Available via license: CC BY 4.0
Content may be subject to copyright.
ABSTRACT
We examined the effects of substituting soybean
meal with either yeast protein from Cyberlindnera
jadinii or barley in concentrate feeds on feed intake,
ruminal fermentation products, milk production, and
milk composition in Norwegian Red (NRF) dairy cows.
The concentrate feeds were prepared in pellet form as
soy-based (SBM; where soybean meal is included as a
protein ingredient), yeast-based (YEA; soybean meal
replaced with yeast protein), or barley-based (BAR;
soybean meal replaced with barley). The SBM con-
tained 7.0% soybean meal on a dry matter (DM) basis.
This was replaced with yeast protein and barley in the
YEA and BAR concentrate feeds, respectively. A total
of 48 early- to mid-lactation [days in milk ± standard
deviation (SD): 103 ± 33.5 d] NRF cows in their first to
fourth parity and with initial milk yield of 32.6 kg (SD
= 7.7) were allocated into 3 groups, using a randomized
block design, after feeding a common diet [SBM and
good-quality grass silage: crude protein (CP) and neu-
tral detergent fiber (NDF) content of 181 and 532 g/
kg of DM, respectively] for 14 d (i.e., covariate period).
The groups (n = 16) were then fed one of the dietary
treatments (SBM, YEA, or BAR) for a period of 56 d
(i.e., experimental period). The concentrate feeds were
offered in split portions from 3 automatic feeders us-
ing electronic identification, with ad libitum access to
the same grass silage. Dietary treatments had no effect
on daily silage intake, total DM intake, or total NDF
intake. Dietary CP intake was lower and starch intake
was higher in the BAR group compared with the other
groups. Ruminal fluid pH, short-chain volatile fatty
acid (VFA) concentrations, acetate-to-propionate ratio,
and non-glucogenic to glucogenic VFA ratio were not
affected by dietary treatments. No effects of the dietary
treatments were observed on body weight change, body
condition score change, milk yield, energy-corrected
milk yield, milk lactose and fat percentages, or their
yields. In conclusion, yeast protein can substitute con-
ventional soybean meal in dairy cow diets without ad-
verse effect on milk production and milk composition,
given free access to good-quality grass silage.
Key words: amino acid, dietary nitrogen, milk
composition, soybean, barley
INTRODUCTION
Sustainable meat and milk production is essential for
future agricultural production. Growing environmental
concerns surrounding food and feed production, and
sustainability issues due to increasing population and
demand for food (Foley et al., 2011; Notarnicola et al.,
2017), necessitate the search for local feed resources
(Åby et al., 2014). Diets for high-yielding dairy cows in
the Nordic countries commonly consist of grass silage
(Huhtanen et al., 2013) augmented with concentrate
feeds based on barley and a relatively high proportion
of imported protein feed ingredients such as soybean
meal, corn gluten meal, and rapeseed meal (Åby et al.,
2014).
Norway has a challenging climate for agriculture, with
a typical grassland of only about 3% cultivated land
and limited potential to grow food crops. Therefore,
a growing need exists to develop novel, sustainable,
nonfood protein sources that can be used in animal
diets to allocate food protein to the increasing human
population. Recent efforts, in Norway and elsewhere,
have focused on the effects of partial or complete sub-
stitution of imported protein ingredients with alterna-
tive protein sources in animal feeds (Neal et al., 2014;
Dalle Zotte et al., 2019; Cruz et al., 2020a,b). Yeast-
derived microbial protein is one such emerging protein
ingredient, with favorable AA composition in animal
feeds (Øverland and Skrede, 2017). With a forest cover
of about 38% of the total land area (Government of
Cyberlindnera jadinii yeast as a protein source in early- to mid-lactation dairy
cow diets: Effects on feed intake, ruminal fermentation, and milk production
Alemayehu Kidane,1 Stine Gregersen Vhile,1 Sabine Ferneborg,1 Siv Skeie,2* Martine Andrea
Olsen,2 Liv Torunn Mydland,1 Margareth Øverland,1 and Egil Prestløkken1
1Faculty of Biosciences, Norwegian University of Life Sciences, N-1432 Ås, Norway
2Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, N-1432 Ås, Norway
J. Dairy Sci. 105
https://doi.org/10.3168/jds.2021-20139
© 2022, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®.
This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Received January 7, 2021.
Accepted November 9, 2021.
*Corresponding author: siv.skeie@ nmbu .no
Journal of Dairy Science Vol. 105 No. 3, 2022
Norway, 2014) and the country experiencing a large
accumulation of forest biomass with steady increase in
net growth over the recent years (Solberg et al., 2021),
yeast produced using wood biomass can provide high-
quality protein. For instance, a typical Candida utilis
grown on biomass hydrolysate with ammonium sulfate
as a nitrogen source (Sharma et al., 2018) had an AA
profile comparable to that of soybean meal (Cavins et
al., 1972). Sabbia et al. (2012) reported that DEMP—a
yeast-derived microbial protein based on Saccharomyces
cerevisiae (Alltech Inc.)—had an AA profile similar to
that of ruminal microbial protein. As a result, Sabbia
et al. (2012) reported that DEMP could replace plant
protein in dairy cow diets without negative effects on
milk production when used from 1.14 to 3.41% of the
diet DM. Neal et al. (2014) reported a tendency for in-
creased milk production when adding 1.15% of the diet
DM as yeast microbial protein. In addition, Higginson
et al. (2017) reported improved metabolic status (e.g.,
reduced metabolic stress and adipose tissue mobiliza-
tion) in transition cows during the postpartum period
when fed yeast-derived microbial protein. However,
Manthey et al. (2016) reported reduced feed efficiency
(energy-corrected milk per kilogram of DMI) and milk
fat yield for cows fed 2.25% of diet DM as yeast-derived
microbial protein. These studies were based on corn
silage and alfalfa forages, in contrast with the Scan-
dinavian grass-based silage, with expected differences
in nutrient composition and density. Such differences,
especially in the carbohydrate fraction of the diet,
would be expected to influence the utilization efficiency
of the dietary protein in dairy cow diets (Hristov et
al., 2005). Furthermore, the microbial proteins used
in these studies were largely based on Saccharomyces
cerevisiae, which might differ from other yeasts in the
level of CP, AA profile, and other nutrients (Øverland
and Skrede, 2017).
We hypothesize that Cyberlindnera jadinii yeast pro-
tein can replace soybean meal or barley in early- to
mid-lactation Norwegian Red (NRF) dairy cow diets
without adverse effects on milk yield and milk composi-
tion. The main objective of this study was, therefore,
to evaluate the effects of total substitution of soybean
meal in concentrate feeds by C. jadinii yeast protein
in grass silage-based rations of early- to mid-lactation
NRF cows on feed intake, ruminal fermentation param-
eters, milk yield, and milk composition. Furthermore,
as barley can be produced in Norway and is the most
used concentrate feed ingredient, a diet with barley
replacing both yeast protein and soybean meal in the
concentrate feed was compared against those 2 protein
sources.
MATERIALS AND METHODS
Experimental Animals, Diets, and Design
This experiment was performed at the Livestock Pro-
duction Research Center of the Norwegian University of
Life Sciences (Ås, Norway), with all animal procedures
approved by the national animal research authority of
the Norwegian Food Safety Authority (FOTS ID no.
18038).
A total of 48 NRF dairy cows of mixed parity (pri-
miparous = 27, second lactation = 10, and third lacta-
tion and above = 11) in their early- to mid-lactation
period, averaging (mean ± SD) 103 ± 33.5 DIM, 623
± 72.7 kg of BW, and 32.6 ± 7.7 kg milk yield at the
beginning of the experiment were used in a completely
randomized block design (Figure 1). All animals had
free access to the same grass silage, prepared from pri-
mary growth using a bunker silo, for a period of 70 d.
Chemical composition of the grass silage is presented
in Table 1. The silage was distributed through 40 au-
tomatic feeders (BioControl AS) equipped with verti-
cally moving gates with electronic cow identification
and feed intake registration for each individual cow.
All cows had free access to the 40 automatic feeders.
The feed troughs were filled twice every day (between
0800 and 1000 h, and between 1500 and 1600 h) with
fresh grass silage. The silage was chopped using a Silok-
ing chopping and mixing machine (DUO1814, Silok-
ing Kverneland, Kverneland Group Ireland Ltd.) until
uniform mixture and particle length was achieved, to
restrict feed selection by cows.
The first 14 d were considered a covariate period,
during which all 48 cows were fed a soybean-based con-
centrate feed (SBM) in addition to grass silage (Figure
1). The ratio between grass silage and concentrate was
39:61 (CP and NDF shown in Table 1). The amount
of concentrate feed for each animal was calculated to
meet requirements for maintenance and production at
the start of the experiment using the NorFor feeding
system (TINE OptiFôr; NorFor, 2011).
At the end of the covariate period, the 48 cows were
randomly assigned to 1 of 3 treatment groups blocked
for parity (i.e., first and second or greater lactation)
and balanced for DIM and milk yield, giving 16 cows in
each treatment group. The groups were then randomly
allocated to 1 of the 3 different concentrate feeds:
SBM (continuation of covariate period feeding), yeast
(YEA), or barley (BAR; see Figure 1 for experimen-
tal layout). The SBM concentrate feed contained 7.0%
(on DM basis) soybean meal. This was quantitatively
replaced by yeast and barley in the YEA and BAR
Kidane et al.: CYBERLINDNERA JADINII YEAST PROTEIN IN DAIRY COW DIETS
Journal of Dairy Science Vol. 105 No. 3, 2022
concentrate feeds, respectively. Ingredient and chemical
compositions of the feeds used are provided in Table
1, whereas data on AA composition of the feeds are
provided in Table 2. The yeast protein was supplied
by Lallemand (produced by Danstar Ferment A.G.),
and all concentrate feeds were prepared at Felleskjøpet
Agri (FKA, Vestnes, Norway). A brief summary of the
chemical composition of C. jadinii is provided in the
footnotes of Table 1. All 3 concentrate feeds were formu-
lated to be isoenergetic (Table 1). The SBM and YEA
concentrate feeds were formulated to be isonitrogenous,
whereas the BAR concentrate feed was formulated to
ensure the dietary protein supply needed for the good-
quality grass silage. The concentrate feeds were offered
in split portions daily, maximum of 4 kg per cow per
visit, from 3 FSC40 DeLaval feeding stations, with ad-
ditional small portions (~1.0 kg of SBM split over 3
visits) fed in a milking robot. All cows had free access
to their feeding stations. The level of concentrate feeds
offered was adjusted twice over the experimental period
(reduced 15% on d 28, and an additional 10% on d 50,
relative to covariate-period feeding) for all groups, to
account for the increasing stage of lactation and declin-
ing yield.
Individual feed intake of grass silage and concentrate
feeds as well as milk yield were measured daily for 70
d. The cows were housed in a freestall with concrete
slatted floors and lying cubicles with rubber mats and
sawdust bedding. Cow BW and BCS (on a scale from
1.0 = emaciated to 5.0 = obese) were recorded multiple
times (mean = 2.5; SD = 0.85) per cow per day when
cows visited the milking robot. The BCS was recorded
by a DeLaval BCS camera mounted on a DeLaval sort
gate (DeLaval VMS Classic). The camera took a 3-di-
mensional image of the lower back of cows, which was
then analyzed with DeLaval BCS software, determin-
ing the amount of fat covering the loin, rump tailhead,
hooks, pins, and short ribs to calculate the automated
BCS, as recently described by Mullins et al. (2019).
The BW of cows was recorded just after milking with a
BioControl weighing scale (BioControl AS). Changes in
BW and BCS over the experimental period, calculated
as the difference between mean BW and BCS in the
last week of the experiment (i.e., d 50–56) relative to
the covariate period BW and BCS, in respective order,
were later used in the statistical analysis.
Feed Sampling and Analyses
About 400 g of each of the 3 concentrate feeds and
500 g of grass silage samples were taken once every week
and stored at −20°C pending further processing. At
completion of the experiment, the grass silage samples
were pooled at 3 time points (i.e., covariate period, first
28 d, and last 28 d of the experimental period), whereas
the concentrate feeds were pooled at the latter 2 time
points. The samples were then dried in duplicates at
45°C for 48 h in preparation for milling. The duplicates
were mixed and milled using a cutting mill (SM 200,
Retsch GmbH) at different sieve sizes for the planned
analyses as subsequently described.
Concentrate feed samples for starch analysis were
milled through a 0.5-mm sieve, whereas both concen-
trate feed and silage samples for other analysis were
milled through a 1.0-mm sieve. The DM content of the
samples was determined by drying at 103°C overnight
(ISO, 1999), whereas the ash content was determined
by incinerating the samples at 550°C (ISO, 2002). The
nitrogen content of the feeds was analyzed using AOAC
method 2001.11 (Thiex et al., 2002), with a Kjeltec
2400/2460 Auto Sampler System (Foss Analytical). To-
tal starch content of the concentrate feed samples was
analyzed using AACC method 76-13.01 (Megazyme
amyloglucosidase/α-amylase method; AACC, 2000)
Kidane et al.: CYBERLINDNERA JADINII YEAST PROTEIN IN DAIRY COW DIETS
Figure 1. Schematic of the experiment, with dairy cows fed grass silage augmented with 3 different concentrate feeds, where soybean meal
(SBM) was substituted with either yeast protein from Cyberlindnera jadinii (YEA) or barley (BAR) over an experimental period of 56 d.
Journal of Dairy Science Vol. 105 No. 3, 2022
with starch hydrolyzed to glucose and determining the
concentration of glucose colorimetrically using an RX
DaytoNa+ spectrophotometer (Randox Laboratories
Ltd.). The content of NDF was determined with an An-
kom 220 fiber analyzer (Ankom Technology) according
to Mertens (2002), using sodium sulfite and α-amylase,
and further corrected for residual ash. Water-soluble
carbohydrate content was determined as described in
Randby et al. (2010), whereas residual carbohydrate
content was determined as the difference between DM
and analytical components (sum of starch, CP, NDF,
crude fat, and ash for concentrate feeds, with addition-
al adjustment for silage fermentation products for the
grass silage) according to the Nordic feed evaluation
system (NorFor, 2011). The AA contents (except for
tryptophan; not analyzed) of the silage and concentrate
feeds were determined by ion-exchange chromatogra-
phy according to commission regulation no. 152/2009
Kidane et al.: CYBERLINDNERA JADINII YEAST PROTEIN IN DAIRY COW DIETS
Table 1. Ingredients (%, DM basis) and chemical composition of grass silage and 3 concentrate feeds (SBM =
soybean meal-based; YEA = yeast-based; BAR = barley-based, with barley replacing both yeast and soybean
meal)
Item Grass
silage SBM YEA BAR
Ingredient composition
Barley 48.9 49.2 55.4
Corn gluten meal 2.14 2.13 2.15
Oat 4.94 4.93 4.97
Wheat 9.89 9.85 9.94
Molasses 4.20 4.19 4.23
Beet pulp 15.3 15.3 15.4
Soybean meal 7.00 — —
Yeast1 — 7.29 —
Calcium soap of fatty acids2 3.38 3.04 3.29
Limestone 0.30 0.53 0.31
Monocalcium phosphate 0.66 0.37 0.77
Sodium bicarbonate 1.16 1.27 1.35
Magnesium oxide 0.51 0.51 0.51
Sodium sulfate 0.12 0.03 0.20
Salt (NaCl), feed-grade 1.00 1.00 1.01
Micromineral premix3 0.11 0.11 0.11
Selenpremiks4 0.14 0.14 0.15
Vitamin premix5 0.09 0.09 0.09
Agolin Ruminant6 0.07 0.07 0.07
Chemical composition7 and energy value
DM content (g/kg) 300 875 881 875
CP 181 161 157 134
NDF8522 175 169 174
Fat 46.3 38.3 36.9 38.0
Starch — 385 365 406
Ash 75.8 65.9 67.5 69.2
FPF999.9 — — —
WSC10 16.7 61.5 58.5 56.8
Residual CHO11 58.4 113 146 122
NEL (MJ/kg of DM)12 6.6 7.1 7.1 7.0
1Cyberlindnera jadinii yeast (composition on DM basis): CP (N × 6.25) 479 g/kg; ash 69 g/kg; crude fat 54 g/
kg; total carbohydrates 397 g/kg; and macrominerals P 14.4 g/kg, Na 0.86 g/kg, Mg 1.34 g/kg, and Ca 1.03
g/kg.
2Calcium soap of palm fatty acids provided as Akofeed Kalkfett (AAK).
3Micromineral premix (mg/kg of feed): 19.3 Cu; 0.25 Co; 5.3 I; 86 Zn; 40 Mn; and 100 Fe.
4Selenium premix (Vilomix) providing 0.4 mg Se per kg of feed.
5Vitamin premix providing 5,004 IU of vitamin A, 2,010 IU of vitamin D, and 80 mg of vitamin E per kilogram
of feed.
6Agolin Ruminant is a feed additive produced by Agolin SA; https: / / agolin .ch/ .
7Mean values for chemical composition are based on a minimum of triplicate analysis.
8NDF in feeds corrected for ash.
9Sum of fermentation products (silage fermentation acids and alcohols).
10Water-soluble carbohydrates in feeds.
11Calculated residual carbohydrates (difference between DM content and sum of all analytical values) according
to NorFor (2011).
12Estimated NEL at 20 kg of DMI (NorFor, 2011).
Journal of Dairy Science Vol. 105 No. 3, 2022
of the European Communities (EC, 2009; Table 2). Si-
lage fermentation products and ammonia nitrogen were
analyzed on fresh silage samples at Eurofins (Eurofins
Agro Testing Norway AS, Moss, Norway) as recently
described by Randby et al. (2020).
Ruminal Fluid Samples
Ruminal fluid samples were taken from all animals
at 3 time points: at the end of the covariate period
(d −2 to 0), at the middle of the experimental period
(d 26–28), and at the end of the experimental period
(d 54–56; see Figure 1 for explanation of days). Each
sampling point constituted 3 consecutive days (cover-
ing all 48 cows) with roughly one-third of the cows
in each group included per sampling day. The cows
within each feeding group were randomly assigned to
1 of 3 sampling days for the first sampling, and the
same groupings were used accordingly for the later
samplings. On the sampling days, the cows were moved
to the holding area before the morning feed distribution
(between 0800 and 0830 h). The samples were taken
between 0900 and 1030 h by aspiration using manually
operated esophageal tubing (Akselsens Agenturer A/S)
fitted with a perforated steel endpoint to restrict suc-
tion of large particles. The first portion of the ruminal
fluid (approximately 200–300 mL) was discarded to
avoid saliva contamination, and an equivalent volume
was withdrawn for analysis. This was strained through
4 layers of cheesecloth, and 9.5 mL was preserved with
0.5 mL of concentrated formic acid (98%; vol/vol) and
stored in a cold room (4°C) until completion of the
experiment. The pH of the remaining ruminal fluid
was measured using a digital pH 3310 meter (Xylem
Analytics Germany GmbH). The stored samples were
later analyzed for ruminal fluid VFA by GC (TRACE
1300 Gas Chromatograph equipped with a Stabilwax-
DA column, 3 m, 0.53-mm internal diameter, 0.25 μm;
Thermo Scientific) and for ruminal fluid ammonia-N
using AOAC method 2001.11 (Thiex et al., 2002) with
a modification that block digestion was not carried out.
Milk Yield and Milk Sample Registration
The cows were milked using a robotic milking system
(DeLaval VMS Classic) with the minimal milking in-
terval set to 5.5 h. Daily milk yield was summed from
multiple milkings (mean ± SD: SBM = 2.86 ± 0.66,
YEA = 2.64 ± 0.70, and BAR = 2.80 ± 0.75) per cow
per day. Milk samples were taken at the end of the
covariate period (i.e., d 0), and on d 14, 28, 35, and 56
of the experimental period. On the milk sampling days,
1 composite milk sample per cow was taken from 1900
h to 0700 h the next morning. The samples were pre-
served with a bronopol tablet (2-bromo-2-nitropane-1,3
diol, Broad-Spectrum Microtabs II, Advanced Indus-
tries Inc.) and stored in a cold room (4°C) until analysis
for milk protein, fat, lactose, and urea using a Bentley
FTS/FCM instrument (Bentley Instruments Inc.). The
ECM yield over the experimental period was calculated
for each individual cow based on mean milk chemical
composition and milk yield according to Sjaunja et al.
(1991).
Statistical Analysis
One cow from the BAR group had mastitis during
the experimental period and was separated from the
group until she completed medication (i.e., 14 d). No
data collected on this cow for this period were included
in the statistical analysis. The data were analyzed
using the PROC MIXED procedure of SAS (SAS for
Windows 9.4; SAS Institute Inc.). The respective vari-
ables from the covariate period (d −14 to 0) were used
as covariates. The covariate structure that minimized
Akaike’s information criterion was used, primarily
Toeplitz, compound symmetry, or autoregressive. Cow
(diet × parity) was considered a repeated subject in
all models. The full model for the effect of different
concentrate feeds on feed intake variables, milk yield,
milk component yields, and milk N efficiency (NUE,
expressed as N secreted in milk as a percentage of N
intake) was as follows:
Kidane et al.: CYBERLINDNERA JADINII YEAST PROTEIN IN DAIRY COW DIETS
Table 2. Amino acid composition (% of total AA) and total AA
content of grass silage and the 3 concentrate feeds (SBM = soybean
meal-based; YEA = yeast-based; BAR = barley-based)
Amino acid Grass
silage SBM YEA BAR
Ala 7.97 4.33 4.87 4.48
Arg 4.26 5.59 5.03 5.01
Asp 12.3 8.11 7.58 6.95
Cys 0.77 1.72 1.61 1.84
Glu 13.4 26.7 26.9 28.6
Gly 5.31 3.72 3.78 3.71
His 2.40 2.70 2.60 2.64
Ile 5.09 3.98 4.07 3.81
Leu 9.04 8.19 8.26 8.31
Lys 6.19 4.22 4.34 3.67
Met 1.62 1.31 1.33 1.39
Phe 5.75 5.41 5.20 5.03
Pro 7.18 9.24 8.90 10.6
Ser 4.63 4.69 4.75 4.60
Thr 5.30 3.77 4.12 3.69
Tyr 3.03 2.10 2.21 1.24
Val 5.73 4.25 4.49 4.37
Total AA1115.1 124.5 114.6 100.2
1Total amino acid content of the feeds (g/kg of DM) excluding Trp,
which was not analyzed.
Journal of Dairy Science Vol. 105 No. 3, 2022
Yijkl = μ + Dieti + Dayj + Parityk + (Diet × Day)ij
+ DIM + covXikl + Cowikl + eijkl,
where Yijkl = response variable (e.g., milk yield); μ =
overall mean; Dieti = fixed effect of concentrate feed
type (i.e., BAR, SBM, YEA); Dayj = fixed effect of day
of measurement (j = 1–56); Parityk = fixed effect of
parity (k = primiparous or multiparous); DIM = effect
of days in lactation of each individual cow at the start
of the covariate period; covXikl = effect of covariate
period data for each cow within diet and parity (e.g.,
covariate period milk yield for milk yield data); (Diet
× Day)ij = interaction effect of concentrate feed type
and day of measurement; Cowikl = random effect of cow
nested within diet and parity (l = 1–16); and eijkl = re-
sidual error. Models for VFA and ruminal fluid pH also
included the number of minutes since the last feeding
occasion. The effects of dietary treatments on BW and
BCS changes were tested using the general linear model
in SAS (PROC GLM) with diet, parity, DIM, and co-
variate period BW and BCS values included in their
respective model. Tukey-Kramer was used to test for
differences between means. Data are presented as least
squares means, with statistical significance declared at
P ≤ 0.05 and tendencies discussed at 0.05 < P < 0.1.
RESULTS
Intake, BCS, and BW
Data on daily DM and nutrient intake are presented
in Table 3. Daily total DMI, silage DMI, concentrate
feed DMI, and NDF intake were not affected by the
dietary treatment (i.e., concentrate feed type). Cows in
all 3 dietary treatments achieved a similar level of NDF
intake per unit BW (13.8 ± 0.29 g/kg of BW). How-
ever, total starch intake was higher and total dietary
CP intake was lower in the BAR group. The propor-
tions of silage and concentrate feed in the DMI of all
groups were calculated to be roughly 65% and 35%,
respectively.
The effect of dietary treatments on BW change was
not significant, although BW increased over the experi-
mental period (22.3, 29.8, and 25.4 kg in SBM, YEA,
and BAR, respectively) over the experimental period.
Similarly, BCS change was not affected by the dietary
treatments over the experimental period.
Rumen Fermentation Products
Data on rumen fermentation characteristics are pre-
sented in Table 4. Dietary treatment did not influence
ruminal ammonia nitrogen, total ruminal fluid VFA,
or molar proportions of individual VFA. All ruminal
fermentation parameters were affected (P < 0.04) by
sampling day, except the molar proportion of butyr-
ate. As a result, total ruminal fluid VFA and molar
proportions of acetate were higher in the middle of
the experimental period than at the end. Conversely,
ruminal fluid ammonia nitrogen concentration, molar
proportions of propionate, valerate, isobutyrate, and
isovalerate were lower in the middle than at the end of
the experimental period. The effects of dietary treat-
ments on ruminal fluid pH, acetate-to-propionate ratio,
and non-glucogenic to glucogenic VFA ratio were not
significant. These variables were significantly higher on
samples taken in the middle than at the end of the
experimental period.
Milk Yield, Milk Composition, and Component Yields
Data on milk yield, milk composition, and compo-
nent yields are presented in Table 5. Both daily milk
yield and ECM yield were not affected by the dietary
treatments. Similarly, milk fat and lactose contents
did not differ among the dietary treatments, but a sig-
nificant interaction effect of sampling day by treatment
was found for milk fat content (P < 0. 01). This was
observed with YEA showing the highest fat content
(mean ± SEM, g/kg of milk: YEA = 45.1 ± 0.79; SBM
= 43.7 ± 0.82; BAR = 44.2 ± 0.80) with samples taken
on d 35. Furthermore, milk protein content (P = 0.10)
and MUN (P = 0.06) were marginally lower in the
BAR group than in the other 2 dietary groups. Milk
component yields and dietary NUE were not affected
by the dietary treatments.
DISCUSSION
For most parameters, a significant effect of day was
observed. This effect is most likely due to the change
in amount of concentrate at d 28 and 50. Except for
fat concentration in milk, no diet day interaction was
observed, and the day effect is not further discussed.
Feed Intake
Feed intake was not affected by substituting yeast
or barley for soybean meal in dairy cow diets. The
concentrate feeds were offered in restriction based on
individual cow requirements as calculated by NorFor,
the Nordic feed evaluation system (TINE OptiFôr;
NorFor, 2011), and hence were expected to remain
similar between the groups. However, grass silage was
offered ad libitum, allowing variations in DMI between
cows based on individual cow intake capacity. Despite
this, the ratio of silage to total DMI remained similar
between the groups. Previous studies with yeast-based
Kidane et al.: CYBERLINDNERA JADINII YEAST PROTEIN IN DAIRY COW DIETS
Journal of Dairy Science Vol. 105 No. 3, 2022
protein on dairy cow diets produced mixed results.
Neal et al. (2014) reported decreased intake of DM and
nutrients with dairy cows fed total mixed ration supple-
mented with yeast-based microbial protein (YMP).
They stated that the observed effect was unexpected
and was difficult to explain. With dairy cows fed
high-forage diets containing increasing levels of YMP
(i.e., 0, 1.14, 2.28, and 3.41% DM of YMP replacing
soybean meal), Sabbia et al. (2012) reported a cubic
response on DMI over the YMP inclusion range, with
the 2.28% YMP inclusion level producing DMI similar
to the control diet. This is comparable to our YEA
diet (about 7.0% yeast in the concentrate feed, which
constituted 35% of the achieved DMI, producing ap-
proximately 2.45% inclusion of yeast in the total diet).
In our study, because all the concentrate feeds were
roughly isoenergetic, and cows were fed one quality si-
lage over the experimental period, energy intake would
not have differed between the groups. Furthermore,
the early-cut grass silage used here was above average
quality based on the chemical composition (e.g., high
in CP and intermediate in NDF), and hence intake
limitation due to rumen fill would have been minimal.
Indeed, dietary NDF is heterogeneous in nature, and
Kidane et al.: CYBERLINDNERA JADINII YEAST PROTEIN IN DAIRY COW DIETS
Table 3. Feed and nutrient intake with dairy cows fed grass silage augmented with 3 different concentrate feeds containing soybean meal (SBM),
yeast (YEA), and barley (BAR)
Item
Treatment
SE
Statistics (P-value)
SBM YEA BAR Diet Day Diet × Day
Feed intake (kg/d)
Total DMI 22.1 22.0 21.9 0.17 0.80 <0.01 0.32
Silage DMI 14.5 14.5 14.1 0.18 0.18 <0.01 0.12
Concentrate feed DMI17.74 7.60 7.68 0.05 0.20 <0.01 0.41
Other parameters
CP intake (kg/d) 3.84a3.81a3.60b0.03 <0.01 <0.01 0.24
Starch intake (kg/d) 2.98b2.77c3.13a0.02 <0.01 <0.01 0.21
NDF intake2 (kg/d) 8.92 8.85 8.70 0.10 0.27 <0.01 0.20
DMI/kg of BW (g/kg) 35.2 35.4 34.2 0.65 0.41 <0.01 0.22
DMI/kg of BW0.75 (g/kg) 176.5 177.3 171.7 3.23 0.43 <0.01 0.24
Mean achieved AAT intake and others3
Total AAT (g/d) 2,233 2,216 2,159
Met/AAT intake (%) 2.12 2.13 2.16
His/AAT intake (%) 2.49 2.47 2.47
Lys/AAT intake (%) 6.63 6.68 6.63
a–cDifferent superscript letters within a row indicate significant differences between treatments at P ≤ 0.05.
1The concentrate feeds were offered in split portions, maximum of 4 kg per cow per visit, each day from 3 DeLaval FSC40 feeding stations, with
additional small portions (~1.0 kg of SBM split over 3 visits) fed in a milking robot.
2NDF in the feed corrected for ash.
3Estimated achieved total amino acids absorbed in the intestine (AAT) and intakes of Met, Lys, and His as percentage of the total AAT calcu-
lated using NorFor feeding standards (TINE OptiFôr; NorFor, 2011) at a diet level.
Table 4. Ruminal fermentation parameters from dairy cows fed grass silage augmented with 3 different concentrate feeds from soybean meal
(SBM), yeast (YEA), and barley (BAR)
Item
Treatment
SE
Statistics (P-value)
SBM YEA BAR Diet Day Diet × Day
NH3-N (mg/L) 79.5 74.8 91.0 15.3 0.66 <0.01 0.23
Total VFA (mM) 70.2 67.4 77.9 4.7 0.35 0.04 0.42
Individual VFA (molar % of total VFA)
Acetate 66.9 65.2 64.6 1.0 0.44 <0.01 0.54
Propionate 16.8 17.9 17.9 0.89 0.74 0.01 0.66
Butyrate 13.3 13.6 14.4 0.31 0.13 0.50 0.83
Valerate 1.10 1.20 1.29 0.091 0.47 <0.01 0.78
Isobutyrate 0.85 0.83 0.87 0.032 0.68 <0.01 0.40
Isovalerate 1.03 1.02 1.09 0.054 0.47 <0.01 0.52
Ruminal fluid pH 7.21 6.80 6.87 0.094 0.11 <0.01 0.67
Acetate: propionate 3.99 3.67 3.64 0.027 0.73 <0.01 0.48
NGR15.15 4.85 4.85 0.032 0.82 <0.01 0.54
1Non-glucogenic to glucogenic VFA ratio, calculated according to Morvay et al. (2011) as [acetate + (2 × butyrate) + (2 × branched-chain
VFA)]/[propionate + branched-chain VFA].
Journal of Dairy Science Vol. 105 No. 3, 2022
equating rumen NDF pool based on NDF intake has
limitations (Huhtanen et al., 2016). Here, over 97.5% of
the NDF intake originated from a common NDF pool
(65% from the common grass silage, and 32.5% from
the concentrate feed component, as barley, yeast, and
soybean meal substitutions accounted for about 7.0%
of the concentrate feed ingredients). Therefore, NDF
intake expressed per kilogram of BW could be used
as an indicator of rumen fill (NorFor, 2011; Huhtanen
et al., 2016). To this end, calculated NDF intake per
kilogram of BW was similar between the treatments.
Ruminal Fluid VFA and pH
Marked changes in the molar proportions of the con-
centrations of VFA in the ruminal fluid can be observed
in response to dietary manipulations (Chalupa, 1977;
Sutton et al., 2003). Here, we did not observe any dif-
ference between the 3 dietary treatments on ruminal
fluid VFA. The observed VFA levels were lower than
those reported for dairy cows fed nonrestrictive diets
(Sabbia et al., 2012; Neal et al., 2014; Kidane et al.,
2018). It has been reported that method of sampling
(i.e., via rumen canula vs. esophageal tubing) could af-
fect the total VFA content, with esophageal tubing un-
derestimating the VFA content (Raun and Burroughs,
1962; Geishauser and Gitzel, 1996; Shen et al., 2012;
van Gastelen et al., 2019). However, the molar percent-
ages of specific VFA have been reported to be unbiased
by the method of sampling (Raun and Burroughs,
1962; van Gastelen et al., 2019) and also were not in-
fluenced by the dietary treatments in our experiment.
Furthermore, ruminal fluid acetate-to-propionate ratio
and non-glucogenic to glucogenic VFA ratio were not
altered by the dietary treatments. For both ratios, the
values are higher than those observed in cows fed TMR
(Kidane et al., 2018) using samples taken at multiple
time points over a 24-h cycle.
Ruminal fluid pH was not affected by the dietary
treatments, despite our expectation that the BAR diet
would decrease rumen pH compared with the other
treatments because of increased and rapid starch deg-
radation (Nikkhah, 2012). Ruminal fluid pH usually
oscillates depending upon, among other factors, meals
and feeding times (Palmonari et al., 2010; Kidane et
al., 2018). Our samples were collected before morning
feeding, and the observed elevated ruminal fluid pH
would suggest low VFA concentration, due to active
uptake and reduced fermentable OM in the rumen.
High ruminal fluid pH could also be partly due to sa-
liva contamination (Grünberg and Constable, 2009),
despite our attempts to avoid this.
Milk Yield, Milk Composition, and Milk
Nitrogen Efficiency
Milk yield and milk composition were not affected
by the dietary treatments. Achieved dietary CP levels
were not restrictive, with the lowest for the BAR group
being 164 g/kg of DM. With early- to mid-lactation
Holstein dairy cows, Law et al. (2009) demonstrated
a tendency toward a greater milk yield response when
increasing dietary CP from 114 to 144 g/kg of DM than
from 144 to 173 g/kg of DM. Others (Cunningham et
al.,1996; Leonardi et al., 2003) observed no improve-
ment in milk yield when dietary CP increased over a
range (e.g., 161–189 g/kg of DM) that contained what
was achieved in our experiment. The supply of amino
Kidane et al.: CYBERLINDNERA JADINII YEAST PROTEIN IN DAIRY COW DIETS
Table 5. Milk yield, milk composition, milk component yields and dietary milk nitrogen efficiency from dairy cows fed grass silage augmented
with 3 different concentrate feeds containing protein from soybean meal (SBM), yeast (YEA), and barley (BAR)
Item
Treatment
SE
Statistics (P-value)
SBM YEA BAR Diet Day Diet × Day
Milk yield
Milk yield (kg/d) 30.8 30.0 29.7 0.45 0.20 <0.01 0.62
ECM1 (kg/d) 32.6 32.8 31.6 0.58 0.32 <0.01 0.66
Milk composition
Fat (g/kg) 43.7 45.1 44.2 0.81 0.45 <0.01 <0.01
Protein2 (g/kg) 36.0 36.2 34.9 0.47 0.10 0.01 0.37
Lactose (g/kg) 47.9 48.0 47.8 0.19 0.75 <0.01 0.66
MUN (mg/dL) 14.7 14.8 14.2 0.19 0.06 <0.01 0.13
Milk component yields
Fat (kg/d) 1.32 1.36 1.31 0.031 0.56 <0.01 0.32
Protein (kg/d) 1.09 1.09 1.04 0.024 0.23 <0.01 0.24
Lactose (kg/d) 1.48 1.44 1.42 0.022 0.26 <0.01 0.19
NUE328.4 28.5 29.5 0.49 0.24 <0.01 0.16
1ECM = milk yield (kg) × [(38.3 × fat (g/kg) + 24.2 × protein (g/kg) +16.54 × lactose (g/kg) +20.7)/3,140], according to Sjaunja et al. (1991).
2Milk true protein.
3NUE = gross dietary milk nitrogen efficiency (nitrogen secreted in milk as a percentage of nitrogen intake).
Journal of Dairy Science Vol. 105 No. 3, 2022
acids absorbed in the intestine (AAT) for milk syn-
thesis is mainly contributed by rumen microbial pro-
tein and rumen undegraded dietary protein absorbed
in the small intestine. These are, in turn, influenced
by both rate of protein degradation in the rumen and
rate of passage. As a result, differences in the rate of
degradation of different types of protein and rate of
passage from the rumen make it difficult to compare
the bypass protein level of different feeds given com-
parable dietary CP (Owens and Bergen, 1983). To our
knowledge, values of ruminal degradation and passage
rate of the yeast protein used here are unknown. Our
effort to compare ruminal degradation rates of the
yeast protein and soybean meal ingredients using an
in sacco technique (38-μm pore size; NorFor, 2011) was
not successful because of substantial particle loss (over
80% on DM basis) upon washing with the yeast pro-
tein. Sabbia et al. (2012) speculated that yeast-derived
microbial protein would flow with the liquid phase out
of the rumen, rendering it some degree of protection
due to a high rumen escape rate. This was observed
with a linear decrease in ruminal ammonia concentra-
tions with increasing yeast-derived microbial protein in
the diets. Our YEA concentrate feed created only nu-
merical difference compared with SBM on ruminal fluid
ammonia concentration, failing to support the above
hypothesis. However, Owens and Bergen (1983) argued
that plant proteins, including soybean, have a higher
degree of protein degradation in the rumen compared
with other protein sources with a high bypass fraction
(e.g., distillers products).
Lysine, methionine, and histidine have been identi-
fied most often as the limiting AA for milk production
(Schwab and Broderick, 2017). Which AA is the first
limiting depends on the feed protein source. Here, cal-
culated dietary intakes of total AAT and these 3 AA
fell within a narrow range for all groups, with Lys and
Met intake (percentage of AAT) close to milk yields
allowable by the achieved AAT intake (NRC, 2001).
Thus, the observed milk yield, milk protein content,
and protein yield from the SBM and YEA diets sug-
gested that the diets supplied comparable levels of AA
absorbed in the small intestine.
It has been reported that His could be the first
limiting AA for milk production when grass silage
constituted the main part of the diet with barley- and
oat-based concentrate feeds (Kim et al., 1999; Schwab
et al., 2005). This was more pronounced when rumen
microbial protein provided most of the MP supply to
the small intestine (Lee et al., 2012). However, the
observed numeric differences in milk and milk protein
yields between the dietary groups here were not as
large as expected. It has been reported that endogenous
reserves (e.g., carnosine, anserine, and hemoglobin) can
release His to sustain metabolic needs during periods of
deficiency (Clemens et al., 1984; Lapierre et al., 2008),
indicating some degree of phenotypic plasticity in His-
deficient diets. Therefore, it can be argued that, only
with an extended period of feeding, shortage of dietary
His in the BAR diet would have penalized milk protein
synthesis.
Furthermore, microbial protein supplies a large por-
tion of the AAT (Storm and Ørskov, 1983; Clark et
al., 1992), with an AA profile comparable to that of
milk. Thus, increasing the concentration of rumen-fer-
mentable carbohydrates would be expected to influence
microbial protein synthesis in the rumen (Meyer et al.,
1967) and improve milk production at a given dietary
CP intake (Broderick, 2003). Our barley-based diet had
higher starch but lower dietary CP content relative to
the SBM and YEA diets. Given the proportion of grass
silage in the total DMI and its high CP content (with
550 g of soluble CP per kg of CP), and the high starch
intake from barley in the concentrate feed, microbial
CP synthesis would be expected to be higher (Keady
et al., 1998; Cone and Becker, 2012) in the BAR group.
Therefore, even with the observed lower CP intake in
the group relative to the other 2 diets, it might be that
an increased microbial CP synthesis in the BAR could
have compensated for this. This could explain the ob-
served similar milk yield across treatments, in contrast
to our hypothesis.
In dairy cow feeding, dietary nitrogen intake, nitro-
gen secretion in milk, and excretion in manure regulate
environmental impacts. As a result, efforts are being
made to improve NUE and reduce nitrogen loss. About
25 to 35% of dietary nitrogen is captured and secreted
in milk (Broderick, 2003; Kidane et al., 2018). A large
part of the remaining nitrogen is lost in manure, which
is undesirable both in terms of cost and from the en-
vironment perspective. In our experiment, NUE was
numerically higher in the BAR group compared with
the others, but the absence of contrasting difference
among the dietary treatments could be explained by
the narrow range of dietary CP in the DMI.
CONCLUSIONS
Our results indicate that yeast can be used as a pro-
tein source in diets for early- to mid-lactation NRF
dairy cows, without negative effects on milk yield and
milk composition. Replacement of soybean meal and
yeast with barley, in combination with a grass silage
of good quality, showed a tendency for decreased milk
protein content. Further research on the long-term ef-
fects of these diets, in combination with varying silage
qualities, may be required to adequately describe ef-
fects on milk production and milk composition, without
Kidane et al.: CYBERLINDNERA JADINII YEAST PROTEIN IN DAIRY COW DIETS
Journal of Dairy Science Vol. 105 No. 3, 2022
interfering effects of metabolic plasticity in response to
changes in nutrient supply.
ACKNOWLEDGMENTS
We thank the staff members of the Livestock Produc-
tion Research Center of the Norwegian University of
Life Sciences (Ås, Norway) for their help in the trial.
We also thank the anonymous reviewers for the criti-
cal review, valuable comments, and suggestions, which
helped us improve the quality of this manuscript.
This work was part of the Foods of Norway Project
(Centre for Research-Based Innovation; grant number
237841), funded by the Research Council of Norway
(Norges Forskningsråd, Oslo). The concentrate feeds
were produced at Felleskjøpet Agri (Felleskjøpet Agri,
Lillestrøm, Norway). The authors have not stated any
conflicts of interest.
REFERENCES
Åby, B. A., J. Kantanen, L. Aass, and T. Meuwissen. 2014. Current
status of livestock production in the Nordic countries and future
challenges with a changing climate and human population growth.
Acta Agric. Scand. A Anim. Sci. 64:73–97. https: / / doi .org/ 10
.1080/ 09064702 .2014 .950321.
AACC. 2000. Approved Methods of the AACC. 10th ed. Am. Assoc.
Cereal Chem.
Broderick, G. A. 2003. Effects of varying dietary protein and energy
levels on the production of lactating dairy cows. J. Dairy Sci.
86:1370–1381. https: / / doi .org/ 10 .3168/ jds .S0022 -0302(03)73721
-7.
Cavins, J. F., W. F. Kwolek, G. E. Inglett, and J. C. Kowan. 1972.
Amino acid analysis of soybean meal: Interlaboratory study. J.
AOAC 55:686–691. https: / / doi .org/ 10 .1093/ jaoac/ 55 .4 .686.
Chalupa, W. 1977. Manipulating rumen fermentation. J. Anim. Sci.
45:585–599. https: / / doi .org/ 10 .2527/ jas1977 .453585x.
Clark, J. H., T. H. Klusmeyer, and M. R. Cameron. 1992. Microbial
protein synthesis and flows of nitrogen fractions to the duode-
num of dairy cows. J. Dairy Sci. 75:2304–2323. https: / / doi .org/ 10
.3168/ jds .S0022 -0302(92)77992 -2.
Clemens, R. A., J. D. Kopple, and M. E. Swendseid. 1984. Metabolic
effects of histidine-deficient diets fed to growing rats by gastric
tube. J. Nutr. 114:2138–2146. https: / / doi .org/ 10 .1093/ jn/ 114 .11
.2138.
Cone, J. W., and P. M. Becker. 2012. Fermentation kinetics and pro-
duction of volatile fatty acids and microbial protein by starchy
feedstuffs. Anim. Feed Sci. Technol. 172:34–41. https: / / doi .org/ 10
.1016/ j .anifeedsci .2011 .12 .006.
Cruz, A., H. Sterten, F. S. Steinhoff, L. T. Mydland, and M. Øverland.
2020a. Cyberlindnera jadinii yeast as a protein source for broiler
chickens: Effects on growth performance and digestive function
from hatching to 30 days of age. Poult. Sci. 99:3168–3178. https: /
/ doi .org/ 10 .1016/ j .psj .2020 .01 .023.
Cruz, A., A.-H. Tauson, C. F. Matthiesen, L. T. Mydland, and M.
Øverland. 2020b. Cyberlindnera jadinii yeast as a protein source
for growing pigs: Effects on protein and energy metabolism. Liv-
est. Sci. 231:103855. https: / / doi .org/ 10 .1016/ j .livsci .2019 .103855.
Cunningham, K. D., M. J. Cecava, T. R. Johnson, and P. A. Ludden.
1996. Influence of source and amount of dietary protein on milk
yield by cows in early lactation. J. Dairy Sci. 79:620–630. https: / /
doi .org/ 10 .3168/ jds .S0022 -0302(96)76407 -X.
Dalle Zotte, A., Y. Singh, J. Michiels, and M. Cullere. 2019. Black
soldier fly (Hermetia illucens) as dietary source for laying quails:
Live performance, and egg physico-chemical quality, sensory pro-
file and storage stability. Animals (Basel) 9:115. https: / / doi .org/
10 .3390/ ani9030115.
EC (European Communities). 2009. Commission regulation no.
152/2009 of 27 January 2009, laying down the methods of sam-
pling and analysis for the official control of feed. Off. J. Eur. Union
L 54:24–27.
Foley, J. A., N. Ramankutty, K. A. Brauman, E. S. Cassidy, J. S. Ger-
ber, M. Johnston, N. D. Mueller, C. O’Connell, D. K. Ray, P. C.
West, C. Balzer, E. M. Bennett, S. R. Carpenter, J. Hill, C. Mon-
freda, S. Polasky, J. Rockström, J. Sheehan, S. Siebert, D. Tilman,
and D. P. M. Zaks. 2011. Solutions for a cultivated planet. Nature
478:337–342. https: / / doi .org/ 10 .1038/ nature10452.
Geishauser, T., and A. Gitzel. 1996. A comparison of rumen fluid sam-
pled by oro-ruminal probe versus ruminal fistula. Small Rumin.
Res. 21:63–69. https: / / doi .org/ 10 .1016/ 0921 -4488(95)00810 -1.
Government of Norway. 2014. Forestry. Accessed Aug. 2, 2021. https:
/ / www .regjeringen .no/ en/ topics/ food -fisheries -and -agriculture/
skogbruk/ innsikt/ skogbruk/ id2009516/ .
Grünberg, W., and P. D. Constable. 2009. Chapter 6: Function and
dysfunction of the ruminant forestomach. Pages 12–19 in Food
Animal Practice, 5th ed. D. E. Anderson and D. M. Rings, ed. W.
B. Saunders.
Higginson, V., B. Baurhoo, Y. Schuermann, M. Taibi, A. St-Yves, R.
Duggavathi, and A. Mustafa. 2017. Effects of yeast-derived rumen
escape microbial protein on lactational performance and meta-
bolic status of transition dairy cows. J. Anim. Sci. 95(Suppl. 2):39.
https: / / doi .org/ 10 .2527/ asasmw .2017 .083.
Hristov, A. N., J. K. Ropp, K. L. Grandeen, S. Abedi, R. P. Etter, A.
Melgar, and A. E. Foley. 2005. Effect of carbohydrate source on
ammonia utilization in lactating dairy cows. J. Anim. Sci. 83:408–
421. https: / / doi .org/ 10 .2527/ 2005 .832408x.
Huhtanen, P., E. Detmann, and S. J. Krizsan. 2016. Prediction of ru-
men fiber pool in cattle from dietary, fecal, and animal variables. J.
Dairy Sci. 99:5345–5357. https: / / doi .org/ 10 .3168/ jds .2015 -10842.
Huhtanen, P., S. Jaakkola, and J. Nousiainen. 2013. An overview of
silage research in Finland: From ensiling innovation to advances
in dairy cow feeding. Agric. Food Sci. 22:35–56. https: / / doi .org/
10 .23986/ afsci .6632.
ISO. 2002. 5984: Animal feeding stuffs—Determination of crude ash.
International Organization for Standardization.
ISO. 1999. 6496: Animal feeding stuffs—Determination of moisture
and other volatile matter content. International Organization for
Standardization.
Keady, T. W. J., C. S. Mayne, and M. Marsden. 1998. The effects of
concentrate energy source on silage intake and animal performance
with lactating dairy cows offered a range of grass silages. Anim.
Sci. 66:21–33. https: / / doi .org/ 10 .1017/ S1357729800008808.
Kidane, A., M. Øverland, L. T. Mydland, and E. Prestløkken. 2018.
Interaction between feed use efficiency and level of dietary crude
protein on enteric methane emission and apparent nitrogen use
efficiency with Norwegian Red dairy cows. J. Anim. Sci. 96:3967–
3982. https: / / doi .org/ 10 .1093/ jas/ sky256.
Kim, C.-H., J.-J. Choung, and D. G. Chamberlain. 1999. Determina-
tion of the first-limiting amino acid for milk production in dairy
cows consuming a diet of grass silage and a cereal-based supplement
containing feather meal. J. Sci. Food Agric. 79:1703–1708. https:
/ / doi .org/ 10 .1002/ (SICI)1097 -0010(199909)79: 12<1703::AID-
JSFA424>3.0.CO;2-5.
Lapierre, H., D. Ouellet, L. Doepel, G. Holtrop, and G. Lobley. 2008.
Histidine, lysine and methionine: From metabolism to balanced
dairy rations. Pages 14–31 in Proc. Eastern Nutrition Conference
of Animal Nutrition Association of Canada. University of Guelph.
Law, R. A., F. J. Young, D. C. Patterson, D. J. Kilpatrick, A. R.
Wylie, and C. S. Mayne. 2009. Effect of dietary protein content
on animal production and blood metabolites of dairy cows during
lactation. J. Dairy Sci. 92:1001–1012. https: / / doi .org/ 10 .3168/ jds
.2008 -1155.
Lee, C., A. N. Hristov, K. S. Heyler, T. W. Cassidy, H. Lapierre, G. A.
Varga, and C. Parys. 2012. Effects of metabolizable protein sup-
ply and amino acid supplementation on nitrogen utilization, milk
Kidane et al.: CYBERLINDNERA JADINII YEAST PROTEIN IN DAIRY COW DIETS
Journal of Dairy Science Vol. 105 No. 3, 2022
production, and ammonia emissions from manure in dairy cows. J.
Dairy Sci. 95:5253–5268. https: / / doi .org/ 10 .3168/ jds .2012 -5366.
Leonardi, C., M. Stevenson, and L. E. Armentano. 2003. Effect of two
levels of crude protein and methionine supplementation on perfor-
mance of dairy cows. J. Dairy Sci. 86:4033–4042. https: / / doi .org/
10 .3168/ jds .S0022 -0302(03)74014 -4.
Manthey, A. K., K. F. Kalscheur, A. D. Garcia, and K. Mjoun. 2016.
Lactation performance of dairy cows fed yeast-derived microbial
protein in low- and high-forage diets. J. Dairy Sci. 99:2775–2787.
https: / / doi .org/ 10 .3168/ jds .2015 -10014.
Mertens, D. R. 2002. Gravimetric determination of amylase-treated
neutral detergent fiber in feeds with refluxing in beakers or cru-
cibles: Collaborative study. J. AOAC Int. 85:1217–1240.
Meyer, R. M., E. E. Bartley, C. W. Deyoe, and V. F. Colenbrander.
1967. Feed processing. I. Ration effects on rumen microbial protein
synthesis and amino acid composition. J. Dairy Sci. 50:1327–1332.
https: / / doi .org/ 10 .3168/ jds .S0022 -0302(67)87624 -0.
Morvay, Y., A. Bannink, J. France, E. Kebreab, and J. Dijkstra. 2011.
Evaluation of models to predict the stoichiometry of volatile fatty
acid profiles in rumen fluid of lactating Holstein cows. J. Dairy Sci.
94:3063–3080. https: / / doi .org/ 10 .3168/ jds .2010 -3995.
Mullins, I. L., C. M. Truman, M. R. Campler, J. M. Bewley, and J. H.
C. Costa. 2019. Validation of a commercial automated body condi-
tion scoring system on a commercial dairy farm. Animals (Basel)
9:287. https: / / doi .org/ 10 .3390/ ani9060287.
Neal, K., J. S. Eun, A. J. Young, K. Mjoun, and J. O. Hall. 2014.
Feeding protein supplements in alfalfa hay-based lactation diets
improves nutrient utilization, lactational performance, and feed
efficiency of dairy cows. J. Dairy Sci. 97:7716–7728. https: / / doi
.org/ 10 .3168/ jds .2014 -8033.
Nikkhah, A. 2012. Barley grain for ruminants: A global treasure or
tragedy. J. Anim. Sci. Biotechnol. 3:22. https: / / doi .org/ 10 .1186/
2049 -1891 -3 -22.
NorFor. 2011. NorFor: The Nordic Feed Evaluation System. H. Volden,
ed. EAAP Scientific Series, No. 30. Wageningen Academic Pub-
lishers.
Notarnicola, B., G. Tassielli, P. A. Renzulli, V. Castellani, and S. Sala.
2017. Environmental impacts of food consumption in Europe. J.
Clean. Prod. 140:753–765. https: / / doi .org/ 10 .1016/ j .jclepro .2016
.06 .080.
NRC. 2001. Nutrient Requirements of Dairy Cattle. 7th ed. National
Academy Press.
Øverland, M., and A. Skrede. 2017. Yeast derived from lignocellulosic
biomass as a sustainable feed resource for use in aquaculture. J.
Sci. Food Agric. 97:733–742. https: / / doi .org/ 10 .1002/ jsfa .8007.
Owens, F. N., and W. G. Bergen. 1983. Nitrogen metabolism of ru-
minant animals: historical perspective, current understanding and
future implications. J. Anim. Sci. 57(Suppl. 2):498–518. https: / /
doi .org/ 10 .2527/ animalsci1983 .57Supplement _2498x.
Palmonari, A., D. M. Stevenson, D. R. Mertens, C. W. Cruywagen,
and P. J. Weimer. 2010. pH dynamics and bacterial community
composition in the rumen of lactating dairy cows. J. Dairy Sci.
93:279–287. https: / / doi .org/ 10 .3168/ jds .2009 -2207.
Randby, Å. T., H. N. Halvorsen, and A. K. Bakken. 2020. Losses and
grass silage quality in bunker silos compacted by tractor versus
wheel loader. Anim. Feed Sci. Technol. 266:114523. https: / / doi
.org/ 10 .1016/ j .anifeedsci .2020 .114523.
Randby, Å. T., P. Nørgaard, and M. R. Weisbjerg. 2010. Effect of
increasing plant maturity in timothy-dominated grass silage on
the performance of growing/finishing Norwegian Red bulls. Grass
Forage Sci. 65:273–286. https: / / doi .org/ 10 .1111/ j .1365 -2494 .2010
.00745 .x.
Raun, N. S., and W. Burroughs. 1962. Suction strainer technique in
obtaining rumen fluid samples from intact lambs. J. Anim. Sci.
21:454–457. https: / / doi .org/ 10 .2527/ jas1962 .213454x.
Sabbia, J. A., K. F. Kalscheur, A. D. Garcia, A. M. Gehman, and
J. M. Tricarico. 2012. Soybean meal substitution with a yeast-
derived microbial protein source in dairy cow diets. J. Dairy Sci.
95:5888–5900. https: / / doi .org/ 10 .3168/ jds .2011 -5237.
Schwab, C. G., and G. A. Broderick. 2017. A 100-Year Review: Protein
and amino acid nutrition in dairy cows. J. Dairy Sci. 100:10094–
10112. https: / / doi .org/ 10 .3168/ jds .2017 -13320.
Schwab, C. G., P. Huhtanen, C. Hunt, and T. Hvelplund. 2005. Nitro-
gen requirements of cattle. In Nitrogen and Phosphorus Nutrition
of Cattle: Reducing the Environmental Impact of Cattle Opera-
tions. E. Pfeffer and A. N. Hristov, ed. CABI Publishing.
Sharma, S., L. Hansen, J. Ø. Hansen, L. Mydland, S. Horn, M. Øver-
land, V. Eijsink, and K. Vuoristo. 2018. Microbial protein pro-
duced from brown seaweed and spruce wood as a feed ingredient.
J. Agric. Food Chem. 66:8328–8335. https: / / doi .org/ 10 .1021/ acs
.jafc .8b01835.
Shen, J. S., Z. Chai, L. J. Song, J. X. Liu, and Y. M. Wu. 2012. In-
sertion depth of oral stomach tubes may affect the fermentation
parameters of ruminal fluid collected in dairy cows. J. Dairy Sci.
95:5978–5984. https: / / doi .org/ 10 .3168/ jds .2012 -5499.
Sjaunja, L. O., L. Baevre, L. Junkarinen, J. Pedersen, and J. Setälä.
1991. A Nordic proposal for an energy corrected milk (ECM) for-
mula. Pages 156–157 in Performance Recording of Animals: State
of the Art, 1990: Proceedings of the 27th Biennial Session of the
International Committee for Animal Recording (ICAR), Paris,
France. Wageningen Academic Publishers.
Solberg, B., A. Moiseyev, J. Ø. Hansen, S. J. Horn, and M. Øverland.
2021. Wood for food: Economic impacts of sustainable use of forest
biomass for salmon feed production in Norway. For. Policy Econ.
122:102337. https: / / doi .org/ 10 .1016/ j .forpol .2020 .102337.
Storm, E., and E. Ørskov. 1983. The nutritive value of rumen micro-
organisms in ruminants: 1. Large-scale isolation and chemical com-
position of rumen micro-organisms. Br. J. Nutr. 50:463–470. https:
/ / doi .org/ 10 .1079/ BJN19830114.
Sutton, J. D., M. S. Dhanoa, S. V. Morant, J. France, D. J. Napper,
and E. Schuller. 2003. Rates of production of acetate, propionate,
and butyrate in the rumen of lactating dairy cows given normal
and low-roughage diets. J. Dairy Sci. 86:3620–3633. https: / / doi
.org/ 10 .3168/ jds .S0022 -0302(03)73968 -X.
Thiex, N. J., H. Manson, S. Anderson, J.-Å. Persson, S. Anderson,
E. Bogren, G. Bolek, D. Budde, C. Ellis, S. Eriksson, G. Field,
E. Frankenius, C. Henderson, C. Henry, M. Kapphahn, L. Lun-
dberg, H. Manson, J. Moller, M. Russell, J. Sefert-Schwind, and
M. Spann. 2002. Determination of crude protein in animal feed,
forage, grain, and oilseeds by using block digestion with a copper
catalyst and steam distillation into boric acid: Collaborative study.
J. AOAC Int. 85:309–317. https: / / doi .org/ 10 .1093/ jaoac/ 85 .2 .309.
van Gastelen, S., F. Schumacher, J. W. Cone, J. Dijkstra, and W. F.
Pellikaan. 2019. In dairy cattle, the stomach tube method is not a
feasible alternative to the rumen cannulation method to examine
in vitro gas and methane production. Anim. Feed Sci. Technol.
256:114259. https: / / doi .org/ 10 .1016/ j .anifeedsci .2019 .114259.
ORCIDS
Alemayehu Kidane https: / / orcid .org/ 0000 -0003 -2945 -7189
Stine Gregersen Vhile https: / / orcid .org/ 0000 -0003 -1104 -2647
Sabine Ferneborg https: / / orcid .org/ 0000 -0002 -9218 -9407
Siv Skeie https: / / orcid .org/ 0000 -0003 -1928 -4085
Martine Andrea Olsen https: / / orcid .org/ 0000 -0003 -0293 -7027
Liv Torunn Mydland https: / / orcid .org/ 0000 -0002 -9361 -3687
Margareth Øverland https: / / orcid .org/ 0000 -0003 -1142 -6624
Egil Prestløkken https: / / orcid .org/ 0000 -0003 -3151 -6782
Kidane et al.: CYBERLINDNERA JADINII YEAST PROTEIN IN DAIRY COW DIETS
