ArticlePDF Available

Technical Note: Validation of a System for Monitoring Individual Feeding and Drinking Behavior and Intake in Group-Housed Cattle

Authors:

Abstract and Figures

The objective of this study was to validate a system for monitoring individual feeding and drinking behavior and intake in group-housed cattle. A total of 42 Holstein cows were tested with access to 24 feed bins and 4 water bins. For the purposes of this validation experiment, we focused our observations on 4 water bins and 13 feed bins. When the cow approached the feed or water bin, an antenna detected the cow's unique passive transponder and lowered the barrier, allowing the cow access to the feed or water. For each visit to the bin, the system recorded the cow number, bin number, initial and final times and weight and calculated the visit duration and intake. Bins were also monitored by direct observation and time-lapse video recording for 2 d per bin, with observations for 4 and 6 h/d for the feed and water bins, respectively. Data from direct observations were compared with the electronic data recorded by the system. Feed disappearance over 24 h was assessed by using an external scale over 3 consecutive 24-h periods, and these values were compared with the sum of intakes across all visits to that bin for the same time periods. The system showed a high specificity (100%) and sensitivity (100 and 99.76% for the feed and water bins, respectively) for cow identification. The duration of the feeding and drinking visits and the feed and water intake per visit, as estimated by the monitoring system, were highly correlated with those obtained by direct observation (R(2) >/= 0.99 in all the cases). The comparison of the total feed that disappeared from each bin in 24 h with the sum of the feed cows consumed from that bin during the same period differed by less than 1 kg (29.92 +/- 0.90 kg and 29.24 +/- 0.90 kg as estimated by manual weighing and by the electronic system, respectively). This difference could be attributed to changes in feed moisture during the 24-h period. In conclusion, this electronic system is a useful tool for monitoring intakes and feeding and drinking behavior of loose-housed cows.
Content may be subject to copyright.
J. Dairy Sci. 90:5732–5736
doi:10.3168/jds.2007-0331
©American Dairy Science Association, 2007.
Technical Note: Validation of a System for Monitoring Individual Feeding
and Drinking Behavior and Intake in Group-Housed Cattle
N. Chapinal,* D. M. Veira,† D. M. Weary,* and M. A. G. von Keyserlingk*
1
*Animal Welfare Program, The University of British Columbia, 2357 Main Mall, Vancouver V6T 1Z4, Canada
†Pacific Agri-Food Research Centre, Agriculture and Agri-Food Canada, PO Box 1000, Agassiz, British Columbia V0M 1A0, Canada
ABSTRACT
The objective of this study was to validate a system
for monitoring individual feeding and drinking behav-
ior and intake in group-housed cattle. A total of 42
Holstein cows were tested with access to 24 feed bins
and 4 water bins. For the purposes of this validation
experiment, we focused our observations on 4 water
bins and 13 feed bins. When the cow approached the
feed or water bin, an antenna detected the cow’s
unique passive transponder and lowered the barrier,
allowing the cow access to the feed or water. For each
visit to the bin, the system recorded the cow number,
bin number, initial and final times and weight and
calculated the visit duration and intake. Bins were
also monitored by direct observation and time-lapse
video recording for 2 d per bin, with observations for
4 and 6 h/d for the feed and water bins, respectively.
Data from direct observations were compared with the
electronic data recorded by the system. Feed disap-
pearance over 24 h was assessed by using an external
scale over 3 consecutive 24-h periods, and these values
were compared with the sum of intakes across all visits
to that bin for the same time periods. The system
showed a high specificity (100%) and sensitivity (100
and 99.76% for the feed and water bins, respectively)
for cow identification. The duration of the feeding and
drinking visits and the feed and water intake per visit,
as estimated by the monitoring system, were highly
correlated with those obtained by direct observation
(R
2
0.99 in all the cases). The comparison of the total
feed that disappeared from each bin in 24 h with the
sum of the feed cows consumed from that bin during
the same period differed by less than 1 kg (29.92 ±
0.90 kg and 29.24 ±0.90 kg as estimated by manual
weighing and by the electronic system, respectively).
This difference could be attributed to changes in feed
moisture during the 24-h period. In conclusion, this
Received May 2, 2007.
Accepted August 19, 2007.
1
Corresponding author: nina@interchange.ubc.ca
5732
electronic system is a useful tool for monitoring in-
takes and feeding and drinking behavior of loose-
housed cows.
Key words: feeding behavior, dairy cow, validation
Traditional nutrition research in dairy cattle has
focused on assessing the relationship between DMI
and production (e.g., Grant and Albright, 1995). The
majority of this research has been with animals
housed in tie stalls (e.g., Mulligan et al., 2002) or group
housed but trained to access a particular feed bin so
that each animal’s daily feed intake can be monitored
(e.g., Daniels et al., 2006). These methods for collecting
individual intake may not accurately reflect the in-
takes of cows housed in groups, where they need to
compete for access to food (DeVries and von Keyser-
lingk, 2006; Huzzey et al., 2006).
Feeding behavior is often thought to provide some
indication of how much cows are eating (e.g., Murphy,
1992; Nielsen, 1999), and drinking behavior may also
be a useful predictor of both water and feed intake.
Unfortunately, monitoring feeding and drinking be-
havior in loose-housed cows is also difficult. Tradition-
ally, these behaviors have been monitored through
direct observation or time-lapse video recording
(Friend et al., 1977; Vasilatos and Wangsness, 1980;
Huzzey et al., 2005), both of which are labor intensive.
More recently, electronic systems for monitoring feed-
ing behavior have been developed and validated (e.g.,
DeVries et al., 2003).
The Insentec monitoring system (Insentec, Mark-
nesse, the Netherlands) provides measures of feed and
water intake (e.g., Tolkamp et al., 2000; Huzzey et
al., 2007) and allows researchers to collect continuous
feeding and drinking behavioral data for loose-housed
cows that can freely access a number of feeding and
drinking stations. However, to date no data have been
published that validate either the behavioral mea-
sures or intakes from this system. Thus, the objective
of this study was to validate these measures by com-
paring estimates collected electronically with those
from direct human observation and time-lapse video
recordings.
TECHNICAL NOTE: MONITORING SYSTEM FOR GROUP-HOUSED CATTLE 5733
A total of 42 Holstein cows at The University of
British Columbia Dairy Education and Research Cen-
ter (Agassiz, British Columbia, Canada) were tested
with access to 24 feed bins and 4 water bins. Each
individual feed bin was 1.00 m wide, 0.75 m high, and
had a depth of 0.84 m. Each feed bin was assigned to
either a single cow or multiple cows. Feed bins could
hold approximately 40 kg as-fed TMR. Individual wa-
ter bins were identical in size to that described above,
and all cows had access to the water bin. The water
bins were programmed to hold 40 kg of water and were
refilled after each visit, provided cows had drunk at
least 1 kg. Each cow was fitted with an ear tag con-
taining a unique passive transponder (High-Perfor-
mance ISO Half Duplex Electronic ID Tag, Allflex Can-
ada, St-Hyacinthe, Quebec, Canada). When the cow
approached the feed or water bin, an antenna detected
the cow’s transponder and lowered the barrier,
allowing the cow access to the feed or water. When
the cow finished eating or drinking and left the bin,
the barrier would close until the next cow approached.
For each visit to the bin, the system recorded the cow
number, the bin number, the initial and final times
and weight and calculated the duration and intake.
For the purposes of this validation experiment, we
focused our observations on 4 water bins and 13 feed
bins. During the course of the experiment, prepartum
cows were fed a close-up TMR consisting of 21.3% corn
silage, 42.8% alfalfa hay, and 35.9% concentrate and
mineral mix on a DM basis (DM: 50.8 ±1.19%; CP:
14.4 ±1.01% of DM; ADF: 35.0 ±2.74% of DM; NDF:
45.6 ±2.57% of DM; and NE
L
: 1.40 Mcal/kg). Lactating
cows were fed a TMR consisting of 21.3% grass silage,
14.7% corn silage, 12.3% alfalfa hay, and 51.7% con-
centrate and mineral mix on a DM basis (DM: 51.1 ±
1.82%; CP: 17.7 ±1.00% of DM; ADF: 23.7 ±1.42% of
DM; NDF: 36.1 ±1.82% of DM; and NE
L
: 1.66 Mcal/
kg). Fresh feed was provided at 0600 and 1600 h. Video
cameras (Panasonic WV-BP330, Panasonic, Osaka,
Japan) monitored the feed bins and water bins, and
were connected to a video multiplexer (Panasonic WJ-
Table 1. Least squares means ±SE, maximum and minimum value for feeding and drinking visit duration (s), and intake per visit (kg)
estimated by electronic and direct observations and regression equation and coefficient between the 2 recording methods, for a total of 665
visits to 13 feed bins and 268 visits to 4 water bins
Electronic observations Direct observations Regression
1
Item LSM Minimum Maximum LSM Minimum Maximum Equation R
2
Feeding visits
Duration, s 222.5 ±9.88 1.0 1,722.0 223.6 ±9.88 1.0 1,725.0 y = x 1.07 1
Intake, kg 0.8 ±0.04 0.1 6.6 0.8 ±0.04 0.0 6.6 y = x 1
Drinking visits
Duration, s 78.3 ±3.45 2.0 269.0 79.7 ±3.45 4 270.0 y = x 1.35 1
Intake, kg 5.6 ±0.22 0.1 25.8 5.5 ±0.22 0.2 25.8 y = 0.99x + 0.15 0.99
1
x = direct observations; y = electronic observations.
Journal of Dairy Science Vol. 90 No. 12, 2007
FS216) and a time-lapse videocassette recorder (Pana-
sonic AG-6540). The clocks on the video recorder and
the computer collecting the data were synchronized.
Cows were individually identified with ear tags and
with symbols dyed onto both sides of their bodies.
Trained observers monitored 2 bins/d per person,
beginning immediately after the delivery of fresh feed
and continuing for 4 h for feed bins and 6 h for water
bins. In this way, each of the bins was followed for a
total of 2 d. For each visit, the observer recorded the
cow’s ear tag. The observer also recorded the weight
of water or feed consumed by using the digital display
of the bin. The time that the cow entered and left the
bin was assessed by using the video recordings. The
start and end times of the visit were assessed by using
the opening and closing of the feed barrier.
Feed disappearance over 24 h was assessed manu-
ally during 3 consecutive 24-h periods by using a digi-
tal scale and subtracting the weight of any orts re-
maining in the bin the next morning from the amount
of feed provided the previous day. This value was then
compared with the sum of feed disappearances across
all the visits to that bin for the same time period.
For all measures, we calculated sensitivity (likeli-
hood that a cow present at the bin is detected as pres-
ent by the monitoring system) and specificity (likeli-
hood that a cow that is absent from the bin is detected
as absent by the monitoring system). All the statistical
analyses were conducted with SAS software (SAS In-
stitute, 1999). Measures of duration and feed and wa-
ter intake per visit generated by the electronic moni-
toring system (dependent variables) were regressed
onto those from direct observation (independent vari-
ables), testing for slope and intercept effects. Feed
disappearance assessed manually was compared with
the sum of feed disappearances across all the visits
recorded by the electronic monitoring system by using
a mixed model, where the method of obtaining the
data (manual vs. electronic) was treated as a fixed
effect and the bin as a random effect.
CHAPINAL ET AL.5734
During the observation periods, both the electronic
system and direct observation detected a total of 819
visits to the feed bins and 274 visits to the water bins.
However, 2 of the visits to the water bins were un-
usual: on both occasions one cow was displaced by
another at the bin, without the barrier raising and
dropping between cows. The intruding cow was pushed
out of the bin when the barrier raised after 5 and 12
s, respectively, but in neither case was the identity of
the intruding cow recorded by the monitoring system.
Instead, a single visit was recorded by the electronic
system, with the combined visit duration and intake
allocated only to the initial cow. Therefore, sensitivity
was 100% for the feed bins but 99.76% for the water
bins, and specificity was 100% for both the feed and
water bins. Other systems that monitor visits to the
Figure 1. Relationship between the feed (A) and water (B) intake per visit (kg) obtained from the electronic observations and the direct
observations for a total of 665 visits to 13 feed bins and 268 visits to 4 water bins.
Journal of Dairy Science Vol. 90 No. 12, 2007
feeder have recorded slightly lower values. DeVries
et al. (2003) described an electronic feeding behavior
system that monitored cow presence at the feeding
area every 6 s, and reported a sensitivity and specific-
ity of 87.7 and 99.2%, respectively. Bach et al. (2004)
described a computerized system for monitoring feed-
ing behavior and individual feed intake consisting of
load cells located in front of individual self-locking
stations, and reported a sensitivity and specificity of
99.6 and 98.8%, respectively.
The electronic system provided measures of feeding
and drinking behavior and feed and water intake that
were similar to those recorded by direct observation
(Table 1). For these analyses, we discarded data from
1 d for 4 feed bins because of problems with the video
recordings. Our final data set included 665 feeding
TECHNICAL NOTE: MONITORING SYSTEM FOR GROUP-HOUSED CATTLE 5735
visits and 268 drinking visits for which we had elec-
tronic and direct observations. The resulting coeffi-
cients of determination were significant (P<0.001),
and slopes did not differ from 1 (P>0.2) for all mea-
sures tested. The intercept was greater than 0 (P<
0.05) for all measures except the feed intake per visit.
In all cases, the direct observations of visit duration
were close to 1 s longer than the value recorded by the
electronic monitoring system. The electronic system
defined the start and end of the visit on the basis of
the transponder coming within range of the antennae
located at the top of the feeder. In contrast, visit dura-
tions during direct observations were assessed by us-
ing opening and closing times of the feed barrier. There
was a slight delay between the time a cow withdrew
her head from the feeder and the barrier closing, ac-
counting for the slightly longer visit duration assessed
by direct observation. These differences were smaller
than that found by Bach et al. (2004), and the regres-
sion coefficient was slightly higher than that calcu-
lated for the meal duration (not calculated in this
study) by DeVries et al. (2003).
In one visit to the feed bins and 5 visits to the water
bins, negative values were recorded for intake by the
electronic monitoring system. These negative values
were always small (0.1 kg), and were likely due to
slight instabilities in the scales, because in all the
cases but one visit to the water bin, intakes were re-
corded as 0 by the direct method. The higher frequency
of negative values for the water bins may have been
due to wave motion within the bin. The concordance
between estimates of intake from the direct and auto-
mated methods was lowest when water intakes were
low (R
2
= 0.27, 0.49, 0.93, and 1.00 for the first to
fourth quartile intakes). The few outlying values in
water intake (Figure 1) were likely due to human error
during direct observation. The water bins automati-
cally refilled after visits with intakes greater than 1
kg, making observations on these bins more difficult
than those on the feed bins. During observations, only
one cow was observed pushing feed out of the bin and
onto the floor, suggesting that this is not a frequent
source of error in estimates of intake from these bins.
The comparison of the total feed that disappeared
in 24 h with the sum of bin visit intakes recorded
electronically over the same period differed by 0.68 kg
(29.92 ±0.90 kg and 29.24 ±0.90 kg as estimated by
manual weighing and by the electronic system, respec-
tively). This difference could be partially attributed to
changes in feed moisture during the 24-h period. In
any case, the visits were short enough that intake
per visit recorded by the monitoring system was not
affected by moisture changes. Thus, the electronic re-
cording of intake on a per-visit basis is likely a more
Journal of Dairy Science Vol. 90 No. 12, 2007
accurate measure of wet feed intakes, although DMI
may be better estimated by using 24-h feed disap-
pearance.
In conclusion, the electronic system described
herein is a useful tool for monitoring intakes and feed-
ing and drinking behavior of loose-housed cows. The
system provides reliable estimates of the number of
visits per animal, duration of each visit, intake per
visit, and therefore feeding and drinking rate, total
time spent in the feed and water bins daily per animal,
and total amount of feed and water consumed daily
per animal.
ACKNOWLEDGMENTS
We gratefully acknowledge the staff and students at
The University of British Columbia’s Dairy Education
and Research Centre and the University’s Animal
Welfare Program, and especially Audrey Nadalin and
Gabi Krisinger for their help with the live observa-
tions. The equipment described in this study was pur-
chased, in part, by a grant awarded to M. A. G. von
Keyserlingk by the Canadian Foundation for Innova-
tion and the British Columbia Knowledge Develop-
ment Fund. The project was funded by the Natural
Sciences and Engineering Research Council of Can-
ada, through the Industrial Research Chair in Animal
Welfare, and by contributions from the Dairy Farmers
of Canada, the British Columbia Dairy Foundation,
members of the British Columbia Veterinary Medical
Association, and many other donors listed on the Ani-
mal Welfare Web site at http://www.landfood.ubc.ca/
animalwelfare.
REFERENCES
Bach, A., C. Iglesias, and I. Busto. 2004. A computerized system
for monitoring feeding behavior and individual feed intake of
dairy cattle. J. Dairy Sci. 87:4207–4209.
Daniels, K. M., K. E. Webb Jr., M. L. McGilliard, M. J. Meyer, M.
E. Van Amburgh, and R. M. Akers. 2006. Effects of body weight
and nutrition on mammary protein expression profiles in Hol-
stein heifers. J. Dairy Sci. 89:4276–4288.
DeVries, T. J., and M. A. G. von Keyserlingk. 2006. Feed stalls
affect the social and feeding behavior of lactating dairy cows.
J. Dairy Sci. 89:3522–3531.
DeVries, T. J., M. A. G. von Keyserlingk, D. M. Weary, and K. A.
Beauchemin. 2003. Validation of a system for monitoring feeding
behavior of dairy cows. J. Dairy Sci. 86:3571–3574.
Friend, T. H., C. E. Polan, and M. L. McGilliard. 1977. Free stall
and feed bunk requirements relative to behavior, production and
individual feed intake in dairy cows. J. Dairy Sci. 60:108–116.
Grant, R. J., and J. L. Albright. 1995. Feeding behavior and manage-
ment factors during the transition period in dairy cattle. J.
Anim. Sci. 73:2791–2803.
Huzzey, J. M., T. J. DeVries, P. Valois, and M. A. G. von Keyserlingk.
2006. Stocking density and feed barrier design affect the feeding
and social behavior of dairy cattle. J. Dairy Sci. 89:126–133.
Huzzey, J. M., D. M. Veira, D. M. Weary, and M. A. G. von Keyser-
lingk. 2007. Prepartum behavior and dry matter intake identify
dairy cows at risk for metritis. J. Dairy Sci. 90:3220–3233.
CHAPINAL ET AL.5736
Huzzey, J. M., M. A. G. von Keyserlingk, and D. M. Weary. 2005.
Changes in feeding, drinking, and standing behavior of dairy
cows during the transition period. J. Dairy Sci. 88:2454–2461.
Mulligan, F. J., P. J. Caffrey, M. Rath, J. J. Callan, P. O. Brophy,
and F. P. O’Mara. 2002. An investigation of feeding level effects
on digestibility in cattle for diets based on grass silage and high
fibre concentrates at two forage:concentrate ratios. Livest. Prod.
Sci. 77:311–323.
Murphy, M. R. 1992. Symposium: Nutritional factors affecting ani-
mal water and waste quality. Water metabolism of dairy cows.
J. Dairy Sci. 75:326–333.
Journal of Dairy Science Vol. 90 No. 12, 2007
Nielsen, B. L. 1999. On the interpretation of feeding behaviour
measures and the use of feeding rate as an indicator of social
constraint. Appl. Anim. Behav. 63:79–91.
SAS Institute. 1999. SAS User’s Guide. SAS Institute Inc., Cary,
NC.
Tolkamp, B. J., D. P. N. Schweitzer, and I. Kyriazakis. 2000. The
biologically relevant unit for the analysis of short-term feeding
behavior of dairy cows. J. Dairy Sci. 83:2057–2068.
Vasilatos, R., and P. J. Wangsness. 1980. Feeding behavior of lactat-
ing dairy cows as measured by time-lapse photography. J. Dairy
Sci. 63:412–416.
... Several companies have developed systems for this purpose, including the Calan Broadbent Feeding System, the Controlling and Recording Feed Intake System, the GrowSafe System (recently acquired by Vytelle, a precision livestock company), Intergado Efficiency, and the Roughage Intake Control System. Many researchers have also evaluated the efficiency of these systems [71][72][73][74][75][76][77][78]. Nevertheless, these systems are rarely extensively used in commercial operations due to their exorbitant cost and the substantial cleaning and maintenance requirements. ...
Article
Full-text available
Increasing feed efficiency in beef cattle is critical for meeting the growing global demand for beef while managing rising feed costs and environmental impacts. Challenges in recording feed intake and combining genomic and nutritional models hinder improvements in feed efficiency for sustainable beef production. This review examines the progression from traditional data collection methods to modern genetic and nutritional approaches that enhance feed efficiency. We first discuss the technological advancements that allow precise measurement of individual feed intake and efficiency, providing valuable insights for research and industry. The role of genomic selection in identifying and breeding feed-efficient animals is then explored, emphasizing the benefits of combining data from multiple populations to enhance genomic prediction accuracy. Additionally, the paper highlights the importance of nutritional models that could be used synergistically with genomic selection. Together, these tools allow for optimized feed management in diverse production systems. Combining these approaches also provides a roadmap for reducing input costs and promoting a more sustainable beef industry.
... Several companies have developed systems for this purpose, including the Calan Broadbent Feeding System, Controlling and Recording Feed Intake System, GrowSafe System (recently acquired by Vytelle, a precision livestock company), Intergado Efficiency, and Roughage Intake Control system. Many researchers have also evaluated the efficiency of these systems [73][74][75][76][77][78][79][80]. Nevertheless, these systems are rarely extensively used in commercial operations due to their exorbitant cost and the substantial cleaning and maintenance requirements. ...
Preprint
Full-text available
Increasing feed efficiency in beef cattle is critical for meeting the growing global demand for beef while managing rising feed costs and environmental impacts. This review examines the progression from traditional data collection methods to modern genetic and nutritional approaches that enhance feed efficiency. We first discuss the technological advancements that allow precise measurement of individual feed intake and efficiency, providing valuable insights for research and industry. The role of genomic selection in identifying and breeding feed-efficient animals is then explored, emphasizing the benefits of integrating multi-population data to improve prediction accuracy. Additionally, the paper highlights the importance of nutritional models that could be used synergistically with genomic selection, allowing for optimized feed management in diverse production systems. Combining these approaches provides a roadmap for reducing input costs and promoting a more sustainable beef industry.
... The electronic bins (validated by Chapinal et al., 2007) were only accessible by one cow at a time and opened after reading the cow's radio frequency identification ear tag when it came within the read range of the reader positioned at the feed bin gate. For each visit, the software associated with the electronic bin system recorded the cow ID, date, start time, end time, and duration of the visit, and the weight of water in the bin at the beginning and the end of the visit. ...
... The automated feed bins continuously measured and recorded the weight of PMR at the start and end of each bin visit (as validated by Chapinal et al., 2007), such that the number of bin visits, feeding time, feeding rate, and PMR DMI for each cow could be calculated. The PMR DMI for each bin visit was determined by multiplying the as-fed intake at each bin visit by the weekly average DM% of the diet, as determined by DM analysis of fresh PMR feed samples. ...
Article
Full-text available
Livestock feeding behaviour is an influential research area in animal husbandry and agriculture. In recent years, there has been a growing interest in automated systems for monitoring the behaviour of ruminants. Current automated monitoring systems mainly use motion, acoustic, pressure and image sensors to collect and analyse patterns related to ingestive behaviour, foraging activities and daily intake. The performance evaluation of existing methods is a complex task and direct comparisons between studies is difficult. Several factors prevent a direct comparison, starting from the diversity of data and performance metrics used in the experiments. This review on the analysis of the feeding behaviour of ruminants emphasise the relationship between sensing methodologies, signal processing, and computational intelligence methods. It assesses the main sensing methodologies and the main techniques to analyse the signals associated with feeding behaviour, evaluating their use in different settings and situations. It also highlights the potential of the valuable information provided by automated monitoring systems to expand knowledge in the field, positively impacting production systems and research. The paper closes by discussing future engineering challenges and opportunities in livestock feeding behaviour monitoring.
Article
Full-text available
To determine whether visits or meals are the most biologically relevant unit of short-term feeding behavior, we analyzed 209,025 records of visits to feeders by 37 cows. Two feeds were used that differed in protein content. Cows were divided into control groups for the low and high protein feeds and a choice group that had access to both. Daily number of visits and intake per visit were very variable. Cows fed low protein feed had lowest daily intakes, but single-visit characteristics were poorly correlated with daily intake. The probability of cows ending a visit did not change greatly with visit length. Log-normal models were used to estimate individual meal criteria (44.7+/-2.1 min), and visits were grouped into meals. Meal duration (36.9+/-1.3 min) and daily number of meals (6.1+/-0.1) were not affected by treatment. Feeding rate and intake per meal were lowest for cows fed low protein feed. Meal size decreased systematically during the day. The probability of cows ending and starting a meal increased with meal length and interval between meals, as predicted by the satiety concept. Meals are, therefore, a biologically relevant unit of short-term feeding behavior and visits are not.
Article
Full-text available
An electronic system has been designed that allows for passive monitoring of feeding behavior of individual cows housed in a free-stall barn. The objective of this study was to validate the data generated by this GrowSafe feed alley monitoring system. Twelve lactating cows were each monitored for 24 h using both the GrowSafe system and time-lapse video. The GrowSafe estimation of number of meals consumed by each cow showed perfect agreement with meal frequency identified using the video recordings. The duration of these meals, as estimated by GrowSafe, was highly correlated with the meal duration derived from the video (R2 = 0.98). Despite the excellent agreement for these meal-based measures, for each cow we found some instances in which the video showed that a cow was present at the feed alley but GrowSafe failed to detect cow presence (12.6% of observations) and a few instances in which the reverse was true (3.5% of observations). However, all the missed or extraneous data from the GrowSafe system were closely associated in time with known periods of feeding. These results indicate that this feed alley monitoring system can provide very good measures of meal frequency and meal duration and reasonable estimates of instantaneous feed alley attendance for loose-housed dairy cattle.
Article
Full-text available
The objective of this study was to develop and validate a computerized system to monitor feeding behavior and feed intake of loose-housed dairy cattle. The system consisted of 28 scales located in front of each self-locking place of a regular feedbunk. All cows had access to all scales indifferently. Each visit to the feedbunk was monitored by a transponder in the ear of each cow that was detected by a proximity reader located at the top right corner of each headlock. The data from the scales and the proximity readers were continuously recorded by a computer with an average scanning time of 3.5 s. The monitoring system was validated using all 28 feeding places and 51 lactating cows in a series of 4-h observations during 5 different d. During the observation periods, for each feeder, 2 observers recorded the cow number and the exact time of the visit. The observed data were then compared with the computer records. To validate the ability of the system to monitor feed consumption, on separate days, the amount of feed consumed by a cow during a visit was also measured manually with an external scale, and the feed that disappeared from each scale in 2 different 24-h periods was compared with the sum of feed consumed in each scale during these 2 periods. The average time spent in a given scale by each cow determined by direct observations was similar to that determined by the computer. The system was accurate and showed a high specificity (98.8%) and sensitivity (99.6%) for cow detections. Feed weights determined by the computer system were similar to those measured manually with an external scale, implying that the system was also accurate in measuring individual intake weights. In conclusion, the system provided a reasonable estimate of the number of visits per animal, length of each visit, amount of feed consumed per visit and animal, the total amount of feed consumed daily by each animal, and the rate at which animals consume feed.
Article
Twelve dairy cows were used to determine behavior with varying number of free stalls and length of feed bunk. A least squares procedure which regressed for numbers of observations was adopted for obtaining dominance values. Available free stalls were 1.0, .83, .67, .50, .33 per cow. With 1.0 free stalls, linear feed bunk was .5, .4, .3, .2, .1 m per cow changed at 7-day intervals. Cow behavior and locations were quantified by time-lapse photography at 1-min intervals during last 3 days of each treatment. Behavior was altered when less than .67 free stalls or .2 m of linear feed bunk was available per cow. Minimum stalls needed per cow without altering daily free stall usage = [14.2 h (average use)] ÷ [hours per day that free stalls are available to the herd × .93 (maximum efficiency before crowding)] . Linear feed bunk of .2 m appears adequate to ensure desired amount of eating time when individuals have access to food in bunk 21 h per day. Estimated individual dry matter intakes were the same at .5 m and .25 m of feed bunk per cow. Intake was affected by time spent eating for .25 m. In 10-variable models, time spent eating, or in free stalls, and individual dry matter intake were described predominantly by production variables.
Article
This paper highlights a number of issues associated with the use and interpretation of feeding behaviour measures using examples from the literature on rats, cows and pigs. The inter-relatedness of six feeding behaviour variables is illustrated. Different meal patterns adopted to achieve similar intakes are briefly discussed in the context of flexibility of feeding behaviour. The relative constancy of feeding rate of an individual in a given environment is described, and the notion of a preferred rate of eating is introduced. It is suggested that an animal kept individually will eat a given amount of food at a preferred rate of eating. Changes in feeding rate by individuals are examined and different causes discussed. These include increased feeding motivation (hunger), and influences from the social environment. It is proposed that changes in the feeding rate of individual group housed animals may reflect concomitant changes in the social environment, and could thus be used as an indicator of social constraint. In addition, it is proposed that the depression in daily food intake seen in group housed animals compared to single kept individuals may reflect a shift in behavioural priorities.
Article
This experiment investigated the effect of feeding level on diet digestibility at two forage:concentrate ratios for diets containing grass silage. Four rumen fistulated Holstein–Friesian steers were used in a latin square experiment comprising a 2×2 factorial arrangement of treatments. Treatments were maintenance and 1.6×maintenance feeding level and 500 or 850 g of soya hulls/kg DM. Digestibility was determined by total collection, with fractional soya hulls (k1p) and fractional solute outflow rate (k1f) determined for the rumen using Cr-mordanted soya hulls and Co-EDTA, respectively. Rate of digestion was determined in-sacco. Increasing feeding level and soya hulls inclusion decreased diet DM, OM, CP, NDF, ADF and GE digestibility (P<0.01). The depression due to feeding level was greater for 850 g/kg DM soya hulls diets for GE (P<0.05), DM (P=0.06) and OM (P=0.06) digestibility. Rate of slowly degradable soya hulls DM and NDF digestion was lower (P<0.05 and P=0.07) for 850 g/kg DM soya hulls diets. Soya hulls k1p and k1f increased with feeding level but only the increase in k1f was significant (P<0.01). Diet OM, NDF and GE digestibility were positively related to rumen pH 6 h after feeding (P<0.01; R2=0.50 to 0.69). These results suggest that lower rumen pH values may be an important component of the larger digestibility depressions often observed for high concentrate diets.
Article
Lactating dairy cows metabolize large amounts of water and are affected rapidly by water deprivation. Total body water, half-life of body water, size of component pools, and exchanges among them have been quantitated in several studies. The data underscore the dynamic nature of water metabolism in lactating cows. Water is lost in milk, urine, feces, and various forms of evaporation. Sources of water include drinking, feed, and metabolic (oxidation) water. Factors that have been shown to influence drinking behavior include eating pattern, water temperature, whether water is given in a trough or water bowl, flow rates into water bowls, animal dominance if water bowls are shared, and stray voltage. Important environmental factors modulating water consumption of dairy cattle are DMI, nature of the diet, milk production, temperature, and humidity. Equations have been proposed to predict water consumption based on measures of some of these variables. Water is of paramount importance both physiologically and nutritionally; therefore, it is not surprising that its metabolism indirectly may affect many feeding and management decisions. Ample water of acceptable quality must be provided to maximize milk production.
Article
Evaluation of feeding behavior of ad libitum-fed lactating dairy cows by time-lapse photography revealed 68% of the total feeding activity occurred between the daylight hours of 0600 and 1800. Cows consumed an average of 12.1 meals/day, each 20.9 min in duration. Only 58% of the total defined meal time actually was spent eating, or 253.6 min/cow per day. Estimated meal size and rate of eating, as well as total daily time spent eating, were greater for cows as compared to animals with lower energy demand. Certain feeding characteristics, such as meal frequency and duration, were variable among animals, suggesting that these behaviors may be characteristics of individual cows. Results by time-lapse photography compared well with direct measurement by weigh-cell apparatus.
Article
Little research has focused specifically on the relationships among feeding behavior, management strategy, and optimal intake by the transition cow. Most information must be extrapolated from studies of cattle at other stages of lactation. The transition period can be divided into two distinct phases: 5 to 7 d prepartum, characterized by a 30% reduction in DMI, and 0 to 21 d postpartum, during which time intake should increase rapidly. Feed restriction can reduce number of daily meals by 50%, but when feed is offered for ad libitum consumption, with consistent time of feeding, access can be limited to 8 h daily with no adverse effects on performance of midlactation cows. Sequence of offering feeds may affect intake, but relative degradabilities of dietary protein and starch need to be considered. During early lactation, increased feeding frequency of a total mixed diet may most improve intake when dietary fermentability is moderate to high and management quality is poor. High-producing dairy cows achieve greater intake by increasing meal size and spending less time eating and ruminating per unit of intake. Control of feed intake and meal patterns may differ by parity and should be considered when grouping cattle. Daily exercise of tied dairy cows may not affect intake. Grouping strategy and group feeding behavior influence cow productivity and profitability. Competition for feed and space can be reduced by fenceline feeding vs bunks. Optimum intake during the transition period will occur only if feeding management accommodates normal feeding behavior of dairy cows.