrates (RFC) on TMR particle fractions > 26.9 mm (A), > 1.65 mm (B), and

pan (C) at 0, 8, 16, and 24 h after feeding1



Chapter 5

Effect of Subacute Ruminal Acidosis on Total Mixed Ration Preference in

Lactating Dairy Cows

Abstract

Subacute ruminal acidosis (SARA) is a condition where the pH of the rumen becomes

abnormally acidic because of increased and altered production of volatile fatty acids. The

objective of this experiment was to determine how SARA affects total mixed ration selection in

dairy cows. In this study 8 multiparous, lactating, ruminally cannulated Holstein cows were given

a choice between a long forage particle size diet with slow-fermenting starch (LC) and a short

forage particle size diet with fast-fermenting starch (SF). Cows were allowed to adapt to this

feeding scheme and were then subjected to a rumen challenge to induce a bout of SARA. The

rumen challenge successfully decreased rumen pH and altered rumen volatile fatty acid profiles.

Daily weighted average rumen pH decreased from 6.02 to 5.77, and average minimum rumen pH

decreased from 5.59 to 5.28. In addition, following the rumen challenge concentrations of acetate,

butyrate, and valerate and acetate to propionate ratio increased. In response to the rumen

challenge, intake of LC increased from the baseline level of 18.1% of total daily dry matter intake

to 38.3% for that d. During the first recovery d after the rumen challenge, LC intake moderated to

28.0% of total daily dry matter intake. On the second recovery d LC intake returned to baseline

levels at 18.6%. These results indicate that cows are able to alter their diet preference for higher

physically effective fiber and slower starch fermentability during a bout of SARA and that they

can effectively fully recover from this type of SARA within 72 h when appropriate diets are

available.

112

Key Words: acidosis, diet selection, particle size, sorting

Introduction

Subacute ruminal acidosis (SARA) is a major concern in the modern high producing

dairy cow. It is defined as a moderately depressed rumen pH in the range of 5.5 to 5.0 (Nocek,

1997; Krause and Oetzel, 2006). Krause and Oetzel (2006) suggested that there are 3 major

causes of SARA in dairy herds: excessive intake of rapidly fermentable carbohydrates,

inadequate ruminal adaptation to a highly fermentable diet, and inadequate ruminal buffering

caused by inadequate dietary fiber or inadequate physical fiber. The negative effects of SARA are

vast and varied; ranging from decreased DMI (Britton and Stock, 1986; Nocek, 1997) and

reduced feed efficiency (Huntington, 1993; Nocek, 1997) to decreased milk fat yield (Nocek,

1997) and contributing to lameness (Nocek, 1997; NRC, 2001; Stone, 2004). A study that

evaluated 154 cows in 14 Wisconsin dairy herds determined that 20.1% of lactating cows had

SARA when tested using rumenocentesis (Oetzel et al., 1999). In a case study of a 500-cow dairy

in central New York state, Stone (1999) estimated that SARA could cost up to $475/cow per yr in

lost milk production and components only. Clearly, SARA warrants extensive research and

management.

There are several studies that have examined diet and feed selection changes when sheep

or lambs were subjected to acidotic rumen conditions. For example, in a study by Phy and

Provenza (1998b) lambs were fed a meal of rolled barley and then offered a choice of flavored

(onion or oregano) rabbit pellets that either contained NaHCO3 and lasalocid or NaCl. The

authors determined that after a grain meal lambs preferred rabbit pellets that contained NaHCO3

and lasalocid over pellets that contained NaCl. Another study by Phy and Provenza (1998a)

examined the effect eating a meal of rapidly fermentable feed had on the preference for rapidly

113

fermentable feed later in the d. Lambs fed a lower amount (400 g) of rolled barley for a meal

exhibited equal preference for rolled barley and alfalfa pellets (52 and 48% of total intake

respectively) during the next 4 h. However, when a higher amount (1,200 g) of rolled barley was

fed the lambs increased their preference for alfalfa pellets over rolled barley (71 and 29% of total

intake respectively) during this same time (Phy and Provenza, 1998a). All of these results show

that lambs prefer feeds that attenuate acidosis after a grain meal to maintain ruminal health.

In addition, studies have examined the influence of SARA in dairy cows on eating

behavior. Keunen et al. (2002) conducted an experiment where 25% of DMI of cows being fed a

TMR was replaced by wheat and barley pellets in order to induce SARA. The choice of 2 feeds,

long alfalfa hay and alfalfa pellets, was then offered 2 times per d for 30 min each. Cows with

SARA increased their consumption of long alfalfa hay over alfalfa pellets when compared to their

consumption without SARA; 85 and 60% of test feeds were consumed as long alfalfa hay for

SARA and non-SARA cows respectively (Keunen et al., 2002). DeVries et al. (2008) used a

rumen challenge model to induce SARA in early and mid-lactation Holstein cows. The rumen

challenge consisted of restricting feed to 50% of ad libitum DMI for 1 d followed by feeding 4 kg

of barley and wheat and then ad libitum access to TMR. Changes in eating behavior were

measured by determining the particle size distribution of offered feed and refusals and calculating

a selection index for each particle fraction. After the rumen challenge, cows in both groups

changed their sorting behavior. DeVries et al. (2008) determined that early lactation cows

generally increased their sorting for medium particles and against short and fine particles and

exhibited no change in sorting long particles. Mid-lactation cows exhibited variable responses

with sorting activity changing with d and period. DeVries et al. (2008) suggested that both early

and mid-lactation cows altered their sorting behavior to consume a diet that would help attenuate

their bout of SARA.

114

Despite there being evidence of dairy cattle altering their eating behavior or diet choice

based on their rumen environment, there has been no research published where cattle had access

to 2 distinct diets to observe the influence of SARA on diet preference and eating behavior.

Therefore the objective of this experiment was to induce a bout of SARA in lactating dairy cows

that had ad libitum access to 2 distinct diets that varied in forage particle size and starch

fermentability and to determine how SARA affects TMR selection in dairy cows.





Eight lactating, multiparous, ruminally cannulated, Holstein cows averaging 219 ± 61 DIM and

44 ± 7 kg/d milk production, weighing 702 ± 56 kg, and with parity of 3.13 ± 0.99 (mean ± SD)

were studied. The trial consisted of a 7-d adaptation period followed by an 8-d collection period.

For the duration of the study, cows were fed 2 different diets simultaneously: a long

particle size diet with slowly fermentable starch (LC) and a short particle size diet with fast starch

fermentabilty (SF). Diets were offered to cows in tie-stall feed bunks divided into halves via a

plywood panel that eliminated cross contamination of TMR. The side of the feed bunk that the

diets were offered was alternated each d to limit the possibility for bias of bunk location or

relationship to water bowls. The 2 rations fed contained identical ingredients and proportions, but

varied in the particle length of corn silage and the particle size of dry ground corn. The LC diet

included long corn silage (LCS) and dry cracked corn (CC) and the SF diet included short corn

silage (SCS) and dry fine ground corn (FC). Ingredients and their percentage of ration DM were:

115

corn silage (42.6), dry ground corn (22.2), alfalfa haylage (15.4), canola meal (9.4), roasted split

soybeans (7.1), mineral/vitamin mix (2.5), salt (0.4), and Optigen (Alltech, Nicholasville, KY;

0.4).

Corn silage hybrid was Pioneer 34M78 (Pioneer Hi-Bred International, Inc., Johnston,

IA) that was planted on 4/19/2010 and harvested on 8/30/2010. Corn silage was harvested with a

John Deere 6750 forage harvester (John Deere, Moline, IL) equipped with a kernel processor set

at approximately 6.35 mm. The cutterhead of the harvester used 16 knives (maximum capacity is

48 knives) with the length-of-cut transmission at its highest setting to produce a theoretical length

of cut of 47.1 mm. After harvesting, corn silage was ensiled in an Ag-Bag (Ag-Bag, St. Nazianz,

WI) and allowed to ferment for 62 d before beginning the study. Corn silage that was removed

from the Ag-Bag and mixed into TMR without further processing was considered LCS. A cut-

and-throw type, single row, forage harvester that was modified to operate on a trailer and be fed

manually with a 25 horsepower V-Twin small gas engine was used to reduce the particle size of

corn silage to produce SCS. Corn silage was rechopped twice through the custom forage chopper

on a daily basis to minimize the chemical variance between LCS and SCS. Dry corn was ground

through a Roskamp roller mill (California Pellet Mill Co., Crawfordsville, IN) to produce the CC

used in this study. This corn was then ground further with a Case International 1250 grinder-

mixer (Case IH, Racine, WI) using a 3.18 mm screen to produce FC. Diets were mixed separately

using an I. H. Rissler model 1050 TMR mixer (E. Rissler Mfg. LLC, New Enterprise, PA).

The 4 consecutive d immediately following the adaptation period (d 8 to 11) were

designated the baseline for feed preference and rumen conditions. On d 12 feed intakes for each

diet were restricted to 75% of baseline intake. Following feed restriction, on d 13 at 0745 4 kg

(as-fed) of fine ground wheat was thoroughly mixed into the rumen digesta of each cow via the

rumen cannulae to provide a rumen challenge by initiating SARA. Each cow was then allowed ad

libitum access to both diets at 0800, the amount of TMR offered allowed for approximately 115%

116

of total daily baseline intake to be consumed from either diet offered. Ad libitum TMR feeding

continued on d 14 and 15 to monitor recovery from the rumen challenge.

Animals were housed in individual stalls, milked twice/d at 0500 and 1700 h, and fed

once/d at approximately 0800 h for ad libitum consumption. Cows were fed for a 10% refusal rate

except when either treatment diet intake was below 6 kg/d DM, which was set as the minimum

amount of feed to be offered to always allow for an opportunity to choose either diet. Feed was

pushed up 3 times/d at 1230, 1730, and 2400 h. Rations were balanced to meet or exceed NRC

(2001) requirements for cows producing 52.2 kg of milk/d containing 3.75% fat and 3.07% true

protein assuming a DMI of 29.5 kg/d and water was available for ad libitum consumption.

Rumen Sampling

On d 11 of the study, ruminal contents were collected from dorsal, ventral, cranial,

caudal, and medial areas of the rumen at 0.0, 1.5, 3.5, 5.5, 8.5, 11.5, 14.5, 18.0, 21.5, and 24.5 h

after feeding (Kononoff et al., 2003b) to determine baseline rumen conditions. Rumen sampling

also occurred on d 12 (feed restriction) at 11.5, 14.5, 18.0, and 21.5 h after feeding, d 13 (rumen

challenge) at 0.0, 1.5, 3.5, 5.5, 8.5, 11.5, 14.5, 18.0, 21.5, and 24.5 h after feeding, and d 14

(recovery) at 3.5, 8.5, 14.5, and 21.5 h after feeding. At each rumen sampling collected digesta

was mixed thoroughly, sampled, and filtered through 2 layers of cheesecloth. Rumen liquid pH

was immediately determined using a handheld pH meter (HI 98121, HANNA Instruments Inc.,

Woonsocket, RI). Approximately 15 mL of filtered liquid was placed into bottles containing 3

mL of 25% metaphosphoric acid and 3 mL of 0.6% 2-ethylbutyric acid (internal standard) and

stored at approximately 2C. Within 24 h after collection, samples were centrifuged 3 times at

4000 g for 30 min at 4C to obtain a clear supernatant and were analyzed for VFA

concentration using gas chromatography (Yang and Varga, 1989).

117


Feed bunk contents for each animal were weighed and sampled on d 8 to 15 at 0 and 24 h

after feeding to determine particle size distribution and DM content of the remaining feed.

Additionally, feed bunk contents were weighed on d 8 to 11 and d 13 at 2, 4, 8, and 16 h after

feeding. All samples were sieved in the American Society of Agriculture and Biological

Engineers (ASABE) forage particle separator, which can determine 6 particle fractions (> 26.9, >

18.0, > 8.98, > 5.61, > 1.65, and < 1.65 mm; screen diagonal; ASABE, 2007). Whole samples

were then placed in a forced air oven at 65°C for 48 h to determine DM content. Samples of

forages, ground corn, and TMR were taken on d 11 and 13 and analyzed by Cumberland Valley

Analytical Services, Inc. (Hagerstown, MD) for CP (AOAC, 2000), ADF (AOAC, 2000), NDF

(Van Soest et al., 1991), ash (AOAC, 2000), NFC (Van Soest et al., 1991), and NEL (NRC,

2001). Starch contents of forages, ground corn, and TMR were determined by grinding (0.5-mm

screen; Wiley Mill, Arthur H. Thomas Co. Inc., Swedesboro, NJ) dried samples and then using

the starch procedure reported by Zanton and Heinrichs (2009). Particle size distributions of

forages and TMR were determined via sieving with the ASABE forage particle separator

(ASABE, 2007). To determine particle size distributions of ground corn, samples were placed on

a series of stacked sieves (sizes 0.15, 0.425, 0.60, 0.85, 1.18, 1.70, 2.36, 3.35, 4.75, and 6.7 mm;

VWR, Arlington Heights, IL) contained in a Retsch AS 200 Control sieve shaker (Retsch, Haan,

Germany) and were sieved for 10 min at 2.5 mm amplitude. There was approximately a 41%

increase between each sieve screen size, except between the 0.15- and 0.425-mm sieves. Particles

retained on each sieve were then weighed to determine their proportion of total sample DM.

There were 2 procedures used to calculate physically effective NDF (peNDF): peNDF8.0 = % of

particles > 8.98 mm × NDF of whole sample (similar to top 2 sieves of the Penn State particle

separator) and peNDF1.18 = % of particles > 1.65 mm × NDF of whole sample (similar to top 3

118

sieves of the Penn State particle separator; Kononoff et al., 2003a). Corn grain fermentability was

determined via in situ bags incubated in quadruplicate in the rumen of 2 lactating cows (each cow

incubated 2 bags of each sample for each time point) for 0.5, 1, 2, 4, 6, 8, 12, 16, 24, and 48 h.

After removal from the rumen, bags were rinsed in cold water by hand until water was almost

clear. Bags were then dried in a forced-air oven at 65°C for 48 h and then weighed to determine

remaining DM.


Statistical analysis was conducted using PROC MIXED of SAS (Version 9.2, SAS

Institute, Cary, NC). Dependent variables were analyzed as a cross over design. All denominator

degrees of freedom for F-tests were calculated according to Kenward and Roger (1997) and

repeated measurements for ruminal pH, ruminal VFA concentrations, and ground corn DM

disappearance were analyzed using the first order autoregressive covariance structure (Littell et

al., 1998) as well as terms for time and interaction of treatment by time. Because of unequally

spaced rumen sampling, the weighted mean daily pH and VFA concentrations were determined

by calculating the area under the response curve according to the trapezoidal

rule (Shipley and

Clark, 1972). Area under the curve for the SARA thresholds of 5.8 and 5.5 were also calculated

using the trapezoidal rule (Shipley and Clark, 1972). For each cow, the 4 baseline d (8, 9, 10, and

11) were averaged before analysis to provide equal number of observations between baseline and

rumen challenge d. A selection index based on refusals was calculated for each of the 6 particle

size fractions. This index was calculated as the actual intake of each fraction (Yi to pan)

expressed as a percentage of the expected intake. Expected intake of Yi equals intake multiplied

by the fraction of Yi in the fed TMR (Leonardi and Armentano, 2003). Values > 1.0 indicate

cows were sorting for the particle fraction and values < 1.0 indicate cows were sorting against the

119

particle fraction. The 95% confidence limits were used to determine if a selection index was

significantly different from 1.0. All data are presented as least squares means and treatment

effects are considered significant when P < 0.05 and a trend when P < 0.10.


Chemical Composition and Particle Size Distribution of Diets

Particle size distributions and chemical compositions of forages used in this study are

shown in Table 5-1. There was a large difference in particle size distribution between LCS and

SCS. When separated with the ASABE particle separator, LCS had many more particles retained

on 26.9 and 18.0 mm screens, equal particles on the 8.98 mm screen, and many fewer particles on

5.61 and 1.65 mm screens and the pan than SCS. The approximate equivalency of Penn State

particle separator fractions to the ASABE screens are: top (26.9 + 18.0 mm), middle (8.98 mm),

lower (5.61 + 1.65 mm), and pan (pan). The particle size distribution of alfalfa haylage was

similar to SCS. Chemical compositions of the corn silages were similar and not practically

different despite some statistically significant differences for DM, ADF, NDF, NFC, and NEL.

Sampling error may be responsible for the small differences seen between LCS and SCS since

they were taken from the same bag each d as a single batch, with part being re-chopped as the

only difference. Rechopping of corn silage could conceivably increase DM content through

increased drying rate. The peNDF measures were, as expected, very different between corn

silages, but there was a much greater difference for peNDF8.0 than for peNDF1.18. The LCS was

1.81 and 1.15 times greater than SCS for peNDF8.0 and peNDF1.18 respectively.

The particle size distributions, chemical compositions, and rates of disappearance for

corn grains used in this study are shown in Table 5-2. The particle size distributions of CC and

120

FC were different at all 11 particle fractions. The greatest differences occurred at screen sizes

2.36 mm and larger, where CC had 67.4% and FC had 5.6% of particles retained, and at screen

sizes 1.18 mm and smaller, where CC had 18.4% and FC had 78.2% of particles retained. The

chemical compositions of CC and FC were similar and not practically different despite being

statistically different in DM and CP content. The rates of disappearance of CC and FC were

different at every time point except 48 h (P-value = 0.15). The greatest differences between CC

and FC were in the first 2 h of incubation, where FC had about 2.1 times more DM disappearance

than CC. The disappearance of FC continued to be greater than CC at each time point (except 48

h), but the differences between them decreased with increasing incubation time. These data

should be interpreted with caution as the impact of eating and rumination on ground corn was not

a factor in this analysis and it is reasonable to assume that chewing would have a larger impact on

CC because of its greater potential for further particle size reduction.

The particle size distributions and chemical compositions of the treatment TMR are

shown in Table 5-3. Each particle fraction was different between LC and SF; the 4 largest particle

fractions (> 26.9, > 18.0, > 8.98, and > 5.61 mm) were greater for LC, while the 2 smallest

particle fractions (> 1.65 mm and pan) were greater for SF. The chemical compositions of the

TMR were similar and not practically different. The CP, NDF, forage NDF, and starch content of

the TMR were approximately 16.3, 31.8, 21.5, and 30.1% of DM respectively. The peNDF

measures were very different between LC and SF diets, with the greatest difference occurring

with peNDF8.0, where LC was 2.12 times higher than SF (13.8 versus 6.5%). The LC diet was

only 1.30 times higher than SF for peNDF1.18 (27.7 versus 21.3%).

121


The effect of the rumen challenge model on rumen pH is shown in Figure 5-1. On the

baseline d rumen pH gradually decreased after feeding to a low of 5.61 at 11.5 h post- feeding.

Rumen pH then gradually increased to pre-prandial levels by 24 h after feeding. The following d

(feed restriction d) rumen pH was measured starting at 11.5 h after feeding and rumen pH was not

different from baseline levels at 11.5 and 14.5 h after feeding. Rumen pH then increased faster

and remained higher than baseline levels for the remainder of the d. The following d (rumen

challenge d) ground wheat was mixed into the rumen via the cannulae of all cows 15 min before

feeding. Rumen pH, which began at a higher level than baseline, then dropped sharply after

feeding until 3.5 h after feeding and remained constant for 18 h after feeding. Rumen challenge d

rumen pH had a 3.8-fold larger drop from feeding to 1.5 h after feeding and a 6.1-fold larger drop

from feeding to 3.5 h after feeding compared to baseline d rumen pH. Also rumen challenge d

rumen pH was lower than baseline d rumen pH at 3.5, 5.5, 8.5, and 18.0 h after feeding. By 21.5

h after feeding on rumen challenge d, rumen pH had returned to baseline levels and stayed at

baseline levels for the remainder of the rumen challenge d and the following recovery d, except at

21.5 h after feeding on the recovery d; the cause of this difference is not apparent.

Ruminal pH daily weighted average was lower and the area under ruminal pH 5.8 and 5.5

was greater during the rumen challenge d compared to baseline (Table 5-4). This indicates that

the rumen challenge was successful in inducing SARA and the drop in average rumen pH (0.25

unit decrease) was comparable to other studies attempting to induce SARA in dairy cattle such as

Keunen et al. (2002) and Dohme et al. (2008); 0.14 and 0.35 unit decreases respectively. The area

under ruminal pH of 5.8 was increased by 5 fold (50.9 to 254.8 pH units × min/d) on the rumen

challenge d and the area under ruminal pH of 5.5 was essentially 0.0 during baseline but

increased to 37.3 pH units × min/d on the rumen challenge d. Dohme et al. (2008) showed similar

122

areas under the curve for their early lactation cows subjected to rumen challenges where areas

under 5.8 ruminal pH were 136, 231, and 475 pH units × min/d and under 5.5 ruminal pH were

42, 91, and 291 pH units × min/d (during the 1st, 2

nd and 3

rd rumen challenge respectively). In

addition, there was more variation in rumen pH on the rumen challenge d as it had a lower

minimum (5.28 versus 5.59) and a higher maximum (6.95 versus 6.69) over 24 h. Rumen VFA

concentrations were also determined to be different between these 2 d. The daily weighted

average concentration for acetate, butyrate, valerate, and isobutyrate, as well as acetate to

propionate ratio increased on rumen challenge d. Propionate and isovalerate concentrations were

not affected by the rumen challenge.

TMR Preference, Dry Matter Intake, and Refusals

TMR preference was measured as the amount of LC diet DM consumed divided by total

daily DMI and expressed as a percentage. Average LC consumption for all cows over the 4

baseline d was 18.1% of total daily DMI (Figure 5-2). This ratio remained the same for the feed

restriction d as both diets were restricted to 75% of baseline intake and there were virtually no

ration refusals for either diet (Table 5-5). These results are in agreement with the results of Castle

et al. (1979) where 3 grass silages of different particle lengths were fed simultaneously to 3

pregnant Ayrshire heifers. The heifers consumed 15.9, 31.9, and 52.2% of total DMI as long,

medium, and short silages respectively. After the rumen challenge, LC intake increased

dramatically to 38.3%, followed by 28.0% on the first recovery d. On the second recovery d after

rumen challenge, LC intake returned to baseline levels at 18.6% of total daily DMI. These results

clearly show that the cows very consistently (small SE values) changed their TMR preference in

response to a rumen challenge and also they appear to fully recovered from this rumen challenge

within 72 h.

123

The DMI and refusals for each diet for the baseline, feed restriction, rumen challenge,

and recovery d are shown in Table 5-5. The average daily DMI during the baseline period was

30.7 kg/d (5.3 and 25.4 kg/d for LC and SF respectively). The following d feed was restricted to

75.9% of baseline intake at 23.3 kg/d (4.0 and 19.3 kg/d for LC and SF respectively). Rumen

challenge d DMI increased from the baseline for LC by 136% and decreased for SF by 20% for a

total daily intake of 32.7 kg/d (excluding ground wheat). Intake of LC recovered to baseline

levels by recovery d 1 and SF DMI recovered to baseline levels by recovery d 2. The amount of

TMR delivered to the cows was adjusted daily to maintain a refusal rate of 10%, with the

exception of the restricted intake d prior to the rumen challenge. However, since the minimum

amount of feed offered per diet per d was set at 6 kg of DM and most cows consumed less than

this amount of the LC diet, LC refusals were much higher than SF refusals during the baseline

period (31.8 versus 9.6%). On the rumen challenge d there was a drastic increase in refusals for

both diets because cows were offered approximately 115% of total daily baseline intake for each

diet (230% of total daily baseline intake combined) so they had the ability to consume their entire

daily intake from only one diet if they preferred. Therefore, LC refusals were 63.0% and SF

refusals were 45.5% on the rumen challenge d. Refusal rates remained elevated during the 2

recovery d because larger amounts of feed continued to be offered to allow cows to return to their

baseline LC:SF intake ratios without the influence of low diet refusals. Whether TMR was

delivered on the left or right side of the feed bunk or whether TMR was delivered to the side of

the feed bunk that was adjacent to a water bowl did not affect the percent of LC consumed as a

percentage of total daily DMI (P = 0.68 and 0.63, respectively) during the baseline period.

The cumulative percentages of daily intakes for each diet for the baseline and rumen

challenge d are shown in Figure 5-3. The cows consumed approximately 21.9% of their total

daily intake by 2 h after feeding and there were no differences in the cumulative percentages of

daily intakes among the diets or d. At 4 h after feeding the cows consumed an average of 32.1%

124

of their daily diet intakes; however, there were some small differences among the diets and d. The

SF intake was lower on the baseline d compared to LC and SF intake on the rumen challenge d.

By 8 and 16 h after feeding the cows had consumed approximately 51.7 and 81.5% respectively

of daily diet intakes and there were no differences among diets and d. These results show that the

cows consumed the diets simultaneously and in the same ratio throughout the d, independent of

which d it was. In other words the cows did not consume a larger proportion of 1 diet at certain

times of the d and a larger proportion of the second diet at another time of d. These data also

show how heavily a cows’ daily DMI is skewed toward immediately after feeding when only 1

meal is fed per d in an individual stall housing system, even though ample feed was available

before feeding based on consistently having high levels of refusal.

Ration Sorting

Ration sorting was measured via selection indices calculated by comparing TMR particle

size distributions at time of feeding to 24 h after feeding. The selection index uses the actual

consumption of a particle fraction divided by the estimated consumption of the particle fraction if

no sorting occurred to produce the index value. Index values > 1.0 indicate sorting for a particle

fraction and values < 1.0 indicate sorting against. It was found that there were no differences in

sorting indices among the baseline, rumen challenge, and recovery d (P all > 0.10); therefore all d

were averaged (Table 5-6). Based on these selection indices, sorting occurred in all 6 particle

fractions when cows were fed the LC diet. They sorted against particles retained on the 3 largest

screens (26.9, 18.0, and 8.98 mm) and for particles in the 3 smallest particle fractions (> 5.61, >

1.65 mm, and pan). All of the LC sorting indices were different from 1.0 based on their 95%

confidence limits, even though they did not have very large numerical differences. There was

much less ration sorting on the SF diet, as 4 of the 6 particle fractions had no significant sorting

125

occurring. The cows fed the SF diet did sort against particles retained on the 18.0 and 8.98 mm

screens. In addition, the sorting indices for each particle fraction were different between the diets

except for the 18.0 mm screen. It is unlikely that the minimal amount of sorting described in this

study influenced cow performance and rumen fermentation, because in a previous study by

Maulfair et al. (2010) much greater ration sorting activity was found to have no effects on milk

production or rumen fermentation patterns.

Conclusions

Lactating cows were given the choice between a diet with long forage particle size and

slowly fermentable starch and a diet with short forage particle size and rapidly fermentable

starch. Cows were allowed to adapt to this 2-diet feeding scheme until the intake ratio of LC:SF

remained constant. Cows were then given a rumen challenge to induce a bout of SARA. Results

of this study show that dairy cattle can significantly alter their TMR preference, when faced with

SARA, to a diet with increased peNDF and slower starch fermentability that may help alleviate

their acidotic condition. In addition, this study showed that dairy cattle with this severity of a

single bout of SARA can fully recover within 72 h after onset. Since cattle were only fed once per

d and only subjected to one bout of SARA in this study, further research is warranted to evaluate

effects of multiple SARA bouts and different feeding times and feeding systems on diet selection.

Acknowledgements

Sincere appreciation is extended to Growmark FS, LLC (Sangerfield, NY) for generously

allowing the use of their modified forage harvester for the duration of this trial. This research was

126

supported in part by agricultural research funds administered by The Pennsylvania Department of

Agriculture.

References





Britton, R. A., and R. A. Stock. 1986. Acidosis, rate of starch digestion and intake. Pages 125–

137 in Proceedings of the Feed Intake Symposium, Oklahoma Agricultural Experiment Station,

Norman, OK.

Castle, M. E., W. C. Retter, and J. N. Watson. 1979. Silage and milk production: comparisons

between grass silage of three different chop lengths. Grass and Forage Science. 34:293–301.

DeVries, T. J., F. Dohme, and K. A. Beauchemin. 2008. Repeated ruminal acidosis challenges in

lactating dairy cows at high and low risk for developing acidosis: Feed sorting. J. Dairy Sci.

91:3958–3967.

Dohme, F., T. J. DeVries, and K. A. Beauchemin. 2008. Repeated ruminal acidosis challenges in

lactating dairy cows at high and low risk for developing acidosis: Ruminal pH. J. Dairy Sci.

91:3554–3567.

Huntington, G. B. 1993. Nutritional problems related to the gastro-intestinal tract. Pages 474–480

in The Ruminant Animal: Digestive Physiology and Nutrition. D. C. Church, ed. Waveland Press,

Inc., Long Grove, IL.



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.






Sci. 86:3343–3353.

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Krause, K. M., and G. R. Oetzel. 2006. Understanding and preventing subacute ruminal acidosis

in dairy herds: A review. Anim. Feed Sci. Technol. 126:215–236.







93:4791–4803.




Oetzel, G. R., K. V. Nordlund, and E. F. Garrett. 1999. Effect of ruminal pH and stage of

lactation on ruminal lactate concentration in dairy cows. J. Dairy Sci. 82 (Suppl. 1):38. (Abstr.)

Phy, T. S., and F. D. Provenza. 1998a. Eating barley too frequently or in excess decreases lambs'

preference for barley but sodium bicarbonate and lasalocid attenuate the response. J. Anim. Sci.

76:1578–1583.

Phy, T. S., and F. D. Provenza. 1998b. Sheep fed grain prefer foods and solutions that attenuate

acidosis. J. Anim. Sci. 76:954–960.


New York, NY.

Stone, W. C. 1999. The effect of subclinical rumen acidosis on milk components. Pages 40–46 in

Proceedings of the Cornell Nutrition Conference for Feed Manufacturers, Cornell University,

Ithaca, NY.

Stone, W. C. 2004. Nutritional approaches to minimize subacute ruminal acidosis and laminitis in

dairy cattle. J. Dairy Sci. 87:E13–E26.



74:3583–3597.



Zanton, G. I., and A. J. Heinrichs. 2009. Digestion and nitrogen utilization in dairy heifers limit-

fed a low or high forage ration at four levels of nitrogen intake. J. Dairy Sci. 92:2078–2094.

128

Table 5-1. Chemical compositions and particle size distributions determined with the ASABE

particle separator for alfalfa haylage and long and short corn silage

Alfalfa Corn Silage

Item Haylage Long Short SEM1 P-value

1


26.9 mm 1.9 12.6 0.9 0.65 < 0.01

18.0 mm 6.0 31.3 13.2 0.54 < 0.01

8.98 mm 26.3 28.7 28.7 1.02 1.00

5.61 mm 24.0 13.7 21.7 1.95 0.04

1.65 mm 31.8 11.9 26.8 1.32 < 0.01

Pan 10.0 1.8 8.8 1.83 0.03


DM 46.0 39.2 40.7 0.92 < 0.01

CP 18.5 8.6 8.5 0.06 0.15

ADF 36.0 20.7 19.1 0.47 0.04

NDF 46.4 34.9 32.6 0.67 0.05

peNDF8.03

15.9 25.4 14.0 0.66 < 0.01

peNDF1.184

41.8 34.3 29.7 0.97 < 0.01

Ash 10.4 3.1 3.2 0.11 0.49

NFC 23.3 50.2 52.8 0.72 0.05

Starch 0.77 39.7 41.4 1.61 0.49

NEL, Mcal/kg 1.36 1.73 1.78 0.01 0.05 1Associated with corn silages.







129


determined via in situ incubation for dry cracked corn, dry fine ground corn, and ground wheat

Ground

Wheat

Ground Corn

Item Cracked Fine SEM1 P-value

1


6.70 mm 0.0 2.2 0.0 0.18 < 0.01

4.75 mm 0.0 10.0 0.1 1.38 < 0.01

3.35 mm 0.3 29.7 0.3 1.51 < 0.01

2.36 mm 9.3 25.5 5.2 1.29 < 0.01

1.70 mm 25.9 14.2 16.2 1.22 0.05

1.18 mm 22.9 6.5 17.7 0.54 < 0.01

0.85 mm 12.7 3.2 13.3 0.29 < 0.01

0.60 mm 7.9 2.7 11.0 0.33 < 0.01

0.425 mm 5.5 2.0 11.4 0.80 < 0.01

0.15 mm 8.9 3.0 22.3 0.78 < 0.01

Pan 6.7 1.0 2.5 0.22 0.01


DM 86.4 90.3 88.3 0.36 0.03

CP 11.7 8.8 9.4 0.16 0.04

ADF 4.4 5.1 4.4 0.38 0.27

NDF 14.1 11.3 11.1 0.52 0.77

Ash 1.5 1.4 1.6 0.12 0.31

NFC 71.0 75.1 74.7 0.94 0.75

NEL, Mcal/kg 2.27 1.96 1.96 0.00 1.00

Rate of disappearance2, %

0.5 h – 17.7 36.1 2.96 < 0.01

1.0 h – 18.4 38.3 2.96 < 0.01

2.0 h – 19.0 42.0 2.96 < 0.01

4.0 h – 27.6 48.8 2.96 < 0.01

6.0 h – 34.2 58.2 2.96 < 0.01

8.0 h – 41.9 65.8 2.96 < 0.01

12.0 h – 56.1 76.9 2.96 < 0.01

16.0 h – 59.1 83.8 2.96 < 0.01

24.0 h – 76.4 92.0 2.96 < 0.01

48.0 h – 90.7 96.2 2.96 0.15 1Associated with ground corn.

2Nylon bags were incubated in quadruplicate in the rumen of 2 lactating cows (each cow

incubated 2 bags of each sample for each time point).

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Table 5-3. Chemical composition and particle size distributions determined with the ASABE

particle separator for TMR containing long forage and slowly fermentable starch (LC) or short

forage and rapidly fermentable starch (SF)

Item LC SF SEM P-value


26.9 mm 3.2 0.7 0.10 < 0.01

18.0 mm 15.0 4.3 0.26 < 0.01

8.98 mm 19.9 17.3 0.32 < 0.01

5.61 mm 22.0 18.0 1.08 0.04

1.65 mm 24.2 32.6 0.64 < 0.01

Pan 15.6 27.2 1.53 0.01


DM, % 50.1 50.7 0.49 0.26

CP 16.4 16.1 0.36 0.54

ADF 20.8 19.1 0.42 0.04

NDF 32.8 30.7 0.64 0.06

Forage NDF 21.8 21.1 0.42 0.19

peNDF8.02 13.8 6.5 0.88 < 0.01

peNDF1.183 27.7 21.3 0.76 < 0.01

Ash 6.6 6.1 0.18 0.03

NFC 41.0 44.2 0.32 < 0.01

Starch 28.9 31.3 0.89 0.04

NEL, Mcal/kg 1.68 1.70 0.01 0.10 1Approximate equivalency to Penn State particle separator: top sieve (26.9 + 18.0 mm), middle






131

Table 5-4. Effect of rumen challenge while offering 2 free choice TMR containing long forage

and slowly fermentable starch or short forage and rapidly fermentable starch on rumen pH and

VFA for baseline and rumen challenge d

Item Baseline Challenge SEM P-value

Rumen pH

Weighted average1 6.02 5.77 0.04 < 0.01

Minimum 5.59 5.28 0.06 < 0.01

Maximum 6.69 6.95 0.10 < 0.01

AUC2 < 5.8, pH units × min/d 50.85 254.83 24.31 < 0.01

AUC < 5.5, pH units × min/d 0.11 37.22 7.81 < 0.01

VFA weighted average, µM/mL

Acetate 81.78 86.50 1.42 < 0.01

Propionate 38.39 36.97 3.24 0.39

Butyrate 15.00 17.90 0.57 < 0.01

Valerate 2.69 2.90 0.38 0.01

Isovalerate 2.25 2.30 0.11 0.53

Isobutyrate 1.05 1.20 0.04 0.01

Acetate: Propionate 2.29 2.58 0.25 0.03 1Weighted averages determined by calculating



2AUC = Area under curve (area below pH threshold (5.5 or 5.8) and above pH profiles of cows.

132

Table 5-5. Effect of rumen challenge while offering 2 free choice TMR containing long forage

and slowly fermentable starch (LC) or short forage and rapidly fermentable starch (SF) on DMI

and refusals for baseline, feed restriction, rumen challenge, and recovery d

Day

DMI, kg Refusal, %

LC SF LC SF

Baseline 5.3bc

25.4a 31.8

c 9.6

d

Feed restriction 4.0c 19.3

c 4.8

d 0.5

e

Rumen challenge 12.5a 20.2

c 63.0

a 45.5

a

Recovery 1 7.8b 21.6

bc 42.7

bc 27.1

b

Recovery 2 5.0c 23.5

ab 49.5

b 18.4

c

SEM 1.4 2.1 5.7 3.1

P-value < 0.01 < 0.01 < 0.01 < 0.01 a–e

Means within a column with different superscripts differ (P ≤ 0.05).

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Table 5-6. Effect of offering 2 free choice TMR containing long forage and slowly fermentable

starch (LC) or short forage and rapidly fermentable starch (SF) on mean selection indices1 of

baseline, rumen challenge, and recovery d (4 d)

Screen, mm2 LC SF SEM P-value

26.9 0.78 0.983 0.05 < 0.01

18.0 0.85 0.90 0.03 0.21

8.98 0.91 0.96 0.01 < 0.01

5.61 1.03 1.003 0.01 0.02

1.65 1.06 1.013 0.01 < 0.01

Pan 1.15 1.023 0.02 < 0.01





3Sorting index is not significantly different from 1.00 based on a 95% confidence limit.

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Figure 5-1. Effect of rumen challenge while offering 2 free choice TMR containing long forage

and slowly fermentable starch or short forage and rapidly fermentable starch on rumen pH over

time for baseline, feed restriction, rumen challenge, and recovery d.

135


and slowly fermentable starch or short forage and rapidly fermentable starch on preference for

TMR with long forage (expressed as a percentage of total daily intake).

a–cMeans with different superscripts differ (P ≤ 0.05).

136


and slowly fermentable starch or short forage and rapidly fermentable starch on cumulative

percent of diet daily intake at various times after feeding for baseline and rumen challenge d.

a–bMeans within a time point with different superscripts differ (P ≤ 0.05).

Chapter 6

Conclusions

The first study of this dissertation concluded that replacing alfalfa silage with dry

chopped alfalfa hay at levels of 5, 10, 20, and 40% of forage DM had only minimal influence on

sorting behavior in lactating dairy cows and only during the first 4 h after feeding. Significant

sorting occurred early in the d but by 24 h after feeding cattle had consumed rations that were

similar in composition, when measured via sorting indices or actual particle fraction

consumption, to their offered TMR indicating that cows were changing their sorting behavior

throughout the d. Dry chopped alfalfa hay was included at levels up to 23.5% of ration DM

without negative effects on milk production and rumen fermentation. These results suggest that

refusal particle size distribution is not a good measure to determine if sorting is a problem in

dairy cows. This study also determined that the Penn State and Ro-Tap particle separators

produce different results when separating the same samples; indicating that data obtained from

these 2 methods of particle separation should not be used interchangeably.

The second study investigated the interaction of forage particle size (FPS) and ruminally

fermentable carbohydrates (RFC). Four diets were fed that varied in corn silage particle size and

corn grain grind size. It was determined that RFC increased milk yield and milk protein content

while decreasing milk fat content. Ruminal pH, NH3, lactate, VFA, and volume were not affected

by either FPS or RFC. Changes in starch, NDF, and particle size composition of the refusals

throughout the d and selection indices indicated that ration sorting was occurring and diets

containing long FPS and high RFC were sorted to a greater degree than diets containing short

FPS but no interaction between FPS and RFC was present. There was, however, an interaction

between FPS and RFC for DMI. It was shown that DMI decreased with increasing FPS when the

138

diet included low RFC and did not change when the diet included high RFC, and DMI increased

with RFC for the long diets and did not change with RFC on the short diets.

The final study of this dissertation fed lactating cows 2 diets simultaneously and allowed

ad libitum consumption of both rations. After cattle were adapted to this feeding system the

effects of inducing subacute ruminal acidosis (SARA) with a rumen challenge on diet preference

were studied. After adaptation, cattle consumed 18.1% of their total daily intake as the long

forage particle size and slowly fermentable starch diet versus a short forage particle size and

rapidly fermentable starch diet. When faced with a bout of SARA, cows drastically increased

their consumption of the long forage particle size and slowly fermentable starch diet to 38.3% of

total daily intake, possibly to help attenuate the SARA. These cattle were fully recovered within

72 h after the initial rumen challenge.

This dissertation concludes the following: that ration sorting in lactating dairy cows,

despite the general consensus by the majority of dairy cattle nutritionists and researchers, is not of

major concern because negative effects seldom occur; that the critical particle size for rumen

escape is larger than the previously held 1.18 mm and it is probably close to 6.7 mm; that RFC

have a greater influence than FPS on DMI, ruminal fermentation and milk yield and components;

and that dairy cattle can alter their diet preference during a bout of subacute ruminal acidosis to

consume more physically effective fiber and less rapidly fermentable starch, possibly to attenuate

the acidosis.

A major finding of this dissertation is that the critical particle size for rumen escape is

much greater than 1.18 mm and is likely close to 6.7 mm. The implication of a critical particle

size that is much greater than previously thought is that the method for calculating physically

effective NDF (peNDF) may have to be revised. Particles that are 1.18 mm in length may not be

as effective at stimulating chewing activity in lactating dairy cows as previously thought and this

may a reason for the many inconsistencies in the literature about the effects of peNDF. The use of

139

the 8.0-mm sieve of the Penn State particle separator to calculate peNDF therefore may be a more

accurate method than using the 1.18-mm sieve of the same separator. Further research should be

conducted analyzing alterations in the proportions of forages and diets greater than 6.7 mm on

chewing activity, ruminal fermentation, and milk composition.

Another major finding of this dissertation is that lactating dairy cattle can alter their diet

selection when faced with a bout of SARA. These results seem to lead to a lot of exciting

research. The mechanism or mechanisms that allow cows to quickly recognize variation in their

ruminal environment and the need for changes in feed choice and then correctly identify the

feedstuff that would best attenuate the ruminal condition is an area that holds great potential for

increasing our knowledge of ruminants. The more practical aspects of this behavior also hold

much potential for further research. How successfully cows can correct imbalances in their

ruminal environment through diet selection needs to be determined. If cows can use diet selection

or feed sorting to bring about improvements in ruminal health then practical applications should

be researched to determine if this behavior can be used commercially to the advantage of dairy

farmers. Perhaps feeding free choice long hay could decrease the incidence of SARA on farms.

Feeding 2 TMR that vary in peNDF and monitoring intake of each may provide a sign of

increased susceptibility to SARA if intake of the high peNDF ration increases.

Several results of this dissertation point to the possibility of cows altering their feeding

behavior to seemingly improve their ruminal environment or its consistency. The feeding

behavior of dairy cattle and its many interactions with feedstuffs, ration compositions, and

feeding management have seen very little research. As the physiological systems of the dairy cow

are continued to be pushed to their limits with ever increasing energy intake and milk production;

how and why cows alter their feed sorting, diet selection, eating, and ruminating behavior may

hold the key to increasing animal performance and health in the coming decades.

Appendix A

Technical Note: Evaluation of Procedures for Analyzing Ration Sorting and

Rumen Digesta Particle Size in Dairy Cows

Journal of Dairy Science Vol. 93 No. 8, 3784-3788, 2010

D. D. Maulfair and A. J. Heinrichs

Abstract

Collecting total mixed ration (TMR) samples throughout the d to measure sorting activity

of dairy cows may cause changes to sorting behavior of cows or may make it more difficult to

elucidate effects of sorting on TMR particle size distributions. Also, forage particle size research

commonly includes analysis of the solid portion of rumen digesta for particle size distribution

after digesta has been squeezed through several layers of cheesecloth. Therefore, the first

objective of this experiment was to determine if collecting TMR samples throughout the d

affected sorting behavior of cows and resulted in a different particle size distribution than when

TMR was not artificially altered during the d. The second objective of this experiment was to

determine if squeezing rumen digesta samples through cheesecloth changed particle size

distribution when analyzed by a wet sieving technique. It was determined that small, significant

differences existed in particle size distribution between the 2 sampling methods of TMR for

sorting behavior. These differences were more likely to occur at time points later in the d. This

resulted in small changes in sorting indices calculated from these data; sampling and mixing

TMR throughout the d reduced the degree of sorting. Squeezing rumen digesta through 4 layers

141

of cheesecloth had no effect on particle size distribution of particles > 0.15 mm but reduced the

amount of rumen fluid-associated dry matter contained in the sample.

Key words: dairy cow, feeding behavior, particle size, sorting

When collecting TMR samples to analyze ration sorting it is necessary to thoroughly mix

the remaining TMR in order to collect representative samples. Several studies (Hosseinkhani et

al., 2008; Kononoff et al., 2003; Leonardi and Armentano, 2003) have sampled the remaining

TMR several times throughout the d. This method may lead to incorrect conclusions about sorting

because any sorting that had occurred up to sampling time would be nullified during sample

collection. It has not been determined if mixing TMR to take samples affects further ration

sorting behavior of dairy cows. Therefore, the first objective of this experiment was to determine

if collecting TMR samples throughout the d affected sorting behavior of cows and resulted in a

different particle size distribution than when TMR was not artificially altered during the d. When

taking rumen samples from fistulated dairy cows during feeding studies, it is common procedure

to squeeze rumen digesta through several layers of cheesecloth to obtain the fluid fraction for

analysis (Kononoff et al., 2003). The solid fraction retained on the cheesecloth sometimes is used

for particle size distribution analysis via wet sieving. However, it is not known if squeezing

through cheesecloth affects particle size distribution of the solid fraction. Therefore, the second

objective of this experiment was to determine if squeezing rumen digesta samples through

cheesecloth changed particle size distribution when analyzed by a wet sieving technique.

Data for this paper were collected during the final period of a feeding trial designed to

study the effects of varying forage particle size on ration sorting in lactating dairy cows (Maulfair

et al., 2010). Cows were cared for and maintained according to a procedure approved by The

Pennsylvania State University Institutional Animal Care and Use Committee. Eight lactating,

multiparous, Holstein cows averaging 90 ± 32 DIM, weighing 642 ± 82 kg, and with parity of

2.25 ± 0.46 (mean ± SD) were randomly assigned to replicated 4 × 4 Latin squares; 1 square of

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cows was rumen fistulated. The periods were 21 d in length, with a 13-d adaptation period

followed by an 8-d collection period. During each of the 4 periods, cows were fed 1 of 4 rations

that contained identical feed ingredients and proportions. Ration ingredients and their percentage

of ration DM were: corn silage (29.4), alfalfa haylage (17.6), grass hay (11.8), ground corn

(22.9), roasted soybeans (6.7), canola meal (5.7), heat-treated soybean meal (3.2),

mineral/vitamin mix (2.4), and salt (0.3). Rations contained 15.9% CP, 34.0% NDF, and 1.65

Mcal/kg NEL and varied only in chop length of the dry grass hay included in the ration. Particle

sizes (geometric mean ± SD, mm) of the rations were: short (4.46 ± 3.02), medium (5.10 ± 3.56),

long (5.32 ± 3.92), and extra long (5.84 ± 4.39) as determined by ASABE (2007). All diets were

mixed separately using an I. H. Rissler model 1050 TMR mixer (E. Rissler Mfg. LLC, New

Enterprise, PA). Animals were housed in individual stalls in a mechanically ventilated barn,

milked twice per d at 0700 and 1900 h, and fed once per d at approximately 0730 h for ad libitum

consumption and a 10% refusal rate to allow for maximum opportunity to sort the ration. Feed

was pushed up but not mixed at 1230, 1730, and 2400 h. All rations were balanced to meet or

exceed NRC (2001) requirements, and water was available ad libitum.

To examine whether mixing and sampling remaining feed affected sorting behavior of

dairy cows, TMR samples were taken on d 20 and 21 at 0, 2, 4, 8, 12, 16, and 24 h after feeding

(Mixed). In addition, samples were taken at 0 and 8 h (d 19 and 22); 0 and 16 h (d 23 and 24);

and 0 and 24 h (d 25 and 26) after feeding (Unmixed). During the study, average DMI was 25.89

± 0.88 kg/d and refusals averaged 12.21 ± 0.70% of DMI; DMI and refusals were not different

between treatments (P > 0.24 and 0.22, respectively). At each sampling point TMR was removed

from the feed bunk, weighed, thoroughly mixed, sampled, and then returned to the cow, which is

standard procedure in feeding studies. All samples were sieved in the American Society of

Agriculture Engineers forage particle separator, which can determine 6 particle fractions (> 26.9,

> 18.0, > 8.98, > 5.61, > 1.65, and < 1.65 mm; screen diagonal; (ASABE, 2007). Geometric mean

143

particle length (Xgm) and standard deviation of particle length (Sgm) were calculated according to

the ASABE (2007) procedure. Since > 1% of material was retained on the top screen, 3 samples

of each diet were randomly selected, and all particles retained on the top screen were measured

manually (with a ruler) before drying. Measured mean particle sizes for the top screen were:

118.8 ± 3.6, 105.7 ± 9.1, 84.5 ± 2.6, 74.8 ± 6.6 (mean ± SD, mm) for the extra long, long,

medium, and short diets, respectively. Whole samples were then placed in a forced air oven at

55°C for 48 h to determine DM content. Sorting indices based on refusals were calculated for

particle size fractions at 8, 16, and 24 h after feeding. Actual intake of each particle fraction was

divided by expected intake of each particle fraction (Leonardi and Armentano, 2003). Values > 1

indicate selective consumption and values < 1 indicate selective refusal of the DM retained on an

individual sieve. Additionally, Xgm sorting indices were calculated for the same time points by

dividing the Xgm of TMR consumed up to each time point by Xgm at time 0. Values > 1 indicate

cows were consuming rations with longer particle size and values < 1 indicate cows were

consuming rations with shorter particle size than the diets fed. Statistical analysis was conducted

using the MIXED procedure of SAS (2006). The model included sampling method, time, and diet

as fixed effects, cow as a random effect, and the interaction of sampling method and time. All


(1997). All data are presented as least squares means and sampling method effects are considered

significant when P < 0.05 and a trend when P < 0.10.

It was found that TMR did differ slightly when particle distribution was expressed by

individual screens (Table A-1). There were some small, significant differences in several of the

particle fractions, and sampling method generated more significant and larger differences as the

time after feeding increased. In general, where there were significant differences, the Unmixed

sampling protocol had a longer particle size than Mixed. When particle distribution of the

unconsumed TMR was expressed as geometric mean particle length (Table A-2) there were no

144

differences for 0 and 8 h. However, there was a trend at 16 h for Unmixed to be longer than

Mixed, and at 24 h samples of remaining TMR collected with the Unmixed protocol had

significantly longer particles than the Mixed sampling scheme. In Table A-2 the Xgm and Sgm

values are from the unconsumed TMR or what is left in the feed bunk at that time point. They are

the same data that are found in Table A-1 converted from individual screens to an average

particle size. There was a significant linear contrast for the sampling procedure by time

interaction, indicating that the Xgm of the uneaten diet increased to a greater extent for Unmixed

than for Mixed with increasing time. When sorting index was calculated using all 6 particle

fractions, virtually no significant differences in sorting were found between sampling methods at

any of the time points (data not shown). Using the sorting index calculated with Xgm (Table A-2),

cows were eating shorter rations than they were fed when analyzed using both sampling

procedures at all time points. There were no differences at 8 and 16 h, but at 24 h the Unmixed

sampling procedure resulted in a lower selection index than the Mixed sampling procedure.

Again, the linear contrast for the sampling procedure by time interaction was significant because

sorting index increased for the Mixed and decreased for the Unmixed sampling method over time.

Diet effects on sorting behavior were found to be significant and are discussed in Maulfair et al.

(2010). Based on these results, mixing TMR several times throughout the d to obtain a

representative sample caused the particle size of the unconsumed TMR to be smaller and biased

conclusions about sorting behavior toward less sorting than what actually occurred.

To determine if squeezing rumen digesta through cheesecloth affected particle size

distribution obtained via wet sieving, rumen samples were taken on d 15 at 0.0, 1.5, 3.5, 5.5, 8.5,

11.5, 14.5, 18.0, 21.5, and 24.5 h after feeding. Samples were taken from 5 rumen locations

(dorsal, ventral, cranial, caudal, and medial areas), mixed thoroughly, and then separated into 2

equal parts. One part was squeezed though 4 layers of cheesecloth, and the solid fraction retained

on the cheesecloth was stored in a -20°C freezer. The second part was stored the same way, but

145

without the initial squeezing. To determine particle size distribution the 2 samples were then wet

sieved using a procedure modified from Beauchemin (1997). Sub-samples (approximately 30 g)

were placed on a series of stacked sieves (sizes 0.15, 0.6, 1.18, 3.35, 6.7, 9.5 mm; VWR,

Arlington Heights, IL) contained in a Retsch AS 200 Control sieve shaker (Retsch, Haan,

Germany) and sieved in duplicate. The samples were sieved for 10 min at 2.5 mm amplitude with

cold water flow rate at approximately 1.5 to 2.0 L/min to ensure particles were separated

thoroughly. Contents retained on the sieves were rinsed with cold water into a funnel with rumen

in situ bags (5 × 10 cm, 53 μm pore size; ANKOM, Macedon, NY) attached to the stem to collect

the sample. Bags were then dried in a forced air oven at 55°C for 24 h and weighed to determine

DM retained on each sieve. A portion of each sample was also dried at 55°C for 24 h in a forced

air oven without sieving to determine the DM content of the original sample. The rumen fluid-

associated fraction of the sample was calculated as the DM lost during the sieving and drying

process. Statistical analysis was conducted using the MIXED procedure of SAS (2006). The

model included sampling method, time, and diet as fixed effects and cow as a random effect. All


(1997). All data are presented as least squares means, and treatment effects are considered

significant when P < 0.05 and a trend when P < 0.10.

There were no significant differences found between the 2 sampling techniques for any of

the fractions retained on screens (Table A-3). There was significantly more (46.22 vs. 34.87%)

rumen fluid-associated DM per unit of solid-associated DM for samples that were not squeezed

through cheesecloth. If the proportion of rumen fluid-associated DM is of importance to the

objective of the experiment, then rumen digesta should not be squeezed before wet sieving.

However, if only particles retained on screens are of importance, using rumen digesta after

squeezing will have no effect on results.

146

In conclusion, mixing TMR to take representative samples several times throughout the d

had a small effect of decreasing the particle size of uneaten feed, which may lead to the

conclusion that cows are sorting their ration to a lesser extent (when cows are sorting against

longer particles and for shorter particles) than if sorting samples were taken only at the end of the

sampling interval. Also, squeezing rumen digesta through 4 layers of cheesecloth had no effect on

particle fractions > 0.15 mm, but it reduced the proportion of rumen fluid-associated DM per unit

of solid-associated DM.

Acknowledgements

Sincere appreciation is extended to Geoff Zanton (Penn State, University Park, PA) for

statistical advice and support. This research was supported in part by agricultural research funds

administered by The Pennsylvania Department of Agriculture.

References

American Society of Agricultural and Biological Engineers. 2007. Method of Determining and

Expressing Particle Size of Chopped Forage Materials by Screening. ANSI/ASAE S242.1:663–

665.

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(2):324–333.

Hosseinkhani, A., T. J. DeVries, K. L. Proudfoot, R. Valizadeh, D. M. Veira, and M. A. G. von

Keyserlingk. 2008. The Effects of Feed Bunk Competition on the Feed Sorting Behavior of

Close-Up Dry Cows. J. Dairy Sci. 91(3):1115–1121.

Kenward, M. G., and J. H. Roger. 1997. Small Sample Inference for Fixed Effects from

Restricted Maximum Likelihood. Biometrics 53(3):983–997.

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(10):3343–3353.

147

Leonardi, C., and L. E. Armentano. 2003. Effect of Quantity, Quality, and Length of Alfalfa Hay

on Selective Consumption by Dairy Cows. J. Dairy Sci. 86(2):557–564.

Maulfair, D. D., G. I. Zanton, M. Fustini, and A. J. Heinrichs. 2010. Effect of Feed Sorting on

Chewing Behavior, Production, and Rumen Fermentation in Lactating Dairy Cows. J. Dairy Sci.

93:4791–4803.



SAS Institute. 2006. SAS User's Guide: Statistics. Version 9.1.3. in SAS Inst. Inc., Cary, NC.

148

Table A-1. Percentage of uneaten TMR particles (DM basis) retained on sieves at 8-h intervals

after feeding when sampled by 2 different procedures

Item1 Mixed

2 Unmixed

3 SE P-value

Hour 0

26.9 mm 6.72 5.35 1.29 0.42

18.0 mm 4.17 3.80 0.26 0.17

8.98 mm 20.3 20.4 0.43 0.89

5.61 mm 21.2 20.6 0.33 0.27

1.65 mm 23.7 24.9 0.65 0.10

Pan 23.9 24.9 0.76 0.24

Hour 8

26.9 mm 8.15 5.95 1.54 0.28

18.0 mm 4.71 4.17 0.29 0.10

8.98 mm 21.9 21.3 0.53 0.44

5.61 mm 20.9 21.2 0.41 0.58

1.65 mm 22.7 23.3 0.74 0.46

Pan 21.7 24.0 0.87 0.03

Hour 16

26.9 mm 10.9 11.6 1.54 0.72

18.0 mm 4.65 6.38 0.29 <0.01

8.98 mm 22.1 26.6 0.53 <0.01

5.61 mm 21.4 20.6 0.41 0.17

1.65 mm 21.1 21.1 0.74 0.99

Pan 19.9 13.7 0.87 <0.01

Hour 24

26.9 mm 12.0 17.1 1.54 0.01

18.0 mm 5.70 5.19 0.29 0.12

8.98 mm 24.2 22.0 0.53 <0.01

5.61 mm 21.3 19.8 0.41 <0.01

1.65 mm 19.6 19.0 0.74 0.55

Pan 17.2 16.9 0.87 0.72 1Pore size of screens.

2TMR was mixed, sampled, and returned to cow at 2, 4, 8, 12, 16, and 24 h after feeding.

3TMR was not mixed until sample collections at the respective time point.

149

Table A-2. Geometric mean particle length of uneaten TMR and sorting index of the consumed

diet1 obtained with 2 different sampling procedures

Item Mixed2 Unmixed

3 SE P-value

Hour 0

Xgm4, mm 4.87 4.58 0.57 0.71

Sgm5, mm 3.72 3.59 0.07 0.13

Hour 8

Xgm, mm 5.63 4.90 0.70 0.45

Sgm, mm 3.82 3.65 0.08 0.12

Index6 0.89 0.95 0.03 0.17

Hour 16

Xgm, mm 6.72 8.40 0.70 0.08

Sgm, mm 3.95 3.60 0.08 <0.01

Index 0.92 0.95 0.02 0.17

Hour 24

Xgm, mm 7.84 10.1 0.70 0.02

Sgm, mm 3.85 4.04 0.08 0.07

Index 0.93 0.89 0.01 <0.01 1Xgm treatment×time interaction linear contrast P = 0.01, quadratic contrast P = 0.58; index

treatment×time interaction linear contrast P = 0.03, quadratic contrast P = 0.52.

2TMR was mixed, sampled, and returned to cow at 2, 4, 8, 12, 16, and 24 h after feeding.

3TMR was not mixed until sample collections at the respective time point.



6Index = (Xgm consumed up to timei) / (Xgm at time 0).

150

Table A-3. Percentage of rumen digesta particles (DM basis) retained on sieves after wet sieving

when digesta samples were prepared with or without being squeezed through cheesecloth

Item1 Squeezed Non-Squeezed SE P-value

9.5 mm 17.3 16.4 0.96 0.58

6.5 mm 6.56 6.63 0.24 0.84

3.35 mm 18.4 18.7 0.62 0.76

1.18 mm 20.4 20.9 0.27 0.21

0.6 mm 12.9 12.8 0.18 0.63

0.15 mm 24.5 24.6 0.38 0.90

Fluid-assoc/ Solid-assoc2 34.9 46.2 1.04 <0.01

1Pore size of screens.

2Rumen fluid-associated DM per unit of solid-associated DM.

Appendix B

Effect of Feed Sorting on Chewing Behavior, Production, and Rumen

Fermentation in Lactating Dairy Cows


D. D. Maulfair, G. I. Zanton, M. Fustini, and A. J. Heinrichs

Abstract

Ration sorting is thought to allow cows to effectively eat different rations throughout the

d causing fluctuations in rumen fermentation patterns that can be detrimental to production and

possibly animal health. The objective of this experiment was to study the effects of varying total

mixed ration (TMR) particle size on sorting behavior of lactating dairy cows and to evaluate

effects on chewing behavior, milk yield, milk components, and rumen fermentation. Eight

multiparous, Holstein cows (90 ± 32 d in milk; 4 rumen cannulated) were randomly assigned to

replicated 4 × 4 Latin squares. Cows were fed diets that varied in the chop length of dry grass

hay. The diet consisted of 29.4% corn silage, 22.9% ground corn, 17.6% alfalfa haylage, and

11.8% dry grass hay on a dry matter basis. The percentage of hay particles > 26.9 mm was 4.2,

34.1, 60.4, and 77.6% for the short (S), medium (M), long (L), and extra long (XL) hays

respectively. This resulted in the TMR of each diet having 1.5 (S), 6.5 (M), 8.6 (L), and 11.7%

(XL) of particles > 26.9 mm. Daily ruminating min/kg dry matter intake (DMI; 19.3, 19.2, 22.4,

and 21.3; S, M, L, and XL) and eating min/kg DMI (13.9, 14.6, 17.2, and 16.1; S, M, L, and XL)

increased linearly as TMR particle size increased. Daily DMI decreased linearly as TMR particle

size increased and was 26.9 (S), 27.0 (M), 24.1 (L), and 25.1 (XL) kg/d. No differences were

152

found in rumen volatile fatty acids and NH3 and there were only slight changes in rumen pH.

Milk production and milk components were also similar among diets. Despite large differences in

particle size among these diets and certain chewing and ruminating differences, there were no

changes in rumen fermentation, milk production, or milk components found in this study.

Key Words: chewing, particle size, rumination, sorting

Introduction

The NRC (2001) recommends a minimum NDF level of 25% of DM and a forage NDF

level of 19% of DM for lactating dairy cows. However, the NRC states that these values are

based on cows fed: a TMR, alfalfa or corn silage as the predominant forage, forage with adequate

particle size, and dry ground corn as the predominant starch source. These recommendations are

therefore limited to rather specific conditions due to the limited data available and they do not

define adequate particle size in a measurable manner. Fiber with adequate length is thought to

increase chewing in cattle, which increases salivary secretion of NaHCO3 and buffers the rumen

digesta (Nocek, 1997; Allen, 1997; Krause et al., 2002b). Beauchemin et al. (2008) showed that

rate (g/min) of salivation stayed constant during eating; however, changes in the rate of eating

affected the amount of saliva secreted per unit of DMI when cows were fed barley silage, alfalfa

silage, long-stemmed alfalfa hay, or barley straw. Particle size, DM, and NDF content of forages

are factors affecting rate of eating and time spent eating (Bailey, 1961; Beauchemin et al., 2008)

and it has been suggested that time spent chewing is a good measure of a feed’s physical

effectiveness (Balch, 1971; Sudweeks et al., 1981). Physically effective NDF (peNDF), which

combines the physical and chemical properties of a feedstuff, is commonly defined as the NDF

concentration multiplied by the percentage of particles retained on a 1.18 mm sieve (1.65 mm;

153

screen diagonal) and greater. This definition presumes that the cows consume the ration as

formulated.

Dairy cows have been shown to selectively consume or sort their rations when fed a

TMR. Cows generally sort against long particles and for finer particles (Leonardi and Armentano,

2003; Kononoff et al., 2003; DeVries et al., 2007). This is thought to create problems because,

not only are they reducing the particle size of the diet consumed, but also reducing NDF intake

because the longer particles of the TMR contain a higher proportion of NDF than the rest of the

ration (Leonardi and Armentano, 2003). Feeding longer alfalfa hay versus chopped alfalfa hay

increased sorting of rations, but intake of long particles still increased when fed the long alfalfa

hay because of the higher concentration in the diet (Leonardi and Armentano, 2003). A potential

problem for dealing with sorting on dairy farms is the fact that variability of sorting among cows

can be very substantial, especially with the longest fraction (Leonardi and Armentano, 2003;

Leonardi et al., 2005a).

Therefore, the objective of this experiment was to study the effects of varying TMR

particle size on sorting behavior and to evaluate its effects on chewing behavior, milk yield, milk

components, and rumen fermentation in lactating dairy cows.





Eight lactating, multiparous, Holstein cows averaging 90 ± 32 DIM, weighing 642 ± 82 kg, and

with parity of 2.25 ± 0.46 (mean ± SD) were randomly assigned to replicated 4 × 4 Latin squares;

154

1 square contained rumen cannulated cows. The periods were 21 d in length, with a 13-d

adaptation period followed by an 8-d collection period.

During each of the 4 periods, cows were fed 1 of 4 rations that contained identical feed

ingredients and proportions but varied in the length of the dry grass hay included in the ration.

Ingredients and their percentage of ration DM were: corn silage (29.4), ground corn (22.9), alfalfa

haylage (17.6), grass hay (11.8), roasted soybeans (6.7), canola meal (5.7), heat-treated soybean

meal (3.2), mineral/vitamin mix1 (2.4), and salt (0.3). Grass hay inclusion level (20% of forage

DM) was chosen based on previous research that showed it allowed for rations to be properly

balanced while still creating adequate variations in particle size distributions between rations.

Grass hay lengths of short (S), medium (M), long (L), and extra long (XL) were produced using

several bale choppers. The XL and L hay was chopped once and twice, respectively, with a Case

IH model 8610 bale processor (Case IH, Racine, WI). The M and S hay was chopped once and 3

times, respectively, with a Roto Grind model 760 tub grinder (Burrows Enterprises Inc., Greeley,

CO); the S hay was additionally run once through a New Holland model 718 forage harvester

(New Holland Ag, Racine, WI). All diets were mixed separately using an I. H. Rissler model

1050 TMR mixer (E. Rissler Mfg. LLC, New Enterprise, PA).

Animals were housed in individual stalls, milked twice/d at 0700 and 1900 h and fed

once/d at approximately 0730 h for ad libitum consumption and for 10% refusal to allow

extensive opportunity to sort the ration. Feed was pushed up 3 times/d at 1230, 1730, and 2400 h.

All rations were balanced to meet or exceed NRC (2001) requirements and water was available

for ad libitum consumption.

1Mineral and vitamin mix contained 12.2% Ca, 0.41% P, 3.88% Mg, 0.48% K, 0.37% S, 3.54% Na, 5.46% Cl, 222

mg/kg of Fe, 1,379 mg/kg of Zn, 455 mg/kg of Cu, 1,363 mg/kg of Mn, 11.2 mg/kg of Se, 7.33 mg/kg of Co, 18.5 mg/kg of I,

298 KIU/kg of vitamin A, 73.9 KIU/kg of vitamin D, 2,853 IU/kg of vitamin E.

155

Chewing Activity

Eating and ruminating activity were recorded on d 14 through 18 of each period using



2000). These recorders analyze jaw movements of cattle and the software can determine eating or

ruminating chews based on the amplitude and frequency of jaw movements. This procedure has

been validated for use with cows housed in tie-stalls by Kononoff et al. (2002). Chewing was

measured for all cows for two 24-h periods including while cows were being milked.

Inter-meal intervals were separated from intra-meal intervals by analysis of the 2 d of

chewing data (minimum interval ≥ 4 s) by a modification of the methodology reported by

Tolkamp et al. (1998) and Yeates et al. (2001). Initial analysis of the probability density functions

(PDF) of all data revealed that inter- and intra-meal histograms were each skewed toward the

point that these histograms crossed. Yeates et al. (2001) reported that a Weibull distribution fitted

to the last population of intervals adequately accounted for the skewness observed in that data set,

whereas skewness in the first population was subdivided into 2 populations of intervals associated

with drinking and non-drinking within-meal intervals. To account for the skewness present in the

current data set, a Weibull distribution was fit to both populations to avoid potential

overparameterization and allow for a skewed representation of the data. As the methodology

employed is based upon the concept of satiety and the treatments administered in the current

experiment were hypothesized to affect meal responses, a meal criterion estimate for the

treatments that could be evaluated for statistical differences would be of value. Frequently, meal

criteria are estimated from pooled or individual cow data; however, a Latin square experimental

design with treatments applied over periods and cows limits the appropriateness of pooling the

data. Since parameter estimates resulting from analysis of the individual cow within period

156

replicates are not estimated from the data without error or correlation, a nonlinear mixed model

methodology was employed to estimate parameters while explicitly accounting for the design of

the experiment within the framework of the nonlinear estimation procedure. The following

Weibull, mixture model was fit to the observed cumulative frequencies (grouped by 0.1 loge

second intervals; CDF) using the nonlinear mixed procedure in SAS (2006) using adaptive

Gaussian quadrature with the Laplace approximation to the marginal likelihood:

where:

and

;

t = loge(interval time);

i= 1,2 = coefficient of the shape parameter of the Weibull distribution for the first or

second population, respectively;

mi= 1,2 = coefficient of the time point of inflection of the CDF and mode (maximum

frequency) of the PDF for the first or second population, respectively;

tc= coefficient of the meal criterion;

jkl = random error component ~N[0,V2], with

V2 =

; that is, the residual standard deviation is weighted by the squared root of the

PDF. Additionally, each coefficient is the sum of the overall mean parameter estimate across

period, treatment, and cow; the fixed effects of period and treatment; and the random effect of

cow:

157

;

where:

Zjkl = generic coefficient of the Weibull mixture model

Z = estimate of coefficient Z across periods, treatments, and cows

ZPj = fixed effect of period j on Z (j = 1, 2, 3, 4) subject to the constraint that ZPj = 0

ZTk = fixed effect of treatment k on Z (k = 1, 2, 3, 4) subject to the constraint that ZTk=0

zcl = random effect of cow l on Z (l=1,…,8) ~N[0, c2].

The model used to calculate meal criteria in this experiment was different from that used

by Yeates et al. (2001) in 3 ways: 1) the CDF was fit with the nonlinear mixed procedure based

on maximum likelihood estimation of the parameters; 2) the scale parameter of the Weibull

distribution was replaced by the expected value parameter of the mode of the PDF or the time

point of inflection of the CDF; 3) the parameter estimate of p (the proportion of intervals in the

first population), was replaced by the expected value parameter for the time when the PDF of the

2 populations intersected (that is, the meal criterion: tc) so that the meal criteria could be

explicitly estimated concurrently with the remaining coefficients of the model. Additionally, the

studentized residuals were observed to be heteroscedastic across time and appeared to vary in

association with the PDF. Thus, the variance was weighted by the PDF, which removed the

heteroscedasticity. The parameter 2 was observed to be a far-from-linear parameter according to

the Hougaard skewness calculation; a substitution of Loge 2 for 2 was found to make the

estimate close-to-linear and improve the estimates of the parameters. The variance-covariance

matrix of the random effects was initially considered to be unstructured; however, the only

covariance parameter estimate that significantly contributed to model fit (by the Bayesian

information criterion) was between m1and1, thus only this covariance parameter was retained

in the final fitting of the model. An overall test of the significance of a treatment effect on each of

158

the parameters of the model was carried out by fitting the full and reduced model and using the

likelihood ratio test. Predicted values for tc were computed using the parameter estimates and

empirical Bayes estimates of the random effects; the number of meals was then calculated as the

sum of intervals exceeding the predicted meal criterion within cow and period. Least squares

means and standard errors of the within treatment parameter estimates were calculated from the

solutions and the variance-covariance matrix for the nonlinear mixed model, respectively. Results

were back-transformed differences between treatments evaluated using the 95% confidence

intervals of the least squares means.

Meal criteria intervals of 5 and 7 min were evaluated in addition to the calculated meal

criteria. The 5-min interval was used for comparison to studies that used manual observation

(Maekawa et al., 2002; Beauchemin et al., 2003; Leonardi et al., 2005b) or video observation

(Bhandari et al., 2008) at 5-min intervals to determine chewing activity. The 7-min meal criterion

was used because it is the default inter-meal interval for the Graze program (Rutter, 2000), and it

is similar to research from several studies (Dado and Allen, 1993; Mooney and Allen, 2007) that

used 7.5 min.

Rumen Sampling

On d 15 of each period, ruminal contents were collected from dorsal, ventral, cranial,

caudal, and medial areas of the rumen at 0.0, 1.5, 3.5, 5.5, 8.5, 11.5, 14.5, 18, 21.5, and 24.5 h

after feeding (Kononoff et al., 2003). Collected digesta was mixed thoroughly, sampled, and

filtered through 4 layers of cheesecloth. Rumen liquid pH was immediately determined using a

handheld pH meter (phTestr 10 BNC, Oakton, Vernon Hills, IL). Approximately 15 mL of

filtered liquid was placed into bottles containing 3 mL of 25% metaphosphoric acid and 3 mL of

0.6% 2-ethylbutyric acid (internal standard) and stored at -20C. After thawing, samples were

159

centrifuged 3 times at 4000 g for 30 min at 4C to obtain a clear supernatant and were analyzed

for NH3 using a phenol-hypochlorite assay (Broderick and Kang, 1980) and VFA concentration

using gas chromatography (Yang and Varga, 1989).


Feed bunk contents for each animal were weighed and sampled on d 20 and 21 at 0, 2, 4,

8, 12, 16, and 24 h after feeding to determine particle size distribution and DM of the remaining

feed. At 0, 8, 16, and 24 h after feeding refusals were also analyzed for NDF and starch content to

determine intake of these components between each time point. All samples were sieved in the

American Society of Agriculture and Biological Engineers forage particle separator, which can

determine 6 particle fractions (> 26.9, > 18.0, > 8.98, > 5.61, > 1.65, and < 1.65 mm; screen

diagonal; ASABE, 2007). Since > 1% of material was retained on the top screen, 3 samples of

each diet were randomly selected, and all particles retained on the top screen were measured

manually (with ruler) before drying. Whole samples were then placed in a forced air oven at 55°C

for 48 h to determine DM content. Geometric mean particle length (Xgm) and standard deviation

of the particle length (Sgm) were calculated according to ASABE (2007) procedure. Samples were

then ground (1 mm screen; Wiley Mill, Arthur H. Thomas Co. Inc., Swedesboro, NJ) to determine

NDF using heat-stable α-amylase and Na2SO3 according to Van Soest (1991) and ground (0.5

mm screen; Wiley Mill, Arthur H. Thomas Co. Inc., Swedesboro, NJ) to analyze starch using a

modified procedure from Knudsen (1997). Samples of forages and TMR were taken 3 times/wk,

composited by period, and analyzed by Cumberland Valley Analytical Services, Inc.

(Hagerstown, MD) for CP, ADF, NDF, ash, NFC, and NEL. There were 2 procedures used to

calculate peNDF; peNDF8.98 = % of particles > 8.98 mm × NDF of whole sample (similar to top 2

sieves of PSPS) and peNDF1.65 = % of particles > 1.65 mm × NDF of whole sample (similar to

160

top 3 sieves of PSPS). A sorting index based on the refusals was calculated for the particle size

fractions at 2, 4, 8, 12, 16, and 24 h after feeding and for NDF and starch at 8, 16, and 24 h after

feeding. Sorting activity was calculated as the actual intake of each fraction (Y1 to pan) expressed

as a percentage of the expected intake. Expected intake of Yi equals intake multiplied by the

fraction of Yi in the TMR (Leonardi and Armentano, 2003). Sorting indices were calculated using

both the expected intake since time point 0 (cumulative) and the expected intake since the

previous time point (interval). Additionally, Xgm sorting indices were calculated for the same time

points by dividing the Xgm of TMR consumed up to each time point by Xgm at time 0. Values > 1

indicate cows were consuming rations with longer particle size and values < 1 indicate cows were

consuming rations with shorter particle size than the diets fed.

Milk Production

Milk production was recorded and samples were taken on d 20 and 21 at morning and

evening milkings. Samples were collected and preserved using 2-bromo-2-nitropropane-1,3 diol.

Milk samples were analyzed for fat, true protein, lactose, MUN, and SCC by the Dairy One milk

testing laboratory (State College, PA) using infrared spectrophotometry (Foss 605B Milk-Scan;

Foss Electric, Hillerod, Denmark).


Statistical analysis was conducted using PROC MIXED of SAS (2006). Dependent

variables were analyzed as a 4 4 Latin square design. All denominator degrees of freedom for

F-tests were calculated according to Kenward and Roger (1997) and repeated measurements for

rumen samples and refusal particle size, NDF, and starch were analyzed using the first order

161

autoregressive covariance structure (Littell et al., 1998), as well as terms for time and interaction

of treatment by time. Because of unequally spaced rumen sampling, the weighted mean daily pH,

NH3, and VFA concentrations were determined by calculating the area under the response curve

according to the trapezoidal rule (Shipley and Clark, 1972). The data were analyzed for

orthogonal contrasts using the fed TMR Xgm that was corrected for unequal spacing according to

Robson (1959). All data are presented as least squares means and treatment effects are considered

significant when P ≤ 0.05 and a trend when P ≤ 0.10.



The chemical composition, particle size distribution, and Xgm of forages included in the

rations are shown in Table B-1. Particle size was determined with the ASABE forage particle

separator because particle length of some diets was so great that the Penn State Particle Separator

(PSPS) did not adequately separate samples. The PSPS particle fractions and their approximate

equivalent ASABE separator screens are: top (26.9 + 18.0 mm), middle (8.98 mm), lower (5.61 +

1.61 mm), and pan (pan). The grass hays had large differences in particle size, particularly with

the particles retained on the 26.9 mm screen although all particle fractions had differences among

the hays. In addition, the Xgm increased greatly from the shortest to the longest ration, with a 13-

fold difference between S grass hay and XL grass hay. The M hay had lower ADF and NDF and

higher NFC values than other hay lengths; this was probably due to individual bale variation.

Although all bales were from the same field and cutting, each length of hay was composed of

different bales. These differences however did not affect TMR chemical composition. Particle

size distribution of the fed TMR also varied greatly (Table B-2). The greatest differences were in

162

the particle fraction > 26.9 mm. The only particle fraction that did not show differences among

diets was particles retained on the pan. Measured mean particle lengths for the top screen to

calculate Xgm were: 74.8 ± 6.6, 84.5 ± 2.6, 105.7 ± 9.1, and 118.8 ± 3.6 (mean ± SD, mm) for

short (S), medium (M), long (L), and extra long (XL) diets, respectively. Particle lengths

(geometric mean ± SD, mm; ASABE, 2007) of the fed rations were: 4.46 ± 0.13, 5.10 ± 0.13,

5.32 ± 0.13, and 5.84 ± 0.13 for S, M, L, and XL diets, respectively. The Xgm of the rations were

approximately equally spaced with differences averaging 0.46 mm between each ration from S to

XL; Sgm increased linearly with increasing ration particle size. Chemical compositions were

similar among the rations with only slight differences in DM, linearly increased with increasing

particle size. It is interesting to note that although there were large differences in mean particle

length among rations peNDF1.65 remained constant. This occurs because all particles greater than

1.65 mm are weighted equally regardless of length, a weakness of calculating peNDF this way.

There was a linear trend for peNDF8.98 to increase with increasing TMR particle size but the

numerical difference was small.

Ration Sorting

Figure B-1 shows the Xgm of refusals increased in all rations throughout the d. The

amount of change varied by diet; the shortest diet changed very little between feeding and

removal of orts while the longest diet had a very drastic change in Xgm during the same time

period. This effect can also be seen in Figure B-2, where the percentage of particle fractions in

refusals in relation to their percentage in fed TMR is shown. The top screen percentages increased

in all diets with very large increases in the longest 2 rations (107, 157, 193, and 283%; S, M, L,

and XL). The pan percentages decreased in all rations, again with greater changes between TMR

fed and TMR refused as the TMR particle size increased. The pan percentages after 24 h were:

163

82, 74, 61, and 49% of the amount in the fed TMR for the S, M, L, and XL rations respectively.

Only graphs of the top screen and pan are shown due to space constraints, but the 2 largest

screens showed very similar patterns, the middle 2 screens did not show substantial differences

among the rations or from the original diet, and the bottom 2 fractions showed similar patterns.

These results are supported by the finding that NDF concentration in refusals increased more in

the longer rations throughout the d than the shorter rations (Figure B-3). In addition, the level of

starch decreased in the 2 longest rations and remained unchanged or even increased in the 2

shorter rations. These data would lead to the conclusion that animals consumed very different

amounts of starch and NDF during the d due to sorting activity. However, when the amount of

these components consumed per d was calculated, rations had similar levels of NDF and starch

intakes (Table B-3). Cumulative sorting indices for particle size, NDF, and starch intake

expressed as the actual intake of each component divided by the predicted intake of that

component are shown in Figures B-4 and B-5. Sorting indices of S and M rations for the top

screen were higher than L and XL sorting indices at 8 h and less, and after 8 h there were no

differences among the rations. Pan sorting indices showed that at 2 h L and XL were highest, M

was intermediate, and S was lowest. After 2 h the differences diminished and eventually

disappeared by 12 h. Only the top screen and pan fractions are shown for space saving, but the

top 3 screens showed similar patterns, the fourth screen did not show substantial differences

among the rations, and the bottom 2 fractions showed similar patterns. The S and M rations had

higher NDF sorting indices and lower starch sorting indices than L and XL rations at 8 h after

feeding. By 24 h after feeding there were no longer differences among the 4 rations for NDF or

starch sorting indices. Figure B-6 shows the cumulative Xgm selection index which combines all

six particle fractions to make an easier comparison. The S and M rations had much higher

selection indices for the first 8 h than the L and XL rations; the ration being consumed was longer

than the ration fed for S and M and shorter for L and XL. After 8 h the rations became much

164

closer in values but remained different, at 24 h the longest 3 rations remained below 1.0 and S

was equal to 1.0.

Intake of DM, NDF, Starch, and Particle Fractions

There was a linear trend for decreased DMI as TMR particle size increased (Table B-3);

this trend was probably due to increased gut fill associated with the bulkier diets, as has been

noted previously (Kononoff and Heinrichs, 2003; Leonardi et al., 2005b). These results are

contrary to other studies (Krause et al., 2002a; Beauchemin and Yang, 2005) that showed no

effect of forage particle size on DMI. The diets had NDF and starch intakes that were not

different despite very different sorting characteristics among the rations throughout the d.

Analysis of intake of individual particle fractions revealed that intake of particles retained on the

26.9-mm sieve increased linearly from 0.39 to 2.43 kg/d with increasing ration particle size, as

seen in Table B-3. In contrast, intake of particles retained on the 18.0- and 8.98-mm sieves

showed a linear decrease as particle size of the ration increased. Intake of particles retained on the

5.61-mm sieve was similar among rations. Intake of particles retained on the 1.65-mm screen and

the pan were also not different among rations, most likely influenced by the equal concentrate fed

to all groups. The consumed Xgm was much closer among rations than the Xgm fed. Consumed

Xgm for all rations was between 4.44 and 5.10 mm. This probably occurred because cows on the

shorter rations made up for being offered fewer particles > 26.9 mm by increasing their intake of

particles retained on the 18.0- and 8.98-mm sieves. Also, intake of NDF and starch remained

similar among rations despite different intakes of particle fractions because the particles retained

on the top 3 sieves that varied in intakes were primarily grass hay, and thus had similar

composition. Finally, refusal percentages met or slightly exceeded the goal of 10% and were not

different among rations.

165

Chewing Activity

Observed meal criteria (Table B-4) were 7.6, 13.8, 10.5, and 11.2 min for S, M, L, and

XL rations, respectively, with S meal criterion being significantly less than the other rations. The

modes of the intra-meal intervals were found to be 13.7, 13.5, 14.1, and 12.0 s for S, M, L, and

XL respectively and were not different from each other. The modes of the inter-meal intervals

were 51.8, 72.1, 58.7, and 68.4 min for S, M, L, and XL, with S having a shorter interval than the

other diets. The DMI per meal was determined to be similar among diets and averaged 2.35

kg/meal. There were no differences among diets for ruminating, eating, or total chewing time per

d for all meal criteria and averaged 515, 388, and 903 min/d respectively (Table B-5). When

chewing activity was expressed as time/kg DMI there were significant linear contrasts for

increased ruminating, eating, and total chewing time/kg DMI as TMR particle size increased and

averaged 20.6, 15.5, and 36.0 min/kg respectively. Ruminating, eating, and total chewing activity

values expressed as min/d were higher than those reported in other studies (Kononoff and

Heinrichs, 2003; Beauchemin et al., 2003; Beauchemin and Yang, 2005). However, when these

data were expressed as min/kg DMI they were found to be similar to the data reported in these

same studies. This may be explained by the fact that the DMI in this study was on average 4.4

kg/d higher than these 3 other studies increasing the total amount of daily chewing activity.

Chewing activity expressed as min/d was probably not different among rations because DMI

decreased linearly while chewing activity (min/kg DMI) increased linearly with increasing TMR

particle size effectively nullifying the changes. The mean total time that chewing activity was

recorded was not different among rations and was 23.8 h. Chewing activity was also expressed as

number of chews/d and chews/kg DMI. Again there were no differences among diets for

ruminating, eating, and total chewing when calculated on a daily basis. There were no changes in

number of ruminating chews/kg DMI, but there was a linear trend for number of eating and total

166

chews/kg DMI to increase as TMR particle size increased. There were no differences among diets

in number of meals eaten/d. Similar to chewing time/d, number of boli/d showed no differences

among diets, but when expressed as boli/kg DMI there was a linear increase with increasing TMR

particle size.

Chewing data were also analyzed using 5-min and 7-min meal criteria. Eating and total

chewing time increased slightly as length of the meal criteria interval increased. The number of

eating and total chews increased slightly as length of the meal criteria interval increased. Number

of meals/d decreased as length of the meal criteria increased. Therefore, these data show that

exact meal criterion used is not important as there are only small changes in values of variables

and there are no changes to conclusions made based on these values.


The weighted mean rumen pH was similar among rations (Table B-6) but there was a

trend for a quadratic contrast for these data. Kononoff and Heinrichs (2003) found a similar

quadratic contrast when increasing alfalfa haylage particle size in TMR. The average weighted

mean for all diets was 5.98. This was similar to results from some studies (Krause et al., 2002b;

Beauchemin et al., 2003), higher than some (Beauchemin and Yang, 2005), and lower than others

(Kononoff et al., 2003; Leonardi et al., 2005b). There were no differences found in the minimum

rumen pH among rations and average minimum pH for all rations was 5.42. There was a linear

tendency for decreasing maximum rumen pH with increasing ration particle size. Perhaps the

intake of starch at any one time was not great enough to overcome the buffering capacity of the

rumen with a large amount of forage still retained from the previous d. This slow digesting fiber

allows the cow to create a more uniform rumen environment than the actual intake of feed would

allow otherwise. The weighted mean, minimum, and maximum NH3 concentrations were found

167

to have no significant contrasts among rations and the mean averaged 8.3 mg/dL. These results

are similar to what was found by Kononoff et al. (2003). The concentrations of acetate,

propionate, butyrate, valerate, isovalerate, isobutyrate were also shown to have no significant

contrasts among diets. Concentrations of all VFA measured were similar to those found by

Kononoff and Heinrichs (2003)

Milk Production and Composition

Milk production averaged 38.7 kg/d and the rations had no effect on milk, FCM, fat,

protein, or lactose yield (Table B-7). Milk fat percentage was similar among diets, as was milk

protein percentage. These results are in agreement with some studies that found that changes in

forage particle size did not affect milk production or components (Krause et al., 2002a;

Beauchemin et al., 2003; Bhandari et al., 2008) but are in disagreement with others that found

that changes in forage particle size influence milk production or components (Kononoff et al.,

2003; Leonardi et al., 2005b). There were linear trends for lactose and MUN to decrease with

increasing ration particle size, but there were only slight numerical differences. There was a

significant quadratic contrast for SCC but the reason for tendency is not apparent. One cow was

removed from the milk production and composition analysis because of chronically high SCC.

Removing this cow from the analysis decreased average SCC by 135,350 cells/mL and increased

average percent fat by 0.09; however, it did not change the conclusions for any production

parameters.

168

Conclusions

In this experiment 4 diets that varied only in the particle size of their grass hay were fed

to lactating dairy cows to measure differences in sorting activity and the effect of these

differences on production parameters. Great differences were observed among rations when

sorting activity was determined by the change in composition of refusals (particle size, NDF, and

starch) compared to the ration fed. However, actual intake of these components after 24 h was

similar for all rations and as a result milk production, milk components and rumen characteristics

were similar among the rations. Therefore, cows were essentially receiving different rations

throughout the d, but the final daily outcome was not different. When measuring sorting activity

in lactating dairy cattle it is important not to only consider composition of the orts (which

comprise only a small percentage of the daily intake) but also actual intakes of various ration

components. In addition, although the diets fed varied greatly in Xgm, the Xgm of what was

consumed by cows were very similar. Cows on the S ration ate a ration similar in Xgm to what

was offered, and cows on all other rations ate a shorter ration than what was offered. Since the

ration the cows actually consumed had similar Xgm and the cows sorted the ration that was

offered, perhaps these cows were sorting to achieve a desired Xgm. If this is the case, a ration with

the proper Xgm may be able to limit or eliminate ration sorting by lactating cows.

Acknowledgments



169

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172

Table B-1. Chemical composition and particle size distributions determined with the ASABE

particle separator for corn silage, alfalfa haylage, and short (S), medium (M), long (L), or extra

long (XL) grass hay

Item

Corn

silage

Alfalfa

haylage

Grass hay

S M L XL SEM P-value


26.9 mm 0.96 3.03 4.17d 34.1

c 60.4

b 77.6

a 4.10 < 0.01

18.0 mm 3.39 6.65 13.1a 12.9

a 11.5

a 6.83

b 1.38 0.04

8.98 mm 53.0 32.8 17.8a 15.7

a 10.4

b 5.30

c 1.38 < 0.01

5.61 mm 29.1 27.3 20.1a 9.64

b 6.21

c 3.66

d 0.65 < 0.01

1.65 mm 12.1 22.9 22.5a 12.7

b 6.54

c 4.17

c 0.82 < 0.01

Pan 1.44 7.28 22.3a 15.0

b 4.91

c 2.43

c 1.61 < 0.01

Xgm2, mm 9.01 7.01 5.15

c 14.6

c 38.0

b 65.4

a 3.67 < 0.01

Sgm3, mm 1.83 2.54 3.48

c 4.93

a 4.22

b 3.43

c 0.18 < 0.01


DM 34.5 43.5 90.5a 89.8

ab 90.1

ab 89.4

b 0.28 0.14

CP 7.20 22.6 8.20 10.5 10.5 8.50 . .

ADF 23.6 29.9 38.6 33.8 38.4 39.9 . .

NDF 37.0 34.5 66.6 59.7 67.1 67.3 . .

peNDF8.984

21.2 14.7 22.3 37.4 55.3 60.4 . .

peNDF1.655

36.5 32.0 51.7 50.7 63.8 65.7 . .

Ash 3.00 11.4 5.30 6.20 6.30 6.10 . .

NFC 50.0 29.1 18.8 22.3 15.2 17.3 . .

NEL, Mcal/kg 1.65 1.52 1.35 1.48 1.35 1.30 . . a–d


1Approximate equivalency to PSPS: top sieve (26.9 + 18.0 mm), middle sieve (8.98 mm), lower

sieve (5.61 + 1.65 mm), and pan (pan).




2 sieves of PSPS).


3 sieves of PSPS).

173

Table B-2. Chemical composition and particle size distributions determined with the ASABE

particle separator for TMR containing short (S), medium (M), long (L), or extra long (XL) grass

hay

Item S M L XL SEM Linear Quadratic


26.9 mm 1.47 6.52 8.61 11.7 0.52 < 0.01 0.31

18.0 mm 4.75 4.52 3.79 3.22 0.15 < 0.01 0.12

8.98 mm 23.8 22.2 20.3 19.2 0.41 < 0.01 0.96

5.61 mm 22.6 20.9 21.0 20.2 0.29 < 0.01 0.22

1.65 mm 25.1 23.6 23.7 23.4 0.34 < 0.01 0.15

Pan 22.3 22.2 22.6 22.3 0.48 0.92 0.91

Xgm2, mm 4.46 5.10 5.32 5.84 0.13 < 0.01 1.00

Sgm3, mm 3.02 3.56 3.92 4.39 0.06 < 0.01 0.65


DM, % 55.1 56.4 56.3 57.0 0.56 0.02 0.67

CP 15.8 15.9 16.0 16.1 0.24 0.31 0.94

ADF 22.3 22.5 21.7 23.0 0.30 0.26 0.12

NDF 33.7 34.2 34.0 34.3 0.40 0.41 0.83

Forage NDF 24.8 24.0 24.8 24.9 . . .

peNDF8.984 10.2 11.4 11.1 11.7 0.39 0.03 0.43

peNDF1.655 26.2 26.6 26.3 26.6 0.39 0.55 0.86

Ash 6.90 7.15 7.18 7.25 0.21 0.26 0.73

Starch 27.6 27.4 27.0 26.8 0.83 0.43 0.96

NEL, Mcal/kg 1.65 1.65 1.65 1.65 0.01 0.59 0.32 1Approximate equivalency to PSPS: top sieve (26.9 + 18.0 mm), middle sieve (8.98 mm), lower





2 sieves of PSPS).


3 sieves of PSPS.

174

Table B-3. Effect of feeding TMR containing short (S), medium (M), long (L), or extra long

(XL) grass hay on DM, NDF, and starch intake at various times after feeding and total

consumption (measured 24 h after feeding) of various sized particles


DMI, kg

8 h 14.7 16.4 14.0 15.7 0.96 0.45 0.94

16 h 23.8 23.9 22.1 23.8 1.01 0.63 0.20

24 h 26.9 27.0 24.1 25.1 1.08 0.04 0.70

NDF, kg

8 h 4.89 5.71 4.33 5.00 0.36 0.81 0.70

16 h 7.79 8.13 7.13 7.81 0.36 0.71 0.64

24 h 8.72 9.22 6.71 8.06 0.50 0.13 0.53

Starch, kg

8 h 3.06 3.53 2.97 3.38 0.26 0.31 0.82

16 h 5.55 5.58 5.19 5.59 0.26 0.84 0.24

24 h 6.22 6.40 4.99 5.78 0.33 0.11 0.42

Particles consumed, kg1

26.9 mm 0.39 1.67 1.69 2.43 0.20 < 0.01 0.34

18.0 mm 1.27 1.21 0.88 0.78 0.08 < 0.01 0.61

8.98 mm 6.19 6.03 4.70 5.00 0.24 < 0.01 0.56

5.61 mm 5.93 5.80 5.16 5.49 0.24 0.11 0.42

1.65 mm 6.64 6.75 6.10 6.69 0.31 0.86 0.48

Pan 5.98 6.43 6.00 6.54 0.34 0.32 0.89

Xgm2, mm 4.44 4.90 4.82 5.10 0.15 < 0.01 0.65

Sgm3, mm 3.03 3.54 3.76 4.08 0.09 < 0.01 0.60

Refusal, % 12.49 11.98 12.70 12.70 0.01 0.68 0.58 1Approximate equivalency to PSPS: top sieve (26.9 + 18.0 mm), middle sieve (8.98 mm), lower




175

Table B-4. Observed meal characteristics for diets containing short (S), medium (M). long (L), or

extra long (XL) grass hay

Item S M L XL SEM1

Intra-meal interval, s2

Mode 13.7 13.5 14.1 12.0 .

95% CI 16.8,11.2 16.5,11.0 17.2,11.5 14.7,9.8 .

Inter-meal interval, min3

Mode 51.8b 72.1

a 58.7

a 68.4

a .

95% CI 70.1,38.3 97.6,53.3 79.4,43.4 92.5,50.5 .

Meal criterion,

min 7.6b 13.8

a 10.5

a 11.2

a .

95% CI 10.0,5.7 18.3,10.4 14.0,8.0 14.8,8.4 .

DMI/meal, kg4 2.34 2.49 2.28 2.30 0.32

a–bMeans within a row with different superscripts differ after transforming (P ≤ 0.05).

1For model output, back-transformed 95% confidence intervals are shown.

2Intra-meal interval = bout of no eating within meals.

3Inter-meal interval = bout of no eating outside of meals.

4Calculated based on daily DMI

176


(XL) grass hay on chewing behavior as determined by observed meal criteria1


Min/d

Ruminating 518 525 495 523 15.5 0.95 0.45

Eating. 376 400 383 394 19.9 0.50 0.68

Total chewing 894 924 878 916 25.2 0.58 0.85

Total time recorded 1,424 1,434 1,425 1,434 9.45 0.53 0.93

Min/kg

Ruminating 19.3 19.2 22.4 21.3 1.21 0.04 0.69

Eating 13.9 14.6 17.2 16.1 1.11 0.03 0.37

Total chewing 33.2 33.8 39.6 37.4 2.07 0.01 0.44

Chews/d

Ruminating 23,690 23,874 20,711 25,100 2,809 0.76 0.22

Eating 19,699 20,322 19,462 21,775 1,207 0.21 0.38

Total chews 43,388 44,196 40,173 46,874 3,409 0.43 0.20

Chews/kg

Ruminating 881 872 984 1042 142 0.12 0.57

Eating 731 743 874 891 63 0.01 0.78

Total chews. 1,612 1,615 1,857 1,933 187 0.03 0.62

Meals/d 13.4 11.5 13.4 11.9 0.87 0.35 0.83

Boli, number/d 740 766 772 838 73.9 0.13 0.59

Boli, number/kg DMI 27.3 28.1 34.1 33.6 2.68 0.02 0.94 1Observed meal criteria use intervals predicted from current dataset.

177


(XL) grass hay on rumen fermentation


Rumen pH

Weighted average1 5.96 6.05 5.98 5.92 0.05 0.39 0.07

Minimum 5.36 5.45 5.43 5.44 0.05 0.32 0.41

Maximum 7.05 6.99 6.99 6.96 0.10 0.04 0.65

NH3, mg/dL

Weighted average 7.56 8.02 8.01 9.42 1.03 0.21 0.57

Minimum 2.10 2.76 3.20 5.04 1.04 0.08 0.51

Maximum 17.9 19.5 17.2 19.0 2.07 0.81 0.96

VFA Weighted average, µM/mL

Acetate 84.6 84.6 84.2 85.6 1.71 0.50 0.44

Propionate 31.8 30.3 31.8 32.7 2.39 0.70 0.54

Butyrate 15.4 15.7 16.4 15.9 0.70 0.51 0.58

Valerate 2.72 2.74 2.93 3.11 0.14 0.07 0.62

Isovalerate 2.45 2.46 2.46 2.55 0.11 0.31 0.55

Isobutyrate 1.90 1.85 1.86 1.98 0.06 0.29 0.11 1Weighted averages determined by calculating the area under the response curve according to the


178


(XL) grass hay on milk production and components1


Milk yield, kg/d 39.3 40.1 37.4 37.8 1.73 0.27 0.77

3.5% FCM, kg/d 38.7 38.6 37.2 38.2 2.00 0.64 0.69

Fat, % 3.44 3.28 3.46 3.52 0.12 0.35 0.12

Fat, kg/d 1.34 1.31 1.29 1.35 0.08 1.00 0.41

Protein, % 2.92 2.92 2.94 2.94 0.04 0.21 0.84

Protein, kg/d 1.14 1.70 1.10 1.11 0.05 0.40 0.64

Lactose, % 4.83 4.86 4.81 4.79 0.06 0.04 0.07

Lactose, kg/d 1.91 1.95 1.80 1.81 0.10 0.17 0.66

MUN, mg/dL 12.6 12.6 11.7 11.6 0.48 0.05 0.78

SCC, 1,000 cells/mL 71.4 51.9 57.0 60.2 13.5 0.14 0.04 1One cow was removed from analysis due to chronic high SCC.

179

Figure B-1. Effect of feeding TMR of increasing particle size on refusal geometric mean particle

size.

180

A

B

Figure B-2. Effect of feeding TMR of increasing particle size on refusal particle distribution as a

percentage of original diet. Selected data shown; 26.9-mm sieve (A) and pan (B).

181

A

B

Figure B-3. Effect of feeding TMR of increasing particle size on refusal NDF (A) and starch (B)

concentration.

182

A

B

Figure B-4. Effect of feeding TMR of increasing particle size on cumulative particle size

selection index. Selected data shown; 26.9-mm sieve (A) and pan (B).

183

A

B

Figure B-5. Effect of feeding TMR of increasing particle size on cumulative NDF (A) and starch

(B) selection indices.

184

Figure B-6. Effect of feeding TMR of increasing particle size on cumulative geometric mean

length (Xgm) selection index.

Appendix C

Effect of Varying TMR Particle Size on Rumen Digesta and Fecal Particle

Size and Digestibility in Lactating Dairy Cows


D. D. Maulfair, M. Fustini, and A. J. Heinrichs

Abstract

The objective of this experiment was to evaluate the effects of feeding rations of different

particle sizes on rumen digesta and fecal matter particle size. Four rumen cannulated,

multiparous, Holstein cows (104 ± 15 d in milk) were randomly assigned to a 4 × 4 Latin square.

The diets consisted of 29.4% corn silage, 22.9% ground corn, 17.6% alfalfa haylage, and 11.8%

dry grass hay (20% of forage dry matter) on a dry matter basis. Dry grass hay was chopped to 4

different lengths to vary the total mixed ration particle size. Geometric mean particle sizes of the

rations were 4.46, 5.10, 5.32, and 5.84 mm for Short, Medium, Long, and Extra Long diets

respectively. The ration affected rumen digesta particle size for particles ≥ 3.35 mm, and had no

effect on distribution of particles < 3.35 mm. All rumen digesta particle size fractions varied by

time after feeding; with soluble particle fractions increasing immediately after feeding while 0.15,

0.6, and 1.18 mm particle size fractions decreased slightly after feeding. Particle fractions > 1.18

mm had ration by time interactions. Fecal neutral detergent fiber and indigestible neutral

detergent fiber concentrations decreased with increasing total mixed ration particle size. Fecal

particle size expressed as total geometric mean particle length followed this same tendency. Fecal

particle size expressed as retained geometric mean particle length averaged 1.13 mm with greater

186

than 36% of particle being larger than 1.18 mm. All fecal nutrient concentrations measured were

significantly affected by time after feeding with NDF and INDF increasing after feeding and

peaking at about 12 h later and then decreasing to pre-prandial levels. Starch concentrations were

determined to have the opposite effect. Additionally, apparent digestibility of diet nutrients was

analyzed and dry matter digestibility tended to decrease with increasing total mixed ration

particle size, while other nutrient digestibilities were not different among rations. These results

show that the critical size for increased resistance to rumen escape is larger than 1.18 mm and this

critical size is constant throughout the d. This study also concludes that, when using average

quality grass hay to provide the range of particle sizes fed, dry matter digestibility tends to

decrease with increasing ration particle size.

Key Words: digestibility, particle size, rumen escape

Introduction

The sieve size 1.18 mm has been widely used as the size in which feed particles retained

on or above are considered physically effective for dairy cows. This number originated from

research of Evans et al. (1973) and Poppi et al. (1980; 1981), where resistance of particles leaving

the rumen of cattle and sheep was measured. It was determined that 1.18 mm was a threshold

particle size for both cattle and sheep for greatly increased resistance to particles leaving the

rumen and < 5% of fecal particles are generally retained on a 1.18-mm sieve (Poppi et al., 1980,

1981). It should be noted that a wet sieving technique was used in these studies to measure

particle size and this procedure is very different from the dry vertical sieving procedure used by

Mertens (1997) to develop the physical effectiveness factor of feeds (using particles retained on a

1.18-mm sieve) that is used by some ration formulation software today. Therefore it should not be

assumed that these two different sieving methods will produce similar results. Some researchers

187

have suggested that the critical particle size for rumen escape in dairy cattle may be larger than

1.18 mm (Yang et al., 2001; Oshita et al., 2004), however determining this has proven difficult.

Also little is known if diet particle size or time after feeding affects this critical particle size for

passage from the rumen.

There is some controversy regarding effect of ration particle size on DM digestibility

(DMD). Kononoff and Heinrichs (2003a) and Yang and Beauchemin (2005) reported that

increasing ration particle size increased DMD; however, Kononoff and Heinrichs (2003b)

observed that increasing ration particle size decreased DMD. In addition there are several studies

that reported no effect of ration particle size on DMD (Krause et al., 2002; Yang and

Beauchemin, 2006; 2007). Clearly this effect is variable based on other aspects of the diet or

management. Therefore, the objective of this experiment was to study effects of varying TMR

particle size on rumen digesta and fecal particle size in lactating dairy cows to determine the

critical size for particles leaving the rumen and if rumen and fecal particle size change throughout

the d and according to diet particle size.





Four lactating, multiparous, rumen cannulated, Holstein cows averaging 104 ± 15 DIM, weighing

659 ± 88 kg, and with parity of 2.25 ± 0.50 (mean ± SD) were randomly assigned to a 4 × 4 Latin

square. Periods were 21 d in length, with a 13-d adaptation period followed by an 8-d collection

period. During each of the 4 periods, cows were fed 1 of 4 rations that contained identical feed

188

ingredients and proportions but varied in the length of dry grass hay included in the ration.

Ingredients and their percentage of ration DM were: corn silage (29.4), ground corn (22.9),

haylage (17.6), grass hay (11.8), roasted soybeans (6.7), canola meal (5.7), heat-treated soybean

meal (3.2), mineral/vitamin mix (2.4), and salt (0.3).

More detailed information regarding diets was reported by Maulfair et al. (2010). Rations

were designated as short (S), medium (M), long (L), and extra long (XL). Animals were housed

in individual stalls, milked twice/d at 0700 and 1900 h and fed once/d at approximately 0730 h

for ad libitum consumption with 12% refusal to allow maximum opportunity to sort the ration.

Feed was pushed up 3 times/d at 1230, 1730, and 2400 h. All rations were balanced to meet or

exceed NRC (2001) requirements, and water was available ad libitum.

Rumen Sampling

On d 15 of each period, ruminal contents were collected from dorsal, ventral, cranial,

caudal, and medial areas of the rumen at 0.0, 1.5, 3.5, 5.5, 8.5, 11.5, 14.5, 18.0, 21.5, and 24.5 h

after feeding (Kononoff et al., 2003b). Collected digesta was mixed thoroughly, sampled, and

filtered through 4 layers of cheesecloth. Solid portions of digesta samples retained on cheesecloth

were stored at -20C for later determination of particle size distribution via the wet sieving

technique of Maulfair and Heinrichs (2010). Maulfair and Heinrichs (2010) determined that

squeezing rumen digesta through cheesecloth before wet sieving had no effect on particle size

distribution of particles > 0.15 mm but reduced the amount of soluble DM contained in the

sample. Representative samples (approximately 30 g) were mixed in 1 L of water and soaked for

10 min. Samples were then placed on a series of stacked sieves (sizes 0.15, 0.6, 1.18, 3.35, 6.7,

9.5 mm; VWR, Arlington Heights, IL) contained in a Retsch AS 200 Control sieve shaker

(Retsch, Haan, Germany) and sieved in duplicate. Samples were sieved for 10 min at 2.5 mm

189

amplitude with the sprayer ring located between 3.35- and 1.18-mm screens and cold water flow

rate at approximately 1.5 to 2.0 L/min to ensure particles were separated thoroughly. Sieve

contents were rinsed into a funnel with rumen in situ bags (5 x 10 cm, 53 μm pore size; ANKOM,

Macedon, NY) attached to the stem to collect the sample. Bags were then dried in a forced air

oven at 55°C for 24 h and weighed to determine DM retained on each sieve. A portion of each

sample was also dried at 55°C for 24 h in a forced air oven without sieving to determine DM

content of the original sample. Soluble fraction of the sample was calculated as the DM lost

during sieving and drying. Data were analyzed using each particle fraction as a percentage of DM

retained on ≥ 0.15-mm screen (retained) and also as the percentage of DM of the entire sample

sieved (total).

Fecal Sampling

Fecal sampling occurred at the same time points as rumen sampling (d 15 at 0.0, 1.5, 3.5,

5.5, 8.5, 11.5, 14.5, 18.0, 21.5, and 24.5 h after feeding) via grab samples from the rectum.

Samples were stored at -20C until later determination of particle size distribution and

concentration of DM, NDF, indigestible NDF (INDF), starch, and ash. Particle size of

subsamples was determined using the same wet sieving technique used for rumen digesta, with

the exception of eliminating the top screen (9.5 mm). Geometric mean particle length (Xgm) and

standard deviation of particle length (Sgm) were calculated according to American Society of

Agricultural and Biological Engineers (ASABE) (2007) procedure. Xgm was calculated using 2

procedures; the first, retained Xgm (XgmRet), only considered particles retained on the 0.15-mm

screen or larger, the second procedure, total Xgm (XgmTot), considered all particle fractions

including the soluble fraction that passed through the bottom screen (0.15 mm). Mean particle

length of the soluble fraction was assumed to be 0.106 mm, which is half of the diagonal screen

190

diameter (0.212 mm) of the bottom screen; this is the assumption that ASABE (2007) uses for

mean length of particles on the pan. Subsamples were also placed in a forced air oven at 55°C for

48 h to determine DM content and were then ground (1-mm screen; Wiley Mill, Arthur H.

Thomas Co. Inc., Swedesboro, NJ) to determine NDF using heat-stable α-amylase and Na2SO3

according to Van Soest (1991) and ground (0.5-mm screen; Wiley Mill, Arthur H. Thomas Co.

Inc., Swedesboro, NJ) to analyze starch using a modified procedure from Knudsen (1997;

modification included 150 mg of sample, 45 units of amyloglucosidase, and analysis of released

glucose monomers with procedure no. 1075, Stanbio Laboratory Inc.) For INDF determination,

subsamples were enclosed in F57 filter bags (ANKOM Technology, Macedon, NY) in

sextuplicate, then incubated in the rumen of 2 cows (each cow incubated 3 bags of each sample)

for 12 d. After removal from the rumen, bags were rinsed in cold water by hand until water was

almost clear. Bags were then dried in a forced air oven at 55°C for 48 h and later processed using

the same procedure used for NDF determination. Ash was determined by combustion at 600°C

for 6 h (AOAC, 1990).

Digestibility

Dry matter intakes were recorded daily and feed bunk contents were sampled at 0 and 24

h after feeding on d 21 and 22 and were analyzed for DM, NDF, INDF, and starch using identical

procedures to those of fecal samples. Intakes of NDF, INDF, and starch were determined by

subtracting the amount of each in refusals (refused TMR weight × refused TMR concentration)

from the amount of each fed (fed TMR weight × fed TMR concentration). Fecal output was

calculated by dividing intake of INDF by INDF concentration (24.5 h weighted mean) in feces.

Since intake is based on a 24 h period and fecal output is based on 24.5 h period it must be

assumed that the 30 min difference will not significantly affect the results. Apparent

191

digestibilities for all parameters were calculated by the following formula: (intake – (24.5 h

weighted mean concentration in feces × fecal output)) ÷ intake.


Statistical analysis was conducted using PROC MIXED of SAS (2006). Dependent

variables were analyzed as a 4 4 Latin square design. All denominator degrees of freedom for

F-tests were calculated according to Kenward and Roger (1997) and repeated measurements for

rumen and fecal samples were analyzed using the first order autoregressive covariance structure

(Littell et al., 1998), as well as terms for time and interaction of treatment by time. Because of

unequally spaced rumen and fecal sampling, weighted mean daily concentrations and proportions

were determined by calculating the area under the response curve according to the trapezoidal

rule

(Shipley and Clark, 1972). Data were analyzed for orthogonal contrasts using the fed TMR Xgm

that was corrected for unequal spacing according to Robson (1959). All data is presented as least

squares means; treatment effects are considered significant when P ≤

0.05 and a trend when P ≤

0.10.



Chemical composition, particle size distribution, and Xgm of forages included in the

rations are shown in Table C-1. The M hay had lower ADF and NDF and higher NFC values than

other hay lengths; this was probably due to individual bale variation. Although all bales were

from the same field and cutting, each length of hay was composed of different bales. These

192

differences however did not affect TMR chemical composition because the 0.86% expected

change in TMR NDF concentration was probably masked by sampling and lab error (Table C-2).

Particle size was determined with the ASABE forage particle separator because particle length of

some diets was so great that the Penn State Particle Separator (PSPS) did not adequately separate

samples and small particles were improperly retained on the top screen. The PSPS particle

fractions and their approximate equivalent ASABE separator screens are: top (26.9 + 18.0 mm),

middle (8.98 mm), lower (5.61 + 1.61 mm), and pan (pan). Particle size distribution of the fed

TMR varied greatly among treatments, but chemical compositions were similar (Table C-2).

More detailed information regarding forages and diets was reported by Maulfair et al. (2010).

Rumen Particle Size

There were no differences in study conclusions between analysis of particle fractions as

percentage of retained or total DM, therefore discussion and graphs of rumen digesta will be

based on total DM. Particles that passed through the 3.35-mm screen were affected by time after

feeding but not by ration; these particle fractions were 1.18, 0.6, 0.15 mm, and soluble, while

particles retained on 9.5-, 6.7-, and 3.35-mm screens were affected by both time and ration. This

finding is similar to Kononoff and Heinrichs (2003a) where rumen digesta particles retained on

13.2- and 6.7-mm sieves increased with increasing ration particle size while particles retained on

0.6- and 0.15-mm sieves were not affected by ration particle size. However, Kononoff and

Heinrichs (2003a) determined that particles retained on the 1.18-mm sieve decreased with

increasing ration particle size in contrast to the present study. This contradiction may be caused

by differences in forages used in these studies, the present study used corn silage, alfalfa haylage,

and dry grass hay, while Kononoff and Heinrichs (2003a) used only corn silage. Evans et al.

(1973) also determined that coarse particles retained on the largest screen (2.4 mm) responded to

193

effects of time and feeding and smaller particles had less response. These particle fractions, and

additionally the soluble DM to retained DM ratio, are shown in Figure C-1 and expressed as the

mean of the 4 treatments. Particles in the soluble fraction and the soluble DM to retained DM

ratio markedly increased after feeding, remained elevated, and began to decrease slowly at 11.5 h

after feeding, eventually returning to pre-prandial levels just prior to the next feeding. Particles

retained on the 0.15-mm screen exhibited the opposite effect, decreased after feeding, remained

lowered, and began to slowly increase at about 11.5 h after feeding to pre-prandial levels.

Particles retained on 1.18-and 0.6-mm screens had less substantial changes compared to the other

particle fractions. These fractions followed a similar pattern as the 0.15-mm fraction, as they

decreased after feeding and began to slowly increase at about 11.5 h after feeding. Figure C-2

shows that the 6.7-mm particle fraction was least abundant for all rations. The most abundant

fraction for S was 3.35 mm, L and XL was 9.5 mm, and M alternated between 3.35 and 9.5 mm.

The M diet started with 3.35 mm being most abundant; by 8.5 h after feeding 9.5 mm became

most abundant; finally at 24.5 h after feeding 3.35 mm was again the most abundant particle

fraction. There were ration by time interactions as the 9.5-mm fraction increased after feeding in

S and L diets while it decreased in M and XL diets. The 3.35-mm particle fraction increased in

XL diets, decreased in S and L diets, and maintained its level in M diets. It seems that the 9.5-mm

and 3.35-mm particle fractions acted inversely of each other after feeding. The 6.7-mm particle

fraction did not have substantial changes over time after feeding.

Fecal Particle Size and Composition

The weighted means for fecal concentrations of NDF, INDF, starch, ash, and DM are

shown in Table C-3. The weighted mean represents the average value over the course of the d

even though sampling time points were not equally spaced. There was a significant linear contrast

194

for fecal NDF concentrations to decrease (from 50.7 to 47.2%) with increasing TMR particle size

(from S to XL) even though NDF intake was not different across treatments (Maulfair et al.,

2010). Fecal INDF concentration also followed this tendency, decreasing from 30.0 to 27.4%

with increasing TMR particle size. There were no differences in weighted means for starch, ash,

and DM. When determining fecal particle size distribution no particles were retained on the 6.7-

mm screen. Fecal particle size was expressed as Xgm using 2 different procedures. The XgmRet

procedure (using only particles retained on ≥ 0.15-mm screens) did not result in any differences

among rations for weighted means, and Xgm of all rations averaged 1.13 mm. These values agree

with results of Kononoff and Heinrichs (2003a; 2003b) that reported fecal Xgm averaged 1.13 and

1.03 mm respectively and did not change based on ration particle size. These fecal particle size

data are lower than those reported by Yang et al. (2001), which averaged 1.86 mm and also did

not differ due to ration particle size. The XgmTot procedure (using all particle fractions) had much

lower values than XgmRet and had a significant linear contrast for fecal Xgm to decrease with

increasing TMR particle size, decreasing from 0.33 to 0.31 mm for S to XL respectively. This

effect was caused by the increasing proportion of the soluble DM fraction with increasing ration

particle size while all other particle fractions exhibited no effect of ration (Table C-4). One

possible explanation for increased soluble DM in feces is because chewing min/kg of DMI

increased with TMR of larger particle size (Maulfair et al., 2010) possibly increasing saliva

secretion, therefore increasing liquid passing out of the rumen and causing a greater proportion of

particles < 0.15 mm to leave the rumen (Owens and Isaacson, 1977). Another possible cause of

increased soluble DM in feces is increased hind gut fermentation, leading to higher numbers of

bacteria which would be included the soluble fraction. The fecal particle distribution resulted in

approximately 16% of particles > 3.35 mm and 37% > 1.18 mm as a proportion of DM retained

on the 0.15-mm sieve. The distribution had approximately 7% of particles > 3.35 mm and 17% >

1.18 mm as a proportion of total sample DM. These results are similar to Kononoff and Heinrichs

195

(2003a; 2003b), who reported that 48 and 46% respectively of fecal particles were > 1.18 mm as

a proportion of DM retained on a 0.15-mm sieve; however, they are much higher than those

observed by Poppi et al. (1981; 1985) where < 5% of particles were > 1.18 mm as a proportion of

total sample DM in mature steers fed exclusively forage. The reasons for the 3- to 4-fold increase

in particles > 1.18 mm escaping the rumen are probably due to large differences in DMI and

passage rate of high producing dairy cows compared to steers being fed a maintenance diet. When

fecal nutrients were analyzed over time it was determined that NDF, INDF, starch, ash, and DM

concentrations were all affected by time after feeding (Figure C-3). In all rations both NDF and

INDF concentrations increased after feeding to a peak at about 11.5 h after feeding, and then

decreased to pre-prandial levels. Fecal starch concentrations however exhibited the opposite

tendency with starch concentrations decreased in all rations after feeding to a low at about 11.5 h

and then increased to pre-prandial levels. In all rations fecal ash concentrations followed a pattern

over time similar to NDF and INDF concentrations, and fecal DM concentrations followed a

pattern over time similar to starch concentrations. Neither XgmRet nor XgmTot (Figure C-4) was

affected by time after feeding; however several individual particle size fractions did change

significantly over time. The fractions that were affected by time after feeding were 0.6 and 0.15

mm using the retained procedure and 0.15 mm and soluble using the total procedure (data not

shown). Figure C-4 also shows that generally XgmTot decreased with increasing TMR particle

size.

Intakes, Fecal Output, and Digestibility

Dry matter intakes ranged from 23.6 to 27.1 kg/d and were not affected by treatment

(Table C-5). This effect was also present for INDF intake and fecal output. Dry matter

digestibility averaged 61.6% and decreased linearly (P = 0.08) as ration particle size increased.

196

This effect was also seen by Kononoff and Heinrichs (2003b), where DMD decreased from 66.5

to 63.1% with increasing ration particle size. However this effect is in contrast to Kononoff and

Heinrichs (2003a) and Yang and Beauchemin (2005), where DMD increased with increasing

ration particle size. Digestibility of NDF and starch averaged 45.6 and 94.8% respectively, and

neither was different among rations in this current study. There are many conflicting results

comparing changes in DMD with NDF digestibility (NDFD) and starch digestibility (StarchD)

when ration particle size is increased. Some studies reported no differences in DMD, NDFD, or

StarchD (Yang and Beauchemin, 2006; 2007) while another study reported no differences in

DMD and NDFD but StarchD decreased (Krause et al., 2002) with increasing ration particle size.

In addition, Yang and Beauchemin (2005) reported an increase in DMD and NDFD with no

change in StarchD, but Kononoff and Heinrichs (2003a) did not see a change in NDFD with an

increase in DMD (StarchD was not determined in this study) when ration particle size was

increased. These differing results are likely caused by interactions between forage type, forage to

concentrate ratio, and starch fermentability with forage particle size. None of the experiments

with steam-rolled barley grain as the main starch source had any effect of ration particle size on

StarchD when fed with multiple forage types (alfalfa, barley, and corn silage) (Yang and

Beauchemin, 2005; 2006; 2007). Only one of these studies using corn grain as the main starch

source measured StarchD and it was determined that StarchD decreased with increasing ration

particle size when feeding high –moisture shelled corn and dry cracked shell corn with alfalfa

silage (Krause et al., 2002). Therefore it seems that barley grain digestibility is independent of

forage particle size while corn grain digestibility may not be. Forage source did not have

consistent results for NDFD with differing ration particle size. Studies feeding an alfalfa silage

based ration had both no effect of ration particle size on NDFD (Krause et al., 2002; Yang and

Beauchemin, 2007) and a decrease in NDFD with increasing ration particle size (Kononoff and

Heinrichs, 2003b). Corn silage based rations were also inconsistent with one study having an

197

increase in NDFD with increasing ration particle size (Yang and Beauchemin, 2005) and one

study had no effect of ration particle size on NDFD (Kononoff and Heinrichs, 2003a). There are

probably many factors that are influencing these differences in NDFD within each forage source.

Conclusions

In this experiment, 4 diets that varied in particle size were fed to lactating dairy cows. It

was determined that rumen digesta particle size increased with increasing ration particle size for

sieves ≥ 3.35 mm and remained the same for sieves < 3.35 mm. Fecal particle size was not

different among rations and averaged 1.13 mm with more than 36% of particles being retained on

an 1.18-mm sieve or larger. Therefore it can be concluded that the critical size threshold for

increased resistance to rumen escape is larger than 1.18 mm in modern high producing dairy

cows. In addition, this critical size is constant throughout the d as fecal particle size fractions >

1.18 mm were not affected by time after feeding. This study also concludes that for the range of

TMR particle sizes fed, which was achieved using various lengths of dry grass hay, dry matter

digestibility tends to decrease with increasing ration particle size.

Acknowledgments



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Table C-1. Chemical composition and particle size distributions determined with the ASABE

particle separator for corn silage, alfalfa haylage, and short (S), medium (M), long (L), or extra

long (XL) grass hay

Item

Corn

silage

Alfalfa

haylage

Grass hay

S M L XL SEM P-value


26.9 mm 1.0 3.0 4.2d 34.1

c 60.4

b 77.6

a 4.10 < 0.01

18.0 mm 3.4 6.7 13.1a 12.9

a 11.5

a 6.8

b 1.38 0.04

8.98 mm 53.0 32.8 17.8a 15.7

a 10.4

b 5.3

c 1.38 < 0.01

5.61 mm 29.1 27.3 20.1a 9.6

b 6.2

c 3.7

d 0.65 < 0.01

1.65 mm 12.1 22.9 22.5a 12.7

b 6.5

c 4.2

c 0.82 < 0.01

Pan 1.4 7.3 22.3a 15.0

b 4.9

c 2.4

c 1.61 < 0.01

Xgm2, mm 9.0 7.0 5.2

c 14.6

c 38.0

b 65.4

a 3.67 < 0.01

Sgm3, mm 1.8 2.5 3.5

c 4.9

a 4.2

b 3.4

c 0.18 < 0.01


DM 34.5 43.5 90.5a 89.8

ab 90.1

ab 89.4

b 0.28 0.14

CP 7.2 22.6 8.2 10.5 10.5 8.5 . .

ADF 23.6 29.9 38.6 33.8 38.4 39.9 . .

NDF 37.0 34.5 66.6 59.7 67.1 67.3 . .

peNDF8.04

21.2 14.7 22.3 37.4 55.3 60.4 . .

peNDF1.185

36.5 32.0 51.7 50.7 63.8 65.7 . .

Ash 3.0 11.4 5.3 6.2 6.3 6.1 . .

NFC 50.0 29.1 18.8 22.3 15.2 17.3 . .

NEL, Mcal/kg6 1.65 1.52 1.35 1.48 1.35 1.30 . .

a–dMeans within a row with different superscripts differ (P ≤ 0.05).

1Approximate equivalency to Penn State Particle Separator (PSPS): top sieve (26.9 + 18.0 mm),

middle sieve (8.98 mm), lower sieve (5.61 + 1.65 mm), and pan (pan).




sieves of PSPS) (Kononoff et al., 2003a).


3 sieves of PSPS) (Kononoff et al., 2003a).

6NEL = Net energy of lactation, as described by NRC (2001).

201

Table C-2. Chemical composition and particle size distributions determined with the ASABE

particle separator for TMR containing short (S), medium (M), long (L), or extra long (XL) grass

hay



26.9 mm 1.5 6.5 8.6 11.7 0.52 < 0.01 0.31

18.0 mm 4.8 4.5 3.8 3.2 0.15 < 0.01 0.12

8.98 mm 23.8 22.2 20.3 19.2 0.41 < 0.01 0.96

5.61 mm 22.6 20.9 21.0 20.2 0.29 < 0.01 0.22

1.65 mm 25.1 23.6 23.7 23.4 0.34 < 0.01 0.15

Pan 22.3 22.2 22.6 22.3 0.48 0.92 0.91

Xgm2, mm 4.46 5.10 5.32 5.84 0.13 < 0.01 1.00

Sgm3, mm 3.02 3.56 3.92 4.39 0.06 < 0.01 0.65


DM, % 55.1 56.4 56.3 57.0 0.56 0.02 0.67

CP 15.8 15.9 16.0 16.1 0.24 0.31 0.94

ADF 22.3 22.5 21.7 23.0 0.30 0.26 0.12

NDF 33.7 34.2 34.0 34.3 0.40 0.41 0.83

Forage NDF 24.8 24.0 24.8 24.9 . . .

peNDF8.04 10.2 11.4 11.1 11.7 0.39 0.03 0.43

peNDF1.185 26.2 26.6 26.3 26.6 0.39 0.55 0.86

Ash 6.9 7.2 7.2 7.3 0.21 0.26 0.73

Starch 27.6 27.4 27.0 26.8 0.83 0.43 0.96

NEL, Mcal/kg6 1.65 1.65 1.65 1.65 0.01 0.59 0.32

1Approximate equivalency to Penn State Particle Separator (PSPS): top sieve (26.9 + 18.0 mm),

middle sieve (8.98 mm), lower sieve (5.61 + 1.65 mm), and pan (pan).




sieves of PSPS) (Kononoff et al., 2003a).


3 sieves of PSPS) (Kononoff et al., 2003a).

6NEL = Net energy of lactation, as described by NRC (2001).

Table C-3. Effect of feeding TMR containing short (S), medium (M), long (L), or extra long

(XL) grass hay on daily weighted means of fecal NDF, indigestible NDF (INDF), starch, ash, DM

and Xgm

Item, % of DM1 S M L XL SEM Linear Quadratic

NDF 50.7 48.2 47.2 47.2 0.98 0.03 0.28

INDF 30.0 29.6 28.6 27.4 0.99 0.01 0.40

Starch 3.8 2.9 4.0 3.9 0.57 0.69 0.39

Ash 9.3 9.3 9.3 9.0 0.28 0.47 0.56

DM, % 15.3 15.5 15.8 15.9 0.39 0.19 0.97

XgmRet2 1.13 1.16 1.11 1.10 0.05 0.41 0.42

SgmRet3 1.28 1.29 1.27 1.28 0.01 0.50 0.43

XgmTot4 0.33 0.32 0.30 0.31 0.01 0.03 0.64

SgmTot5 1.52 1.53 1.49 1.50 0.02 0.13 0.79




2XgmRet = geometric mean particle length determined by ASABE (2007) using data from screens

≥ 0.15 mm.

3SgmRet = particle length standard deviation determined by ASABE (2007) using data from

screens ≥ 0.15 mm.

4XgmTot

= geometric mean particle length determined by ASABE (2007) using data from all

particle fractions and assuming a mean particle length of 0.106 mm for particles passing through

bottom screen.

5SgmTot

= particle length standard deviation determined by ASABE (2007) using data from all

particle fractions and assuming a mean particle length of 0.106 mm for particles passing through

bottom screen.

203


(XL) grass hay on daily weighted mean fecal particle size distribution.

Screen, mm1 S M L XL SE Linear Quadratic

Retained, % of DM

3.35 15.7 17.0 14.9 15.0 1.55 0.32 0.53

1.18 21.0 21.1 21.1 20.9 0.75 0.89 0.85

0.6 13.5 13.1 13.3 13.5 0.26 0.71 0.24

0.15 49.8 48.9 50.1 50.6 1.49 0.38 0.43

Total, % of DM

3.35 7.6 7.8 6.7 6.8 0.71 0.12 0.86

1.18 10.2 9.9 9.5 9.5 0.41 0.18 0.74

0.6 6.5 6.0 6.0 6.1 0.18 0.20 0.16

0.15 24.0 22.8 22.5 22.9 0.84 0.14 0.12

Soluble 51.8 53.5 55.3 54.7 0.91 0.02 0.17 1Weighted means determined by calculating



204


(XL) grass hay on DMI, indigestible NDF intake (INDFI), fecal output and apparent

digestibilities of DM, NDF, and starch


DMI, kg 25.9 27.1 23.6 25.3 1.28 0.21 0.87

INDFI, kg 2.8 3.1 2.5 2.8 0.17 0.62 0.74

Feces, kg 9.4 10.7 8.9 10.3 0.74 0.27 0.96

DMD1, % 63.7 60.9 62.4 59.3 1.43 0.08 0.88

NDFD2, % 45.5 45.2 47.7 44.0 1.58 0.70 0.31

StarchD3, % 95.1 95.7 94.4 94.0 0.81 0.22 0.40

1DMD = DM digestibility.

2NDFD = NDF digestibility.

3StarchD

= starch digestibility.

205

Figure C-1. Mean rumen digesta particles of all treatments retained on 1.18-, 0.6-, 0.15-mm

screens, soluble fraction, and soluble DM to retained DM ratio throughout the d.

206

A

B

207

C

D

Figure C-2. Effect of feeding Short (A), Medium (B), Long (C), and Extra Long (D) TMR on

rumen digesta particles retained on 9.5-, 6.7-, and 3.35-mm screens throughout the d.

208

A

B

209

C

Figure C-3. Effect of feeding TMR of increasing particle size on fecal NDF (A), indigestible

NDF (B), and starch (C) concentration throughout the d.

210

Figure C-4. Effect of feeding TMR of increasing particle size on fecal geometric mean particle

length (calculated using data from all particle fractions) throughout the d.

Vita

Daryl D. Maulfair

Education

Doctor of Philosophy in Animal Science August 2011

The Pennsylvania State University, University Park, PA

Bachelor of Science in Animal Sciences May 2006

The Pennsylvania State University, University Park, PA

Business/Management Option

Professional Experience

Research Assistant: The Pennsylvania State University, University Park, PA 2006–11

Dairy Sales/Marketing Internship: Pennfield Corporation, Lancaster, PA 2005

Dairy Sales Externship: Pennfield Corporation, Lancaster, PA 2005

Work Study: The Pennsylvania State University, University Park, PA 2002–05

Dairy Consultant Internship: Red Dale Ag Service, Orwigsburg, PA 2003

Dairy Production: Maulfair-Acres, Jonestown, PA 1996–2006

Peer Reviewed Publications

Maulfair, D. D., M. Fustini, and A. J. Heinrichs. 2011. Effect of varying TMR particle size on

rumen digesta and fecal particle size and digestibility in lactating dairy cows. J. Dairy

Sci. 94:3527–3536.


chewing behavior, production, and rumen fermentation in lactating dairy cows. J. Dairy

Sci. 93:4791–4803.


analyzing ration sorting and rumen digesta particle size in dairy cows. J. Dairy Sci.

93:3784–3788.

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