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,
6
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
9
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
13
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
20
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,
21
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.
68
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.
71
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
76
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
77
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
78
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
79
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
82
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
83
(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.
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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
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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, %.
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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, %.
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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.