USING DUAL-FLOW CONTINUOUS CULTURE SYSTEM TO ESTIMATE RUMINAL FERMENTATION RESPONSES TO DIETARY MANIPULATIONS

By

VIRGINIA LUCIA NEVES BRANDAO

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2019

© 2019 Virginia Lucia Neves Brandao

To my mom, sister, brother and Marcos With love and eternal gratitude

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ACKNOWLEDGMENTS

I would like to express my deepest and sincere gratitude to my advisor Dr.

Antonio Faciola. He has believed in my work since day one and I am sure that my

accomplishments during this period would never be possible without his support in

every single step of the journey. When he asked during my interview for this position

why I wanted to do my PhD in the USA, I answered that it was my dream. Ever since

that day, he has believed in me, and has helped me to accomplish this dream. I stop

counting how many times I cried in his office because I was stuggling with experiments,

manuscripts, classes, etc. and he always found a way to calm me down and find

solutions. Thank you for pushing me to go further, to work out of my comfort zone, to

allow me to express my scientific curiosity, to let me find my own questions and answer

them, to offer me countless opportunities to learn from the best, and to show me that

there is no such thing as dreaming too much or too big.

My appreciation to the committee members Drs. Staples (in memory), Ferraretto,

Laporta, and Sollenberger. Thank you for valuable inputs and advice on my projects

and career. A special thanks to Dr. Ferraretto for helping me on my meta-analysis

projects and for answering the many questions I had about career opportunities. To Dr.

Laporta, thank you for the opportunity to work as your TA. Even though my time spent

with the committee was short I deeply appreciate their constructive criticism and

feedback; they really contributed to my growth and development as scientist.

A very special acknowledgment goes to my lab group, especially the ones that

were right by my side since the beginning: Lorrayny (our little mommy), Eduardo (my

brother), and Xiaoxia (my little Chinese sister). Graduate school is very hard to go

through and you made this period very pleasant, my best memories of graduate school

5

are with you. One day I was very busy and stressed with my experiments and I was not

eating well. Lorrayny and Eduardo noticed that and brought me a lunch box full of

homemade food. That meant so much to me, I will be forever grateful for having you as

friends. They taught me that we are stronger as a group than as an individual, and that

the hard work always pays off! Thank you for taking care of me when I had my appendix

removed, for making me feel loved and at home, you were my family here. Thank you

for all the laughs and tears. A very especial thanks to Lorrany who taught me that

perfectionism is important, that there was no such thing as giving up on something, for

always noticing when I was broken hearted and for helping me recover from all battles.

My appreciation to my lab mates that started later, but also have especial place in my

heart: Hugo (boy), Jose (Alberto), Jim and Sarah. Thank you for helping to go through

the challenges, for always cheering and helping me to achieve my goals. Working at the

Faciola lab was the most amazing experience of my life, that has shaped me forever. I

will miss this lab dearly. I wanted to thank the visiting scholars that stayed with us for a

short time but were crucial to the success of my projects: Ana Laura Lelis, Lais

(querida) Tomaz, Richard Lobo, Bruna Calvo, Mariana Nehme, Patricia, Claudinha

Sampaio, Perivaldo Carvalho, Leni Lima, Rasiel Restelatto, Andre Avila, and Andressa

Faccenda.

I express appreciation to the Department of Agriculture, Nutrition, & Veterinary

Sciences of the University of Nevada, Reno, for the opportunity to start my PhD, and to

Dr. Teshome Shenkoru for assistance on my first projects. I am grateful to the Animal

Sciences Department of the University of Florida for receiving me with very open arms,

for the opportunity to earn my degree, and most important for the opportunity to learn,

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grow and develop as scientist. The UF faculty, staff and students made me feel

welcomed and at home. Special thanks for Joyce Hayen, Debbie Nagy, Renee Parks-

Jaime, Pam Krueger and Karen Webb for helping in everything needed for the lab.

To Dr. Katie Schoenberg, which has been my mentor since 2016, I say thank you

for guiding, inspiring and mentoring me during very tough decisions. Thank you for

sharing your experience and perspective, for your constant support during this journey,

for empowering me to be the best version of myself, and for being a role model of a

scientist and women. I am very grateful to have you in my life. To Joanna Karavolias,

thank you for the friendship, for the tail gates and barbecues. Thank to all my friends,

that were close or far away and that somehow managed to be present during this

journey.

Finally, thank you to my beloved mom, who gave up so many things in her life to

make sure that her kids were pursuing their dreams. Thanks, Mom, for teaching me that

education is the only way to change our lives, and for never allowing me to give up. You

have been and will always be my biggest inspiration and role model. Thank you for

enduring my absence, for being present even though you were many miles away, for

sending your love every day, and for teaching me that we need to spread our love

everywhere we go. Thank you for giving me wings to fly as high as I wanted. Your

lessons will never be forgotten. Thanks to my sister, beloved godson Henrique, my

brother and nieces, for encouraging me to do my best, to work hard and to always

believe that I could go further. You are my best friend since the day I was born, and

your videos, messages, and pictures gave me strength and filled me with love. To all my

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family: Eduardo, Paula, Socorro, Armando, Amanda, Felipe and Fernando, thank you

for being always cheering my little victories.

To Marcos, words cannot describe my gratitude for having you by side every day.

Thank you for showing me that I have no limitations, that the sky is the limit and that

there is nothing so good that it cannot be improved. Thank you for showing that if I work

hard enough, I can accomplish everything I set my mind and heart to. You are not only

the love of my life, but an inspiration. Thank you for tireless help, for being my best

intern, and your patience when we were far away from each other.

May my dreams keep taking me further.

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TABLE OF CONTENTS page

ACKNOWLEDGMENTS ...................................................................................................... 4

LIST OF TABLES .............................................................................................................. 11

LIST OF FIGURES ............................................................................................................ 13

LIST OF ABBREVIATIONS ............................................................................................... 14

ABSTRACT ........................................................................................................................ 17

1 INTRODUCTION ........................................................................................................ 19

2 LITERATURE REVIEW .............................................................................................. 22

Camelina sativa........................................................................................................... 22 Chemical Composition ......................................................................................... 22 Use in Ruminant Diets ......................................................................................... 23

Dual-flow Continuous Culture System ....................................................................... 28 Overview ............................................................................................................... 28 Comparisons Between Dual-flow Continuous Culture and in vivo

Fermentation Data ............................................................................................ 31

3 EFFECT OF REPLACING CALCIUM SALTS OF PALM OIL WITH CAMELINA SEED AT 2 DIETARY ETHER EXTRACT LEVELS ON DIGESTION, RUMINAL FERMENTATION, AND NUTRIENT FLOW IN A DUAL-FLOW CONTINUOUS CULTURE SYSTEM ................................................................................................... 35

Summary ..................................................................................................................... 35 Introductory Remarks ................................................................................................. 36 Materials and Methods ............................................................................................... 37

Experimental Design and Diets ........................................................................... 37 Dual-Flow Continuous Culture System ............................................................... 39 Experimental Procedures and Sample Collections ............................................. 40 Chemical Analysis ................................................................................................ 41 Statistical Analysis................................................................................................ 45

Results and Discussion .............................................................................................. 45 Overall Ruminal Metabolism Effects .................................................................... 45 Ruminal Nitrogen Metabolism and Amino Acids ................................................. 52

Conclusions ................................................................................................................. 54

4 EFFECTS OF REPLACING CANOLA MEAL WITH SOLVENT EXTRACTED CAMELINA MEAL ON MICROBIAL FERMENTATION IN A DUAL-FLOW CONTINUOUS CULTURE SYSTEM ......................................................................... 65

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Summary ..................................................................................................................... 65 Introductory Remarks ................................................................................................. 66 Materials and Methods ............................................................................................... 68

Experimental Design and Diets ........................................................................... 68 Dual-Flow Continuous Culture System ............................................................... 69 Experimental Procedures and Sample Collections ............................................. 70 Chemical Analysis ................................................................................................ 72 Calculations .......................................................................................................... 74 Statistical Analysis................................................................................................ 74

Results ........................................................................................................................ 75 Nutrient Digestibility and Volatile Fatty Acids ...................................................... 75 Nitrogen Metabolism and Amino Acid Outflow .................................................... 76

Discussion ................................................................................................................... 77 Conclusions ................................................................................................................. 84

5 UNVEILING THE RELATIONSHIPS BETWEEN DIET COMPOSITION AND FERMENTATION PARAMETERS RESPONSE IN DUAL-FLOW CONTINUOUS CULTURE SYSTEM: A META-ANALYTICAL APPROACH ...................................... 95

Summary ..................................................................................................................... 95 Introductory Remarks ................................................................................................. 96 Materials and Methods ............................................................................................... 98

Data Collection and Preparation .......................................................................... 98 Model Derivation Procedure ................................................................................ 99

Results and Discussion ............................................................................................100 Effects on True Ruminal Digestibility .................................................................100 Effects on Volatile Fatty Acid .............................................................................102 Effects on N Metabolism ....................................................................................108

Conclusions and Implications ...................................................................................111

6 HOW COMPARABLE IS MICROBIAL FERMENTATION DATA FROM DUAL-FLOW CONTINUOUS CULTURE SYSTEM TO OMASAL SAMPLING TECHNIQUE? A META-ANALYTICAL APPROACH ...............................................119

Summary ...................................................................................................................119 Introductory Remarks ...............................................................................................120 Materials and Methods .............................................................................................122

Data Collection and Preparation ........................................................................122 Data Cleaning and Model Derivation Procedure ...............................................124

Results ......................................................................................................................125 Independent Variables: NDF digestibility and Dietary NFC ..............................125 Independent Variables: CP Digestibility and Efficiency of Microbial Protein

Synthesis .........................................................................................................127 Discussion .................................................................................................................129

Carbohydrates ....................................................................................................129 Nitrogen Metabolism ..........................................................................................131 Dependent Variables Affected by Method .........................................................134

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Implications ...............................................................................................................137

7 RESEARCH SUMMARY ..........................................................................................147

LIST OF REFERENCES .................................................................................................151

BIOGRAPHICAL SKETCH ..............................................................................................168

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LIST OF TABLES

Table page 3-1 Ingredient and chemical composition of the experimental diets ........................... 56

3-2 Fatty acid composition of the experimental diets .................................................. 57

3-3 Amino acids composition of the experimental diets .............................................. 58

3-4 Effects of camelina seed supplementation at two dietary ether extract levels on ruminal true digestibility in dual-flow continuous culture system ..................... 59

3-5 Effects of camelina seed supplementation at two dietary ether extract levels on volatile fatty acids concentration in dual-flow continuous culture system ....... 60

3-6 Effects of camelina seed supplementation at two dietary ether extract levels on ruminal effluent fatty acid concentration and biohydrogenation in dual-flow continuous culture system ...................................................................................... 61

3-7 Effects of camelina seed supplementation at two dietary ether extract levels on ruminal pH and nitrogen metabolism in dual-flow continuous culture system ..................................................................................................................... 63

3-8 Effects of camelina seed supplementation at two dietary ether extract levels on amino acid ruminal effluent outflow in dual-flow continuous culture system ... 64

4-1 Ingredient and chemical composition of the experimental diets (% DM unless otherwise stated) .................................................................................................... 86

4-2 Nutrient composition of protein supplement used on the experimental diets ....... 87

4-3 Amino acid composition of experimental diets and protein supplements ............. 88

4-4 Effects of replacing canola meal with solvent extracted camelina meal on nutrient true digestibility of DM, OM and CP, NDF and ADF in dual-flow continuous culture system ...................................................................................... 89

4-5 Effects of replacing canola meal with solvent extracted camelina meal on volatile fatty acids total concentration and molar proportion in pooled effluent in dual-flow continuous culture system .................................................................. 90

4-6 Effects of replacing canola meal with solvent extracted camelina meal on nitrogen metabolism in dual-flow continuous culture system ................................ 91

4-7 Effects of replacing canola meal with solvent extracted camelina meal on amino acid flow in dual-flow continuous culture system ....................................... 92

5-1 Descriptive statistics .............................................................................................113

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5-2 Equations using fermentor dry matter intake (DMI) as independent variable ....114

5-3 Equations using dietary neutral detergent fiber (NDF) as independent variable .................................................................................................................115

5-4 Equations using dietary crude protein (CP) as independent variable.................116

6-1 Descriptive statistics for dataset comparing microbial fermentation from continuous culture system and omasal sampling technique studies ..................139

6-2 Regressions developed using neutral detergent fiber digestibility as independent variable comparing microbial fermentation from continuous culture system and omasal sampling technique studies .....................................140

6-3 Regressions developed using dietary non-fiber carbohydrate as independent variable comparing microbial fermentation from continuous culture system and omasal sampling technique studies ..............................................................141

6-4 Regressions developed using true crude protein digestibility as independent variable comparing microbial fermentation from continuous culture system and omasal sampling technique studies ..............................................................142

6-5 Regressions developed using efficiency of microbial protein synthesis as independent variable comparing microbial fermentation from continuous culture system and omasal sampling technique studies .....................................143

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LIST OF FIGURES

Figure page 4-1 Effect of replacing canola meal with camelina meal solvent extracted on

diurnal variation of acetate and propionate concentration inside the fermentors in dual-flow continuous culture system. .............................................. 93

4-2 Effect of replacing canola meal with solvent extracted camelina meal on diurnal variation of ammonia nitrogen concentration (NH3-N) inside the fermentors in dual-flow continuous culture system. .............................................. 94

5-1 Concentration of total volatile fatty acids, and molar proportion of acetate, and propionate using dietary NDF as independent variable. ..............................117

5-2 Ammonia nitrogen concentration and non-ammonia nitrogen flow using dietary crude protein (CP) as independent variable. ...........................................118

6-1 Adjusted molar proportion of acetate and propionate regressed with neutral detergent fiber digestibility, regressed with dietary non-fiber carbohydrates, and regressed with true crude protein digestibility. .............................................144

6-2 Adjusted concentration of ammonia regressed with neutral detergent fiber digestibility and true crude protein digestibility. ...................................................145

6-3 Adjusted proportion of bacterial nitrogen and nonammonia nonmicrobial nitrogen from total nitrogen flow, efficiency of microbial protein synthesis and efficiency of nitrogen use regressed with true crude protein digestibility ...........146

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LIST OF ABBREVIATIONS

AA Amino acids

ADF Acid detergent fiber

Arg Arginine

ß0 Intercept

ß1 slope

BCAA Branched-chain amino acids

BCVFA Branched-chain volatile fatty acids

BH Biohydrogenation

BW Body weight

CAM Camelina meal

CH4 Methane

CLA Conjugated linoleic fatty acids

CM Canola meal

CO Camelina oil

CO2 Carbon dioxide

CoEDTA Cobalt ethylenediaminotetraacetate

CP Crude protein

Cr-EDTA Chromium ethylenediaminotetraacetate

Cr-mordant Chromium mordant

CS Camelina seed

CS5 7.7% camelina seed supplementation at 5% ether extract

CS8 17.7% camelina seed supplementation at 8% EE

D Day

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DFCCS Dual-flow continuous culture system

DM Dry matter

DMI Dry matter intake

EE Ether extract

EMPS Efficiency of microbial protein synthesis

ENU Efficiency of nitrogen use

FA Fatty acids

FAME Fatty acid methyl ester

GC Gas chromatograph

His Histidine

HPLC High pressure liquid chromatograph

iNDF Indigestible neutral detergent fiber

LiCoEDTA Lithium cobalt ethylenediaminotetraacetate

Lys Lysine

MEG5 Calcium salts of palm oil supplementation at 5% ether extract

MEG8 Calcium salts of palm oil supplementation at 8% ether extract

Met Methionine

Min Minutes

MP Metabolizable protein

MUFA Monounsaturated fatty acids

N Nitrogen

NAN Non-ammonia nitrogen

NANMN Nonammonia nonmicrobial nitrogen

NDF Neutral detergent fiber

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NDFD Neutral detergent fiber digestibility

NFC Non-fiber carbohydrate

NH3-N Ammonia nitrogen

NH3-Nf Ammonia nitrogen flow

OM Organic matter

OST Omasal sampling technique

PUFA Polyunsaturated fatty acid

RDP Rumen degraded protein

RUP Rumen undegraded protein

S Seconds

SCAM Solvent extracted camelina meal

SFA Saturated fatty acids

T3 Triiodothyronine

T4 Thyroxine

TCPD True crude protein digestibility

TOMD True organic matter digestibility

UFA Unsaturated fatty acid

VFA Volatile fatty acids

Yb-acetate Ytterbium acetate

Yb-chloride Ytterbium chloride

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Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

USING DUAL-FLOW CONTINUOUS CULTURE SYSTEM TO ESTIMATE RUMINAL

FERMENTATION RESPONSES TO DIETARY MANIPULATIONS By

Virginia Lucia Neves Brandao

August 2019

Chair: Antonio Faciola Major: Animal Sciences

Camelina is an oilseed (CS) high in unsaturated fatty acids, while camelina meal

(CAM) has approximately 39% crude protein (CP) and comparable amino acid profile

(AA) to canola meal (CM). The dual-flow continuous culture system (DFCCS) is an in

vitro system developed to simulate the differential flows of liquid and solids from the

rumen. This dissertation had three major objectives: 1) to evaluate the use of CS and

solvent-extracted CAM (SCAM) in dairy diets, using the dual-flow continuous culture

system (DFCCS); and 2) to summarize the literature and, using a meta-analytical

approach, investigate the relationship between diet composition and microbial

fermentation end-products in a DFCCS and finally; 3) to evaluate carbohydrate and

nitrogen (N) metabolism, comparing two methods: DFCCS or omasal sampling

technique (OST). The first study aimed to assess the effects of replacing calcium salts

of palm oil (MEG) with CS on ruminal fermentation, digestion, and nutrient flows in a

DFCCS when supplemented at 5 or 8% dietary ether extract (EE). We concluded that

supplementation of CS resulted in a greater proportion of biohydrogenation

intermediates and propionate, and at 5% EE, had similar N metabolism as diet using

CM. This suggests CS can be used up to 5% dietary EE without compromising N

18

metabolism and AA outflow. The second study aimed to assess the effects of replacing

CM with solvent extracted camelina meal (SCAM) in lactating dairy cow diets. We

concluded that SCAM inclusion increased propionate and decreased ammonia N,

indicating that it can be a potential replacement for CM. The objective of the third study

was to investigate the functional form of the relationship between diet composition

(dietary CP, NDF) and amount of substrate (fermentor DMI) with microbial fermentation

end-products in a DFCCS using a meta-analysis. It was concluded that overall the

responses to dietary manipulation in a DFCSS are similar to the responses commonly

observed in vivo studies. The fourth study aimed to evaluate carbohydrate and N

metabolism using a meta-analytical approach, comparing two methods: DFCCS or

omasal sampling technique (OST). It was concluded that overall, functional responses

to dietary manipulations in DFCCS is similar to OST.

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CHAPTER 1 INTRODUCTION

Camelina sativa (CS) is an oilseed from the mustard family (Brassicaceae

family). Recently, the interest in research evaluating camelina has been renewed due to

increased demand and governmental policies stimulating the use of renewable biofuel

sources (Sorda et al., 2010). Camelina has several agronomic features that make it

suitable to grow in certain regions of the USA and Canada, such as tolerance to drought

and cold climates (Zubr, 1997). The oil has up to 74% unsaturated fatty acids, and

contains approximately 20 to 40% C18:3, 10 to 20% C18:2, 12 to 25% C18:1 and 13 to

21% C20:1 (Hurtaud and Peyraud, 2007a). However, it also contains anti-nutritional

factors, such as erucic acid, which ranges from 2 to 5% (Hurtaud and Peyraud, 2007a),

and glucosinolates (Kramer et al., 1990). Glucosinolates can potentially affect thyroid

function; however, this effect in ruminant might be attenuated. The studies of

Cappellozza et al. (2012) and Lawrence et al. (2016) using heifers fed CAM at 10% DM

demonstrated that circunlating T3 and T4 concentrations were not affected by CAM

intake.

The by-product from CS biofuel industry is camelina meal (CAM), and it is

obtained through mechanical pressing using an expeller (Fröhlich and Rice, 2005; Ye et

al., 2016). The chemical composition of this meal can vary widely, containing

approximately 38% CP (Hixson and Parrish, 2014; Brandao et al., 2018). However,

because CAM is usually generated using a cold-press process, the residual oil is still

relatively high, ranging from 10 to 20% (Pekel et al., 2009, 2015). It has comparable

amino acid (AA) composition as canola, differing primarily on its greater arginine content

(Colombini et al., 2014). However, due to relatively high concentration of anti-nutritional

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factors such as erucic acid and glucosinolates, the inclusion of CAM in ruminants’ diets

is limited at 10% DM, by the Food and Drug Administration (FDA).

Camelina seed has the potential to alter milk fatty acids composition due to its

high unsaturated fatty acids conconcentration; while CAM could be used as high-quality

protein source with comparable AA profile as canola meal. Therefore, this dissertation

aimed to assess the effects of supplementing CS and CAM on microbial fermentation,

digestion, and flows of FA and AA in a dual-flow continuous culture system. The first

study presented in Chapter 3, investigated the effects of replacing calcium salts of palm

oil with CS on ruminal fermentation, digestion, and flows of FA and AA in a dual-flow

continuous culture system when supplemented at 5 or 8% dietary EE. While, in Chapter

4 a solvent-extraction was performed on ground CS in an experimental scale, yielding

solvent extracted CAM (SCAM). In this chapter, the effects of replacing canola meal

(CM) with SCAM were assessed in lactating dairy cow diets and determined the effects

of SCAM on microbial fermentation and AA flow in a dual-flow continuous culture

system.

The rumen is the main site of fiber, carbohydrates, and protein digestion in

ruminants; therefore, understanding the ruminal fermentation process is crucial to

improve animal performance. However, digestion and passage rates are two

competitive processes (Mertens, 1977) that are difficult to study separately in vivo. In

the dual-flow continuous culture system (DFCCS), liquid and solid passage rates are

controlled, which allows evaluation of microbial fermentation under the same dry matter

intake (DMI) and passage rates. The DFCCS was described by Hoover et al. (1976),

aiming to simulate the continuous differential flows of liquid and solids from the rumen,

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with continuous removal of fermentation end-product. It is a long-term fermentation

system, with studies of 3-4 experimental periods varying from 8 (Calsamiglia et al.,

2002a) to 11 days each period (Dai et al., 2019), which might explain the more close

response to in vivo, than closed vessel incubations (Hoover et al., 1976). Furthermore,

the system allows for intense sampling, determination of degradation rates, and testing

feed additives in early development stages that are not yet produced in large scale,

under tightly controlled conditions.

Currently, the omasal sampling technique (OST), described by Huhtanen et al.

(1997) and modified by Ahvenjärvi et al. (2000), has been widely used for determining

ruminal fermentation and nutrient flow in dairy and beef cattle. Although this technique

provides valuable results and is considered adequate to estimate ruminal fermentation

and nutrient flow, it is laborious and expensive. Therefore, alternative techniques

capable of accurately simulating ruminal fermentation, such as DFCCS, are important to

be evaluated.

The use of the DFCSS to simulate microbial fermentation has been increased in

the past decade due to increased concerns regarding the use of animals in research,

the high cost of in vivo trials, and the possibility of testing a large variety of dietary

treatments in a short period of time. In Chapter 5, the functional form of the relationship

between diet composition and amount of substrate (fermentor DMI) with microbial

fermentation end-products was investigated in a dual-flow continuous culture system

using a meta-analytical approach. In Chapter 6, carbohydrate and N metabolisms were

evaluated using a meta-analytical approach comparing two methods: DFCCS or OST.

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CHAPTER 2 LITERATURE REVIEW

Camelina sativa

Camelina Chemical Composition

The recent governmental incentives to expand biofuel production have

encouraged research, development, and production of alternative oilseeds around the

world (Sorda et al., 2010), primarily oilseeds with high oil yield that can grow in areas

where traditionally used crops cannot. This justifies the renewed interest on Camelina

sativa. This is an oil seed from the Brassicaceae family, also known as the Mustard

family, which also encompasses other oilseeds such as canola, rapeseed, and carinata.

Camelina is adapted to dry and cold regions, which makes it particularly

interesting for certain areas of the USA and Canada. Camelina has multiple important

agronomic features, such as moderate to low nutrient requirements, tolerance to saline

soils, and relatively greater resistance to diseases, which results in lesser need for

pesticides (Zubr, 1997). The seed contains approximately 42% oil (%DM; Zubr, 1997),

in which up to 74% is polyunsaturated fatty acids (PUFA; Hurtaud and Peyraud, 2007).

Camelina seed (CS) has comparable concentration of C18:3n-3 to linseed, representing

approximately 30 to 40% of linolenic and 20% of linoleic acids in the total fatty acids

(Budin et al., 1995).

The by-product from CS biofuel industry is camelina meal (CAM), and the

chemical composition of this meal can vary widely according to oil extraction method. It

contains approximately 38% CP (Hixson and Parrish, 2014; Brandao et al., 2018), and

has comparable amino acid (AA) composition as canola, differing primarily in greater

arginine content (Colombini et al., 2014). Currently, CAM is obtained industrially through

23

a cold-pressing process which results in a remaining oil concentration ranging from 10

to 20% (Pekel et al., 2009, 2015). However, to enable greater CAM inclusion in

ruminant diets and make this crop more competitive, a more efficient oil extraction is

needed.

Another limiting factor of CAM inclusion is the anti-nutritional factors such as

glucosinolates and erucic acid. Glucosinolates are present in many of the plants

belonging to the Brassicaceae family, and they are biologically inactive molecules.

Therefore, glucosinolates per se are not detrimental to animal health, however their

metabolites (thiocyanate, vinyloxazolidinethiones, and isothiocyanate) have been

associated with depressed thyroid function, by interfering with iodine uptake, which may

affect animal growth and performance (Forss and Barry, 1983; Marillia et al., 2014).

Ruminants are likely more tolerant to glucosinolates than monogastric animals, however

prolonged exposure has been shown to depress thyroid function in cattle (Laarveld et

al., 1981; Vincent et al., 1988), DMI (Lardy and Kerley, 1994), and milk yield (Waldern,

1973).

Camelina contains approximately 3.0% of erucic acids (Budin et al., 1995; Zubr,

1997). Erucic acid can be harmful to human health and the US Food and Drug

Administration (FDA) has established a limit of 2% erucic acid in total dietary FA;

however, in Europe, the limit is 5% of erucic acid in total dietary FA (EFSA, 2016) in

ruminant’s diets. Therefore, due to relatively high erucic concentration and

glucosinolates, CAM inclusion in ruminant diet is limited currently at 10% DM.

Use of Camelina in Ruminant Diets

Camelina has been tested in vivo for dairy cows (Hurtaud and Peyraud, 2007b;

Halmemies-Beauchet-Filleau et al., 2011, 2017; Bayat et al., 2015), beef cattle

24

(Cappellozza et al., 2012), lambs (Noci et al., 2011), dairy sheep (Mierlita and Vicas,

2015), in vitro (Colombini et al., 2014) and in a dual-flow continuous culture system

(Brandao et al., 2018a; b). It has been used as seed, oil, meal or cake, however, due to

FDA regulations, the inclusion in vivo has not been greater than 10% (DM).

In a study using grass silage as forage source and 60 g/kg of camelina oil (CO)

mixed in the concentrate fed to lactating dairy cows, Bayat et al. (2015) reported that

CO reduced CH4 and CO2 emissions compared with the control diet. Cows fed CO also

had lower DMI, milk yield and milk fat yield than cows fed control diet. It has been

reported that feeding high levels of fat can compromise DMI, ruminal fermentation, and

milk yield (Palmquist and Jenkins, 1980; Allen, 2000; Kazama et al., 2010; Rodrigues et

al., 2019). In the Bayat et al. (2015) experiment, the CO diet contained 77.3 g/kg DM of

total fatty acids, while control diet had 21.9 g/kg. Therefore, it is possible that the

deleterious effect reported in DMI, milk yield and components could be attributable to

the high dietary EE instead of the use of CO.

When moderate levels of oil are fed to dairy cows, the possible adverse effect in

performance and intake may be avoided. In an experiment evaluating the effect of

supplementing rapeseed oil, or sunflower oil, CO or CAM, Halmemies-Beauchet-Filleau

et al. (2011) reported similar DMI, total tract nutrient digestibility, and milk yield in dairy

cows fed the fat supplement compared with a control diet. In this study, cows were fed

29 g/kg of lipids from different fat supplements, which resulted in a concentrate

containing approximately 5.4% fatty acids (total concentrate DM) in fat supplemented

diets, and 3.14% in the control diet. Although milk yield, energy corrected milk, and milk

components were not affected by treatments, the milk fat composition was different

25

across treatments. Supplementation of plant oils increased concentration of unsaturated

FA and long-chain FA containing 18 carbons, primarily conjugated linoleic FA (CLA), cis

C18:1 and trans C18:1, compared with the control (no oil supplementation). The

mammary gland synthesizes de novo all FA containing 4 to 14 carbons, most of those

containing 14 carbons and approximately 50% of the 16-carbon FA (Chilliard et al.,

2007). Therefore, this increase in the proportion of 18 carbons in milk FA when these

plant oils were fed suggested that there was an inhibition of de novo FA synthesis in the

mammary gland (Givens and Shingfield, 2006). This response might be explained by

the studies of Shingfield et al. (2010) who reported that long-chain FA have an inhibitory

effect on Acetyl-CoA carboxylase activity, reducing de novo FA synthesis of saturated

FA in the mammary gland.

Therefore, CO or CAM can be used in dairy diet to manipulate milk FA

concentration. However, when comparing CO with CAM they led to different proportions

of 18-carbon FA in milk. Halmemies-Beauchet-Filleau et al. (2011) also reported that

supplementing CAM resulted in less complete biohydrogenation than CO, due to

greater accumulation of numerous biohydrogenation intermediates, such as a 2-fold

greater concentration of trans-10 18:1, trans-11 18:1, and cis-9, trans-11 CLA in fat milk

from cows fed CAM. This difference is likely associated with the partial protection of

ruminal fermentation provided by the seed hulls when fed as CAM, rather than as oil

(CO).

Milk FA composition can be modulated through dietary manipulations; therefore,

CS or CAM can be used to alter milk FA composition. In a study aiming to evaluate the

effects of feeding CS (630 g/d) or CAM (2 kg/d) to dairy cows on fatty acid composition

26

of dairy products and the properties of butter, Hurtaud and Peyraud (2007) reported that

feeding camelina increased milk C18:1 trans isomers, in which CM had 11 times greater

trans-10, and 2.6 times greater trans-11 C18:1. They also reported that CLA, and

primarily rumenic acid (cis-9, trans-11 C18:2) concentration increased in the milk FA

from cows fed camelina diets. Consequently, butter spreadability was affected, making

it softer and more spreadable when camelina was fed. Additionally, in this experiment

feeding CS or CAM did not affect DMI, BW, NEFA, milk yield or ruminal pH; however, it

did reduce milk fat concentration. Regarding production of VFA, feeding camelina

reduced acetate, increased propionate and did not affect total VFA concentration (after

feeding). It is interesting to note that authors reported that CAM had greater day-to-day

variation in intake than control and CS cows, and that one cow refused to eat CAM in

the last experimental period. It is possible that CAM had an oxidation process during the

experiment, which may have led to this effect, or this could have been unrelated to CAM

and due to some other unaccounted factor. Additionally, CAM had stronger effects on

decreasing milk fat than CS, and this may be attributed to the partial protection provided

by the seeds hulls, which result in lower ruminal degradation of seed comparing with

meal.

Aside from the use as a milk composition modulator, camelina can also be used

to attenuate stress caused by transportation (Cappellozza et al., 2012) and increase

plasma PUFA in beef animals (Moriel et al., 2011). In a study evaluating the effects of

replacing corn and soybean meal with CAM on performance and reproduction of

replacement beef heifers for two years, Moriel et al. (2011) reported greater first-service

pregnancy rates to timed artificial insemination in heifers fed CAM. There was no

27

treatment effect for DMI, BW, or ADG during the two years of evaluation, and CAM was

successful in increasing plasma PUFA. Authors hypothesized that feeding PUFA, using

CAM would improve reproductive performance, however they reported only a numerical

and non-significant 17% improvement in pregnancy rate in heifers fed CAM compared

to heifers fed a control diet, and a significant increase in pregnancy rates to timed

artificial insemination. Additionally, authors evaluated the effects of feeding CAM on the

thyroid function for an extended period of time (two years) and did not observe

decreased concentration of thyroid hormones synthesis. Overall, this study

demonstrated that feeding CAM at 0.33% BW can replace corn and soybean meal in

diets for beef heifers, resulting in similar overall performance and with no compromise in

thyroid function.

Beef animals may be susceptible to an immunological challenge when exposed

to stressful procedures such as transportation and weaning (Arthington et al., 2008).

Supplementation of PUFA alleviated the acute-phase response caused by stressful

procedures (Araujo et al., 2010; Cooke and Bohnert, 2011), therefore, CAM could be

strategically supplemented during this phase. In a series of three experiments,

Cappellozza et al. (2012) evaluated the effect of feeding CAM to steers on DMI,

digestibility, performance, and thyroid function. Similar to previous studies (Moriel et al.,

2011), authors reported no effect of feeding CAM on thyroid function. Regarding

immunological response after a corticotropin-releasing hormone (CRH) challenge,

steers fed CAM had reduced ceruloplasmin and tended to have lower haptoglobin

concentrations, which are variables related to acute-phase response. These results

indicate that CAM supplementation lessened the acute-phase protein reaction after an

28

induced immunological challenge, therefore it can be used to attenuate deleterious

effects of stressful events. Additionally, authors also reported an improvement in feed

efficiency when CAM was fed.

Camelina has been evaluated and studied under different diets to modulate milk

fat composition, intake, feed efficiency, and immunological response. However, studies

investigating the effects of CAM on microbial fermentation, digestibility and flows of AA

and FA are warranted. Therefore, in Chapter 3 it was evaluated the effects of CS, and in

Chapter 4 it was investigated the effect of solvent-extracted CAM on microbial

fermentation.

Dual-flow Continuous Culture System

Overview

In vitro ruminal fermentation techniques have been used for at least 85 years

(Ewart, 1974). The dual-flow continuous culture system (DFCCS) was described by

Hoover et al. (1976); however, continuous culture techniques to simulate ruminal

fermentation have been described since 1958 (Adler et al., 1958). The DFCCS consists

of a fermentation vessel, kept under anaerobic conditions through continuous infusion of

N2. It is composed of one feeding port, one port for artificial saliva infusion, and another

port for removal of liquid effluent. The liquid passage rate and artificial saliva infusion

are controlled by peristaltic pumps. Agitation is accomplished using a propeller located

inside the vessel, that is connected to one set of magnets located in the vessel and

another set located inside the base. Temperature is controlled and regulated using a

heater probe and a temperature sensor both located inside the vessel. In the DFCCS,

fermentation end-products are continuously removed from the system through outflow

of liquid and solid effluents during long fermentation periods, that can range from 8

29

(Calsamiglia et al., 2008) to 11 (Dai et al., 2019) days. Additionally, fresh substrate for

fermentation is daily added in the fermentation vessels. The batch culture technique

supplies an initial quantity of substrate that will be fermented, giving rise to fermentation

end-products that can be later quantified.

Therefore, the association of constant supply of feed and removal of fermentation

end-products during extended period assists in reducing the accumulation of

compounds that can inhibit fermentation (Ewart, 1974). Due to the anaerobic and

dynamic nature of the rumen, it has been determined that an in vitro ruminal

fermentation system should 1) be anaerobic, 2) have controlled temperature, 3)

constantly mix the contents, 4) remove products of fermentation, and 5) be continuous

(Adler et al., 1958). Therefore, this supports a hypothesis that continuous culture

systems result in fermentation responses that are more closely related to in vivo than

closed vessel incubations (Hoover et al., 1976b).

Several different types of continuous cultures have been described (Slyter et al.,

1964; Ewart, 1974), and even though bacterial number is maintained the protozoa

number is significantly reduced (Hoover et al., 1976b) compared with in vivo. Therefore,

Hoover et al. (1976) developed a continuous culture system using separate flows of

liquid and solid (DFCCS), in an attempt to have fermentation control, and increase

protozoa number. Their apparatus consists of a fermentation vessel, a water spray

regulated at 39ºC to maintain constant temperature, and inputs of buffer, pelleted solid

feed, N2 infuser, and a 13-mm port that allows the overflow of liquid and solid effluent.

They also developed a filter made of polyester fiber surrounded by a wire mesh of 3 mm

pore size, and a layer of nylon mesh (pore size 105 𝛍). This filter was developed aiming

30

to increase protozoa retention in the system. However, the limitation of this filter is it

needs to be replaced every 48 hours in order to keep the liquid flow at the adjusted

rates. Aside from the fact that it increases the labor, it also can compromise

fermentation because the vessels need to be completely open for filter replacement.

These authors were successful in increasing protozoa compared with single flow

systems. Another important contribution of this study was the description of a

mechanical feeding device that allows frequent feeding of continuous fermentation

systems.

The system described by Hoover et al. (1976) has been widely used in

continuous culture studies, however, some modifications have been made. For

instance, Hoover et al. (1976) used artificial saliva consisting of 60% McDougall solution

and 40% tap water, and currently the majority of DFCCS studies utilize the artificial

saliva developed by Weller and Pilgrim (1974), with addition of 0.4 g/l of urea. Recently,

Karnati et al. (2009) suggested some modifications of the filter developed by Hoover et

al. (1976), and they reported protozoa number relative to substrate concentration that

approximated those found in vivo experiments. However, authors also reported a 5-fold

lower protozoa number than Hoover et al. (1976) and justified this response due to the

greater substrate supplied in Hoover et al. (1976) (twice as much). Currently, most of

DFCCS studies use a regular filter, that is not capable of retaining protozoa inside the

vessel but it requires less maintenance than the filters described previously (Hoover et

al., 1976b; Karnati et al., 2009). Other modifications have been made and were

described previously (Del Bianco Benedeti et al., 2015; Brandao et al., 2018 a,b).

31

Comparisons Between Dual-flow Continuous Culture and in vivo Fermentation Data

Direct comparisons of DFCCS data with in vivo data have been made previously

(Hannah et al., 1986; Mansfield et al., 1995; Salfer et al., 2018). These three studies

were performed feeding the same diet composition to dairy cows and to DFCCS. To

reduce possible confounding effects of passage rate, the in vivo passage rate was

determined in a previous study and the fermentors passage rate was adjusted

accordingly. However, authors acknowledged that due to different DMI, it was not

possible to compare all response variables. When that was the case, the authors

focused on differences among treatments to verify if they were following a similar

pattern. Hannah et al. (1986) observed that digestibilities of OM (true) and CP and AA

degradability were similar in DFCCS to in vivo.

The study of Mansfield et al. (1994) used four lactating dairy cows cannulated in

the rumen and duodenum, and four DFCCS fermentors. They fed two levels of NFC (25

and 40%) and two RDP levels (9 and 12%, %DM). Overall, they observed lower

cellulolytic bacterial and protozoal concentration in DFCCS than in vivo, however

concentrations of amylolytic and proteolytic bacterial were similar. They also reported

greater total non-structural carbohydrates digestion and lower NDF digestibility for

DFCCS. Authors highlighted that this difference was mainly due to low estimation of

NDF digestibility and greater estimation of total non-structural carbohydrates in DFCCS

when the 40% NFC diet was fed. They suggested that using diets with high NFC in the

DFCCS might have resulted in an underestimation of NDF digestibility. Another possible

explanation for these results was the use of pelletized diet to feed the fermentors.

During the pelletization process, heat, moisture and pressure are used to produce

32

pellets, which can increase starch digestibility. Authors concluded that 80% of the

response variables evaluated had similar results comparing DFCCS with in vivo, and

therefore the system could be considered as a valuable tool to study ruminal

fermentation.

In a study evaluating how the microbial community in a DFCCS relates to the

rumen of dairy cattle, and also studying the changes in the microbial community over

time, Salfer et al. (2018) used the next-generation sequencing of 16S rRNA amplicons

approach to characterize microbial community. They fed the same diet to two rumen-

cannulated lactating dairy cows and eight fermentors and reported that richness and

diversity were lower in DFCCS compared with the rumen of dairy cows. This result may

be explained by the more homogeneous environment in the fermentors compared with

the rumen, which does not allow the formation of different microbial niches within the

fermentation vessel. The rumen is composed of various niches that present different

microbial communities, for instance the rumen mat and wall have different microbial

populations (Kong et al., 2010).

Interestingly, it was also demonstrated that the bacterial and archaeal

communities were stable at Day 3 after incubation and do not change until Day 9. This

suggests the time commonly allowed for adaptation in DFCCS (from 6 to 7 days)

studies could be potentially shortened. This study was not in agreement with Hoover et

al. (1976b), which demonstrated that overall fermentation parameters were stable from

days 6 to 9, and from days 11 to 14 of incubation. In a study using a continuous

fermentation system (RUSITEC), Lengowski et al. (2016) reported similar results to

those of Salfer et al. (2018), in which they observed stabilization of methanogens and

33

total bacteria from 2 to 13. The inconsistency among studies demonstrates that further

investigation under a wider range of diet types, fermentation systems and techniques is

warranted.

Other studies were carried out applying similar treatments to DFCCS and in vivo,

however, it was not always the objective to directly compare systems (Devant et al.,

2001; Dann et al., 2006; Sniffen et al., 2006). A systematic approach, using tools such

as meta-analysis may be important to evaluate fermentation responses generated using

DFCCS, because it can provide a quantitative assessment, isolating the effect of

individual studies.

Therefore, Hristov et al. (2012) performed a meta-analysis aiming to compare

ruminal fermentation data and the variability of the in vitro data with in vivo. They

separated the in vitro dataset into two groups: 1) studies using a RUSITEC system (a

continuous fermentation system) and 2) studies using any other system that simulates

ruminal fermentation using continuous culture (non-RUSITEC). In vivo digestibility data

used in this study to compare with in vitro originated from studies using total tract

digestibility. It was reported that non-RUSITEC studies had lower acetate and NDF

digestibility than in vivo, and that in vivo studies had the lowest variability in nutrient

digestibility data. It was reported that total VFA concentration from non-RUSITEC (mean

= 93.8 mM) studies was closer to in vivo (mean = 116.0 mM), than from RUSITEC (78.9

mM) studies. As expected, protozoa counts were lower in vitro studies than in vivo.

However, these in vitro systems can only simulate ruminal fermentation, and an

important limitation of this study was the use of in vivo digestibility data from total tract

compared with ruminal in vitro digestibility.

34

Ruminal fermentation, digestion and nutrient flow can be assessed using

different techniques, such as sampling from fistulae located in the abomasum or

duodenum (Ahvenjärvi et al., 2000a). However, intestinal and abomasal cannulation

require longer recovery time and are more difficult to manage than ruminal cannula

(Fatehi et al., 2015a). Additionally, there is an increased public awareness regarding

animal well-being and invasive techniques to estimate nutrient flow are becoming

difficult to justify. The omasal sampling technique (OST), described by Huhtanen et al.

(1997) and modified by Ahvenjärvi et al. (2000) is a well-accepted technique to assess

ruminal fermentation and nutrient flow. Although this technique provides valuable results

and is considered adequate to estimate ruminal fermentation and nutrient flow, it is

laborious and expensive. Alternatives to invasive experiments should be pursued and

evaluated.

To our knowledge, a systematic comparison of fermentation data generated

using DFCCS compared with ruminal fermentation data generated using OST has not

been done yet. Therefore, in Chapter 5 the functional form of fermentation response to

dietary manipulations was evaluated, performing a meta-analysis using 75 peer-

reviewed studies, and using fermentor DMI, and dietary NDF and CP as dependent

variables. In Chapter 6 carbohydrate and N metabolism were evaluated using a meta-

analytical approach to compare two methods: dual-flow continuous culture system

(DFCCS) and OST.

35

CHAPTER 3 EFFECT OF REPLACING CALCIUM SALTS OF PALM OIL WITH CAMELINA SEED

AT 2 DIETARY ETHER EXTRACT LEVELS ON DIGESTION, RUMINAL FERMENTATION, AND NUTRIENT FLOW IN A DUAL-FLOW CONTINUOUS

CULTURE SYSTEM

Summary

Camelina is a drought- and salt-tolerant oil seed, which in total ether extract (EE)

contains up to 74% polyunsaturated fatty acids. The objective of this study was to

assess the effects of replacing calcium salts of palm oil (Megalac, Church & Dwight Co.

Inc., Princeton, NJ) with camelina seed (CS) on ruminal fermentation, digestion, and

flows of fatty acids (FA) and AA in a dual-flow continuous culture system when

supplemented at 5 or 8% dietary EE. Diets were randomly assigned to 8 fermentors in a

2 × 2 factorial arrangement of treatments in a replicated 4 × 4 Latin square design, with

four 10-d experimental periods consisting of 7 d for diet adaptation and 3 d for sample

collection. Treatments were (1) calcium salts of palm oil supplementation at 5% EE

(MEG5); (2) calcium salts of palm oil supplementation at 8% EE (MEG8); (3) 7.7% CS

supplementation at 5% EE (CS5); and (4) 17.7% CS supplementation at 8% EE (CS8).

Diets contained 55% orchardgrass hay, and fermentors were fed 72 g of dry matter/d.

On d 8, 9, and 10 of each period, digesta effluent samples were taken for ruminal NH3-

N, volatile fatty acids, nitrogen metabolism analysis, and long-chain FA and AA flows.

Statistical analysis was performed using the MIXED procedure (SAS Institute Inc., Cary,

NC). It was detected an interaction between FA source and dietary EE level for acetate,

where MEG8 had the greatest molar proportion of acetate. Molar proportions of

propionate were greater and total volatile fatty acids were lower on CS diets.

Supplementation of CS decreased overall ruminal nutrient true digestibility, but dietary

EE level did not affect it. Diets containing CS had greater biohydrogenation of 18:2 and

36

18:3; however, biohydrogenation of 18:1 was greater in MEG diets. Additionally, CS

diets had greater ruminal concentrations of trans-10/11 18:1 and cis-9, trans-11

conjugated linoleic acid. Dietary EE level at 8% negatively affected flows of NH3-N (g/d),

nonammonia N, and bacterial N as well as the overall AA outflow. The shift from acetate

to propionate observed on diets containing CS may be advantageous from an energetic

standpoint. Moreover, CS diets had greater ruminal outflow of trans-10/11 18:1 and cis-

9, trans-11 conjugated linoleic acid than MEG diets, suggesting a better FA profile

available for postruminal absorption. However, dietary EE at 8% was deleterious to

overall N metabolism and AA outflow, indicating that CS can be fed at 5% EE without

compromising N metabolism.

Introductory Remarks

Camelina sativa is an oil seed crop from the mustard family (Brassicaceae),

which is saline soil- and drought-tolerant. It is adapted to a variety of climate and soil

conditions (Zubr, 1997), including dry regions of the western United States (Keske et al.,

2013). Camelina seed (CS) contains, in total ether extract (EE), up to 74% PUFA, of

which 46% is linolenic acid (Hurtaud and Peyraud, 2007c). Another feature of CS is its

high protein concentration and good AA profile (Zubr, 2003), primarily Arg (Miller et al.,

1962; Zubr, 2003). Therefore, feeding CS may be advantageous because of its energy

content, as well as its FA and AA profiles. However, CS contains antinutritional factors

such as glucosinolates and erucic acid (Kramer et al., 1990; Mawson et al., 1994) that

may affect digestion.

Although UFA (notably PUFA, when expressed in % of total FA) are predominant

in commonly used ruminant feedstuffs, milk and milk products are relatively low in

PUFA content. This is due to extensive biohydrogenation (BH) by the ruminal microbial

37

population (Morimoto et al., 2005). Therefore, extent as well as type of BH determine

the quantity and structures of FA leaving the rumen (Fievez et al., 2007) and ultimately

found in milk. However, it is possible to increase PUFA concentration in milk through

dietary PUFA supplementation using oil seeds such as CS (Theurer et al., 2009; Moate

et al., 2013). Yet diets excessively high in UFA may have deleterious effects on nutrient

digestibility, microbial population, and digestion. Typically, UFA have greater negative

effects on ruminal fermentation than calcium salts of palm oil, due to the calcium salts of

palm oil partial protection from ruminal fermentation.

Thus, CS may be a promising source of UFA and capable of providing

supplementary EAA as well. However, to our knowledge, the literature lacks reports on

the effects of CS supplementation on ruminal fermentation as well as its effects on

ruminal FA and AA outflows. The objective of this study was to assess the effects of

replacing calcium salts of palm oil with CS on ruminal fermentation, digestion, and flows

of FA and AA in a dual-flow continuous culture system when supplemented at 5 or 8%

dietary EE. Therefore, our hypotheses were that (1) when replacing calcium salts of

palm oil at a level of 5% dietary EE, CS would not negatively affect ruminal

fermentation; and (2) CS and calcium salts of palm oil would have different ruminal

fermentation patterns.

Materials and Methods

Animal care and handling were approved by the University of Nevada – Reno

Institutional Animal Care and Use Committee (IACUC protocol # 00588).

Experimental Design and Diets

This study was conducted in a replicated 4 × 4 Latin square design with four 10-d

experimental periods, consisting of 7 d of diet adaptation followed by 3 d of sampling.

38

Each fermentor unit was randomly assigned within the Latin square to receive each diet

once over the 4 periods. Treatments were arranged in a factorial 2 × 2, where factor A

consisted of CS as supplement versus calcium salts of palm oil supplement, and factor

B consisted of 2 dietary EE levels (5 vs. 8%). Therefore, the treatments were (1)

calcium salts of palm oil (Megalac, Church & Dwight Co. Inc., Princeton, NJ)

supplementation at 5% EE (MEG5); (2) calcium salts of palm oil supplementation at 8%

EE (MEG8); (3) 7.7% CS supplementation at 5% EE (CS5); and (4) 17.7% CS

supplementation at 8% EE (CS8).

Fermentors were manually fed 72 g of DM/d, equally distributed twice daily at

0800 and 2000 h. Experimental diets were fed on a DM basis and were formulated to

meet or exceed NRC recommendations (NRC, 2001) for a Holstein dairy cow, with 660

kg of BW and producing 35 kg of milk/d, with 3.5% fat and 3.2% protein. Diets were

formulated to have 16% CP and approximately 35% NDF (Table 3-1). Diets consisted of

orchardgrass hay, ground corn, canola meal, and either ground CS or calcium salts of

palm oil fatty acids. Treatments CS5 and CS8 contained 83.4 and 85.4% UFA,

respectively, and MEG5 and MEG8 contained 42.4 and 47.7% SFA, respectively (Table

3-2).

Camelina seed, genotype Calena, was used in this experiment and contained

35.5% EE and 88.5% UFA; calcium salts of palm oil fatty acids contained 53% SFA. In

addition, CS contained 29.4% CP, 19.8% NDF, and 9.4% ADF. All dietary ingredients

were ground through a 2-mm screen in a Wiley mill (model #2, Arthur H. Thomas Co.,

Philadelphia, PA) and orchardgrass hay was pelleted. Dietary AA composition is

presented in Table 3.

39

Dual-Flow Continuous Culture System

Diets were randomly assigned to 8 dual-flow continuous culture fermentors, with

volume ranging from 1,200 to 1,250 mL (Omni-Culture Plus; Virtis Co. Inc., Gardiner,

NY) similar to that originally described by Hoover et al. (1976), and recently modified by

Del Bianco Benedeti et al. (2015), Silva et al. (2016) and Paula et al. (2017). Briefly, this

system consists of a glass fermentation vessel, in which rumen fluid from donor animals

is maintained at constant temperature and agitation. It has a dual-effluent removal

system consisting of separate liquid and solid flows. Artificial saliva is continuously

infused, and feed is provided through an orifice located in the fermentation vessel lid.

Fermentor contents are continuously stirred by a central propeller apparatus driven by

magnets at the rate of 155 rpm, and N2 is infused to maintain an anaerobic

environment.

Ruminal fluid from 2 rumen-cannulated steers (average BW: 910.5 ± 34.5 kg)

was collected approximately 2 h after morning feeding. Donor steers were fed (DM

basis) the same forage:concentrate ratio established for the experimental diets,

containing 55% alfalfa hay, 45% concentrate, and ad libitum mineral mixture. The

ruminal fluid was manually collected from the ventral, central, and dorsal areas of the

rumen of the donor steers and strained through 4 layers of cheesecloth. Approximately

10 L of ruminal fluid was poured into a warmed, insulated bottle.

Rumen fluid from both steers was homogenized and then infused with N2 to

maintain an anaerobic environment, and the temperature was adjusted to 39°C by

submerging a 5-L Erlenmeyer flask in a heated water bath. The rumen fluid was poured

into each of the warmed fermentors until it cleared the effluent spout, and N2 gas was

continuously infused at a rate of 40 mL/min. Artificial saliva (Weller, R. A. and Pilgrim,

40

1974) was continuously infused at 2.2 mL/min. Liquid and solid dilution rates were

adjusted daily to 11.0 and 5.5%/h; respectively, by adjusting buffer input and liq- uid and

solid removal. Individual pH controllers (model 5997-20, Cole-Parmer, Vernon Hills, IL)

were used to monitor the pH of each fermentor.

Experimental Procedures and Sample Collections

On d 5, effluents (liquid and solid) were homogenized and samples were

collected to determine the background 15N abundance (Calsamiglia et al., 1996).

Subsequently, 0.077 g of 10.2% excess of (15NH4)2SO4 (Sigma-Aldrich Co., St. Louis,

MO) was infused into each fermentor to instantaneously label the NH3-N pool. Saliva

was reformulated and 0.077 g/L of enriched (15NH4)2SO4 was infused in replacement of

isonitrogenous quantities of urea to maintain a steady-state concentration of 15N

enrichment inside the fermentors (Calsamiglia et al., 1996).

Liquid and solid effluents were collected separately in 4.3-L plastic containers.

During the first 7 d of the adaptation period, the effluent containers were weighed once

daily at 0800 h and their contents subsequently discarded. Twenty-four hours before the

first collection and on d 7, 8, 9, and 10 of each period, liquid and solid effluent

containers were immersed half way in a chilled water bath at 1°C to stop microbial

activity.

On d 7, 8, and 9, pH was measured using an Accumet portable AP61 pH meter

(Fisher Scientific, Atlanta, GA) at 7 time points: 0, 1, 2, 4, 6, 8, and 10 h after feeding.

On d 8, 9, and 10, liquid and solid effluents from each fermentor were taken and

mechanically homogenized for 1 min (T25 basics, IKA Works Inc., Wilmington, NC), and

500 mL was removed via vacuum system and stored at −20°C for further analysis of

DM, OM, CP, NDF, ADF, EE, and ash. An additional 2 subsamples were filtered

41

through 4 layers of cheesecloth for analysis of ruminal ammonia nitrogen (NH3-N) and

VFA. The subsamples used for NH3-N analysis were preserved with 0.2 mL of 0.2 N

sulfuric acid plus 10 mL of sample, and subsamples (8 mL) used for VFA were

preserved with 2 mL of 25% metaphosphoric acid. Then, samples for NH3-N and VFA

were centrifuged at 1,000 × g for 15 min at 4°C, and the supernatant was separated,

isolated, and stored at −20°C for subsequent analysis.

On the last day of each period, the entire fermentor content was blended for 30 s,

squeezed through 4 layers of cheesecloth, and washed with 200 mL of saline solu- tion

(0.9% NaCl). Filtered samples were centrifuged for 10 min at 1,000 × g at 5°C, and the

pellets were care- fully discarded. The supernatant was centrifuged for 20 min at 11,250

× g, at 5°C (Avanti JXN-30 refrigerated high-speed centrifuge; Beckman Coulter, Brea,

CA). The supernatant was discarded, and pellets were resuspended in 100 mL of

McDougall’s solution and the last centrifugation was performed at 16,250 × g for 20 min

at 5°C. The bacterial isolation procedure was performed using a modification of Krizsan

et al. (2010).The final supernatant was discarded, and bacterial pellets were freeze-

dried, ground using a mortar and pestle method, and stored for further 15N enrichment

and total N and OM analyses (Bach et al., 2008).

Chemical Analysis

Ruminal digesta samples were freeze-dried for further chemical analysis, and

samples contained on average 94% DM. Feed and effluent samples were analyzed for

DM (method 934.01), ash (method 938.08), and EE (method 920.85) according to

AOAC (1990). Crude protein content of feed and effluent samples was determined

using a Leco combustion N analyzer (Leco CN628 Carbon/N Analyzer, Leco

Instruments Inc., St. Joseph, MI; method 990.13, AOAC International, 2005). The OM

42

was calculated as the difference between DM and ash contents, and NFC (% of DM)

was calculated according to NRC (2001):

NFC = 100 − (% NDF + % CP + % fat + % ash)

For NDF and ADF, samples were sequentially analyzed, being treated with

thermo-stable α-amylase, according to Van Soest et al. (1991) and adapted for the

Ankom200 Fiber Analyzer (Ankom Technology, Macedon, NY). Effluent samples were

analyzed for NH3-N content ac- cording to Chaney and Marbach (1962). Volatile fatty

acids concentration was determined using a gas chromatograph (Agilent 6890N, Agilent

Technologies, Santa Clara, CA) equipped with a capillary column (10.0 m × 530 μm ×

1.00 μm nominal). The flow rate of carrier gas (helium) was 27 mL/min. Oven

temperature was programmed at 90°C for 2 min, increasing from 90°C to 190°C at 15°

/min, and holding at 190°C for 2 min.

Nitrogen metabolism was calculated as follows:

NH3-N (g/d) = mg/dL of effluent NH3-N × (g of total effluent flow/100) (3-1)

NAN flow (g/d) = g of effluent N − g of effluent NH3-N (3-2)

Bacterial N flow (g/d) = [NAN flow × atom percentage excess (ape) of 15N of

effluent]/(ape of 15N of bacteria)

(3-3)

Dietary N flow (g/d) = g of effluent NAN − g of effluent bacterial N (3-4)

RUP-N = total N flow (g/d) on effluent – effluent bacterial N flow (g/d) (3-5)

RDP-N = total N intake (g/d) − RUP-N (g/d) (3-6)

Bacterial efficiency = g of bacterial N flow/ kg of OM truly digested (3-7)

where bacterial N flow and bacterial efficiency were calculated according to Calsamiglia

et al. (1996); RUP-N and RDP-N were calculated according to Paula et al. (2017).

43

Amino acids analysis was performed on feed and digesta samples according to method

999.13 of AOAC International (2005), using an amino acid analyzer (model 8900,

Hitachi, Tokyo, Japan). Analyses were performed with post-ninhydrin detection and

using norleucine as the internal standard. Fatty acids analysis was performed at the

Agricultural Experiment Station Chemical Laboratories at the University of Missouri

(Columbia). Fatty acids profile was determined according to AOAC International (2001;

method 996.06), preparation of FAME was performed according to AOCS (2012;

method Ce 2-66), n-3 FA analysis was performed according AOCS (2012; method Ce

1d-91), and CLA analysis was performed according to AOCS (2012; method Ce 1h-05)

using a Supelco SP2560 (100 m × 0.25 mm × 0.2 μm film) column. The GC (Agilent

7890A with 7683B Autosampler; Agilent Technologies) settings were as follows: Oven

temperature was held for 5 min at 140°C, increased at 4°C/min to 200°C (15 min),

increased at 2°C/min to 240°C (20 min), and held for 15 min (total: 55 min). The inlet

temperature was 250°C, pressure was 35 psi, split ratio was 60:1, and injection volume

was 2 μL. Individual FA are reported as percentage of total FA.

Glucosinolate samples were defatted, mixed with methanol, and filtered through

a 0.45-μm filter into an autosampler vial. For quantification, we used a modification of an

HPLC method developed by Betz and Fox (1993). The extract was run on a Shimadzu

(Columbia, MD) HPLC system (2 LC 20AD pumps; SIL 20A auto-injector; DGU 20 As

degasser; SPD-20A UV-vis detector; and a CBM-20A communication BUS module)

running under the Shimadzu LC solutions software (version 1.25). The column was a

C18 Inertsil reverse phase column (250 mm × 4.6 mm; RP C-18, ODS-3, 5u; GL

Sciences, Torrance, CA). Glucosinolates were detected by monitoring at 237 nm.

44

Background samples and bacterial pellets were analyzed for DM, N, and ash as

described previously for feed and effluent samples, and were analyzed for 15N

enrichment (Werner et al., 1999). Isotope analyses were performed using a Eurovector

model 3000 (Euro EA 3000, Eurovector s.p.a., Milano, Italy) elemental analyzer

interfaced to a Micromass Isoprime (IsoPrime, Micromass UK Ltd., Manchester, UK)

stable isotope ratio mass spectrometer. Bacterial N flow and bacterial efficiency were

calculated as follows: Bacterial N flow (expressed in g/d) = (NAN flow × atom

percentage excess of 15N of effluent)/(atom percentage excess of 15N of bacteria pellet),

with 15N digesta effluents background subtracted from 15N enrichment. Bacterial

efficiency = bacterial N flow (g)/OM truly digestible (kg) according to Calsamiglia et al.

(1996). Ruminal true digestibilities were calculated as described by Soder et al. (2013)

and Del Bianco Benedeti et al. (2015).

Biohydrogenation of PUFA was calculated according to Fievez et al. (2007) using

the following equation:

𝐵𝐻 𝑃𝑈𝐹𝐴𝑖 = 100 × 𝑃𝑈𝐹𝐴 𝑖0ℎ − 𝑃𝑈𝐹𝐴𝑖𝑡ℎ

PUFA i0h

(3-8)

where PUFAdiet = PUFA supplied through the diet (%) and PUFAeffluent = PUFA

present in ruminal effluent (%).

Completeness of BH was calculated according to Alves et al. (2017), using the

following equation:

Completeness (%) =18: 0 rumen

Maximum 18: 0 rumen × 100

(3-9)

45

where 18:0 rumen is the 18:0 in the rumen as a percent- age of total C18 FA, and

Maximum 18:0rumen = (cis-9 18:1diet − cis-9 18:1 rumen) + (18:2n-6 diet − 18:2n-6

rumen) + (18:3n-3 diet − 18:3n-3 rumen) + 18:0diet.

Statistical Analysis

All data were subjected to least squares ANOVA using the MIXED procedure of

SAS (version 9.4, SAS Institute Inc., Cary, NC) as a replicated 4 × 4 Latin square

arrangement. The statistical model used was

Y = μ + S + L + (S × L) + LS ijklm i j ij k + R(k)l + Pm + εijklm 3-10

where Yijklm = dependent variable; μ = overall mean; Si = fixed effect of fat source; Lj =

fixed effect of dietary EE level; (S × L)ij = interaction between fat source and dietary EE

level; LSk = random effect of Latin square; R(k)l = random effect of fermentor within

Latin square; Pm = random effect of period; and εijklm = random error. The interaction

between Latin square and treatment was included in the model; however, it was not

significant and therefore it was removed from the model.

Data from different time points (pH) were included as repeated measures in the

experimental model. The commonly used correlation structures were compared and

autoregressive order 1 (AR1) provided the best fit for repeated measurements data,

based on Akaike’s information criterion. For all analysis, Tukey’s test was used to

provide multiple comparisons, and differences were declared when P ≤ 0.05 and

tendency when 0.05 < P ≤ 0.10.

Results and Discussion

Overall Ruminal Metabolism Effects

Ruminal true digestibilities of DM, OM, CP, NDF, and ADF (P < 0.05; Table 3-4)

were affected only by fat source: CS diets resulted in lower digestibility than did MEG

46

diets. Feeds high in PUFA content, such as CS, can modify the ruminal environment

and consequently fermentation patterns toward decreasing ruminal nutrient digestibility,

especially of fiber (Tice et al., 1993). Similar effects of reduced overall nutrient

digestibility were reported previously when high EE diets were fed (Palmquist and

Jenkins, 1980; Allen, 2000; Kazama et al., 2010). However, in our study, this response

was associated with the CS FA profile and not to dietary EE level, indicating that FA

source may be more determinant than total EE to nutrient digestibility. Moreover, when

the MEG8 diet was fed, less dietary fermentable carbohydrates were available (Table 3-

1); consequently, this diet had less energy readily available to ruminal microorganisms,

which may have played a role in DM and OM digestion.

Previous studies have shown that UFA may reduce DMI and shift BH from

complete to incomplete, increasing the ruminal concentration of BH intermediates

(Shingfield et al., 2003; Alves et al., 2013). On the other hand, diets containing calcium

salts of palm oil fatty acids may increase milk fat and have little effect on ruminal

fermentation profile (Grummer, 1988; Chouinard et al., 1998; Theurer et al., 2009). The

lack of consistency in ruminal fermentation response to fat supplementation may be due

to differences in chemical forms, sources, and inclusion levels (Piantoni et al., 2015).

Our results are likely associated with deleterious effects of PUFA on specific cellulolytic

ruminal bacteria. In fact, in a companion study evaluating the effects of replacing

calcium salts of palm oil with CS on ruminal microbial population, our research group

(Dai et al., 2017a) reported that CS changed the structure and composition of bacterial

communities. A decrease was observed (Dai et al., 2017) in the relative abundance of

Fibrobacter and Ruminococcus, which are genera associated with ruminal fiber

47

digestion (Wu et al., 2012) .Therefore, despite all diets having similar NDF

concentration (35%; Table 3-1), the lower NDF and ADF digestibility observed when CS

diets were fed (Table 3-4) may be explained by the decrease in relative abundance of

cellulolytic bacterial communities caused by high PUFA content of CS. It was expected

minor changes in ruminal digestibility in MEG diets because of the fat source used in

these diets, which was relatively inert to ruminal degradation. This characteristic also

explains why it was observed predominantly an effect of fat source on digestibility.

Similar to our results for fiber digestibility, the molar proportion of acetate and the

acetate:propionate ratio (P < 0.05; Table 3-5) were lower when CS diets were fed. In

contrast, CS supplementation increased the molar proportion of propionate (P = 0.01).

Our results are in agreement with those of Hurtaud and Peyraud (2007), who evaluated

the effects of feeding CS and camelina meal to lactating dairy cows and reported an

increase in propionate and decrease in acetate when cows were fed camelina

compared with a control diet. Similar to OM and DM ruminal digestibility (Table 3-4),

total VFA concentration (mM) was lower in CS diets (P < 0.05). Contrary to our results,

Lawrence et al. (2016) did not observe an effect on total VFA concentration when

evaluating the effects of feeding 10% camelina meal in the total ration of dairy heifers.

However, Lawrence et al. (2016) used camelina meal with a lower EE content than the

CS used in our study. From an animal production standpoint, this shift may be

advantageous considering that diets containing CS had increased propionate

concentration; propionate is the main precursor of glucose and ultimately of milk

production in the mammary gland (Lemosquet et al., 2009). Furthermore, CS had higher

48

molar proportions of valerate and isovalerate, and higher total branched-chain VFA (P <

0.05) concentration.

As expected, fat source affected FA profile in the ruminal outflow of SFA, UFA,

MUFA, and PUFA (P < 0.01; Table 3-6). Notably, CS supplementation had greater

proportions of UFA, MUFA, and PUFA ruminal outflow. The MEG diets resulted in

greater BH of 18:1n-9 (P = 0.01) and CS diets resulted in greater BH of 18:2n-6 (P =

0.05) and 18:3n-3 (P = 0.01). These results can be explained in part by the intake of

these FA in each diet; that is, higher intake of 18:3n-3 and 18:2n-6 on CS diets and

higher intake of 18:1n-9 on MEG diets. In a study evaluating the effects of intestinal flow

of FA in dairy cows fed a high-concentrate diet supplemented with fish oil, sunflower oil,

or linseed oil, Loor et al. (2005) observed BH values of 84.7% for 18:2n-6 and of 95.1%

for 18:3n-3 in cows supplemented with linseed oil. In the present study, BH of 18:2n-6

was 73.8 and 74.6% on CS5 and CS8, respectively; and BH of 18:3n-3 was 83.8 and

80.9% on CS5 and CS8, respectively. However, in a dual-flow continuous culture

system, BH values can be lower and our results are in agreement with those of

AbuGhazaleh and Jacobson (2007), who observed BH ranging from 66.1 to 88.6% for

18:2n-6 and 18:3n-3 in a dual-flow continuous culture system.

In the present study, BH of 18:1n-9 was relatively low compared with detected in

in vivo studies, ranging from 23 to 47.7% in the current study. One possible reason is

that certain bacterial species seem to be preferably enriched in in vitro systems

(Weimer et al., 2011), and those with important roles in hydrogenation of 18:1n-9 to

18:0 might not be present to a large degree in this study. Furthermore, our results are in

agreement with a continuous culture fermentor study by Loor et al. (2003) in which BH

49

of 18:1n-9 ranged from 21.2 to 85.6%. An incomplete BH results in lower accumulation

of 18:0 and greater accumulation of BH intermediates, notably trans-18:1 isomers.

Therefore, supplementation of CS has the potential to reduce the last step of BH as

verified by the low values of the estimated completeness of BH (Table 3-6), increased

concentration of trans-10/11 18:1, and lower concentration of 18:0 observed on CS

diets. Additionally, the accumulation of 18:1 observed in CS diets may be related to

inhibition of reductase enzyme in the ruminal bacteria related to the terminal

hydrogenation of 18:1 to 18:0 (AbuGhazaleh and Jacobson, 2007).Our results are in

agreement with Halmemies-Beauchet-Filleau et al. (2011) , who studied different plant

oils, including camelina oil, and reported increased concentrations of BH intermediates

in milk fat when camelina oil was fed, suggesting that camelina may favor incomplete

BH.

Camelina diets had lower concentrations of 18:1n-9 in the ruminal outflow and

greater (P < 0.01) concentration of trans-10/11 18:1 (Table 3-6), which is consistent with

the literature that describes higher inputs of UFA results in accumulation of trans-18:1

(Loor et al., 2005). Vaccenic acid (trans-11 18:1) is a desirable FA flowing out of rumen

because it can be used as substrate to produce cis-9, trans-11 CLA in animal tissue via

Δ9- desaturase (Griinari et al., 2000). In addition, this FA has been shown to have

beneficial implications for human health (Kritchevsky, 2000). In Hurtaud and Peyraud

(2007), a higher concentration of trans-10 18:1, rather than trans-11 18:1, was reported

in milk FA of cows fed diets containing camelina, which could explain our results

indicating that CS supplementation potentially increases ruminal outflow of these FA.

However, we were not able to detect trans-11 18:1 and trans-10 18:1 individually and,

50

although the sum of these 2 FA was increased in CS diets, it is not clear whether the

amount of trans-11 18:1 was greater than that of trans-10 18:1.

Three CLA were identified in the ruminal effluent (Table 3-6). Ruminal

concentrations of 18:2n-6, the 3 detected CLA, and 18:3n-3 were higher when CS diets

were fed (P < 0.01). In particular, supplementing CS resulted in a greater flow of cis-9,

trans-11 CLA. Previous studies have shown that feeding lipid sources can modulate the

concentration of cis-9, trans-11 CLA in milk fat (Shingfield et al., 2003), as this isomer is

associated with anticarcinogenic properties (Williams, 2000) and immune response

(Kritchevsky, 2000). In a study evaluating the effects of feeding camelina meal or seed

to lactating dairy cows, Hurtaud and Peyraud (2007) observed an increased

concentration of cis-9, trans-11 CLA in milk lipid of cows fed diets containing camelina

compared with a control diet. Similarly, Bayat et al. (2015) studied the effects of

supplementing 60 g of camelina oil/kg of dietary DM on milk profile and observed an

increase in concentration of cis-9, trans-11 CLA on milk fat when cows were fed

camelina oil. Additionally, in the same study, the authors observed an increased

concentration of 18:1n-9, 18:2n-6, and 18:3n- 3 in the milk of cows fed camelina oil

compared with control diets. Although we did observe an increase in cis-9, trans-11

CLA in the ruminal effluent, the major source of cis-9, trans-11 CLA in milk comes from

endogenous desaturation of trans-11 18:1 in the mammary gland (Griinari et al., 2000;

Corl et al., 2001).

It was detected an interaction between fat source and dietary EE level for 16:0 (P

< 0.01; Table 3-6), and treatment MEG8 had the greatest concentration (35.3%)

compared with MEG5, CS5, and CS8 (27.0, 11.9, and 9.88%; respectively); MEG

51

supplementation also increased the concentration of 14:0 in the ruminal effluent (P =

0.01; Table 3-6). These results are likely associated with the greater concentration of

these FA in the MEG diets, and consequently greater intake. Hurtaud and Peyraud

(2007) observed lower concentration of 14:0 and 16:0 in milk of cows fed diets

containing camelina meal or seed compared with control diets. However, their control

diets did not contain calcium salts of palm oil.

Camelina contains antinutritional factors such as erucic acid and glucosinolates.

Therefore, the percentage of erucic acid (C22:1n-9) was higher in the CS diet (P < 0.01;

Table 3-6), as was the concentration of glucosinolate (Table 3-1). This was expected

because MEG diets had no detectable erucic acid. Diets CS5 and CS8 had 2.02 and

2.58% erucic acid, respectively. The US Food and Drug Administration (FDA) has

established a limit of 2% erucic acid in total dietary FA; however, in Europe, the limit is

5% of erucic acid in total dietary FA (EFSA, 2016). Therefore, the CS5 diet was below

the FDA limit and both CS5 and CS8 diets were below the European limit. These limits

were established because of the increased risk of myocardial lipidosis in monogastrics

(Kramer et al., 1990; Guil et al., 1997). The concentration of glucosinolates ranged from

1.18 to 2.27 mg/g in CS5 and CS8, and from 0.36 to 0.34 mg/g in MEG5 and MEG8

(Table 1). Glucosinolates could affect thyroid function; however, in a study evaluating

performance of dairy heifers feeding camelina meal, linseed meal, or distilled dried

grains, Lawrence et al. (2016) did not find negateve effects on T3 (triiodothy- ronine)

and T4 (thyroxine) concentration in heifers fed camelina meal containing 1.24 mg/g of

glucosinolates. Likewise, Cappellozza et al. (2012) reported no effect on thyroid-

stimulating hormone or on T3 and T4, when camelina meal was fed to beef heifers.

52

Ruminal Nitrogen Metabolism and Amino Acids

We detected no differences among treatments on ruminal pH and NH3-N (mg/dL,

Table 3-7). Ruminal pH was analyzed as repeated measures and because there was no

treatment effect, it is reported as the daily mean (Table 3-7). These results are in

agreement with Halmemies-Beauchet-Filleau et al. (2017), who fed diets with increasing

camelina oil levels to lactating dairy cows, and with Martin et al. (2016), who studied the

effects of increased linseed oil inclusion in dairy cow diets. Overall, feeding oilseeds or

cakes do not seem to affect ruminal pH (Pires et al., 1997; Reveneau et al., 2005).

Treatments did not affect flows (g/d) of total N, dietary N, bacterial efficiency, or

RUP-N, or RDP-N supply (P > 0.05; Table 3-7). However, dietary EE level affected NH3-

N (g/d), NAN, and bacterial-N flows (P < 0.05; Table 3-7). Diets with 8% dietary EE had

lower bacterial-N and greater NH3-N flow, indicating reduced bacterial-N synthesis.

Even though RDP-N supply and RUP-N flow were not affected by treatments, dietary

EE level affected NH3-N and bacterial-N. We believe these results could be explained

by 2 factors: (1) the deleterious effect on overall N metabolism observed when the CS8

diet was fed may be due to the inclusion of 17.7% CS and its negative effects on

ruminal microbial population as previously described; and (2) when the MEG8 diet was

fed, less dietary fermentable carbohydrates were available (Table 3-1); consequently,

this diet had less energy readily available to produce bacterial-N. Similar effects on

reduced bacterial-N observed when saturated fat sources were fed were observed by

Dunkley et al. (1977), Canale et al. (1990), and Klusmeyer et al. (1991).

The AA flow was affected by treatments in similar manner as N metabolism;

overall dietary EE level negatively affected outflow of individual AA (Table 3-8) except

for taurine and ornithine. A dietary EE level of 8% resulted in lesser outflow of most AA,

53

which resulted in lower outflow of EAA. These results may be associated with bacterial-

N outflow, because the high EE diets decreased bacterial-N and increased NH3 (g/d)

outflow (P < 0.05; Table 3-7). The high EE level reduced the abundance of Fibrobacter

and Ruminococcus, which are generally associated with ammonia metabolism and fiber

digestion; consequently, these diets had deleterious effects on AA profile flowing out of

the fermentors. The greater ruminal escape of EAA may represent a better AA profile

available for postruminal absorption and utilized for milk production.

The MEG diets had only canola meal as a protein source, whereas the CS diets

contained canola meal plus CS. Diet CS5 had similar results as MEG5 in terms of AA

outflow, indicating that partial replacement of canola meal by CS can produce similar

ruminal AA outflows. High-producing dairy cows rely more on the AA profile that

escapes ruminal degradation (RUP) than low-producing cows, which means that high-

producing cows need good quality protein supplements such as canola and possibly

camelina. In North America, canola meal is widely used as a protein source in dairy

diets (Mulrooney et al., 2009), with positive responses on milk production and DMI, and

reduced MUN when replacing different protein sources (Martineau et al., 2013,

Broderick et al., 2015). Overall, CS5 had similar results as the diet containing

exclusively canola meal (MEG5) for N metabolism and AA outflow, indicating that CS

may be able to partially replace canola without compromising AA flow to the small

intestine; this is likely the most important observation of the present study.

The Arg outflow was affect by fat source and EE level (P = 0.04 and P = 0.03,

respectively; Table 3-8), and diets containing CS had greater Arg outflow. This may be

because Arg is one of the most abundant AA in camelina seed (Zubr, 2003). Even

54

though dairy cows are capable of synthesizing Arg through de novo synthesis (NRC,

2001), Arg is an EAA because the amount produced by de novo synthesis is not

sufficient to meet the requirements of high-producing dairy cows (NRC, 2001).

Mammary gland uptake of Arg can be 1 to 3 times the amount of this AA found in milk

(Doepel and Lapierre, 2011), demonstrating the importance of ensuring the Arg supply

for mammary gland.

Despite the slight difference in Lys and Met concentrations across diets, ruminal

outflow of these AA was only affected by dietary EE level. Diets containing 8% dietary

EE had reduced (P < 0.05; Table 3-8) ruminal outflow. Lysine and Met are often

considered first-limiting EAA for dairy cows (NRC, 2001), and milk production and

composition can respond to supplemental Lys and Met (under low-protein diets) as long

as DMI and other AA such as His are not limiting (Sinclair et al., 2014). Histidine ruminal

outflow was only affected by dietary EE level (P = 0.03; Table 3-8), such that 5% EE

resulted in greater His ruminal outflow. Compared with other EAA, bacterial production

of His is relatively low, around 4% of total bacterial EAA (NRC, 2001), whereas milk and

lean tissue have 6.3 and 5.5% of total EAA as His, indicating the importance of His

supplementation in diets with low MP, especially if most of the MP comes from microbial

protein (Lee et al., 2012). These results suggest that CS supplementation at 5% dietary

EE may positively influence milk yield and composition.

Conclusions

Supplementation with CS resulted in a greater proportion of BH intermediates

18:3n-3 and 18:2n-6 in ruminal effluent compared with diets containing calcium salts of

palm oil (MEG). Greater ruminal escape of beneficial FA might translate into better milk

FA profile; however, this needs to be confirmed in vivo. In addition, CS supplementation

55

resulted in lower acetate: propionate ratio than supplementation with MEG. However,

ruminal true digestibilities of DM, OM, NDF, ADF, and CP were lower in CS diets. It was

not observed differences between CS and MEG in RUP-N and RDP-N when added at

5% dietary EE. However, 8% dietary EE caused negative effects on ruminal N

metabolism and AA outflow regardless of CS inclusion, decreasing NAN and bacterial

N. This suggests CS can be used up to 5% dietary EE without compromising N

metabolism and AA outflow.

56

Table 3-1. Ingredients and chemical composition of the experimental diets

Item Diets1

MEG5 MEG8 CS5 CS8

% DM, unless otherwise stated

Orchardgrass hay 55.0 55.0 55.0 55.0

Ground corn 17.5 12.4 19.2 16.3

Canola meal 22.9 23.9 16.8 9.8

Camelina seed - - 7.7 17.7

Calcium salts of palm oil fatty acids2

3.25 7.48 - -

Minerals3 1.25 1.24 1.25 1.25

Chemical Composition

DM, % 91.9 92.4 91.8 92.3

OM 92.3 91.4 93.0 93.1

CP 16.0 16.0 16.0 16.0

NDF 35.5 35.3 35.4 35.2

ADF 19.5 19.6 19.0 18.6

NFC4 37.1 33.1 37.9 34.9

EE 5.00 8.20 5.00 8.20

Ca5 0.20 0.20 0.20 0.20

P5 0.10 0.10 0.10 0.10

Glucosinolates, mg/g 0.34 0.36 1.18 2.27

NEl6, Mcal/kg DM 1.60 1.80 1.60 1.70

N intake g/d 1.96 1.96 1.96 1.96 1MEG5 = calcium salts of palm oil fatty acids at 5% dietary EE; MEG8 = calcium salts of palm oil fatty acids at 8% dietary EE; CS5 = 7.7% camelina seed supplementation at 5% dietary ether extract; CS8 = 17.7% camelina seed supplementation at 8% dietary EE. 2Megalac, Church & Dwight Co., Inc., Princeton, NJ. 3Provided (per kg of DM): 955 g of NaCl, 3,500 ppm of Zn, 2,000 ppm of Fe, 1,800 ppm of Mn, 280 ppm of Cu, 100 ppm of I, and 60 ppm of Co. 4estimated according to NRC (2001), using the following equation: NFC = 100 – (% NDF + % CP + % fat + % ash). 5Estimated according to NRC (2001). 6NEl = Net energy for lactation, estimated using the NRC (2001) model.

57

Table 3-2. Fatty acid composition of the experimental diets

Item Diet1

MEG5 MEG8 CS5 CS8

% of total

14:0 0.88 1.02 0.19 0.14

15:0 0.11 0.08 0.09 0.07

16:0 36.3 41.5 10.6 8.70

16:1n - 7 0.30 0.24 0.26 0.20

17:0 0.15 0.13 0.11 0.09

18:0 3.54 3.96 2.50 2.63

18:1n - 9 31.7 34.3 17.7 16.6

18:1n - 7 1.84 1.28 1.98 1.45

18:2n - 6 16.4 12.1 25.8 24.2

18:3n - 3 6.48 3.69 24.4 26.5

20:0 0.60 0.49 1.57 1.73

20:1n - 9 0.21 0.18 9.03 11.4

21:0 0.04 0.03 0.03 0.02

22:0 0.30 0.19 0.53 0.48

22:1n - 9 0.01 ND 2.02 2.58

23:0 0.26 0.15 0.27 0.17

24:0 0.30 0.18 0.41 0.33

24:1n - 9 0.03 0.02 0.50 0.62

Unknown 0.64 0.36 2.01 2.10

Saturated 42.4 47.7 16.3 14.4

Unsaturated 57.2 52.1 83.4 85.4

MUFA2 34.1 36.1 31.5 32.9

PUFA3 23.2 16.1 51.9 52.6 1MEG5 = calcium salts of palm oil fatty acids at 5% dietary EE; MEG8 = calcium salts of palm oil fatty acids at 8% dietary EE; CS5 = 7.7% camelina seed supplementation at 5% dietary ether extract; CS8 = 17.7% camelina seed supplementation at 8% dietary EE. 2MUFA = Monounsaturated fatty acids. 3PUFA = Polyunsaturated fatty acids. ND = Not detected.

58

Table 3-3. Amino acids composition of the experimental diets

Item Diets1

MEG5 MEG8 CS5 CS8

AA, % of total

Taurine 1.16 1.17 1.18 1.09

Hydroxyproline 0.49 0.42 0.54 0.55

Asp 8.49 7.73 8.84 8.51

Thr 4.31 3.88 4.42 4.36

Ser 3.84 3.78 4.01 3.98

Glu 14.1 14.5 14.6 15.1

Pro 6.50 6.58 6.63 6.70

Gly 5.21 4.85 5.39 5.30

Ala 6.34 6.26 6.55 6.34

Cys 1.69 1.64 1.74 1.84

Val 5.66 5.14 5.84 5.74

Met 1.96 1.91 2.01 2.02

Ile 4.44 4.02 4.56 4.49

Leu 8.88 9.08 9.17 9.07

Tyr 2.56 2.46 2.67 2.64

Phe 5.21 4.99 5.39 5.23

Hydroxylysine 0.99 0.79 0.98 0.89

Orn 0.07 0.05 0.08 0.07

Lys 5.33 4.53 5.42 5.37

His 2.31 2.25 2.37 2.43

Arg 4.99 4.63 5.37 5.33

Trp 0.96 0.93 0.99 1.01 1MEG5 = calcium salts of palm oil fatty acids at 5% dietary EE; MEG8 = calcium salts of palm oil fatty acids at 8% dietary EE; CS5 = 7.7% camelina seed supplementation at 5% dietary ether extract; CS8 = 17.7% camelina seed supplementation at 8% dietary EE.

59

Table 3-4. Effects of camelina seed supplementation at two dietary ether extract levels on ruminal true digestibility in dual-flow continuous culture system

Item, % Diets1

SEM P- value

MEG5 MEG8 CS5 CS8 Source Level Source x

Level2

DM 56.1 54.9 50.2 48.1 4.32 0.02 0.51 0.84

OM 58.7 57.1 53.9 53.3 4.12 0.05 0.58 0.78

CP 55.5 56.9 49.2 51.5 27.9 0.03 0.49 0.85

NDF 56.1 57.6 47.0 44.8 15.5 0.01 0.91 0.52

ADF 49.1 52.4 40.1 36.7 9.04 0.01 0.98 0.44 1MEG5 = calcium salts of palm oil fatty acids at 5% dietary EE; MEG8 = calcium salts of palm oil fatty acids at 8% dietary EE; CS5 = 7.7% camelina seed supplementation at 5% dietary ether extract; CS8 = 17.7% camelina seed supplementation at 8% dietary EE. 2Interaction between dietary ether extract level and fat source. Source = saturated fatty acid source and unsaturated fatty source; Level = 5% of dietary EE and 8% of dietary EE.

60

Table 3-5. Effects of camelina seed supplementation at two dietary ether extract levels on volatile fatty acids concentration in dual-flow continuous culture system

Item Diets1 SEM P- value

MEG5 MEG8 CS5 CS8 Source Level Source x Level2

Total VFA, mM 89.9 89.1 85.1 79.4 2.47 <0.01 0.19 0.32

VFA, % total VFA

Acetate 55.8b 60.6a 49.1c 50.1c 1.73 <0.01 <0.01 0.05

Propionate 25.9 23.5 30.1 28.9 1.14 <0.01 0.13 0.62

Butyrate 14.6 12.9 14.9 14.9 1.44 0.26 0.40 0.42

Isobutyrate 0.44 0.51 0.42 0.57 0.05 0.61 0.02 0.37

Valerate 2.40 1.79 3.23 3.67 0.66 <0.01 0.83 0.19

Isovalerate 0.77 0.65 2.12 1.84 0.43 <0.01 0.45 0.77

Acetate:Propionate 2.21 2.60 1.64 1.77 0.14 <0.01 0.04 0.28

Total BCVFA2, mmol 1.09 1.02 2.20 1.92 0.40 <0.01 0.49 0.67

1MEG5 = calcium salts of palm oil fatty acids at 5% dietary EE; MEG8 = calcium salts of palm oil fatty acids at 8% dietary EE; CS5 = 7.7% camelina seed supplementation at 5% dietary ether extract; CS8 = 17.7% camelina seed supplementation at 8% dietary EE. 2Interaction between dietary ether extract level and fat source. Source = saturated fatty acid source and unsaturated fatty source; Level = 5% of dietary EE and 8% of dietary EE.

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Table 3-6. Effects of camelina seed supplementation at two dietary ether extract levels on ruminal effluent fatty acid concentration and biohydrogenation in a dual-flow continuous culture system

Item, %

Diets1 SEM P- value

MEG5 MEG8 CS5 CS8 Source

Level Source x Level2

14:0 1.15a 1.12a 0.74b 0.47c 0.07 <0.01 <0.01 0.01

15:0 0.91 0.54 0.76 0.48 0.08 0.13 <0.01 0.49

16:0 27.0a 35.3b 11.9c 9.88c 1.04 <0.01 <0.01 <0.01

16:1n - 7 0.36 0.29 0.38 0.31 0.03 0.52 0.03 0.89

17:0 0.40 0.28 0.38 0.23 0.02 0.13 <0.01 0.43

18:0 24.3 19.4 11.3 11.6 3.44 0.01 0.48 0.43

18:1n - 9 16.5b 21.0a 13.5b 11.9b 0.98 <0.01 0.16 0.03

18:1 trans

6/7/8/9 0.94 0.85 1.43 0.45 0.55 0.93 0.30 0.39

10/113 7.20 5.53 16.12 16.03 2.02 <0.01 0.57 0.61

12/134 1.44 1.06 2.13 1.59 0.56 0.21 0.34 0.88

18:1n - 7 2.72 1.91 2.95 1.89 0.20 0.60 <0.01 0.52 9t12c/11t15c/10t15c 18:25 0.88 0.52 6.35 8.07 0.99 <0.01 0.34 0.15

18:2 conjugated

cis-9, trans-11 1.31 0.90 1.98 2.49 0.49 0.01 0.89 0.21

cis-9, cis-11 0.08 0.06 0.12 0.14 0.01 <0.01 0.88 0.07

trans-9, trans-11 0.44 0.32 0.52 0.52 0.06 0.01 0.18 0.17

18:2n - 6 4.68 3.90 6.72 6.27 0.59 <0.01 0.66 0.60

18:3n - 3 1.47 0.98 3.92 5.00 0.44 <0.01 0.51 0.09

20:0 0.57 0.51 2.05 2.84 0.44 <0.01 0.26 0.19

20:1n - 9 0.22c 0.19c 6.07b 9.31a 0.49 <0.01 <0.01 <0.01

22:0 0.46 0.33 0.61 0.58 0.02 <0.01 <0.01 0.19

22:1n - 9 0.04c 0.03c 1.51b 2.32a 0.07 <0.01 <0.01 <0.01

23:0 0.40 0.26 0.39 0.29 0.39 0.15 <0.01 0.11

24:0 0.37b 0.25c 0.44a 0.38b 0.01 <0.01 <0.01 <0.01

24:1n - 9 0.10c 0.09c 0.48b 0.65a 0.02 <0.01 <0.01 <0.01

Unknown 6.17 4.31 6.29 6.52 0.81 0.02 0.06 0.35

Saturated 55.5 58.0 28.7 26.8 2.96 <0.01 0.92 0.50

Unsaturated 38.7 38.1 66.9 70.1 3.66 <0.01 0.72 0.59

MUFA6 29.6 31.0 45.9 45.3 2.40 <0.01 0.85 0.68

PUFA7 9.18 7.13 21.1 24.8 1.64 <0.01 0.58 0.06

62

Table 3-6. Continued

Item, %

Diets1

SEM

P- value

MEG5 MEG8 CS5 CS8 Source Level Source x

Level2

BH8

18:01 47.7 38.6 23 28.1 3.98 0.01 0.59 0.09

18:02 71.1 67.8 73.8 74.4 2.34 0.05 0.54 0.4

18:03 77.2 73.3 83.8 80.9 2.3 0.01 0.11 0.78

Completeness9 42.5 45.2 17.6 17.8 3.92 0.01 0.64 0.69 1MEG5 = calcium salts of palm oil fatty acids at 5% dietary EE; MEG8 = calcium salts of palm oil fatty acids at 8% dietary EE; CS5 = 7.7% camelina seed supplementation at 5% dietary ether extract; CS8 = 17.7% camelina seed supplementation at 8% dietary EE. 2Interaction between dietary ether extract level and fat source. Source = saturated fatty acid source and unsaturated fatty source; Level = 5% of dietary EE and 8% of dietary EE. 3Sum of trans-10 18:1 and trans -11 18:1. 4Sum of trans -12 18:1 and trans -13 18:1. 5Sum of trans-9, cis-12 + trans-11, cis-15 + trans10, cis-15 18:2. 6MUFA = monosaturated fatty acids. 7PUFA = Polyunsaturated fatty acids. 8Biohydrogenation = calculated according to Fievez et al. (2007), using the following equation: BH PUFAi= 100 × (PUFA i0h-PUFAith)/(PUFA i0h), where PUFAi oh = PUFA supply (%) and PUFAith = PUFA (%) present in ruminal effluent. 9Completeness of BH was calculated according to Alves et al. (2017), using the following equation:Completeness (%) =

18:0 rumen

Maximum 18:0 rumen × 100 and 𝑀𝑎𝑥𝑖𝑚𝑢𝑚 18: 0 𝑟𝑢𝑚𝑒𝑛 = (𝑐9 − 18: 1𝑑𝑖𝑒𝑡 − 𝑐9 − 18: 1𝑟𝑢𝑚𝑒𝑛) + (18: 2𝑛 −

6𝑑𝑖𝑒𝑡 − 18: 2𝑛 − 6𝑟𝑢𝑚𝑒𝑛) + (18: 3𝑛 − 3𝑑𝑖𝑒𝑡 − 18: 3𝑛 − 3𝑟𝑢𝑚𝑒𝑛) + 18: 0𝑑𝑖𝑒𝑡. a,b Treatment effects with

different superscript are significantly different (P < 0.05).

63

Table 3-7. Effects of camelina seed supplementation at two dietary ether extract levels on ruminal pH and nitrogen metabolism in a dual-flow continuous culture system

Item

Diets1

SEM

P- value

MEG5 MEG8 CS5 CS8 Source Level Source x

Level2

pH 6.52 6.50 6.44 6.51 0.07 0.71 0.65 0.55

NH3-N, mg/dL 12.1 10.8 12.9 11.8 0.14 0.29 0.18 0.92

N flows, g/d Total N 1.81 1.78 1.92 1.81 0.19 0.42 0.39 0.72

NH3-N3 0.27 0.42 0.24 0.45 0.03 0.49 0.01 0.08

NAN4 1.53 1.35 1.67 1.35 0.16 0.53 0.04 0.59

Bacterial-N5 0.82 0.72 0.84 0.73 0.07 0.73 0.04 0.97

Dietary N6 0.71 0.63 0.83 0.62 0.11 0.47 0.07 0.44

RUP-N7 0.99 1.04 1.07 1.07 0.12 0.31 0.67 0.57

RDP-N8 supply 0.97 0.92 0.89 0.89 0.12 0.20 0.84 0.72

Bacterial efficiency9 21.2 19.7 23.7 20.2 2.27 0.61 0.23 0.74

1MEG5 = calcium salts of palm oil fatty acids at 5% dietary EE; MEG8 = calcium salts of palm oil fatty acids at 8% dietary EE; CS5 = 7.7% camelina seed supplementation at 5% dietary ether extract; CS8 = 17.7% camelina seed supplementation at 8% dietary EE. 2Interaction between dietary ether extract level and fat source. Source = saturated fatty acid source and unsaturated fatty source; Level = 5% of dietary EE and 8% of dietary EE. 3NH3-N (g/d) = mg/dL of effluent NH3-N × (g of total effluent flow/100)

4NAN = nonammonia nitrogen. It was calculated as following: NAN flow (g/d) = g of effluent N − g of effluent NH3-N. 5Bacterial-N flow was calculated according to Calsamiglia et al., 1996, using the following equation: bacterial N flow (g/d) = (NAN flow × atom percentage excess of 15N of effluent)/ (atom percentage excess of 15N of bacteria). 6Dietary N flow (g/d) = g of effluent NAN − g of effluent bacterial N 7RUP-N= rumen undegraded protein nitrogen. Calculated using the following equation: RUP-N = total N flow (g/d) on effluent – effluent bacterial N flow (g/d). Estimated according to Paula et al., 2017. 8RDP-N = rumen degraded protein nitrogen. Calculated using the following equation: RDP-N = total N intake (g/d) - RUP-N (g/d), according to Paula et al. (2017). 9Bacterial efficiency was calculated according to Calsamiglia et al., 1996. Using the following equation: Bacterial efficiency = g of bacterial N flow/kg of OM truly digested.

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Table 3-8. Effects of camelina seed supplementation at two dietary ether extract levels on amino acid ruminal effluent outflow in dual-flow continuous culture system

Item Diets1

SEM P- value

MEG5 MEG8 CS5 CS8 Source Level Source x Level2

AA flow, g/d

Taurine 0.021 0.019 0.023 0.024 0.006 0.092 0.739 0.349

Hydroxyproline 0.031 0.026 0.036 0.034 0.008 0.011 0.103 0.534

Asp 0.835 0.685 0.896 0.781 0.144 0.137 0.017 0.727

Thr 0.426 0.350 0.447 0.383 0.075 0.302 0.015 0.828

Ser 0.329 0.272 0.356 0.316 0.057 0.09 0.024 0.687

Glu 1.146 0.940 1.238 1.095 0.213 0.095 0.023 0.660

Pro 0.427 0.360 0.471 0.417 0.078 0.066 0.030 0.810

Gly 0.482 0.394 0.512 0.449 0.088 0.163 0.018 0.663

Ala 0.567 0.461 0.599 0.537 0.105 0.145 0.028 0.535

Cys 0.114 0.095 0.122 0.107 0.023 0.166 0.029 0.743

Val 0.549 0.449 0.573 0.489 0.098 0.332 0.010 0.808

Met 0.170 0.144 0.185 0.165 0.033 0.128 0.054 0.814

Ile 0.458 0.377 0.478 0.412 0.081 0.333 0.017 0.796

Leu 0.710 0.582 0.761 0.668 0.131 0.132 0.020 0.686

Tyr 0.336 0.276 0.356 0.299 0.056 0.303 0.010 0.929

Phe 0.448 0.369 0.482 0.419 0.081 0.129 0.016 0.764

Hydroxylysine 0.021 0.016 0.022 0.019 0.004 0.148 0.043 0.744

Orn 0.013 0.009 0.012 0.014 0.02 0.125 0.701 0.110

Lys 0.466 0.385 0.479 0.431 0.086 0.358 0.056 0.611

His 0.160 0.132 0.172 0.148 0.032 0.216 0.029 0.816

Arg 0.411 0.337 0.454 0.421 0.081 0.037 0.037 0.553

Trp 0.086 0.076 0.094 0.082 0.015 0.250 0.090 0.871

EAA3 3.797 3.123 4.030 3.525 0.702 0.188 0.021 0.720

1MEG5 = calcium salts of palm oil fatty acids at 5% dietary EE; MEG8 = calcium salts of palm oil fatty acids at 8% dietary EE; CS5 = 7.7% camelina seed supplementation at 5% dietary ether extract; CS8 = 17.7% camelina seed supplementation at 8% dietary EE. 2Interaction between dietary ether extract level and fat source. Source = saturated fatty acid source and unsaturated fatty source; Level = 5% of dietary EE and 8% of dietary EE. 3EAA = essential amino acids (Arg, His, Ile, Leu, Lys, Met, Phe, Thr, Trp, and Val).

65

CHAPTER 4 EFFECTS OF REPLACING CANOLA MEAL WITH SOLVENT EXTRACTED

CAMELINA MEAL ON MICROBIAL FERMENTATION IN A DUAL-FLOW CONTINUOUS CULTURE SYSTEM

Summary

Camelina is an oil seed crop that belongs to the Brassica family (Cruciferae).

Camelina meal is a by-product from biofuel industry that contains on average 38%

crude protein (CP), and between 10 to 20% of residual fat, which limits the inclusion

levels of camelina meal in dairy cow diets as the main protein supplement. Thus, we

conducted a solvent extraction on ground camelina seed on a laboratorial scale. The

objectives of this study were: 1) to assess the effects of replacing canola meal (CM)

with solvent-extracted camelina meal (SCAM) in lactating dairy cow diets; and 2) to

determine the effects of SCAM on microbial fermentation and AA flow in a dual-flow

continuous culture system. Diets were randomly assigned to six fermentors in a

replicated 3 × 3 Latin square with three 10-d experimental periods consisting of 7 d for

diet adaptation and 3 d for sample collection. Treatments were: 0, 50, and 100% SCAM

inclusion, replacing CM as the protein supplement. Diets contained 55:45

forage:concentrate, and fermentors were fed 72 g of DM/d equally divided in two

feeding times. On d 8, 9, and 10 of each period samples were collected for analyses of

pH, volatile fatty acids (VFA), nitrogen (N) metabolism, ammonia N (NH3-N),

digestibility, and AA flow. Statistical analysis was performed using the MIXED procedure

of SAS, and linear and quadratic effects of SCAM inclusion were assessed. Total VFA

concentration and pH were not affected by diets. Molar proportion of acetate decreased,

while molar proportion of propionate increased with SCAM inclusion. Total branched-

chain VFA concentration was the least in fermentors fed diet 0, and greatest in

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fermentor fed diet 50. Digestibility of NDF decreased in fermentors fed SCAM diets, and

dry matter (DM), organic matter (OM) and CP true digestibility were similar across diets.

Concentration of NH3-N linearly decreased, and non-NH3-N (NAN) linearly increased

with SCAM inclusion. Bacterial efficiency (calculated as g of bacterial N flow/kg of OM

truly digested) tended to be greater in fermentors fed diet 100. Outflow of Arg linearly

increased with SCAM inclusion; while overall AA flow was not affected by diet. In

conclusion, replacing CM with SCAM increased propionate molar proportion and NAN

flow, and decreased NH3-N flow and concentration, which may improve animal energy

status and N utilization. Inclusion of SCAM did not change most AA flow, indicating that

it can be a potential replacement for CM.

Introductory Remarks

Camelina sativa is also referred as “false flax” due to its similarity to flax seed

regarding the relatively high proportion of omega-3 fatty acids (Fröhlich and Rice, 2005).

It is a drought and saline-soil tolerant oil seed crop from the Brassica (Cruciferae)

family. Camelina meal (CAM) is the by-product from biofuel industry, which is currently

obtained on an industrial scale via mechanical pressing using an expeller (Fröhlich and

Rice, 2005; Ye et al., 2016). Camelina meal contains on average 38% CP (Hixson and

Parrish, 2014), and 10 to 20% residual fat (Pekel et al., 2009; Kahindi et al., 2014).

The demand for renewable alternative sources of biofuel has increased, leading

to an intensification in camelina growing areas (Moser and Vaughn, 2010). Extracting oil

more efficiently will make this crop more competitive and will enable greater meal

inclusion in ruminant diets. Aside from its high residual fat, CAM use in ruminant diets

as the sole protein supplement is limited by the concentration of anti-nutritional factors,

67

primarily glucosinolates (Berhow et al., 2013), which are associated with deleterious

effects notably in the thyroid gland (Holst and Williamson, 2004).

Camelina belongs to the same family as canola and their meals have similar

protein concentration and AA composition (Colombini et al., 2014). Canola meal (CM) is

widely used in North America as a protein supplement for dairy cows, and it has been

associated with increased milk production, DMI (Martineau et al., 2013) and decreased

MUN (Paula et al., 2018). Therefore, having an alternative protein supplement with

similar fermentation profile and nutrient flow as CM would represent a beneficial

opportunity to the dairy industry.

Recently our research group demonstrated that isonitrogenous (16% CP) diets

containing camelina seed plus CM, had similar N metabolism when fed at 5% EE

compared to diets containing only CM as a protein supplement (Brandao et al., 2018).

However, the greater EE concentration (35%) in camelina seed limited its inclusion in

the diets. In order to overcome this and enable a partial and complete replacement of

CM by CAM, it was performed a solvent fat extraction on ground camelina seed on a

laboratory scale, yielding solvent-extracted CAM (SCAM). We hypothesized that SCAM

could partially or completely replace CM as a protein supplement in lactating dairy cow

diets without negatively affecting microbial fermentation. The objectives were: 1) to

assess the effects of replacing CM with SCAM in lactating dairy cow diets, and 2) to

determine the effects of SCAM on microbial fermentation and AA flow in a dual-flow

continuous culture system. To our knowledge, this was the first time that data on

microbial fermentation and AA flow of SCAM were reported.

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Materials and Methods

The University of Nevada, Reno Institutional Animal Care and Use Committee

(IACUC) approved the animal care and handling protocol used in this experiment

(Protocol # 00588).

Experimental Design and Diets

This study was conducted in a replicated 3 × 3 Latin square with three 10-d

experimental periods consisting of 7 d for diet adaptation and 3 d for sample collection.

Each fermentor unit was randomly assigned to receive each diet once over the three

experimental periods, in which a treatment did not follow the same treatment more than

once. Fermentors were manually fed 72 g/d (DM basis) equally distributed twice daily at

0800 and 1800h. Treatments were: 1) 0% SCAM (0); 2) 50% SCAM (50); and 3) 100%

SCAM (100) inclusion, replacing CM as the protein supplement.

Experimental diets were formulated to meet or exceed NRC recommendations

(NRC, 2001) for a 660-kg Holstein dairy cow producing 35 kg milk/d consisting of 3.5%

fat and 3.2% protein. Diets contained (DM basis) 55% orchardgrass hay and 45%

concentrate and were formulated to be isonitrogenous (16% CP) and to have

approximately 3.4% of ether extract (EE; Table 4-1). Dietary ingredients consisted of

orchardgrass hay, ground corn, and either solvent extracted CM or CAM as protein

supplement.

Before oil extraction, camelina seed (genotype Calena) used in this experiment

contained 35.5% EE and 29.4% CP (DM basis). Fat extraction was performed at the

Applied Research Facility, located in the Department of Natural Resources and

Environmental Science (University of Nevada, Reno), assisted by Dr. Glenn Miller. Fat

extraction was performed using hexane (Sigma-Aldrich Co., St. Louis, MO) as solvent at

69

rate of 6.4 mL hexane/g of ground seed, using a refluxing solvent extractor.

Temperature and pressure were controlled during the entire extraction process, which

yielded a meal containing 6.1% EE and 39% CP (DM basis, Table 4-2). Canola meal

used in this experiment was produced industrially by solvent extraction. In order to have

similar EE across diets and isolate possible confounding effects of EE source, camelina

oil was added to diets 0 and 50 (Table 1). Amino acid composition of diets and protein

supplements are presented in Table 3.

All dietary ingredients used to feed the fermentors were ground to pass through a

2-mm screen in a Wiley Mill (Model #2, Arthur H. Thomas Co., Philadelphia, PA).

Orchardgrass hay was pelleted (ECO-3 Pellet Mill and CME ECO-3; Colorado Mill

Equipment, LLC, Cañon City, CO).

Dual-Flow Continuous Culture System

Ruminal fluid was manually collected approximately two hours after morning

feeding from ventral, central, and dorsal areas of the rumen of two cannulated donor

steers (average BW 872 ± 14.1 kg). Donor animals were adapted to diets 14 d prior to

rumen fluid donation. Donor animals were fed a similar forage to concentrate ratio (DM

basis) as the experimental diets, consisting of 55% forage, 45% concentrate (containing

soybean meal and ground corn) and ad libitum mineral mixture. In each period,

approximately 10 L of ruminal fluid was collected, strained through 4 layers of

cheesecloth and poured into a pre-warmed insulated thermal bottle. Ruminal fluid from

both steers was homogenized and then infused with N2 to maintain an anaerobic

environment. Temperature was kept at 39ºC by submerging a 5,000 mL Erlenmeyer

flask in a pre-heated water bath. Ruminal fluid was then poured into each fermentor

until it cleared the effluent spout.

70

Diets were randomly assigned within squares to six dual-flow continuous culture

fermentors, with volume of 1,225 (± 25) mL (Omni-Culture Plus; Virtis Co. Inc.,

Gardiner, NY) similar to that originally described by Hoover et al. (1976), and modified

by Silva et al. (2016) and Paula et al. (2017) .Fermentor contents were continuously

stirred by a central propeller apparatus driven by magnets at the rate of 155 rpm and N2

was infused at rate of 40 mL/min. Temperature was maintained at 39°C and artificial

saliva (Weller and Pilgrim, 1974) containing 0.4 g/L urea was continuously infused at

2.1 mL/min. Liquid and solid dilution rates were adjusted to 11.0 and 5.5%/h;

respectively, and were constantly monitored throughout experimental days.

Experimental Procedures and Sample Collections

On d 5, liquid and solid effluents were pooled, manually homogenized (within

fermentor) by shaking and sampled for determination of the background 15N abundance

(Calsamiglia et al., 1996). Next, a pulse dose of 0.077 g of 10.2% excess of

(15NH4)2SO4 (Sigma-Aldrich Co., St. Louis, MO) was infused into each fermentor to label

the NH3-N pool. Artificial saliva, enriched with 15N contained 0.077 g/L of the enriched

(15NH4)2SO4, and replaced an isonitrogenous amount of urea to obtain a steady-state

15N enrichment of the NH3 pool in the fermentors (Calsamiglia et al., 1996).

Liquid and solid effluents were collected daily and separately weighted in plastic

containers. During the first 7 d of the adaptation period, solid and liquid effluent

containers were weighed once daily before the morning feeding and contents were

discarded. Twenty-four hours prior to the first sampling and during the 3 d of sampling

during each period, liquid and solid effluents containers were immersed in a water bath

at 1°C to prevent further microbial activity.

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On d 7, 8, and 9, pH was measured using an individual Accumet portable AP61

pH meter (Fisher Scientific, Atlanta, GA) at 6 time points: 0, 1, 2, 4, 6, and 8 h after

morning feeding. Additionally, 8 mL of fermentor fluid samples were collected using a

pipette through the feeding port. Samples were filtered through 4 layers of cheesecloth

and acidified using 2 mL of methaphosphoric acid 25%. Samples were centrifuged at

1,000 × g for 15 min at 4°C, then supernatant was separated, isolated, and stored at -

20°C for further analysis of NH3-N and VFA concentration.

On d 8, 9, and 10 liquid and solid effluents from each fermentor were sampled,

by pouring together and mechanically homogenizing for 30 s using a mixer (T25 basics,

IKA Works Inc., Wilmington, NC 28405). A 500-mL composite of liquid and solid effluent

samples was collected via vacuum system, and these were composited by period.

Samples were stored at -20°C for further chemical analysis of DM, OM, CP, NDF, ADF,

EE, and ash and 15N enrichment. An additional two subsamples were taken across all

three sampling days and filtered through 4 layers of cheesecloth for analysis of pooled

effluent of NH3-N and VFA. These two subsamples were acidified and centrifuged as

described above for time course analysis.

The bacterial isolation procedure was performed using a modification of the

methods of Krizsan et al. (2010). Briefly, on the last d of each period (d 10), the entire

fermentor contents were blended for 30 s, squeezed through four layers of cheesecloth

and washed with 400 mL of saline solution (0.9% NaCl). Filtered samples were

centrifuged three times using a Beckman Coulter Avanti® JXN- 30 refrigerated high-

speed centrifuge at 5°C, to yield the bacterial pellets. The first centrifugation was

performed at 1,000 × g for 10 min, and residual feed particles were discarded. In the

72

second centrifugation, the supernatant was centrifuged at 11,250 × g for 20 min. Lastly,

supernatant was discarded, and pellets were re-suspended in 200 mL of McDougall’s

solution and the third centrifugation was performed at 16,250 × g for 20 min. The final

supernatant was discarded, and bacterial pellets were stored at -20°C for further

analysis of 15N enrichment, total N, and DM analysis. Effluent samples and bacterial

pellets were freeze dried, and ground using a mortar and pestle.

Chemical Analysis

Dietary ingredients were ground to pass through a 1-mm screen in a Wiley Mill

(same as previously described) for further chemical analysis. Ingredients and pooled

effluent samples were analyzed for DM using method 934.01, ash using method 938.08,

and EE using method 920.85, according to AOAC (1990). All samples were analyzed

for CP concentration using a Leco combustion N analyzer (Leco CN628 Carbon/N

Analyzer, Leco Instruments Inc., St. Joseph, MI); method 990.13, AOAC (2005). For

NDF analysis samples were treated with thermo-stable α-amylase and sodium sulfite,

according to Van Soest et al. (1991) and modified for the Ankom200 Fiber Analyzer

(Ankom Technology, Macedon, NY), but without ash correction. For ADF analysis

samples were sequentially analyzed according to Van Soest et al. (1991) and modified

for the Ankom200 Fiber Analyzer (Ankom Technology, Macedon, NY).

Time course samples and pooled effluent samples were analyzed for NH3-N

concentration according to Chaney and Marbach (1962) .Concentration of total and

individual VFA of all samples was determined using gas chromatography (Agilent

6890N, Agilent Technologies, Santa Clara, CA, USA); equipped with a capillary column

(10.0 m x 530 μm x 1.00 μm nominal). The flow rate of carrier gas (helium) was 27

mL/min. Oven temperature was programmed as follows: 90°C for 2 min, increasing from

73

90°C to 190°C at 15°C /min, and holding 190°C for 2 min. Amino acids analysis was

performed on dietary ingredients and composite effluent samples according to the

method 999.13 AOAC (2005), using an Amino Acid Analyzer (Hitachi, model 8900,

Japan) at the Agricultural Experiment Station Chemical Laboratories from the University

of Missouri-Columbia. Analyses were performed with post-ninhydrin detection and

employing norleucine as the internal standard. Sulfur AA were analyzed after a perfomic

acid oxidation.

Glucosinolate analyses was performed at the USDA Agricultural Research

Service (Peoria, IL). Feed samples were prepared according to Berhow et al. (2013).

For glucosinolate quantitation a modification of a HPLC method developed by Betz and

Fox (1994) was used. The extract was run on a Shimadzu (Columbia, MD) HPLC

system (2 LC 20AD pumps; SIL 20A auto-injector; DGU 20 As degasser; SPD-20A UV-

vis detector; and a CBM-20A communication BUS module) running under the Shimadzu

LC solutions software (version 1.25). The column was a C18 Inertsil reverse phase

column (250 mm × 4.6 mm; RP C-18, ODS-3, 5u; GL Sciences, Torrance, CA).

Glucosinolates were detected by monitoring at 237 nm.

Composites of liquid and solid background samples and bacterial pellets were

analyzed for DM, N, and ash as described previously for dietary ingredients and effluent

samples and were analyzed for 15N enrichment according to Werner et al. (1999).

Isotope analyses were performed using a Eurovector model 3000 (Euro EA 3000,

Eurovector S.P.A., Milano, Italy) elemental analyzer interfaced to a Micromass Isoprime

(IsoPrime, Micromass UK Ltd., Manchester, UK) stable isotope ratio mass

spectrometer.

74

Calculations

Bacterial N flow and bacterial efficiency were calculated as follows:

Bacterial N flow (expressed in g/d) = NAN flow x atom percentage excess of

15N of NAN effluent) / (atom percentage excess of 15N of bacteria pellet)

(4-1)

with 15N digesta effluents background subtracted from 15N enrichment (Calsamiglia et

al., 1996).

Nutrient true digestibility was calculated as described by Soder et al. (2013) and

Benedeti et al. (2015). The OM was calculated as the difference between DM and ash.

Nonfiber carbohydrate (NFC; % of DM) was calculated according to NRC (2001):

NFC = 100 − (% NDF + % CP + % fat + % ash) (4-2)

Nitrogen (N) metabolism was calculated as follows:

NH3-N (g/d) = mg/dL of effluent NH3-N × (g of total effluent flow/100) (4-3)

NAN flow (g/d) = g of effluent N − g of effluent NH3-N (4-4)

NANMN flow (g/d) = g of effluent NAN − g of effluent bacterial N (4-5)

Bacterial efficiency = g of bacterial N flow/kg of OM truly digested (4-6)

Efficiency of N use (ENU) = (g of bacterial N/g of available N) × 100 (4-5)

where ENU was calculated according to Bach and Stern (1999)

Statistical Analysis

All data were subjected to least squares ANOVA using the MIXED procedure of

SAS (version 9.4, SAS Inst., Inc., Cary, NC) as a replicated 3 × 3 Latin square

arrangement. The statistical model used was:

Yijklm= µ +Di + LSj + Fk(j) + Pl +Ɛijklm (4-6)

where: Yijkl = dependent variable; µ = overall mean; Di = fixed effect of diets (0, 50 or

100); LSj = random effect of Latin square; Fk(j) = random effect of fermentor within Latin

75

square; Pl = random effect of period and Ɛijklm = random error. Linear and quadratic

effects of SCAM inclusion were assessed using orthogonal contrasts. Data in which

studentized residual was greater than 2 or less than -2, were considered as outliers.

Data from different time points (pH, VFA, and NH3-N) were analyzed as repeated

measurements in the experimental model. Seven covariance structures (AR1, CS, UN,

TOEP, VC, ARH1, and TOEPH) were tested. For VFA and pH data, the UN provided

the best fit, while for NH3-N, AR1 provided the best fit based on Akaike’s information.

Differences were declared when P ≤ 0.05 and a tendency was considered when 0.05 <

P ≤ 0.10.

Results

Nutrient Digestibility and Volatile Fatty Acids

True digestibility of DM, OM, and CP were not affected by diets (Table 4-4).

However, NDF digestibility linearly decreased (P = 0.04) with SCAM inclusion, where

fermentors fed diet 0 had the greatest value (52.5%), diet 50 was intermediate (48.0%)

and diet 100 had the least digestibility (45.0%). Digestibility of ADF was not affected by

diets.

Total VFA concentration and molar proportion of butyrate were not affected by

treatments (Table 4-5). Molar proportion of acetate and propionate were quadratically

affected by treatments (P < 0.01), where fermentors fed diet 0 had the greatest molar

proportion of acetate and diet 100 the least, however the magnitude of the effect was

greater from diet 0 to 50 (6.5%), than from diet 50 to 100 (2.3%). Fermentors fed diet 0

had the least (P < 0.01) molar proportion of propionate, while the ones fed diet 100 had

the greatest. The magnitude of the effect was greater from diet 0 to 50 (6.5%) than from

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diet 50 to 100 (2.0%). Consequently, acetate:propionate ratio was quadratically affected

by treatments (P = 0.01).

Regarding branched-chain VFA (BCVFA) on pooled effluent, molar proportion of

isobutyrate and isovalerate, as well as their sum (in mM) were affected by treatments

(Table 4-5). Isobutyrate linearly decreased (P = 0.01), and isovalerate quadratically

increased (P = 0.01) with SCAM inclusion. Total BCVFA was quadratically affected (P =

0.01) by treatments, where fermentors fed diet 50 had the greatest concentration, diet 0

the least and diet 100 intermediate concentration.

Time course data (collected from fermentor fluid), followed a similar pattern

observed on the pooled effluent data. There was time × diet interaction for acetate (P =

0.02, Figure 4-1A) and only treatment and time effect for propionate (Figure 4-1B).

Acetate concentration was consistently greater (all time points) in fermentors fed diet 0,

intermediate for diet 50 and lesser for diet 100 (Figure 4-1A). At 1, 2, and 8 h after

feeding, acetate concentration linearly increased (P < 0.05) in fermentors fed diet 100

compared to diet 0, while at 6 hours after feeding there was a quadratic effect (P <

0.05). The diurnal variation of propionate concentration in fermentors fed diets 50 and

100 was similar and greater than in fermentors fed diet 0.

Nitrogen Metabolism and Amino Acid Outflow

The pH data followed a typical curve and was not affected (P = 0.31) by

treatment. Due to lack of time × diet interaction (P = 0.98) pH data are presented as

daily means (Table 4-6). The NH3-N concentration in pooled effluent in mg/dL as well as

flow in g/d linearly decreased (P = 0.01) with SCAM inclusion (Table 4-6). On the

contrary, NAN linearly increased (P = 0.02) with SCAM inclusion. Total N flow, bacterial-

N flow, and efficiency of N use were not affected by treatments. Bacterial efficiency

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tended (P = 0.06) to be greater in fermentors fed diet 100, and NANMN flow tended (P

= 0.06) to increase with SCAM inclusion.

Similar to pooled effluent data, fermentors fed diet 100 had decreased (P < 0.01)

diurnal variation of NH3-N concentration, and there was time x diet interaction (Figure 4-

2). At 2 and 4 h after feeding there was a linear (P < 0.05) effect, while at 8 h after

feeding there was a quadratic (P < 0.05) effect on NH3-N concentration. Fermentors fed

diets 50 and 100 reached a peak of NH3-N concentration at 1 hour after feeding, while

fermentors fed diet 0 peaked at 2 hours after feeding; demonstrating that SCAM is

degraded to NH3-N faster than CM.

Outflow of Arg and Ser linearly increased (P = 0.01) with the replacement of CM

by SCAM (Table 4-7). However, flows of total AA, EAA, branched-chain AA (BCAA),

and the remaining individual AA were not affected by treatment.

Discussion

The replacement of CM with SCAM did not affect true digestibility of DM, OM and

CP. The absence of diet effect on total VFA concentration could be partially explained

by the lack of effects on true DM and OM digestibility. Taking into consideration that

SCAM and CM have similar nutritional composition (Table 4-2), it was expected that the

replacement would not affect overall nutrient digestibility. Similarly, Lawrence et al.

(2016) did not find effect on DM, OM, and CP digestibility when comparing diets

containing either CAM, linseed meal, or distilled dried grains fed at 10% of the diet (DM

basis) to growing dairy heifers. Rodriguez-Hernandez and Anderson (2018) did not

observe an effect of feeding 10% of total diet (DM basis) carinata meal (oil seed crop

from the same family as camelina that contains similar CP and fat concentration) to

growing dairy heifers on total tract CP digestibility compared with heifers fed dried

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distilled grains. Both studies evaluated total tract digestibility, while the present study

evaluated nutrient digestibility in a dual-flow continuous culture, which simulates ruminal

digestibility. Partial or complete replacement of CM with SCAM did not affect pH, which

could be attributed to the absence of effect on total VFA. Similar results on pH were

reported by Lawrence et al. (2016) and Rodriguez-Hernandez and Anderson (2018).

Replacement of CM by SCAM decreased NDF, but not ADF digestibility,

suggesting that SCAM inclusion mainly affected hemicellulose digestibility. Various

polysaccharides are present in hemicellulose, such as arabinoxylans, β-glucan,

xyloglucans, and arabinogalactans (Hindrichsen et al., 2006). It is possible that SCAM

cell wall is structured differently from CM or has greater concentration of less digestible

compounds than CM. Reducing NDF digestibility likely reflected on acetate

concentration, which was reduced in diets containing SCAM. Further studies are

necessary to evaluate SCAM hemicellulose composition and whether SCAM

hemicellulose is digested differently than CM.

Our research group observed that feeding ground camelina seed (at 7.7% or

17.7% of dietary inclusion, DM basis) plus CM reduced NDF digestibility when

compared with feeding only CM as protein supplement in a dual-flow continuous culture

system (Brandao et al., 2018). However, we speculate that NDF digestibility of SCAM is

greater than ground camelina seed due to reduced total EE concentration in SCAM.

The difference between dietary NDF comparing diet 0 to 100 was 2.4 percentage

units, and for NFC was 2.1 (Table 4-1). Comparing effluent from fermentors fed diet 0 to

100, molar proportion of acetate differed 8.8 percentage units and propionate 8.5 (Table

4-5). Effluent from fermentors fed diet 100 (which was slightly greater in NFC) also had

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greater propionate concentration, and the effect of increasing dietary NFC resulting in

increased ruminal propionate concentration has been widely documented in the

literature (Batajoo and Shaver, 1994; Schwab et al., 2006). On the other hand, effluent

from fermentors fed diet 0 (which was greater in dietary NDF) had greater acetate

concentration. Therefore, we acknowledge that this dietary difference might have

contributed to the observed results; however, we consider that a change of

approximately 2 percentage units in dietary NDF and NFC, would not be enough to

cause a change of approximately 8 percentage units on effluent acetate and propionate

concentration.

Additionally, when feeding carinata meal to growing heifers, Rodriguez-

Hernandez and Anderson (2018) reported decreased NDF total tract digestibility in

heifers fed carinata than the ones fed distilled dried grains. Although the present study

evaluated ruminal digestibility, not total tract digestibility, their results may be

comparable to ours because the vast majority of fiber is digested in the rumen

(Huhtanen et al., 2010), indicating that the role of the hindgut digestion in the total tract

NDF digestibility is negligible. Therefore, effects on NDF total tract digestibility are likely

to be a reflection of ruminal NDF digestibility.

Concentration of acetate (Figure 1A) inside fermentors fed diet 0 was greater

than in fermentors fed diets 50 and 100 at 1, 2, 6, and 8 hours after feeding. Overall, the

diurnal variation of acetate followed a linear behavior, indicating that as SCAM inclusion

increased acetate concentration decreased. Fermentor concentration of propionate

(Figure 4-1B) had an opposite pattern than acetate, where fermentors fed diet 50 and

100 had the greatest concentration, while diet 0 the least. Acetate and propionate

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diurnal variation is in agreement with pooled effluent results, demonstrating that pooled

effluent was an adequate representation of the culture condition throughout the day. An

energetic shift was observed when CM was replaced with SCAM, where diets

containing SCAM had greater molar proportion of propionate and lesser acetate molar

proportion; however, total VFA concentration was not affected.

Metabolizable energy for a dairy cow is mostly provided by VFA, and it is

estimated that (under normal conditions) acetate, propionate, and butyrate accounts for

95% of total VFA present in ruminal fluid (Bannink et al., 2006). Typically, when

propionate concentration is increased, acetate concentration is decreased. This shift

represents a greater glucogenic potential in diets containing SCAM, which may provide

more energy available for milk production. However, considering that acetate is the

main precursor of milk fat (Grummer, 1991), the shift observed in the present study may

not be always advantageous, as it may represent a decrease in milk fat (Palmquist et

al., 1993).

Branched-chain VFA are required by cellulolytic bacteria to grow (Allison and

Bryant, 1963; Mir et al., 1986). Thus, we speculate that SCAM suppressed cellulolytic

bacteria population, which resulted in accumulation of BCVFA observed in fermentors

fed diets 50 and 100. These results, combined with the reduction in NDF digestibility

and acetate concentration, strengthen the hypothesis that SCAM depressed cellulolytic

bacteria population. In fact, in a study evaluating the effects of feeding camelina seed in

the ruminal bacterial community composition, Dai et al. (2017) reported that camelina

diets suppressed cellulolytic bacteria. One possible explanation for that might be the

fact that camelina has 73.9% of total fatty acids in the form of polyunsaturated fatty

81

acids (PUFA; Hurtaud and Peyraud, 2007), which are associated with possible

detrimental effects on cellulolytic bacteria (Lila et al., 2003; Maia et al., 2007). It is

possible that PUFA present in camelina play a role in suppressing cellulolytic bacteria

by disrupting the bacterial lipid bilayer structure (Keweloh and Heipieper, 1996),

resulting in reduced NDF digestibility, acetate and BCVFA concentrations.

Fermentors fed diets containing SCAM had decreased NH3-N concentration and

lesser NH3-N flow in g/d (Table 4-6). Ammonia concentration in the rumen is used as an

indicator of protein degradation (performed by ruminal microbial population) and of

nonprotein utilization (Broderick and Kang, 1980). Lesser NH3-N accumulation

associated with greater bacterial N would indicate that dietary protein was degraded

and incorporated into microbial protein. In the present study, pooled effluent from

fermentors fed diet 100 had lesser NH3-N concentration than from fermentors fed diets

50 and 0; however, bacterial N was not affected by diets. Ammonia starts to accumulate

when ruminal CP degradation rate exceeds the microbial assimilation and uptake rate of

NH3 (NRC, 2001), leading to greater NH3-N concentration as was observed in diet 0 and

50. To efficiently use dietary CP, it is important to provide protein supplements that

supply RDP that will meet, but not exceed, the N requirements of the ruminal microbial

population (NRC, 2001). Excess NH3 produced in the rumen can be absorbed across

the rumen wall and metabolized in the liver. Then, NH3 is converted to urea and

excreted via urine and milk. Therefore, accumulation of NH3 represents an energy loss,

and ultimately an environmental and economic issue. Improvement on N utilization

efficiency represents a decrease in N losses (Jonker et al., 1998) hence, diets

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containing SCAM may have the potential to decrease N excretion. Additionally, NANMN

flow tended to increase with SCAM inclusion, demonstrating greater escape of RUP.

Interestingly, despite several nutritional similarities between CM and SCAM, CP

degradation had different diurnal variation across diets. Diets containing SCAM (50 and

100) rapidly degraded CP, which is demonstrated by NH3-N concentration peak at one

hour after feeding (Figure 4-2), while diet 0 peaked two hours after feeding. This

relatively rapid NH3-N concentration peak observed in SCAM diets, may be deleterious

to N metabolism if it is not accompanied by a peak of energy supply (NRC, 2001).

Bacterial efficiency (calculated as g of bacterial N flow/ kg OM truly digested) tended to

increase with SCAM supplementation (Table 4-6), suggesting that SCAM tended to

support better microbial energy use. Although bacterial efficiency does not necessarily

predict the efficiency by which bacteria capture available N in the fermentors, it is

considered a reliable indicator of energy use (Bach et al., 2005).

In a lactating dairy cow, the mammary gland is the major AA net user,

representing an uptake of 50% of the total MP (Lapierre et al., 2012). Diet 100 had 17%

more Arg flowing out of the fermentors than diet 0 (Table 4-7), and overall, replacing

CM by SCAM had minor effect on AA flow. This result can be partially explained by

similar AA profile and greater Arg concentration in diets containing SCAM (Table 4-3).

Our results are supported by Colombini et al. (2014) and Brandao et al. (2018) who

reported greater Arg concentration in camelina than canola. Greater Arg supply in

SCAM diet may be important because Arg is a semi-essential AA, and mammary gland

uptake of Arg is on average 2.45 times greater than the amount of Arg present in milk

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(Lapierre et al., 2012). Therefore, feeding SCAM may represent better supply of Arg

available for post-ruminal responses.

Glucosinolate metabolites are known to be associated with depressed thyroid

function by interfering with iodine uptake, which may affect animal growth and

performance (Forss and Barry, 1983; Marillia et al., 2014). However, Lawrence et al.

(2016) did not report negative effects of glucosinolates on T3 (triiodothyronine) and T4

(thyroxine) concentration in heifers fed a diet with camelina meal (fed at 10% DM),

containing on the total diet 1.24 mg/g of glucosinolates. Likewise, Rodriguez-Hernandez

and Anderson (2018), fed a diet containing 10% DM of carinata meal and 2.06 mg/g of

total dietary glucosinolates and did not observe negative effects on DMI and animal

performance. In the present study, total glucosinolate concentration in diet 50 was 1.36

mg/g and in diet 100 was 2.40 mg/g; therefore, it is possible that diet 50 was below a

deleterious threshold and diet 100 above it. Glucosinolates may affect ruminal

fermentation by reducing methane production. A decrease in methane emissions has

been reported in vivo (Sun et al., 2015) and in a continuous culture system (Dillard et

al., 2018) when brassica plants containing high glucosinolates were used. This

decrease in methane is often accompanied by a decrease in acetate concentration and

increase in propionate (Lila et al., 2003). It is possible that glucosinolates modify rumen

microbial population, favoring propionate producing bacteria (Sun et al., 2015), which is

a H2 consuming route and reduces methanogens, ultimately resulting in decreased

methane production. However, in the present study we did not measure methane and

further investigation of these effects is warranted.

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Because we wanted to evaluate the effects of replacing CM with SCAM and they

had different oil concentrations we added camelina oil in the 0 and 50 diets to avoid

potential confounding effects of oil concentration and FA profile. If we account for the

residual oil in SCAM (6% of the meal), all treatments basically received similar

concentration of camelina oil, with only small amounts of canola oil (only from the

residual oil in CM). The total EE concentration across all diets (3.4% DM basis) was

kept as low as possible to avoid any detrimental effects on digestion and fermentation.

Therefore, it is possible; however, very unlikely that the added camelina oil (0.8 and

0.4% of DM) would negatively affect treatments 0 and 50, respectively.

Canola meal is widely used as a protein supplement in dairy farms in North

America, representing the second largest protein feed (including rapeseed meal)

produced globally (Huhtanen et al., 2011). Huhtanen et al (2011) reported increased

DMI and milk yield when cows were fed CM rather than soybean meal. Thus, the similar

fermentation profile reported in the present study indicates possible replacement of CM

with SCAM, which can represent valuable information for the dairy industry. We

hypothesized that SCAM could partially or completely replace CM as a protein

supplement in lactating dairy cow diets without negatively affecting microbial

fermentation in a dual-flow continuous culture system, and overall results support our

hypothesis.

Conclusions

In summary, beneficial effects were observed when SCAM completely replaced

CM. Fermentors fed diet 100 had greater propionate molar proportion and NAN flow,

and lesser NH3-N, which may improve animal energy status and N utilization. However,

SCAM supplementation decreased acetate molar proportion and NDF digestibility,

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indicating potential reduction in fiber digestibility. Fermentors fed diet 100 had the

greatest outflow of Arg, and SCAM supplementation did not change most of the AA

outflow. Overall, our results indicate that SCAM can be a potential replacement for

canola meal. Nevertheless, in vivo studies are necessary to confirm if the effects

observed in a dual-flow continuous culture system will translate into improvements in

animal performance.

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Table 4-1. Ingredient and chemical composition of the experimental diets (% DM unless otherwise stated)

Item Treatments1

0 50 100

Ingredient composition Orchard grass hay 55.0 55.0 55.0

Ground corn 22.3 23.0 23.6

Solvent-extracted canola meal 20.6 10.3 -

Solvent-extracted camelina meal - 10.1 20.2

Camelina oil 0.82 0.41 -

Mineral mix2 1.26 1.25 1.24

Chemical composition DM, % 87.0 86.9 86.8

OM 92.3 92.1 92.0

CP 16.0 16.0 16.0

NDF 41.8 40.6 39.4

ADF 20.4 20.3 20.3

EE 3.42 3.41 3.40

NFC3 32.3 33.4 34.4

NEl, Mcal/kg DM4 1.50 1.51 1.51

Glucosinolates, mg/g 0.30 1.36 2.41 10 = no solvent-extracted camelina meal inclusion, 50 = 50% of solvent-extracted camelina meal inclusion, 100 = 100% of solvent-extracted camelina meal inclusion replacing canola meal as protein supplement. 2Provided (per kg of DM): 955 g of NaCl, 3,500 mg of Zn, 2,000 mg of Fe, 1,800 mg of Mn, 280 mg of Cu, 100 mg of I, and 60 mg of Co. 3Estimated according to NRC (2001), using the following equation: NFC = 100 – (% NDF + % CP + % fat + % ash). 4NEl = Net energy for lactation, estimated using the NRC (2001) model.

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Table 4-2. Nutrient composition of protein supplement used on the experimental diets

Item1 Protein Supplement2

CM SCAM

DM, % 94.5 94.0

OM 93.3 94.9

CP 39.4 39.7

NDF 27.6 19.8

ADF 19.3 19.3

EE 2.32 6.16

Glucosinolates, mg/g GS9 - 3.67

GS10 - 8.16

GS11 - 0.10

Total glucosinolates 1.5 11.93 1% of DM, unless otherwise indicated. 2CM = canola meal, SCAM = solvent-extracted camelina meal.

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Table 4-3. Amino acid composition of experimental diets and protein supplements

Item Protein supplement1 Treatments2

CM SCAM 0 50 100

AA, % of total

Ala 4.72 4.84 6.72 6.71 6.80

Arg 6.47 8.83 5.48 5.72 6.00

Asp 7.48 8.98 8.70 8.82 9.03

Cys 2.78 2.33 1.88 1.83 1.81

Glu 18.6 17.3 14.2 14.1 14.1

Gly 5.38 5.51 5.32 5.31 5.38

His 2.89 2.54 2.32 2.28 2.27

Ile 4.47 4.17 4.57 4.51 4.54

Leu 7.50 7.00 9.09 9.01 9.11

Lys 6.25 5.33 5.57 5.45 5.37

Met 2.13 1.84 2.03 1.99 1.99

Orn 0.03 0.03 0.01 0.01 0.01

Phe 4.37 4.63 5.35 5.36 5.42

Pro 6.71 5.51 7.07 6.92 6.90

Ser 4.17 4.75 4.21 4.26 4.37

Thr 4.58 4.43 4.60 4.55 4.59

Trp 1.34 1.34 0.84 0.84 0.84

Tyr 2.86 3.00 3.03 3.03 3.08

Val 5.70 5.65 5.84 5.80 5.86

EAA3 45.7 45.8 45.7 45.5 46.0

NEAA4 53.1 52.7 52.5 52.3 52.8

BCAA5 17.7 16.8 19.5 19.3 19.5

Others6 1.56 1.95 3.12 3.51 2.50 1CM = solvent-extracted canola meal, SCAM = solvent-extracted camelina meal. 20 = no solvent-extracted camelina meal inclusion, 50 = 50% of solvent-extracted camelina meal inclusion, 100 = 100% of solvent-extracted camelina meal inclusion replacing canola meal as protein supplement. ND = not detected. 3EAA = essential AA (Arg, His, Ile, Leu, Lys, Met, Phe, Thr, Trp, and Val). 4NEAA = nonessential AA (Ala, Asp, Cys, Glu, Gly, Pro, Ser, Orn, taurine, and Tyr). 5BCAA = branched-chain AA (Ile, Leu, and Val). 6Others = sum of hydroxylysine, hydroxyproline, lanthionine, and taurine.

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Table 4-4. Effects of replacing canola meal with solvent-extracted camelina meal on nutrient true digestibility of DM, OM and CP, NDF and ADF in dual-flow continuous culture system

Treatments1 SEM P-value

Digestibility, % 0 50 100 linear quadratic

DM 45.0 44.5 43.2 1.81 0.82 0.46

OM 51.1 48.4 47.2 1.80 0.94 0.22

CP 54.8 51.1 53.4 2.67 0.65 0.27

NDF 52.5 48.0 45.0 1.93 0.04 0.77

ADF 32.2 32.6 29.7 3.11 0.70 0.15 10 = no solvent-extracted camelina meal inclusion, 50 = 50% of solvent-extracted camelina meal inclusion, 100 = 100% of solvent-extracted camelina meal inclusion replacing canola meal as protein supplement.

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Table 4-5. Effects of replacing canola meal with solvent-extracted camelina meal on volatile fatty acids total concentration and molar proportion in pooled effluent in dual-flow continuous culture system

Treatments1 SEM

P-value

Item 0 50 100 linear quadratic

Total VFA, mM 76.5 78.9 77.6 1.80 0.29 0.10

VFA, % total VFA Acetate 63.7 57.2 54.9 1.01 <0.01 <0.01

Propionate 19.4 25.9 27.9 0.81 <0.01 <0.01

Butyrate 14.1 13.6 14.2 1.08 0.83 0.16

Isobutyrate 0.52 0.47 0.45 0.06 0.01 0.42

Valerate 1.56 1.72 1.72 0.10 0.01 0.04

Isovalerate 0.78 1.08 0.84 0.14 0.41 <0.01

Total BCVFA, mM 0.79 0.99 0.85 0.13 0.82 0.01

Acetate:Propionate 3.28 2.22 1.98 0.08 <0.01 <0.01 10 = no solvent-extracted camelina meal inclusion, 50 = 50% of solvent-extracted camelina meal inclusion, 100 = 100% of solvent-extracted camelina meal inclusion replacing canola meal as protein supplement.

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Table 4-6. Effects of replacing canola meal with solvent-extracted camelina meal on nitrogen metabolism in dual-flow continuous culture system

Treatments1 SEM

P-value

Item 0 50 100 linear quadratic

pH 6.44 6.54 6.48 0.05 0.55 0.17

NH3-N mg/dL 18.2 17.9 16.2 1.51 0.01 0.17

N flows, g/d

Total N 2.11 2.14 2.16 0.06 0.58 0.94

NH3-N2 0.58 0.57 0.51 0.04 0.01 0.20

NAN3 1.52 1.56 1.64 0.04 0.02 0.94

Bacterial N4 0.71 0.67 0.72 0.06 0.61 0.19

NANMN5 0.80 0.89 0.94 0.09 0.06 0.72

ENU6, % 70.3 70.4 74.0 5.56 0.55 0.74

Bacterial efficiency7 22.0 21.1 23.9 1.47 0.15 0.06 10 = no solvent-extracted camelina meal inclusion, 50 = 50% of solvent-extracted camelina meal inclusion, 100 = 100% of solvent-extracted camelina meal inclusion replacing canola meal as protein supplement. 2NH3-N (g/d) = mg/dL of effluent NH3-N × (g of total effluent flow/100). 3NAN = nonammonia nitrogen. It was calculated as following: NAN flow (g/d) = g of effluent N − g of effluent NH3-N. 4Bacterial-N flow was calculated as following: bacterial N flow (g/d) = (NAN flow × atom percentage excess of 15N of effluent)/ (atom percentage excess of 15N of bacteria), according to Calsamiglia et al. (1996). 5NANMN (g/d) = nonmicrobial nonammonia nitrogen. Calculated as follows: NANMN = g of effluent NAN − g of effluent bacterial N. 6Efficiency of N use = (g of bacterial N/g of available N) × 100 (Bach and Stern, 1999). 7Bacterial efficiency was calculated according to Calsamiglia et al. (1996). Using the following equation: Bacterial efficiency = g of bacterial N flow/kg of OM truly digested.

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Table 4-7. Effects of replacing canola meal with solvent-extracted camelina meal on amino acid flow in dual-flow continuous culture system

Treatments1 SEM

P-value

AA, g/d 0 50 100 linear quadratic

Ala 0.76 0.79 0.81 0.02 0.05 0.82

Arg 0.53 0.59 0.62 0.02 0.01 0.60

Asp 1.08 1.12 1.39 0.03 0.11 0.67

Cys 0.17 0.17 0.17 0.01 0.59 0.72

Glu 1.42 1.48 1.51 0.04 0.12 0.71

Gly 0.62 0.63 0.64 0.02 0.23 0.79

His 0.21 0.22 0.22 0.01 0.45 0.70

Ile 0.60 0.60 0.60 0.02 0.99 0.83

Leu 0.92 0.95 0.95 0.02 0.33 0.63

Lys 0.68 0.71 0.71 0.02 0.32 0.44

Met 0.22 0.24 0.23 0.01 0.36 0.16

Orn 0.02 0.02 0.02 0.01 0.64 0.32

Phe 0.58 0.6 0.61 0.02 0.25 0.75

Pro 0.52 0.54 0.54 0.01 0.41 0.76

Ser 0.47 0.49 0.51 0.01 0.01 0.64

Thr 0.58 0.59 0.59 0.01 0.58 0.68

Trp 0.11 0.11 0.11 0.01 0.78 0.78

Tyr 0.41 0.42 0.42 0.01 0.67 0.49

Val 0.72 0.74 0.74 0.02 0.48 0.72

EAA2 5.15 5.34 5.37 0.14 0.26 0.62

NEAA3 5.51 5.7 5.79 0.13 0.14 0.75

BCAA4 2.23 2.28 2.28 0.06 0.52 0.70

Others5 0.07 0.07 0.08 0.01 0.17 0.88

Total AA 10.74 11.11 11.23 0.27 0.19 0.68 10 = no solvent-extracted camelina meal inclusion, 50 = 50% of solvent-extracted camelina meal inclusion, 100 = 100% of solvent-extracted camelina meal inclusion replacing canola meal as protein supplement. 2EAA = essential AA (Arg, His, Ile, Leu, Lys, Met, Phe, Thr, Trp, and Val). 3NEAA = nonessential AA (Ala, Asp, Cys, Glu, Gly, Pro, Ser, Orn, taurine, and Tyr). 4BCAA = branched-chain AA (Ile, Leu, and Val). 5Others = sum of hydroxylysine, hydroxyproline, lanthionine, and taurine.

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Figure 4-1. Effect of replacing canola meal with camelina meal solvent-extracted on

diurnal variation of acetate (A) and propionate (B) concentration inside the fermentors in dual-flow continuous culture system. 0 = no solvent-extracted camelina meal inclusion, 50 = 50% of solvent-extracted camelina meal inclusion, 100 = 100% of solvent-extracted camelina meal inclusion replacing canola meal as protein supplement. L = linear contrast P < 0.05; Q = quadratic contrast P < 0.05.

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Figure 4-2. Effect of replacing canola meal with solvent-extracted camelina meal on

diurnal variation of ammonia nitrogen concentration (NH3-N) inside the fermentors in dual-flow continuous culture system. 0 = no solvent-extracted camelina meal inclusion 50 = 50% of solvent-extracted camelina meal inclusion, 100 = 100% of solvent-extracted camelina meal inclusion replacing canola meal as protein supplement. L = linear contrast P < 0.05; Q = quadratic contrast P < 0.05.

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CHAPTER 5 UNVEILING THE RELATIONSHIPS BETWEEN DIET COMPOSITION AND

FERMENTATION PARAMETER RESPONSE IN DUAL-FLOW CONTINUOUS CULTURE SYSTEM: A META-ANALYTICAL APPROACH

Summary

Our objective was to investigate the functional form of the relationship between

diet composition (dietary CP, NDF) and amount of substrate (fermentor DMI) with

microbial fermentation end-products in a dual-flow continuous culture system. A meta-

analysis was performed using data from 75 studies. To derive the linear models, the

MIXED procedure was used, and for non-linear models the NLMIXED procedure was

used. Significance levels to fit the model assumed for fixed and random effects were P

≤ 0.05. Independent variables were: dietary neutral detergent fiber (NDF) and crude

protein (CP), and fermentor dry matter intake (DMI), whereas dependent variables

were: total volatile fatty acids (VFA) concentration, molar proportions of acetate,

propionate, and butyrate, true ruminal digestibility of organic matter (OM), CP, and NDF,

ammonia nitrogen (NH3-N) concentration and flows of NH3-N, non-ammonia nitrogen

(NAN), bacterial-N, dietary-N, and efficiency of microbial protein synthesis (EMPS).

Ruminal digestibilities of OM, NDF and CP decreased as fermentor DMI increased.

Dietary NDF and CP digestibility were quadratically associated, and CP digestibility was

maximized at 48% dietary NDF. Total VFA linearly increased as DMI increased;

exponentially decreased as dietary NDF increased; and was quadratically associated

with dietary CP, in which total VFA concentration was maximized at 18% dietary CP.

Molar proportion of acetate exponentially increased as dietary NDF increased. Molar

proportion of propionate linearly increased and exponentially decreased as DMI and

dietary NDF increased, respectively. Bacterial-N quadratically increased and dietary-N

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exponentially increased as DMI increased. Flows of bacterial-N and dietary-N linearly

decreased as dietary NDF increased, and dietary-N flow was maximized at 18% CP.

The EMPS linearly increased as dietary CP increased and was not affected by DMI or

dietary NDF. In summary, increasing fermentor DMI resulted in increases in total VFA

concentration and molar proportion of propionate, whereas, dietary NDF increased the

molar proportion of acetate. Dietary CP increased molar proportion of propionate and

bacterial-N flow; however, it was also positively associated with NH3-N concentration.

Overall, the analysis of this dataset supports a conclusion that the dual-flow continuous

culture system provides valuable estimates of ruminal digestibility, VFA concentration

and nitrogen metabolism which were closely related to the expected in vivo response. In

addition, this technique can be a valuable tool for testing a large variety of dietary

treatments with continuous removal of fermentation end-products and for longer period

of time than other in vitro systems.

Introductory Remarks

The rumen is the main site of fiber digestion and protein degradation in ruminant

animals. It is estimated that up to 97% of total digested neutral detergent fiber (NDF) is

digested in the rumen (Huhtanen et al., 2010); and 50 to 80% of ingested crude protein

(CP) is degraded in the rumen (NRC, 2001). Digestion and passage rates are two

competitive processes (Mertens, 1977) that are difficult to study separately in vivo.

Therefore, studies to understand how DMI, dietary CP, and NDF affect ruminal

digestion independently of passage rate are warranted. In the dual-flow continuous

culture system (DFCCS), liquid and solid passage rates are controlled, which allows

evaluation of ruminal digestion under controlled conditions and independently from

possible differences in animal passage rate and DMI. The DFCCS is a long-term

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fermentation system with tight variable control, which makes it suitable for evaluating

digestion processes. It was developed by Hoover et al. (1976) and recently modified to

test beef (Benedeti et al., 2015; Amaral et al., 2016; Silva et al., 2017) and dairy diets

(Paula et al., 2017; Brandao et al., 2018 a;b).

The end-products of carbohydrate and protein fermentation are volatile fatty

acids (VFA), microbial cells, CH4 and CO2 (NRC, 2016), in which VFA and microbial

cells represent the majority. Organic acids, notably VFA, largely contribute to

metabolizable energy supply for ruminants (Bergman, 1990; Aschenbach et al., 2011),

and it is estimated that VFA produced in the rumen may account for up to 75% of the

total metabolizable energy from the diet (Siciliano-Jones and Murphy, 1989). The major

VFA produced in the rumen are acetate, propionate, and butyrate, which represent up

to 95% of the total acids produced by fermentation (Bergman, 1990); moreover, VFA

type may affect animal performance and milk and meat composition. In addition to VFA,

microbial protein can also be used as gluconeogenic precursor (Lobley, 1992). In beef

cattle, microbial protein can provide between 50 to 100% of total required metabolizable

protein (NRC, 2016), however, depending on animal energy status and production level,

they may rely more on energy provided by amino acids.

Therefore, the objective was to investigate the functional form of the relationship

between diet composition (dietary CP and NDF) and amount of substrate (fermentor

DMI) with microbial fermentation end-products in a dual-flow continuous culture system

using a meta-analytical approach. We hypothesized that fermentor DMI, dietary CP, and

NDF independently affect microbial fermentation end-products. We acknowledge that

there are other variables that affect ruminal fermentation, such as dietary fatty acids,

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non-fiber carbohydrates, and starch; however, these are not as frequently reported in

other dual-flow continuous cultures and therefore, we focused this review on fermentor

DMI, and dietary CP and NDF as independent variables.

Materials and Methods

Data Collection and Preparation

Data used in this paper were obtained from 75 peer-reviewed published studies,

and the dataset was comprised of 523 treatment means. Articles were published in

Journal of Dairy Science, Journal of Animal Science and PLoS One from 1985 to 2018.

Keywords used to search for relevant papers were: “dual-flow”, “continuous culture”,

“dual-flow continuous culture system”, “microbial fermentation” and “in vitro”. The first

step of study selection was to ensure that the study under consideration used a dual-

flow continuous culture system and not any other in vitro system. Secondly, the study

must have used rumen inoculum from dairy or beef cattle, therefore studies using sheep

or goat inoculum were excluded. All studies included in the database reported the

independent variables of interest (dietary CP, NDF, and fermentor DMI), and only

studies that met all the above cited criteria were included.

The passage rates had minimal variation among studies, in which average solid

passage rate was 5%/h (SD = 0.7), while liquid passage rate averaged 10%/h (SD =

1.4). Most of the dual-flow studies used the same artificial saliva described by Weller

and Pilgrim (1974). Therefore, artificial saliva as well as passage rate were not selection

criterion in our study. In dual-flow continuous culture studies, pH is often maintained

constant (by infusion of either NaOH or HCl) and it is not considered a response

variable. Out of the 75 studies, 39% controlled pH and 25% did not report it. Therefore,

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pH was not used as a variable, due to fact that in the majority of the cases, it did not

reflect a response to treatments, or it was not reported.

Nutrient digestibility (OM, NDF, and CP) used in this study was true ruminal

digestibility, and dietary NDF and CP are expressed as %DM. When molar proportion of

individual VFA, and efficiency of microbial synthesis (EMPS) were not reported, they

were calculated as:

𝑀𝑜𝑙𝑎𝑟 𝑝𝑟𝑜𝑝𝑜𝑟𝑡𝑖𝑜𝑛 𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙 𝑉𝐹𝐴 =𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑜𝑓 𝑖𝑛𝑑𝑖𝑣𝑖𝑑𝑢𝑎𝑙 𝑉𝐹𝐴

𝑇𝑜𝑡𝑎𝑙 𝑉𝐹𝐴 𝑐𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛

(5-1)

𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 𝑜𝑓 𝑚𝑖𝑐𝑟𝑜𝑏𝑖𝑎𝑙 𝑠𝑦𝑛𝑡ℎ𝑒𝑠𝑖𝑠 (𝐸𝑀𝑃𝑆) =𝑔 𝑏𝑎𝑐𝑡𝑒𝑟𝑖𝑎𝑙 𝑛𝑖𝑡𝑟𝑜𝑔𝑒𝑛

𝑘𝑔 𝑜𝑟𝑔𝑎𝑛𝑖𝑐 𝑚𝑎𝑡𝑡𝑒𝑟 𝑡𝑟𝑢𝑙𝑦 𝑑𝑖𝑔𝑒𝑠𝑡𝑒𝑑

(5-2)

Model Derivation Procedure

An initial graphical examination of the data was performed to identify the

relationships that were studied (Sauvant et al., 2008a) and the meta-analysis was

performed according to St-Pierre (2001). All statistical analyses were performed using

SAS (SAS institute Inc., 2004). To derive linear models, the MIXED procedure was

used, and for non-linear models the NLMIXED procedure was used. Significance levels

assumed to fit the model for fixed and random effects were P ≤ 0.05. Independent

variables used were dietary NDF and CP, and fermentor DMI. The dependent variables

as well as descriptive statistics are presented in Table 1. The diets used in the data set

ranged in CP from 4 to 28.7%, NDF from 15.1 to 74.2% (Table5-1) and forage inclusion

in the diets ranged from 9 to 100%. The fermentor DMI, dietary NDF, and CP

(independent variables) effects on response variables were tested using linear,

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quadratic (linear models), exponential, and power models (non-linear models). Random

coefficients model was used considering study as a random effect and including the

possibility of covariance between the slope and the intercept. The covariance parameter

was considered non-zero when P ≤ 0.05. Seventeen variance-covariance structures

were tested and Akaike’s Information Criteria (AIC) was used to define the best fit.

When Cook's distance was greater than 1, the study was removed from the database in

each specific analysis. Outliers were removed when studentized residuals were greater

than 2 or less than -2. It should be noted that our model derivation was not intended to

generate prediction models, instead our objective was to understand and to evaluate

the functional form of the relationship between diet composition (dietary CP and NDF)

and amount of substrate (fermentor DMI) with microbial fermentation end-products in a

dual-flow continuous culture system using a meta-analytical approach.

Results and Discussion

Effects on True Ruminal Digestibility

True organic matter digestibility (TOMD) decreased as DMI increased (Table 5-

2), and a similar response to DMI was also observed for digestibilities of NDF and CP,

suggesting that the decline in true NDF digestibility (NDFD) and CP (TCPD) is

associated with the decline in TOMD. Our results are in agreement with a meta-analysis

that evaluated the effects of feeding level and diet composition on digestibility

performed by Huhtanen et al. (2009). Similar to our results, these authors reported a

negative association between DMI, OM digestibility and NDF digestibility in lactating

dairy cows. Our results indicate that TOMD, NDFD, and TCPD measured in DFCCS

may be comparable to in vivo response. As DMI increases, it is expected that the total

amount of OM truly digested will increase; however, TOMD (expressed as percentage)

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is decreased. In a DFCCS, the passage rate is not affected by DMI or type of feed,

which are the factors typically associated with this response in vivo (Huhtanen et al,

2006). However, increasing DMI in a DFCCS resulted in decreased TOMD, possibly

because at greater DMI, the relationship between feed to fermentor volume decreases.

This context of reduced feed to fermentor volume possibly limits the ability of the

microbial population to colonize and ferment feed. Furthermore, in our dataset TOMD

was not affected by increasing dietary NDF (Table 5-3) and CP (Table 5-4).

Digestibility of NDF linearly decreased as DMI increased (Table 5-2), possibly

due to similar effects observed on TOMD. This result is in agreement with Huhtanen et

al. (2009), who also reported negative association between DMI and NDFD. Similar to

the response observed for TOMD, NDFD was not affected by dietary CP (Table 5-4).

The majority of fiber degradation occurs in the rumen (Van Soest, 1994), and according

to Broderick et al. (2010), the rumen contribution to total tract NDFD can be up to 97%.

This result suggests that the hindgut contribution to NDF digestion is marginal and that

dietary treatment differences observed in the ruminal NDF digestibility closely represent

total tract NDF digestibility.

The NDFD was quadratically associated with dietary NDF (Table 5-3) and NDFD

was maximized at 58% of dietary NDF. Diets containing more than 58% NDF most likely

have greater proportion of poorly digestible nutrients, which could result in lower NDFD

as observed in our data. The NRC (2001) recommends a minimum of 25% NDF, and

Zebeli et al. (2012) suggested that a high producing dairy cow diet should contain

between 14 and 18% of physically effective NDF to avoid issues with low ruminal pH

(sub-acute acidosis) without compromising DMI. Therefore, in a production point of

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view, we acknowledge that feeding dairy cows diet containing 58% NDF is not

recommended

True CP digestibility quadratically decreased as DMI increased (Table 5-2);

however, the minimum point of this curve was out of our data range (210 g/d) since our

DMI data ranged from 12.8 to 120 g/d (Table 5-1). This result indicates that further

investigation of this relationship using a wider range of DMI is warranted. Dietary NDF

and TCPD were quadratically associated (Table 5-3) and TCPD was maximized at 48%

dietary NDF. When dietary NDF was greater than 48%, TCPD was depressed, possibly

due to low energy availability for microbial growth. A similar trend was also observed on

NDFD; when dietary NDF was greater than 58%, there was a decline on NDFD.

Therefore, diets containing elevated NDF can compromise fiber and protein digestion.

Effects on Volatile Fatty Acid

As fermentor DMI increased, total VFA concentration linearly increased (Table 5-

2), and as dietary NDF increased total VFA concentration exponentially decreased

(Table 5-3). Dietary CP was quadratically associated with total VFA concentration,

which was maximized at 18% dietary CP. Diets containing more than 18% CP may

result in greater ruminal NH3-N accumulation, which can compromise fermentation and

result in a decrease in total VFA concentration. As DMI increased, we observed a linear

increase in total VFA concentration, and this response was associated with a greater

OM intake that can be potentially digested in the rumen. The TOMD in this study was

expressed as percentage, therefore, even though the percentage of TOMD decreased

as DMI increased, the total amount of OM digested increased and this can be confirmed

by the increased total VFA concentration observed in the present study. Dietary

composition and DMI affect VFA concentration and the molar proportion of individual

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VFA. In a meta-analysis conducted by Loncke et al. (2009), it was reported that total

VFA concentration was significantly related with digestible OM and ruminal fermented

OM intake. In the same study, authors estimated that an increase of 1 g/d kg-1 of BW of

ruminal fermented OM, represented an increase in VFA net portal appearance of 5.93

mmol/d x kg of BW-1, demonstrating that changes in ruminal fermentation directly affect

energy supply to the animal.

Although in a DFCCS passage rate is controlled, the response of total VFA

concentration to dietary NDF concentration was similar to what is typically observed in

vivo: as dietary NDF increases, total VFA concentration decreases (Figure 5-1A). In

vivo, this response is attributed to two main factors: 1) reduction of passage rate due to

large quantity of fiber intake, resulting in reduced DMI (Zebeli et al., 2012); and 2) as

dietary NDF increases, the concentration of rapidly fermentable carbohydrates

decreases, resulting in a reduction in total VFA concentration. However, due to fixed

and controlled DMI in DFCCS, this response can be attributed mainly to a reduction in

rapidly fermentable carbohydrates due to increasing dietary NDF. Furthermore, there

was a quadratic response of total VFA to dietary CP. This result suggests that at low

dietary CP there is a low availability of nitrogen (N) to ensure adequate microbial

growth, which compromises fermentation end-products. Then, as dietary CP increases

the rumen environment conditions are improved, favoring microbial growth and resulting

in increased total VFA. According to our dataset, at 18% dietary CP, total VFA

concentration was maximized.

Although the overall pattern and response of the data reported in DFCCS

experiments are similar to in vivo, the individual values can be slightly different. In a

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meta-analysis comparing variability of ruminal fermentation data from in vivo and

continuous culture studies, Hristov et al. (2012) reported a mean total VFA

concentration in vivo of 117 mM and from continuous culture system (including a wide

variety of in vitro systems) as 94 mM, while we found a least squares means of 95 mM.

The differences between in vivo and data from continuous culture studies can be

attributed to two main factors: 1) the way in which VFA are absorbed or removed from

the system, and 2) the ratio of fermentor DMI per ruminal fluid volume. In vivo, VFA

removal is mainly accomplished through absorption across the rumen wall (Gäbel et al.,

2002), and on average, disappearance (presumably absorption) of acetate across

rumen wall is 65% (Peters et al., 1992) and 66% for propionate (Peters et al., 1990).

Although the amount of VFA absorbed across the rumen wall increases as production

rate increases, the molar proportion rate typically remains constant (Peters et al., 1990,

1992) .The remaining VFA are washed out with the liquid rumen digesta and absorbed

in the lower gut. In a DFCCS, the digesta flow is continuous and VFA removal occurs

through outflow. The second factor that explains part of the differences in VFA

concentration between in vivo and DFCCS studies is the average DMI:ruminal fluid

volume ratio. For a dairy cow, considering a DMI of 20 kg and 80 L of rumen volume,

this ratio is 250g/L (Hristov et al., 2012), while in a DFCCS we observed a range from

18.3 to 79.6 g/L. In our laboratory the DMI:ruminal fluid volume ratio of the fermentors is

approximately 58 g/L (Benedeti et al., 2015; Silva et al., 2017; Brandao et al., 2018b).

Quantifying in vivo VFA production can be challenging due to the constant

absorption through the rumen wall. For accurate quantification of VFA concentration,

total rumen volume needs to be measured, which can be assessed indirectly with

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ruminal liquid markers or directly by using the rumen emptying technique. In a

comprehensive study, Hall et al. (2015) investigated the relationships among ruminal

VFA concentration, pool size, and amount of ruminal liquid digesta, and pointed out that

differences found between VFA concentration and pool size are likely associated with

amount of ruminal liquid digesta. In vivo, large differences among animals can be

observed for ruminal liquid digesta and ruminal dry matter content, on the other hand, in

DFCCS both liquid digesta and fermentor dry matter content are similar across

treatments, which controls variation and potentially eliminates confounding effects of

these traits and VFA concentration. Therefore, VFA concentration data need to be

cautiously evaluated in in vivo studies when used to explain differences among dietary

treatments. On the other hand, due to lack of absorption, data from continuous culture

studies are more closely related to VFA production (Calsamiglia et al., 2002b).

Molar proportion of acetate was not affected by DMI or dietary CP (Table 5-2 and

5-4, respectively), and the least squares mean for molar proportion of acetate was

59.1%. However, as dietary NDF increased, acetate exponentially increased (Figure. 5-

1B). This result is in agreement with the NDFD data, demonstrating that the increase in

NDFD (up to the maximum point on the curve) resulted in an increase of acetate molar

proportion. The molar proportion of acetate increased with greater magnitude when

dietary NDF was between 30 and 40% than when dietary NDF concentration was

greater than 40% (Figure 5-1B).

Acetate molar proportion typically increases when high forage diets are fed,

which is also associated with greater ruminal pH and NDFD. Therefore, it is difficult in in

vivo studies to isolate effects of diet and pH on ruminal fermentation. In an experiment

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aiming to evaluate the contribution of ruminal pH and different diet compositions on end-

products of fermentation, while isolating pH effect, Calsamiglia et al. (2008) fed eight

fermentors with diets containing 60:40 and 10:90 forage:concentrate and maintained pH

at 8 different levels, ranging from 4.9 to 7.0. The authors reported that acetate was

affected by varying pH, but not by varying forage:concentrate. Whereas propionate

concentration was greater on the high concentrate diet and increased as pH decreased,

demonstrating that acetate is more responsive to ruminal pH than to dietary NDF.

Similarly, Calsamiglia et al. (2002) reported that concentration of acetate was

decreased and propionate increased in fermentors kept at low pH, regardless of diet

composition. The acidic condition decreased digestibility of NDF and ADF, which is

most likely responsible for the decreased acetate. These results demonstrate that the

composition of fermentation end-products is a result of a combination between

substrate type and ruminal pH.

Propionate linearly increased as DMI increased (Table 5-2), exponentially

decreased as dietary NDF increased (Table 5-3), and quadratically increased as dietary

CP increased (Table 5-4). As expected, molar proportions of acetate and propionate

had opposite responses to dietary NDF (Figure 5-1B, 5-2C). The same dietary NDF

range that resulted in larger increases in acetate (30 to 40% NDF), corresponded to the

range of greater decrease in propionate, and after this point, increases in dietary NDF

resulted in lower decline in molar proportion of propionate (Figure 5-1C). Propionate

production is mainly associated with fermentation of rapidly fermentable carbohydrates.

As NDF concentration in the diet increases, typically dietary NFC decreases, which

could explain the response observed in the present study. This response of propionate

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to increased dietary NDF has been widely reported in vivo (Batajoo and Shaver, 1994;

Schwab et al., 2006) and in DFCCS studies (Bas et al., 1989; Silva et al., 2017).

Therefore, according to the present data, propionate and acetate response to dietary

NDF found in DFCCS studies follows a similar pattern as in vivo.

In our data set, molar proportion of butyrate ranged from 1.7 to 42.8 (Table 5-1),

mean of 12.5 (± 6.5), and it was not affected by DMI (Table 5-2) or CP (Table 5-4). The

wider range of molar proportion of butyrate observed in DFCCS, when compared to in

vivo, may be explained by the lack of absorption by the rumen wall. Rémond et al.

(1995) estimated that 18 to 30% of acetate, 30 to 70% of propionate, and 74 to 90% of

butyrate produced during ruminal fermentation are utilized by the rumen wall. The large

butyrate utilization by the rumen wall suggests butyrate values found in vivo may be

lower than in DFCCS studies, due to lack of absorption.

Molar proportion of butyrate linearly decreased as dietary NDF increases (Table

5-3), potentially due to a reduction in NFC (notably starch) as dietary NDF increased.

Butyrate accumulation in the rumen has been reported in animals with greater amounts

of ruminal lactate (Nagaraja et al., 1985; Coe et al., 1999), which indicates that diets

high in starch and low in NDF can result in increased proportion of butyrate and

propionate. Butyrate can be formed from acetate (Nagaraja and Titgemeyer, 2007);

however, it has the opposite response, when compared to acetate, with regards to

increasing dietary NDF. Therefore, strengthening the hypothesis that as dietary NDF

increases, the formation of acetate is preferred over propionate or butyrate (Nagaraja

and Titgemeyer, 2007).

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Effects on N Metabolism

Ammonia N concentration (mg/dL; NH3-N) quadratically decreased as DMI

increased (Table 5-2), while ammonia N flow (NH3-Nf) was not affected by DMI (Table

5-2). The NH3-N and NH3-Nf linearly increased as dietary NDF increased (Table 5-3),

and similar results using DFCCS were previously reported by (Castillejos et al. (2005).

When dietary CP ranged from approximately 15 to 20%, the slope of the regression was

greater, indicating that NH3-N accumulation within this range was accentuated (Figure

5-2A). Both NH3-N and NH3-Nf also increased as dietary CP increased (Table 5-4).

Satter and Roffler (1975) reported a similar response of ruminal NH3-N to dietary CP;

however, they found a quadratic response while the model that best fit our data was

exponential.

As dietary NDF is increased, the concentration of rapidly fermentable

carbohydrates is typically reduced, consequently resulting in less energy available for

microbial growth, and greater NH3-N accumulation. Another potential effect associated

with this response is the lack of protein and energy synchronization, which can also

result in NH3-N accumulation. Rumen degraded protein is broken down into peptides

and amino acids by microorganisms, and the peptides and amino acids are either

deaminated to NH3 or incorporated into microbial protein (Bach et al., 2005). Microbial

protein synthesis is dependent upon carbohydrate supply to provide energy for

microbial metabolism and if the rate of carbohydrate degradation exceeds microbial

assimilation, microbial protein synthesis is compromised. Similarly, when the protein

degradation rate exceeds the carbohydrate degradation rate, then N can be lost in the

form of NH3 (Bach et al., 2005). When there is surplus of rumen degraded protein or

lack of energy, NH3 release rate (from feed) exceeds microbial NH3 uptake NRC (2001),

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resulting in NH3 accumulation in the rumen. In vivo, this can lead to increased N

excretion (Broderick et al., 2015).

In a continuous culture study, Satter and Slyter (1974) reported that once NH3

starts to accumulate inside the fermentors, the growth of NH3 utilizing bacteria is not

enhanced and suggested that 5 mg NH3-N/dL is sufficient to support adequate microbial

growth rates. In the ruminal environment, a minimal NH3 concentration is required to

ensure adequate microbial growth. Data from studies using 15N suggested that at least

50% of the microbial protein produced in the rumen utilizes N from NH3, and the

remaining microbial protein is derived from peptides and amino acids (Leng and Nolan,

1984). However, that proportion can vary depending on the availability and sources of N

within the rumen. Additionally, fibrolytic bacteria preferably utilize NH3-N as N sources,

instead of amino acids and peptides (Russell et al., 1992, 2002). Therefore, the findings

of Griswold et al. (1996) illustrate the importance of maintaining a minimum

concentration of ruminal NH3 not only for adequate microbial fermentation, but also for

adequate fiber digestion.

Low producing animals, and animals fed low CP levels, rely more on N coming

from recycled N and on rumen degraded protein than high producing animals. However,

in a DFCCS, N recycling is simulated through addition of urea in the saliva, thus urea is

continuously added in the system independently of the physiological state that is being

simulated in the study. It is possible that when feeding diets with greater CP to

fermentors, the urea continuously added via saliva will contribute to greater NH3-N

accumulation in the fermentors. This can result in in slightly greater NH3-N

concentration when high protein diets are fed to fermentors compared with values

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observed in vivo under similar dietary CP. For instance, Broderick et al. (2008) fed

lactating cows a control diet containing 16.5% CP, and found NH3-N concentration of

13.9 mg/dL, while Brandao et al. (2018b) fed diets containing 16% CP to continuous

culture fermentors and reported NH3-N ranging from 16.2 to 18.2 mg/dL.

Non-ammonia N (NAN) flow quadratically increased as DMI increased (Table 5-

2), while it linearly decreased as dietary NDF increased (Table 5-3). It was expected

that increasing dietary CP would result in increased NAN flow (Figure 5-2B), as well as

flows of NH3-N and bacterial-N, due to greater N input into the system. Flow of bacterial-

N quadratically increased and dietary-N flow exponentially increased as DMI increased.

Flows of bacterial-N and dietary-N linearly decreased as dietary NDF increased (Table

5-3), while as DMI increases, bacterial-N quadratically increased and dietary-N

exponentially increased (Table 5-4). According to our dataset, dietary-N flow was

maximized at 18% CP. Low dietary CP limits N available for digestion and microbial

growth. Additionally, diets containing low CP, may limit microbial growth due to the lack

of N in the form of amino acids and peptides (Griswold et al., 1996). These findings are

in agreement with our data on bacterial-N flow and VFA response to dietary CP, where

at low dietary CP we observed low bacterial-N flow and total VFA concentration,

suggesting that at low dietary CP overall fermentation is compromised.

Efficiency of microbial protein synthesis linearly increased as dietary CP

increased (Table 4); however, it was not affected by DMI and dietary NDF (Tables 5-2

and 5-3, respectively). Microbial efficiency is a combination of ruminal ATP yield

(Stouthamer, 1973) or amount of OM truly digested, and the efficiency in which ruminal

microbial population use this energy to convert into bacterial-N (Bach et al., 2005). The

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TOMD was not affected by dietary CP; however, total VFA, molar proportion of

propionate, and bacterial-N flow were positively associated with dietary CP. The

fermentation end-products can affect bacterial efficiency, as the metabolic routes to

produce acetate result in greater energy loss than to produce propionate due to greater

gas production (Van Kessel and Russell, 1996).Therefore, we speculate that the

positive association of dietary CP with molar proportion of propionate and bacterial-N

resulted in increased EMPS. Even though increasing dietary CP increased EMPS, it is

not recommended for dairy animals, to feed diets above 18% CP. At this protein level

total VFA concentration, and molar proportion of propionate were maximized; however,

after this point, increases in dietary CP resulted in a decline of these parameters.

Furthermore, feeding CP in excess to cattle results in ruminal NH3-N accumulation,

ultimately increasing urea excretion in milk and urine (Reynolds and Kristensen, 2008).

Conclusions and Implications

Increases in dietary CP resulted in increased microbial efficiency and bacterial-N

flow. However, as dietary CP and NDF increased, we observed that NH3-N also

increased. Fermentor DMI was mostly associated with greater substrate availability,

resulting in increases in VFA concentration and molar proportion of propionate, whereas

dietary NDF increased the molar proportion of acetate, NDF and CP digestibility.

However, it was also positively associated with NH3-N concentration. Overall, the

analysis of this dataset composed of 523 treatment means from 75 peer-reviewed

published studies from 1985 to 2018 supports a conclusion that the dual-flow

continuous culture system technique provides valuable estimates of ruminal digestibility,

volatile fatty acids concentration and nitrogen metabolism which were closely related to

the expected in vivo response. However, further studies comparing in vivo ruminal

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fermentation with dual-flow continuous culture data are warranted. In addition, our data

demonstrate that this technique can be a valuable tool for testing a large variety of

dietary treatments in a short period of time, with continuous removal of fermentation

end-products and for a longer period of time than other in vitro systems.

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Table 5-1. Descriptive statistics

Item n Max Min Average SD

Independent variables

Diet CP (% DM) 513 28.7 4.0 15.9 4.1

Diet NDF (% DM) 504 74.2 15.1 37.1 11.8

DMI (g/d) 511 120.0 12.8 72.6 18.4

Dependent variables True digestibility (%)

OM 400 86.4 23.7 53.5 12.6

CP 373 98.3 15.5 61.5 17.5

NDF digestibility (%) 404 94.0 15.3 53.5 17.4

VFA (%, otherwise stated)1

Total (mM) 460 183.1 24.7 90.7 29.2

Acetate 479 82.2 24.7 59.0 11.5

Propionate 479 64.8 8.9 24.6 8.5

Butyrate 480 42.8 1.7 12.5 6.3

NH3-N (mg/dL) 420 28.1 1.2 12.6 6.2

N flow (g/d)

NH3-N 365 0.8 0.003 0.3 0.2

NAN2 405 3.3 0.06 1.6 0.8

Bacterial-N 422 2.4 0.04 0.9 0.5

Dietary-N 367 2.0 0.003 0.7 0.5

EMPS3 417 74.4 4.3 27.5 10.5 1Volatile fatty acids. 2Non-ammonia nitrogen. 3Efficiency of microbial protein synthesis, calculated as g of bacterial nitrogen/ kg of organic matter truly digested.

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Table 5-2. Equations using fermentor dry matter intake (DMI) as independent variable

Dependent variables Equation AIC MSE R2

P-value4

VFA (%, otherwise stated)1

Total VFA (mM) Ŷ = 33.5202 + 0.8296 × DMI 3253 213.0 0.73 0.01

Acetate Ŷ = 59.1 2794 45.3 - > 0.05

Propionate Ŷ = 17.4964 + 0.09516 × DMI 2674 52.3 0.81 0.01

Butyrate Ŷ = 11.41 1851 12.0 - > 0.05

True digestibility (%)

OM Ŷ = 86.4729 × (1 – 0.005413)DMI 2516 29.9 0.81 0.01

CP Ŷ = 112.11 – 0.8022 × DMI + 0.001905 × DMI2 2679 210.0 0.63 0.04

NDF digestibility Ŷ = 90.0419 – 0.4956 × DMI 2516 111.0 0.71 0.04

NH3-N (mg/dL) Ŷ = 28.0475 – 0.3098 × DMI + 0.001337 × DMI2 -115 13.0 0.08 0.02

N flow (g/d)

NH3-N Ŷ = 0.3224 -335 0.03 - > 0.05

NAN2 Ŷ = 1.0213 – 0.02418 × DMI + 0.000437 × DMI2 16.5 0.06 0.88 0.01

Bacterial-N Ŷ = 0.3224 – 0.00857 × DMI + 0.000228 × DMI2 -1.03 0.05 0.84 0.01

Dietary-N Ŷ = 0.08241 × exp(0.02869 × DMI) -86.1 0.03 0.85 0.01

EMPS3 Ŷ = 30.1485 2475 46.2 - > 0.05 1Volatile fatty acids. 2Non-ammonia nitrogen. 3Efficiency of microbial protein synthesis, calculated as g of bacterial nitrogen/ kg of organic matter truly digested. 4P-value of the model.

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Table 5-3. Equations using dietary neutral detergent fiber (NDF) as independent variable

Dependent variables Equation AIC MSE R2 P-value4

VFA (%, otherwise stated)1

Total VFA (mM) Ŷ = 195 – 154 × (1- exp (- 0.03 × NDF)) 3863 571 0.86 0.01

Acetate Ŷ = - 40.06 + 102.86 × (1 – exp(- 0.1151 x NDF)) 2669 21.9 0.53 0.01

Propionate Ŷ = 189 – 167 × (1 – exp(- 0.1368 x NDF)) 2841 25.5 0.64 0.01

Butyrate Ŷ = 13.3716 – 0.05787 × NDF 2168 6.00 0.58 0.03

True digestibility (%)

OM Ŷ = 58.24 2627 28.0 - > 0.05

CP Ŷ = 20.7049 + 1.9055 × NDF – 0.01995 × NDF2 2976 127.0 0.04 0.01

NDF digestibility Ŷ = 28.4240 + 0.9311 × NDF – 0.00781 × NDF2 3076 64.0 0.11 0.04

NH3-N (mg/dL) Ŷ = 3.3167 + 0.2849 × NDF 2534 12.0 0.18 0.04

N flow (g/d)

NH3-N Ŷ = 0.06133 + 0.007539 × NDF -348 0.01 0.14 0.01

NAN2 Ŷ = 2.2818 - 0.01503 × NDF 143 0.04 0.91 0.01

Bacterial-N Ŷ = 1.3215 – 0.00973 × NDF -133 0.02 0.81 0.02

Dietary Ŷ = 1.0040 – 0.00625 × NDF -31 0.03 0.95 0.01

EMPS3 Ŷ = 29.69 2703 25.0 - > 0.05 1Volatile fatty acids. 2Non-ammonia nitrogen. 3Efficiency of microbial protein synthesis, calculated as g of bacterial nitrogen/ kg of organic matter truly digested. 4P-value of the model.

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Table 5-4. Equations using dietary crude protein (CP) as an independent variable

Dependent variables Equation AIC MSE R2 P-

value4

VFA (%, otherwise stated)1

Total VFA (mM) Ŷ = 29.2671 + 7.9317 × CP – 0.2264 × CP2 3485 135.1 0.13 0.01

Acetate Ŷ = 54.56 2882 28.4 - > 0.05

Propionate Ŷ = 25.723 + 0.3937 × CP – 0.02629 × CP2 3014 26.3 0.64 0.04

Butyrate Ŷ = 13.16 2636 11.0 - > 0.05

True digestibility (%)

OM Ŷ = 55.89 2966 29.2 - > 0.05

CP Ŷ = 57.69 2395 44.3 - > 0.05

NDF digestibility Ŷ = 45.45 2767 62.0 - > 0.05

NH3-N (mg/dL) Ŷ = 4.2717 × exp(0.0647 × CP) 1045 11.0 0.16 0.01

N flow (g/d)

NH3-N Ŷ = 0.09499 × exp(0.07532 × CP) 185 0.03 0.16 0.01

NAN2 Ŷ = 1.0905 + 0.03793 × CP 172 0.04 0.79 0.04

Bacterial-N Ŷ = 0.9246 + 0.002654 × CP -18 0.03 0.83 0.02

Dietary Ŷ = 0.7082 24.7 0.13 - > 0.05

EMPS3 Ŷ = 21.9473 + 0.3668 × CP 2724 25.0 018 0.02 1Volatile fatty acids. 2Non-ammonia nitrogen. 3Efficiency of microbial protein synthesis, calculated as g of bacterial nitrogen/ kg of organic matter truly digested. 4P-value of the model.

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Figure 5-1. Concentration of total volatile fatty acids (VFA; A; AIC = 3863; MSE = 571),

and molar proportion of acetate (B; AIC = 2669; MSE = 21.9), and propionate (C; AIC = 2841; MSE = 25.5) using dietary NDF as independent variable.

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Figure 5-2. Ammonia nitrogen concentration (mg/dL; A; AIC = 1045; MSE = 11) and non-ammonia nitrogen flow (NAN g/d; B; AIC = 172; MSE = 0.04) using dietary crude protein (CP) as independent variable.

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CHAPTER 6 HOW COMPARABLE ARE MICROBIAL FERMENTATION DATA FROM A DUAL-

FLOW CONTINUOUS CULTURE SYSTEM TO AN OMASAL SAMPLING TECHNIQUE? A META-ANALYTICAL APPROACH

Summary

Although the omasal sampling technique (OST) has been successfully used to

estimate ruminal fermentation and nutrient flow, alternatives to invasive animal trials

should be pursued and evaluated. The objective of this study was to compare two

methods: dual-flow continuous culture system (DFCCS) or OST, for evaluating

carbohydrate and N metabolism using a meta-analytical approach. Study inclusion

criteria were: 1) diet chemical composition must be reported; and 2) at least one of the

dependent variables of interest must be reported. A total of 151 articles were included,

in which 96 used the DFCCS and 55 used OST. The independent variables used were

dietary non-fiber carbohydrates (NFC), neutral detergent fiber (NDF) digestibility, true

crude protein (CP) digestibility, and efficiency of microbial protein synthesis (EMPS).

Eleven dependent variables were used. Statistical analyses were performed using the

mixed procedure of SAS. A random coefficients model was used considering study as a

random effect and including the possibility of covariance between the slope and the

intercept. The effect of method (DFCCS or OST) was included and tested in the

estimates of the intercept, linear and quadratic effects of the independent variable.

Significance levels to fit the model assumed for fixed and random effects were P ≤ 0.05.

Molar proportion of acetate and propionate were quadratically related to NDF

digestibility. Both had method effect and differed only in intercept (ß0). There was no

method effect when NDF digestibility was regressed with total VFA concentration, true

CP digestibility, and EMPS. True OM digestibility, bacterial N, efficiency of N utilization

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(ENU), total VFA concentration and molar proportion of butyrate linearly increased as

dietary NFC increased, and none of these variables were affected by method.

Concentration of NH3-N had a linear and positive association with CP digestibility. This

was the only variable for which a method effect was detected when regressed with CP

digestibility, differing only in the estimate of ß0. As EMPS increased, ENU also

increased and it was not affected by method. Overall, the majority of DFCCS responses

were similar to OST. When a method effect was observed, it was mainly the estimate of

the intercept, meaning that the functional relationships among the responses and

predictors were maintained; however, the magnitude of this response was slightly

different in some cases.

Introductory Remarks

Studies aiming to determine fermentation and digestion of feeds and additives, or

aiming to study rumen metabolic processes, often require determination of ruminal

fermentation end-products and nutrient flow using cannulae fitted in the abomasum or

duodenum (Ahvenjärvi et al., 2000b). However, intestinal and abomasal cannulation

require longer recovery time and are more difficult to manage than ruminal cannula

(Fatehi et al., 2015b). The omasal sampling technique (OST), described by Huhtanen et

al. (1997) and modified by Ahvenjärvi et al. (2000) is a well-accepted technique to

assess ruminal fermentation and nutrient flow. It has been successfully used for

estimating nutrient flows and ruminal metabolism of nitrogen (Reynal et al., 2003),

carbohydrates (Owens et al., 2008), fatty acids (Sterk et al., 2012), and minerals (Tuori

et al., 2006). Although this technique provides valuable results and is considered

adequate to estimate ruminal fermentation and nutrient flow, it is laborious and

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expensive. Therefore, alternative techniques capable of accurately simulating ruminal

fermentation are warranted.

The dual-flow continuous culture system (DFCCS) was developed by Hoover et

al. (1976) aiming to simulate the continuous differential flows of liquid and solids from

the rumen. It provides a more close response to in vivo, than closed vessel incubations

(Hoover et al., 1976a). The system consists of a long-term fermentation, with periods

varying from 8 (Calsamiglia et al., 2002b) to 11 days (Dai et al., 2019). It has been used

mainly to evaluate the effect of feedstuffs and additives on fermentation, digestion,

nutrient flow and nitrogen (N) metabolism in dairy (Brandao et al., 2018b) and beef

(Amaral et al., 2016) diets. One of the most important advantages of the DFCCS

compared with other in vitro systems is the continuous removal of fermentation end-

products, which reduces issues with accumulating fermentation products, such as

volatile fatty acids (VFA) and ammonia (NH3) that can potentially inhibit fermentation.

Additionally, the system allows for intense sampling, determination of degradation rates,

and testing feed additives in early developmental stages that are not yet produced in

large scale, under a constant dry matter intake and passage rate.

However, studies quantitatively comparing ruminal fermentation data originated

from DFCCS to OST are still scarce. Hristov et al. (2012) compared the variability of

data from continuous culture systems with in vivo data; however, that study included a

wide variety of different in vitro systems and compared them with in vivo total tract

digestibility, which can be different when compared to ruminal digestibility. Therefore,

we hypothesized that ruminal carbohydrate and N metabolism have similar responses

when estimated using DFCCS and OST. The objective of this study was to summarize

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the literature and evaluate carbohydrate and N metabolism using a meta-analytical

approach to compare two methods: DFCCS and OST.

Materials and Methods

Data Collection and Preparation

A search for relevant articles was made using PubMed, Science Direct and

Google Scholar databases, as well as individual journal databases such as Journal of

Dairy Sciences, Journal of Animal Science, Livestock, Animal Feed Science and

Technology, and Animal from summer 2018 to spring 2019. Data were collected from

peer-reviewed articles published from 1985 to 2019. The keywords used for the DFCCS

dataset were: dual-flow continuous culture system, in vitro, microbial fermentation, and

different combinations of these words; while for the OST dataset the keywords used

were: microbial fermentation, nutrient flow, omasal sampling, omasum, rumen, and

ruminal fermentation. In each article evaluated, including meta-analysis and review

articles, the reference list was also searched for relevant titles and subsequent

screening. Only articles written in English were considered for this evaluation.

As a search criterion for both DFCCS and OST, the study under evaluation

needed to report the chemical composition of the diet (at least dietary CP and NDF) and

at least one of the dependent variables of interest. After a thorough review of the

studies under consideration, the data were entered in the database and the last step

was to verify each data entry. A total of 149 peer-reviewed articles, and 2 unpublished

studies (using DFCCS) from our research group were included. A total of 636 treatment

means met the inclusion criteria, in which 96 studies used the DFCCS and 51 studies

used OST. The mean number of observations per dietary treatment was 4.4 and ranged

from 2 to 9.

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Efficiency of microbial protein synthesis was calculated as grams of microbial N

divided by kg of OM truly digested, and when individual volatile fatty acids (VFA) were

reported as concentration, molar proportion was calculated using total VFA

concentration. Efficiency of N use (ENU) was calculated according to Bach et al. (2005).

In order to allow comparison of nitrogen metabolism data between DFCCS and OST,

bacterial-N and nonammonia nonmicrobial N (NANMN) were divided by the total N flow

and multiplied by 100. Therefore, these data are expressed in percentage of the total N

flow.

Specifically for the OST dataset, studies used the following digesta markers to

determine nutrients flow: for the large particle phase, either indigestible NDF (iNDF; n =

174) or Cr-mordanted fiber (n = 8) was used; for small particle phase, Yb-acetate (n =

58), Yb-chloride (n = 111), or lanthanum (n = 8) was used; and for the liquid phase

CoEDTA (n = 112), Cr-EDTA (n = 58) or LiCoEDTA (n = 4) was used. All studies using

OST used changeover designs, and out of 48 studies included, 3 trials used beef

animals (17 treatment means) and 45 used dairy cows. We only included studies that

performed omasal sampling through rumen-cannula, and only omasal samplings were

included in the OST dataset. No reticulum, abomasum or duodenum sampling studies

were included.

Most of the DFCCS studies aimed to evaluate different feeds and additives. For

the DFCCS dataset, all studies used inoculum from beef or dairy cattle, therefore

studies using inoculum from other ruminant species were not considered in the present

study. This dataset only considered data from the dual-flow continuous culture system,

and data from systems such as Rusitec or any other in vitro system were not included.

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The solid passage rate for DSCCS ranged from 2 to 8%/h, and averaged 5%/h; while

liquid passage rate ranged from 4 to 12.5%/h, and averaged 10%/h. Most experiments

used artificial saliva described by Weller and Pilgrim (1974) with addition of 0.4 g/L of

urea to simulate N recycling. The average volume of the fermentation vessels was 1209

mL, minimum of 700 and maximum of 1830 mL (SD = 289).

Data Cleaning and Model Derivation Procedure

A preliminary graphical examination of the data was performed and aimed to

identify the functional relationships between dependent and independent variables of

interest, and the identification of possible outliers (Sauvant et al., 2008b). In this study,

we used a dataset of in vivo and in vitro studies, therefore it was expected that the in

vivo dataset (OST) would have greater variance within study than the in vitro (DFCCS).

Therefore, to avoid overestimating the DFCCS data weight, we used as a weight factor

the inverse of the standard error of the mean (Roman-Garcia et al., 2016), instead of

the inverse of the squared standard error of the mean. Additionally, to avoid

overweighting studies with very low SEM, the weight factor was normalized within

method (DFCCS and OST) and we truncated the tails in 25%. The normalization

allowed equal weighting across methods, and dependent variables were weighted

without the bias of the different methods used (Roman-Garcia et al., 2016).

This meta-analysis was performed according to St-Pierre (2001), and all

statistical analyses were performed using the mixed procedure of SAS (SAS Institute

Inc., 2004). A random coefficients model was used considering study as a random

effect and including the possibility of covariance between the slope and the intercept.

The covariance parameter was considered non-zero when P ≤ 0.05. Fifteen variance-

covariance structures were tested, and the one that provided the smallest Akaike’s

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Information Criteria was used. Models were tested for linear and quadratic effects of the

independent variable. The effect of method (DFCCS or OST) was always included and

tested in the initial model, and it was tested on the estimates of the intercept, linear and

quadratic effects of the independent variable. Significance levels to fit the model

assumed for fixed and random effects were P ≤ 0.05. We used as independent

variables dietary NFC, NDF digestibility, true CP digestibility, and EMPS; and the

dependent variables and descriptive statistics are presented in Table 6-1. The effect of

method was tested in NDF digestibility, true CP digestibility and EMPS and it was not

significant for any of them (P > 0.05). It should be noted that this study was not intended

to generate prediction models; instead, our objective was to understand and critically

evaluate the functional form of the DFCCS fermentation responses compared to OST.

Results

Independent Variables: NDF digestibility and Dietary NFC

Regressions made using NDF digestibility as the independent variable are

presented in Table 6-2. The NDF digestibility was not associated with bacterial N, ENU,

or molar proportion of butyrate. The intercepts of bacterial N and ENU were affected by

method. Bacterial N presented a study corrected mean of 64.8% for OST and 44.6% for

DFCCS. The study corrected mean of ENU in OST was 65.0% and for DFCCS was

53.3%, meaning that OST was 11.7 percentage units greater than DFCCS. The

NANMN linearly decreased as NDF digestibility increased and presented a method

effect, in which they differed in the estimates of the ß0 and ß1. The DFCCS had greater

ß0 (61.1%) and greater ß1 (-0.51) than OST (39.0% and -0.18, respectively).

True OM digestibility quadratically increased as NDF digestibility increased and

was affected by method (Table 6-2). Only the estimates of ß0 were different, in which

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DFCCS had lower ß0 than OST (47.9% and 58.3% respectively). True CP digestibility

had a positive, linear association with NDF digestibility and EMPS had a negative and

linear association with NDF digestibility. Method did not influence true CP digestibility

and EMPS as function of NDF digestibility.

Total VFA concentration was positively and quadratically associated with NDF

digestibility, with a maximum concentration when NDF digestibility was 46.3% of (Table

6-2). Total VFA concentration was not affected by method. Butyrate did not present

method effect and had study corrected mean of 11.4%. Molar proportion of acetate

(Figure 6-1A) and propionate (Figure 6-1B) were quadratically related with NDF

digestibility. Both presented method effects and differed only in the intercepts (Table 6-

2). Acetate was positively associated with NDF digestibility and was at maximum when

NDF digestibility was 72.9%. Propionate reached its minimum when NDF digestibility

was 70%. Molar proportion of acetate had a greater intercept (47.1%) in studies using

OST, than in DFCCS (41.9%). The opposite response was observed for molar

proportion of propionate, in which the intercept for OST studies was lower (33.1%) than

DFCCS (35.2%).

Concentration of NH3-N linearly increased as NDF digestibility increased, and it

presented a method effect (Figure 6-2A). This was the only variable that had the same

intercept (4.5 mg/dL) but different slopes (0.15 for DFCCS and 0.08 for OST).

The regressions developed using dietary NFC as independent variable are

presented in Table 6-3. Molar proportion of acetate and propionate were linear

associated with dietary NFC, however acetate (Figure 6-1C) was negatively associated

with dietary NFC, while propionate (Figure 6-1D) was positively associated with dietary

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NFC. The method affected only the estimate of ß0, in which OST studies presented a

greater estimate of ß0 for acetate (78.1%) and lower for propionate (14.3%), and

DFCCS presented ß0 estimate for acetate of 73.3% and for propionate 17.4%.

As dietary NFC increased, NDF digestibility and NH3-N linearly decreased and

were not affected by method (Table 6-3). True OM digestibility, bacterial N, ENU, total

VFA concentration and molar proportion of butyrate linearly increased as dietary NFC

increased, and none of these variables were affected by method. Molar proportion of

butyrate presented an overall study corrected mean of 11.7% (SEM = 0.314; N = 545).

True CP digestibility was quadratically and positively associated with dietary

NFC, and it was not affected by method (Table 6-3). The NANMN and EMPS were not

affected by dietary NFC or method, and their study corrected means were 31.6% and

26.8% respectively.

Independent Variables: CP Digestibility and Efficiency of Microbial Protein Synthesis

The regressions developed using true CP digestibility as independent variable

are presented in Table 6-4. Digestibility of OM, bacterial N (Figure A), EMPS (Figure C),

ENU (Figure D) and total VFA were linearly and positively associated with CP

digestibility, and none of these variables were affected by method (Table 6-4). The

NANMN linearly decreased as CP digestibility increased and was also not affected by

method (Figure B). Molar proportion of acetate quadratically increased as CP

digestibility increased (Figure 6-1E), and molar proportion of propionate quadratically

(Figure 6-1F) decreased as CP digestibility increased. Molar proportion of butyrate did

not respond to changes in CP digestibility and presented a mean of 11.35%. Molar

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proportions of acetate, propionate and butyrate were not affected by method (Table 6-

4).

Concentration of NH3-N was linearly and positively associated with CP

digestibility (Figure 6-3B). This was the only dependent variable with a method effect

when regressed with CP digestibility, differing only in the estimate of ß0. Studies using

OST had a lower estimate of ß0 (2.7 mg/dL) than DFCCS (7.03 mg/dL), however they

had the same estimate of ß1.

Regressions developed using EMPS as independent variable are presented

inTable 6-5. True OM digestibility was not associated with EMPS. Only the estimate of

ß0 differed between methods, and the study corrected mean value of OM digestibility

for OST was 68.1% and for DFCCS was 56.8%. Concentration of NH3-N was also not

associated with EMPS, and the study corrected mean value for DFCCS was 8.55 mg/dL

and for OST was 12.19 mg/dL. Bacterial N had a linear and positive association with

EMPS. Method affected only the estimate of ß0, and OST had greater bacterial N than

DFCCS.

The NANMN was linearly and negatively associated with EMPS (Figure 6-3E),

and as EMPS increased, ENU also increased (Figure 6-3F). Total VFA, acetate,

propionate and butyrate were not associated with EMPS. The study corrected mean

value for total VFA concentration was 104 mM, and molar proportions of acetate,

propionate, and butyrate were 60.3%, 22.4%, 11.6% respectively. None of these

variables were affected by method.

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Discussion

Carbohydrates

True OM digestibility positively responded to increases in the two carbohydrate

variables used in the present study (NDF digestibility and dietary NFC; Table 6-2 and 6-

3). Changes in NDF digestibility reflect directly on OM digestibility, because these

variables are highly correlated, especially considering that the rumen is the main site of

fiber digestion (Broderick et al., 2010b). Whereas dietary NFC was likely positively

associated with OM digestibility due to its high fermentability. The most common source

of NFC is starch and ruminal digestibility can vary according to sources and processing

methods (NRC, 2001), however it is generally considered high. In a study including 87

studies from beef and dairy cattle, and a wide variety of starch sources, Offner and

Sauvant (2004) reported ruminal starch digestibility of 71%. Therefore, increases in

dietary NFC will positively impact true OM digestibility by providing a greater amount of

highly fermentable carbohydrates.

The NDF digestibility was used as an independent variable in Table 6-8 and it

was also used as dependent variable and regressed with dietary NFC in Table 6-9.

Typically, dietary content of NFC is increased at the expense of NDF, which results in a

diet with lower fiber content. Additionally, the association of NFC high fermentability with

lower dietary NDF, results in a drop in ruminal pH (Oba and Allen, 2003). Thus,

explaining the linear and negative response of NDF digestibility with increases on

dietary NFC. Additionally, usually increases in dietary supply of rapidly fermentable

carbohydrates is associated with decrase of acetate:propionate ratio, decrease in

ruminal NH3-N concentration and on fiber digestibility (Gao and Oba, 2016).

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Organic acid concentration in the rumen is greatly affected by digestibility; highly

digestible feeds are degraded in the rumen, producing organic acids as end-products

(Russell et al., 1992). Therefore, in agreement with the digestibility responses, total VFA

concentration linearly increased as dietary NFC increased, and quadratically increased

as NDF digestibility increased (Table 6-3 and 6-2 respectively). Total VFA concentration

was not affect by method when regressed with all independent variables used, meaning

that estimates of VFA concentration are similar when made using DFCCS and OST.

Furthermore, similar to the responses observed when molar proportion of acetate and

propionate were regressed with NDF digestibility and dietary NFC (Figure 6-1A, B, C,

and D), when they were regressed with CP digestibility, they also had quadratic

responses (Figure 6-1E and F).

True CP digestibility was positively associated with NDF digestibility (Table 6-2).

Chemical structure of the protein and its interactions with carbohydrates are important

factors that determine ruminal CP digestibility (NRC, 2001). If a large portion of CP is

bound to plant cell wall (primarily lignin), CP digestibility tends to decrease due to

reduced microbial access to the nitrogenous compound. Part of the total CP of feeds is

bound to the plant cell wall and it can be slowly degraded or be of low biological

availability (NRC, 2016). Several factors are responsible for changing the CP fraction of

a feed, including but not limited to plant maturity (Van Soest et al., 1978). As a plant

matures, the contribution of fraction C in the protein fraction increases. This process is

highly correlated with NDF digestibility, because as the plant matures, the lignin and

low-digestible fraction portions also increase (Van Soest et al., 1978). Therefore, this

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positive association between NDF and CP digestibility is likely a reflection of more

digestible feed.

Molar proportion of butyrate usually does not change when commonly used diets

are fed, and ranges from 10 to 20% (Bergman, 1990). In the present study, molar

proportion of butyrate only responded to increments in dietary NFC and presented an

overall study corrected mean within the literature values of 11.7%. Considering that

NFC encompasses starch and sugars, it has been shown in continuous culture

(Vallimont et al., 2004) and in vivo (DeFrain et al., 2004) that increasing dietary NFC

might result in greater butyrate concentration. Additionally, the positive and linear

response to dietary NFC is possibly also associated with accumulation of lactate in the

rumen due to presence of large amount of rapidly fermentable carbohydrates (Nagaraja

and Titgemeyer, 2007). Increased concentration of butyrate has been observed in

animals in high grain diets (Nagaraja et al., 1985; Coe et al., 1999). Ruminal lactate

metabolism can generate acetate, propionate, butyrate, and to a less extent caproate

and valerate (Marounek et al., 1989), however the primary end-product varies

depending on ruminal pH (Satter and Esdale, 1968). It has been proposed that butyrate

can be produced from acetate utilizing the two hydrogens atoms released by the

oxidation of lactate to pyruvate; therefore, butyrate formation might work as a hydrogen

sink (Esdale et al., 1968). This process is associated with changes in pH, such that

when pH is more acidic butyrate is preferably produced from lactate, and in higher pH

acetate is preferably produced (Satter and Esdale, 1968).

Nitrogen Metabolism

As dietary NFC increased, NH3-N decreased (Table 6-3). Carbohydrates,

primarily rapidly fermentable carbohydrates such as NFC, determine the energy

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available for microbial growth and microbial N yield (Hoover and Stokes, 1991).

Therefore, the negative and linear response of NH3-N to increases in dietary NFC is

likely associated with greater microbial protein synthesis, which results in lower NH3-N

accumulation (Sannes et al., 2002; Oba and Allen, 2003; Hristov et al., 2005). Indeed,

bacterial N increased as dietary NFC increased (Table 6-3), while NANMN was not

associated with dietary NFC. Interestingly, even though bacterial N increased in

response to NFC, EMPS was not associated with dietary NFC. Bach et al. (2005)

observed that bacterial N flow decreased as ruminal pH increased, however EMPS was

not associated with rumen pH which commonly respond to changes on carbohydrates

fermentability. Similar to our results, the increase in bacterial N is likely a result of more

OM available, however it does result in changes in EMPS. Additionally, ruminal NH3-N

was insensitive to EMPS (Table 6-5), and similar response has been observed by Bach

et al. (2005) and Oba and Allen (2003).

The ENU was linearly and positively associated with dietary NFC (Table 6-3).

Additionally, as true CP digestibility increased, EMPS and ENU linearly increased

(Figure C and D), however, ENU presented greater slope than EMPS. Suggesting that

changes in CP digestibility are better explained by changes in ENU than EMPS.

Therefore, EMPS provides valuables insights on the energy use, however as an N

indicator the ENU seems to be more appropriate. These two variables are

complementary and their use in association is recommended.

When ENU was regressed with EMPS (Figure F) we observed a linear and

positive association. However, when Bach et al. (2005) regressed these two variables,

they observed a quadratic response, with a maximum EMPS of 29 and 69% ENU. In

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their study, they included only DFCCS data and a smaller number of observation (N =

136) than the present study (N = 381); also, our dataset has a wider range of ENU, and

our model presented lower RMSE (4.63 versus 6.54). It is possible that we found a

linear response, instead of quadratic, due to differences in the dataset. For instance, we

included in vitro and in vivo data, while Bach et al. (2005) only used continuous culture

data, and our dataset has fewer points above 69% ENU.

True CP digestibility was not affected by method in any of the tested variables,

demonstrating that estimate of CP digestibility using DFCCS is similar to OST. True CP

digestibility was positively and linearly associated with ENU. The greater the ENU, more

N is transformed into bacterial N and lesser NH3-N accumulates in the rumen, and Bach

et al. (2005), using data obtained from continuous culture system, reported a negative

association between ruminal NH3-N and ENU. An opposite response was observed for

NANMN, in which as CP digestibility increased NANMN decreased (Figure 6-2B).

Similarly, when NH3-N and bacterial N were regressed with true CP digestibility, they

also presented a linear association, in which the greater true CP digestibility, the greater

bacterial N and ruminal NH3-N (Table 6-4).

Passage rate and digestion are two competitive process (Mertens, 1977), and it

is interesting to note that in DFCCS studies passage rate is constant, while in OST it is

variable and largely affected by intake and diet characteristics. Therefore, the absence

of method effect in the above-mentioned variables suggests that they are more closely

affected by fermentation conditions than by passage rate. Additionally, it is possible that

the continuous removal of fermentation end-products reduces the issue with

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accumulation of products that can inhibit fermentation, resulting in responses similar to

in vivo.

Dependent Variables Affected by Method

True OM digestibility was affected by method when regressed with NDF

digestibility and EMPS, and OST had greater ß0 than DFCCS. On average, studies

using OST had 14.5% greater OM digestibility. Even though method affected OM

digestibility, total VFA concentration was not affected by method in any of the tested

regressions. Therefore, even though the difference in OM digestibility was on average

14.5% greater on OST studies, total VFA concentration was not affected by system.

Molar proportion of acetate increased as NDF digestibility increased up to the

maximum point of 72.9% (Table 6-2). Interestingly, molar proportion of propionate

presented a minimum point close to the maximum point of acetate (at 70% of NDF

digestibility), suggesting that the point that maximizes acetates is similar to the point

that minimizes propionate (Figure 6-1A and B). Due to the method only affecting the

estimate of ß0, the NDF digestibility point that maximizes acetate and minimize

propionate is the same for DFCCS and OST. Similarly, when molar proportions of

propionate and acetate were regressed with dietary NFC, OST presented greater

estimate of ß0 for acetate and lower propionate than DFCCS. Acetate was 11% greater

and propionate was 6% lower in OST when regressed with NDF digestibility, and

acetate was 7.6% greater and propionate 22% lower in OST than in DFCCS.

Demonstrating that when acetate and propionate were studied using independent

variables related with structural and non-structural carbohydrates the magnitude of the

response was slightly different, however due to the intercept shift for DFCCS versus

OST, the functional relationship of the is maintained between methods.

135

Concentration of NH3-N had a linear and positive effect when regressed with CP

or NDF digestibility (Figure 6-2). Overall, NH3-N concentration in DFCCS was 12.19

mg/dL and in OST was 8.55 mg/dL, resulting in a 30% difference. Increments in NDF

digestibility resulted in greater NH3-N accumulation in DFFCS, and this was the only

variable with non-significant ß0 but significant ß1 between methods (Table 6-2). This

result can be partially explained by the fact that in a DFCCS, there is no NH3-N

absorption through the wall, and the only way out of the system is through overflow.

While in vivo, NH3-N is absorbed by the portal-drained viscera, extracted by the liver

and converted to urea (NRC, 2016). Urea is then excreted in the urine or recycled via

saliva or other sections of the gastrointestinal tract (Gozho et al., 2008).

In an experiment aiming to study the effect of increasing N intake on urea

kinetics and recycling, Marini and Van Amburgh (2003) using Holstein heifers, reported

N recycling ranging from 83% in a low N diet and 29% in high N diets. They also

observed that urea production increases as N intake increases, however the N recycling

presents an opposite response, decreasing as N intake increased (Marini and Van

Amburgh, 2003). Additionally, Lapierre and Lobley (2001) concluded that overall urea

returned to the gut ranged on average from 30 to 40% of N ingested. In DFCCS studies,

urea is commonly added in the artificial saliva aiming to simulate N recycling (Busquet

et al., 2005; Cerrato-Sánchez et al., 2007; Silva et al., 2016). This practice is important

when low CP diets are used, and similar to in vivo, the contribution of urea recycling is

important to maintain minimum ruminal N levels to ensure microbial growth. However, in

experiments in which the diet provides enough N, this addition of urea might result in a

136

greater NH3-N input in the system than the microbial population is capable of converting

into microbial N.

In our dataset, dietary CP averaged 16.8% for DFCCS (min = 4.01, max = 28.7,

SD = 3.15) and 16.38% for OST (min = 9.9, max = 23.75, SD = 2.13). Considering a

hypothetical scenario of 1) 0.4g/L of saliva is added in the fermentors; 2) liquid passage

rate of 10%/h and 5%/h for solids; and 3) fermentor volume of 1830 mL, 1.728 g urea

(0.795 g of N) are added to each fermentor daily. Therefore, if the diet was formulated

containing 16.8% CP the urea input by saliva represents approximately 28% of

additional N. Meaning that the animal that this study is simulating would have a 28% of

N recycling, which is within the range previously reported in in vivo studies (Lapierre

and Lobley, 2001; Marini and Van Amburgh, 2003).

The ruminal nitrogen balance in vivo is represented by the N input via rumen

wall, saliva, dietary, and endogenous and output from rumen wall and flow to omasum.

In a DFCCS the only way out of N is by overflow. Therefore, the N added in the artificial

saliva used in DFCCS should represent the balance (N recycling minus N output),

instead of only the N recycling. The NRC (2001) assumed that at an apparent N

balance of zero, that approximately 15.2% of RDP is lost in the rumen. Considering N

recycling of approximately 28%, the balance would be approximately 13%. We

speculate that by reducing urea in the artificial saliva to approximately 0.19 g/L, the

ruminal NH3-N values obtained using DFCCS will be more closely related to OST. We

also hypothesize that considering that N recycling is regulated by N intake (Marini and

Van Amburgh, 2003), the amount of urea added in the artificial saliva needs to be

adjusted according to the dietary CP level. It is possible that depending on the diet fed

137

to the fermentor, this addition of 0.4 g/L urea exceeds the ruminal microbial ability to

metabolize NH3, resulting in accumulation, which might explain the difference on NH3-N

observed between the two methods. Therefore, studies evaluating the possible

adjustment of urea added in the artificial saliva used DFCCS are warranted.

In summary, out of 42 regressions developed in the present study, method only

affected 12 estimates of ß0 and 2 estimates of ß1. Indicating that the DFCCS provides

valuable estimates of ruminal fermentation, and that overall, the functional responses

observed in DFCCS studies are similar to OST. This also indicates that the treatments

effects observed in DFCCS possibly are maintained when tested in vivo, however, the

magnitude of the response might be slightly different. In those cases, results need to be

interpreted cautiously when extrapolating DFCCS data to in vivo, especially regarding

NH3-N concentration.

Implications

This meta-analysis was performed aiming to compare ruminal fermentation

responses in vitro using the continuous cultures system with responses obtained from in

vivo studies using omasal sampling technique. Overall, method affected OM

digestibility, molar proportion of acetate and propionate, however the difference was

observed only in the estimates of intercept. Even though we observed a method effect

for molar proportion of acetate and propionate, total VFA concentration was not affected

by method. Method only affected NANMN and ENU when regressed with NDF

digestibility, while bacterial N was affected by method when regressed with NDF

digestibility and EMPS. Furthermore, true CP digestibility and EMPS responses were

not affected my method.

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Concentration of NH3-N was the only variable that presented method effect on

estimate of intercept and slope, demonstrating that estimation of NH3-N using DFCCS

needs further adjustments and studies investigating this response are warranted.

Therefore, even though we observed differences in the estimate of ß0 for some

variables, in most cases the magnitude of the response was small, and the biological

value of this difference is likely minimum. Most importantly, the functional response to

different dietary NFC, EMPS, and NDF and CP digestibility is overall maintained in the

DFCCS comparing with OST.

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Table 6-1. Descriptive statistics for dataset comparing microbial fermentation from continuous culture system and omasal sampling technique studies

%, otherwise stated Item Dual-flow system Omasal sampling

technique

Independent Variables NDF digestibilitya Mean 49.59 47.09

SD 16.47 14.11

N 308 170 Dietary NFCb Mean 33.48 44.41

SD 14.64 4.83

N 170 69 True CP digestibilityc Mean 59.60 65.15

SD 16.54 9.90

N 275 99 EMPS, g/kgd Mean 28.18 28.89

SD 10.40 7.81

N 336 126 ENUe Mean 52.25 65.26

SD 52.25 65.26

N 451 185 Dependent Variables

True OM digestibilityf Mean 57.51 66.99

SD 11.77 8.01

N 307 109 NH3-N mg/dLg Mean 12.78 9.31

SD 6.28 4.44

N 355 164 Bacterial N/total Nh Mean 45.73 64.61

SD 12.42 9.41

N 451 185 NANMN/total Ni Mean 36.28 31.83

SD 18.14 8.56

N 451 185 Total VFA6 mM j Mean 97.26 108.35

SD 27.65 18.86

N 372 166 Acetate Mean 57.10 64.26

SD 9.44 3.88

N 395 166 Propionate Mean 25.54 20.13

SD 8.56 3.21

N 395 166 Butyrate Mean 11.72 11.73

SD 4.39 1.81 N 387 166

aNDF digestibility = neutral detergent fiber digestibility; bDietary NFC = non-fiber carbohydrate; cCP digestibility = true crude protein digestibility; dEMPS = efficiency of microbial protein synthesis: g bacterial N/ kg OM truly digested;elENU = efficiency of nitrogen use (Bach et al., 2005); fOM digestibility = true organic matter digestibility; gNH3-N = ammonia nitrogen; hBacterialN/totalN = proportion of bacterial nitrogen over total nitrogen, expressed as %;iNANNMN/totalN = proportion nonammonia nonmicrobial nitrogen over total nitrogen, expressed as %; jVFA = volatile fatty acids

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Table 6-2. Regressions developed using neutral detergent fiber digestibility as independent variable comparing microbial fermentation from continuous culture system and omasal sampling technique studies

Methoda Variableb Estimate β0

P-value Estimate

β1 P-value Estimate β2 P-value AICc R2

MSEd

N

β0 Method β1 Method β2 Method

DFCCS OMde

47.92±2.28 0.001 0.001 -0.04±0.09 0.611 0.795 0.004±0.001 0.001 0.214 2000.6 0.94 5.22 356

OST 58.26±2.68

NS CPdf 41.56±3.99 0.001 0.171 0.43±0.07 0.010 0.459 - 0.406 0.823 1923.1 0.79 28.3 278

DFCCS NH3-Ng

4.56±1.27 0.001 0.086

0.15±0.02 0.010 0.001 - 0.768 0.179 2108.2 0.76 5.23 390

OST 4.56±1.27 0.08±0.02

DFCCS BacterialNh

44.56±1.44 0.001 0.001 - 0.531 0.616 - 0.109 0.068 1941.6 0.84 29.8 286

OST 64.76±2.27

DFCCS NANMNi

61.08±3.18 0.001 0.001

-0.51±0.05 0.010 0.024 - 0.077 0.093 1950.0 0.73 29.7 274

OST 39.04±5.91 -0.18±0.13

NS EMPSj 32.64±1.78 0.001 0.282 -0.11±0.03 0.010 0.202 - 0.802 0.602 2315.6 0.82 8.73 384

DFCCS ENUl

53.33±1.78 0.001 0.001 - 0.365 0.674 - 0.440 0.437 2399.5 0.79 38.1 328

OST 64.99±2.49

NS Total VFAm

78.47±8.79 0.001 0.051

1.11±0.33 0.010 0.921 -0.012±0.003 0.001 0.221 3021.2 0.92 31.3 392

DFCCS C2

n 41.96±3.84

0.001 0.001 0.54±0.14 0.010 0.252 -

0.0036±0.001 0.015 0.335

2418.5 0.85 6.35 406

OST 47.15±3.84

DFCCS C3

o 35.23±3.2

0.001 0.024 -0.42±0.12 0.010 0.295 0.003±0.001 0.013 0.319 2093.3 0.77 3.93 397 OST 33.10±3.18

NS C4p 11.43±0.28 0.001 0.952 - 0.513 0.557 - 0.729 0.417 1737.0 0.74 1.94 411

aMethod = Dual-flow continuous culture system (DFCCS) or omasal sampling technique (OST), NS = P-value > 0.05; bDependent variables are expressed in percentage, unless otherwise stated; cAIC = Akaike’s information criteria; dMSE = mean square error; adjusted for the random study effect; eOMd = true organic matter digestibility; fCP digestibility = true crude protein digestibility; gNH3-N = ammonia nitrogen concentration (mg/dL); hBacterial N = proportion of bacterial nitrogen over total nitrogen flow, expressed as %;iNANNMN = proportion nonammonia nonmicrobial nitrogen over total nitrogen flow, expressed as %; jEMPS = efficiency of microbial protein synthesis; lENU = efficiency of nitrogen use (Bach et al., 2005); mVFA = volatile fatty acids concentration, mM.nC2 = acetate, %; oC3 = propionate, %; pC4 = butyrate, %.

141

Table 6-3. Regressions developed using dietary non-fiber carbohydrate as independent variable comparing microbial fermentation from continuous culture system and omasal sampling technique studies

Methoda Variableb Estimate

β0

P-value Estimate

β1 P-value

Estimate β2

P-value AICc R2 MSEd

N

β0 Metho

d β1 Method β2

Method

NS NDFde 72.79±3.94 0.001 0.798 -0.60±0.1 0.001 0.865 - 0.786 0.926 1227.3 0.93 16.8 182

NS OMdf 49.65±3.81 0.001 0.587 0.24±0.08 0.006 0.557 - 0.567 0.512 909.7 0.89 10.2 154

NS CPdg 64.74±5.45 0.001 0.381 -0.70±0.35 0.061 0.919 0.0132±0.005 0.020 0.781 732.9 0.60 34.5 114

NS NH3-Nh 22.59±1.66 0.001 0.910 -0.27±0.04 0.004 0.889 - 0.663 0.912 837.3 0.87 2.19 172

NS Bacterial

Ni 33.63±2.75 0.001 0.184 0.45±0.09 0.001 0.391 - 0.524 0.444 1004.1 0.82 31.4 151

NS NANMNj 31.64±1.68 0.001 0.757 - 0.173 0.899 - 0.247 0.438 1151.0 0.68 33.5 159 NS EMPSl 26.82±1.23 0.001 0.074 - 0.393 0.126 - 0.348 0.866 1059.3 0.91 5.42 191 NS ENUm 28.22±7.33 0.001 0.490 0.70±0.17 0.001 0.615 - 0.424 0.451 1004.9 0.79 53.6 139

NS Total VFAn

53.19±4.73 0.001 0.283 1.22±0.14 0.001 0.301 - 0.412 0.977 1598.9 0.94 38.2 208

DFCCS C2

o 72.33±3.13

0.001 0.001 -0.35±0.07 0.001 0.678

0.511 0.829 1120.4 0.83 9.24 220 OST 78.06±3.41 -

DFCCS C3

p 17.47±1.3

0.001 0.047 0.18±0.03 0.001 0.800

0.821 0.994 1025.5 0.77 6.50 220 OST 14.31±2.06 -

NS C4q 8.15±1.25 0.001 0.258 0.09±0.02 0.002 0.314 - 0.099 0.936 733.0 0.85 1.14 215

aMethod = Dual-flow continuous culture system (DFCCS) or omasal sampling technique (OST), NS = P-value > 0.05; bDependent variables are expressed in percentage, unless otherwise stated; cAIC = Akaike’s information criteria; dMSE = mean square error; adjusted for the random study effect; eNDF digestibility = neutral detergent fiber digestibility; fOM digestibility = true organic matter digestibility; gCP digestibility = true crude protein digestibility; hNH3-N = ammonia nitrogen; iBacterialN/totalN = proportion of bacterial nitrogen over total nitrogen, expressed as %; jNANNMN/totalN = proportion nonammonia nonmicrobial nitrogen over total nitrogen, expressed as %; lEMPS = efficiency of microbial protein synthesis; mENU = efficiency of nitrogen use (Bach et al., 2005); nVFA = volatile fatty acids. oC2 = acetate, %; pC3 = propionate, %; qC4 = butyrate, %.

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Table 6-4. Regressions developed using true crude protein digestibility as independent variable comparing microbial fermentation from continuous culture system and omasal sampling technique studies

Methoda Variableb Estimate

β0

P-value Estimate

β1

P-value Estimate

β2

P-value AICc R2

MSEd

N β0

Method

β1 Method

β2 Method

NS OMde 44.46±2.56 0.001 0.935 0.27±0.03 0.001 0.605 - 0.111 0.520 1895.5 0.96 3.61 326

DFCCS NH3-Nf

7.03±1.87 0.002 0.002 0.09±0.02 0.002 0.002

- 0.833 0.307 1641.2 0.83 3.49 312

OST 2.76±2.07

NS Bacterial

Ng 18.68±3.77 0.001 0.741 0.53±0.06 0.001 0.816 - 0.860 0.490 1491.3 0.88 21.2 234

NS NANMNh 83.80±2.31 0.001 0.375 -0.77±0.03 0.001 0.476 - 0.086 0.585 1311.6 0.98 2.84 243

NS EMPSi 22.35±1.92 0.001 0.894 0.07±0.02 0.011 0.948 - 0.728 0.988 1738.5 0.89 4.68 316

NS ENUj 19.83±4.67 0.001 0.360 0.65±0.02 0.001 0.653 - 0.918 0.120 1999.9 0.85 34.3 277

NS Total VFAl

11.60±1.68 0.001 0.188 0.18±0.09 0.046 0.387 - 0.758 0.627 2372.8 0.92 33.2 309

NS C2m 28.09±6.12 0.001 0.059 0.90±0.18 0.001 0.248

-0.0057±0.001

0.001 0.273 1862.9 0.82 8.74 318

NS C3n 57.43±6.1 0.001 0.088 -0.96±0.18 0.001 0.208 0.0062±0.001 0.001 0.192 1851.7 0.80 7.68 323

NS C4o 11.35±0.01 0.001 0.309 - 0.825 0.470 - 0.773 0.960 1270.7 0.71 2.27 307

aMethod = Dual-flow continuous culture system (DFCCS) or omasal sampling technique (OST), NS = P-value > 0.05; bDependent variables are expressed in percentage, unless otherwise stated; cAIC = Akaike’s information criteria; dMSE = mean square error; adjusted for the random study effect; eOM digestibility = true organic matter digestibility; fNH3-N = ammonia nitrogen; gBacterialN/totalN = proportion of bacterial nitrogen over total nitrogen, expressed as %; hNANNMN/totalN = proportion nonammonia nonmicrobial nitrogen over total nitrogen, expressed as %; iEMPS = efficiency of microbial protein synthesis; jENU = efficiency of nitrogen use; lVFA = volatile fatty acids; mC2 = acetate, %; nC3 = propionate, %; oC4 = butyrate, %.

143

Table 6-5. Regressions developed using efficiency of microbial protein synthesis as independent variable comparing microbial fermentation from continuous culture system and omasal sampling technique studies

Methoda

Variableb Estimate P-value Estimate P-value Estimate P-value AICc R2

MSEd

N

β0 β0 Method β1 β1 Method

β2 β2 Metho

d

DFCCS OMde

56.86±1.26 0.001 0.001

- 0.130 0.222

- 0.075 0.971 2157.6 0.93 6.65 371

OST 68.10±2.13 -

DFCCS NH3-Nf

12.19±1.35 0.001 0.001 - 0.676 0.143

- 0.958 0.651 2012.9 0.74 6.44 364

OST 8.55±1.15 - DFCCS Bacterial

Ng 22.09±1.02

0.001 0.001 0.95±0.01 0.001 0.054 -

0.596 0.916 1956.6 0.87 19.4 300 OST 38.38±1.33 NS NANMNh 51.04±4.90 0.001 0.911 -0.60±0.16 0.001 0.661 - 0.745 0.499 1996.8 0.82 18.9 279 NS ENUi 14.92±3.62 0.001 0.072 1.51±0.15 0.001 0.568 - 0.261 0.778 2700.1 0.91 21.4 381

NS Total VFAj

103.88±2.6

1 0.001 0.289 - 0.127 0.955 - 0.201 0.415 2941.6 0.90 52.7 372

NS C2l 60.28±0.79 0.001 0.096 - 0.601 0.431 - 0.871 0.806 2171.7 0.74 11.1 358

NS C3m 22.39±0.55 0.001 0.344 - 0.544 0.845 - 0.924 0.788 2012.8 0.62 9.94 360

NS C4n 11.66±0.31 0.001 0.478 - 0.832 0.678 - 0.409 0.911 1712.0 0.73 2.48 396

aMethod = Dual-flow continuous culture system (DFCCS) or omasal sampling technique (OST), NS = P-value > 0.05; bDependent variables are expressed in percentage, unless otherwise stated; cAIC = Akaike’s information criteria; dMSE = mean square error; adjusted for the random study effect; eOM digestibility = true organic matter digestibility;fNH3-N = ammonia nitrogen; fBacterialN/totalN = proportion of bacterial nitrogen over total nitrogen, expressed as %; hNANNMN/totalN = proportion nonammonia nonmicrobial nitrogen over total nitrogen, expressed as %; iVFA = volatile fatty acids; lC2 = acetate, %; mC3 = propionate, %; nC4 = butyrate, %.

144

Figure 6-1. Adjusted molar proportion of acetate (A) and propionate (B) regressed with

neutral detergent fiber digestibility, regressed with dietary non-fiber carbohydrates (C and D), and regressed with true crude protein digestibility (E and F). Data obtained from studies using dual-flow continuous culture (○)

and its residuals (☐); and from omasal sampling technique (▲) and its

residuals (■) Residuals (observed – predicted) are represented in the 0-line X axis.

145

Figure 6-2. Adjusted concentration of ammonia regressed with neutral detergent fiber

digestibility (A) and true crude protein digestibility (B). Data obtained from

studies using dual-flow continuous culture (○) and its residuals (☐); and from

omasal sampling technique (▲) and its residuals (■). Residuals (observed – predicted) are represented in the 0-line X axis.

146

Figure 6-3. Adjusted proportion of bacterial nitrogen (A) and nonammonia nonmicrobial

nitrogen (B) from total nitrogen flow, efficiency of microbial protein synthesis (C) and efficiency of nitrogen use (D) regressed with true crude protein digestibility. Adjusted proportion of nonammonia nonmicrobial nitrogen (E) and efficiency of nitrogen use (F) regressed with efficiency of microbial protein synthesis. Data obtained from studies using dual-flow continuous

culture (○) and its residuals (☐); and from omasal sampling technique (▲)

and its residuals (■). Residuals (observed – predicted) are represented in the 0-line X axis.

147

CHAPTER 7 RESEARCH SUMMARY

It was hypothesized that supplementing CS at 5% EE would not negatively affect

microbial fermentation when compared to a diet containing calcium salts of palm oil

(MEG), and that these fat sources would have different fermentation patterns. This

hypothesis was confirmed as supplementation of CS resulted in a greater proportion of

BH intermediates 18:3n-3 and 18:2n-6 in ruminal effluent compared to diets containing

calcium salts of palm oil (MEG). Greater ruminal escape of beneficial FA might translate

into better milk FA profile; however, this needs to be confirmed in vivo. In addition, CS

supplementation resulted in lower acetate: propionate ratio than no CS

supplementation; and ruminal true digestibilities of DM, OM, NDF, ADF, and CP were

lower in CS diets. We observed no differences between CS and MEG in RUP-N and

RDP- N supply when added at 5% dietary EE. However, 8% dietary EE caused

negative effects on ruminal N metabolism and AA outflow regardless of CS inclusion,

decreasing NAN and bacterial N. This suggests CS can be used up to 5% dietary EE

without compromising nitrogen metabolism and AA outflow.

In Chapter 3 it was demonstrated that isonitrogenous (16% CP) diets containing

camelina seed plus CM, had similar N metabolism when fed at 5% EE compared with

diets containing only CM as a protein supplement. However, the greater EE

concentration (35%) in camelina seed limited its inclusion in the diets. In order to

overcome this and enable a partial or complete replacement of CM by CAM, we

evaluated, in Chapter 4, a solvent-extracted CAM (SCAM), produced in our lab

experimentally. Beneficial effects were observed when SCAM completely replaced CM.

Fermentors fed SCAM diet had greater propionate molar proportion and NAN flow, and

148

lesser NH3-N, which may improve animal energy status and N utilization. However,

SCAM supplementation decreased acetate molar proportion and NDF digestibility,

indicating potential reduction in fiber digestibility. Fermentors fed a SCAM diet had the

greatest outflow of arginine, and SCAM supplementation did not change most of the AA

outflow. Overall, these results indicate that SCAM can be a potential replacement for

canola meal. Nevertheless, in vivo studies are necessary to confirm if the effects

observed in a dual-flow continuous culture system will be translated into improvements

in animal performance.

The second major objective was to use a meta-analytical approach to summarize

literature and to investigate the functional relationship between diet composition and

microbial fermentation end-products in a dual-flow continuous culture system (DFCCS).

Additionally, a meta-analytical approach was used to compare carbohydrate and N

metabolism using two methods: DFCCS and omasal sampling technique (OST). For

that, in Chapter 5 we performed a meta-analysis using 75 studies. It was concluded that

increases in dietary CP resulted in increased microbial efficiency and bacterial-N flow.

However, as dietary CP and NDF increased, we observed that NH3-N also increased.

Fermentor DMI was mostly associated with greater substrate availability, resulting in

increases on VFA concentration and molar proportion of propionate, whereas dietary

NDF increased the molar proportion of acetate, NDF and CP digestibility. However, it

was also positively associated with NH3-N concentration. Overall, the analysis of this

dataset demonstrated strong evidence that the dual-flow continuous culture system

technique provides valuable estimates of ruminal digestibility, volatile fatty acids

149

concentration and nitrogen metabolism which were closely related to the expected in

vivo response.

However, further studies comparing in vivo ruminal fermentation with DFCCS

data were needed, to allow comparison of these responses. Therefore, in Chapter 6 a

meta-analysis was performed aiming to compare ruminal fermentation responses in

vitro using the DFCCS with responses obtained from in vivo studies using OST. Overall,

method affected OM digestibility, molar proportion of acetate and propionate, however

the difference was observed only in the estimates of intercept. Even though we

observed a method effect for molar proportion of acetate and propionate, total VFA

concentration was not affected by method. Method only affected NANMN and ENU

when regressed with NDF digestibility, while bacterial N was affected by method when

regressed with NDF digestibility and EMPS. Furthermore, true CP digestibility and

EMPS responses were not affected by method. Concentration of NH3-N was the only

variable that had method effect on the estimates of intercept and slope, demonstrating

that estimation of NH3-N using DFCCS needs further adjustments and studies

investigating this response are warranted. Therefore, even though we observed

differences in the estimate of ß0 for some variables, in most cases the magnitude of the

response was small, and the biological value of this difference is likely minimal. Most

importantly, the functional response to different dietary NFC, EMPS, and NDF and CP

digestibility is overall maintained in the DFCCS compared to OST.

There are two nutritional features of camelina that limit greater inclusion in

ruminant’s diet: 1) concentration of anti-nutritional factors (erucic acid and glusinolates),

and 2) high residual oil concentration in camelina meal. These two limitations can be

150

surpassed through plant genetic breeding, and using a more efficient oil extraction

process, such as solvent-extraction. Camelina is a promising alternative feedstuff that

might have its use increased according to market demand and price of the typically

used feedstuff. However, the mechanism explaining the reduction in fiber digestibility is

yet to be elucidated.

In vitro techniques such as the DFCCS can be used strategically to test additives

that are in early stage of development, not produced in large scale and for treatments

screening. It allows studies to be conducted relatively faster and less expensively than

in vivo trials. The findings reported in this dissertation support that the majority

fermentation responses in DFCCS are similar to in vivo and indicate that the treatments

effects observed in DFCCS possibly are maintained when tested in vivo. Most

importantly, these findings will support an increasing number of in vitro studies and

provide valuable insights to the dairy research.

151

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BIOGRAPHICAL SKETCH

Virginia Lucia Neves Brandao was born in Minas Gerais state, Brazil. Daughter

of Ana Lucia Martins das Neves, she was raised in Sao Paulo city, Brazil. Due to her

passion for animals she studied animal sciences at the Federal University of Vicosa

(Vicosa, Minas Gerais state, Brazil), graduating in 2013. During her undergraduate

program she worked in research projects in the forage and nutrition laboratories for

almost four years. In 2013 she started graduate school, pursing a master’s degree in

forage science under the supervision of Dr. Chizzotti. During her master program she

decided to pursue a scientific industry career and decided to apply for a Ph.D. position

in the United States of America. In 2015 she earned her master’s degree and in the

same year she was accepted in the Ph.D. program at the University of Nevada – Reno,

under Dr. Antonio Faciola supervision. In 2016 she was selected as graduate intern at

Elanco Animal Health (Greenfield, IN), which gave her experience in the industry

research. She was teaching assistant for a lactation physiology and animal nutrition

classes, teaching classes and laboratory sections. In 2017 her advisor was hired by the

Animal Sciences Department of the University of Florida, bringing all his graduate

students to UF. During her Ph.D. program, Virginia was recognized with multiple

awards: in 2016 received the Kleberg Graduate Scholarship; in 2018 first place in the

poster competition in the 4th UF/IFAS animal sciences graduate symposium; and in

2019 she was awarded the Ph.D. student of the year of the Animal Sciences

Department , University of Florida. She received her PhD degree from the University of

Florida in 2019, and her research evaluated the use camelina meal in dairy diets, and

the use of the dual-flow system to estimate ruminal fermentation responses to dietary

manipulations.