animal
Animal (2016), 10:9, pp 1501–1506 © The Animal Consortium 2015
doi:10.1017/S1751731115001809
Feeding behavior improves prediction of dairy cow voluntary feed
intake but cannot serve as the sole indicator
I. Halachmi1†, Y. Ben Meir1,2, J. Miron2 and E. Maltz1
The Volcani Centre, The Institute of Agricultural Engineering, PO Box 6, Bet Dagan 50250, Israel; 2The Volcani Centre, Animal Science Institute – Agricultural
Research Organization (A.R.O.), PO Box 6, Bet Dagan 50250, Israel
1
(Received 6 May 2015; Accepted 8 July 2015; First published online 21 September 2015)
Low-cost feeding-behavior sensors will soon be available for commercial use in dairy farms. The aim of this study was to develop a
feed intake model for the individual dairy cow that includes feeding behavior. In a research farm, the individual cows’ voluntary
feed intake and feeding behavior were monitored at every meal. A feed intake model was developed based on data that exist in
commercial modern farms: ‘BW,’ ‘milk yield’ and ‘days in milking’ parameters were applied in this study. At the individual cow
level, eating velocity seemed to be correlated with feed intake ( R 2 = 0.93 to 0.94). The eating velocity coefficient varied among
individuals, ranging from 150 to 230 g/min per cow. The contribution of feeding behavior (0.28) to the dry matter intake (DMI)
model was higher than the contribution of BW (0.20), similar to the contribution of fat-corrected milk (FCM)/BW (0.29) and not
as large as the contribution of FCM (0.49). Incorporating feeding behavior into the DMI model improved its accuracy by
1.3 (38%) kg/cow per day. The model is ready to be implemented in commercial farms as soon as companies introduce low-cost
feeding-behavior sensors on commercial level.
Keywords: individual cow, eating speed sensor, precision livestock farming
Implications
Feed is the greatest expense in milk production. However,
monitoring the individual cow’s feed intake is currently only
economically feasible under research conditions. Existing
nutrition models to predict feed intake may only fit
groupwise. It can be assumed that low-cost feeding-behavior
sensors will soon be available for commercial use in many
farms. The aim of this study was to develop a feed intake
model for the individual dairy cow that includes feeding
behavior.
Introduction
Feed is the greatest expense in milk production (Buza et al.,
2014). Knowledge of the individual dairy cow’s voluntary dry
matter intake (DMI) could contribute to the design of more
efficient animal nutrition, at either the group level, summing
more accurate predictions of all individual cows concurrently
presented in a group (Maltz et al., 2013) or the individual
cow level. At the latter level, the design of an individualized
animal diet is required when computerized concentrate
†
E-mail: [email protected]
self-feeders either standalone (Livshin et al., 1995) or are
built into a milking robot stall (Halachmi, 2004 and 2005;
Madsen et al., 2010).However, monitoring the individual
cow’s feed intake is currently only feasible under research
conditions (Halachmi et al., 1996 and 1998; Calan, 1997;
Schwartzkopf-Genswein et al., 1999; Grant and Albright,
2001; Huisma, 2002; DeVries et al., 2003; Bach et al., 2004;
Ferris et al., 2006; Wang et al., 2006; Chapinal et al., 2007;
Mendes et al., 2011; Krawczel et al., 2012). Therefore,
several nutrition models have been developed to predict feed
intake, but even the best models have been unable to
account for >70% of the variation in intake (Vandehaar,
1998; Shelley, 2013), that is, the existing models may only fit
groupwise (Arnerdal, 2005). A DMI model at the individual
cow level requires further complexity: the few hundred
coefficients in the model presented by Halachmi et al. (2004)
are not accurate enough (Halachmi et al., 2011).
In addition to supporting the prediction of feed intake,
changes in feeding behavior may indicate approach calving
and may indicate disease. Gonzalez et al. (2008) discovered
that cows with ketosis and lameness show decreased feed
intake, by 10.4 kg and 1.57 kg, respectively. Urton et al.
(2005) claimed that cows with clinical metritis spend 22 min/
day less at the feed bank than those without clinical metritis.
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Halachmi, Ben Meir, Miron and Maltz
Early identification of sick cows could minimize disease
duration (Urton et al., 2005), improve treatment efficacy and
improve animal welfare (Gonzalez et al., 2008), thereby
decreasing economic losses (Urton et al., 2005; Gonzalez
et al., 2008). Therefore, it can be assumed that low-cost
feeding-behavior sensors will soon be available for commercial use in many farms.
Sensors of feeding behavior, that is, time spent in the
feeding lane, duration of each meal and frequency of meals
throughout the day and night, are under development by a
few commercial sensor companies (Halachmi, 2015). The
principal author is familiar with a few Israeli prototype sites
from three different companies that are in the last developmental phases in Israel aimed at low-cost monitoring of
feeding behavior under commercial farm conditions
(Halachmi et al., 2015). Soon these sensors will be incorporated into the dairy farm markets. There are already quite a
few feeding-behavior sensors operating under research
conditions (Shelley, 2013).
However, feeding behavior is not the only parameter determining feed intake. A wide range of predictors can potentially
affect voluntary feed intake of dairy cows (Ingvartsen, 1994;
Kjos, 2002). These include (i) individual animal factors: BW,
milk yield (MY), days in milk (DIM), breed, genetics, age, parity,
gestation, previous feeding, body condition, fatness, eating
velocity, rumen activity, health status, anabolic agents; (ii) food
quality: dietary factors, diet composition, chemical composition, digestibility, degradation profiles, physical form, conservation, dry matter (DM) content, fermentation quality,
palatability, mineral salts alkaline agents, food additives;
(iii) housing and management practices: duration of access to
feed, frequency of feeding, separate (‘cafeteria’) v. complete
feed (total mixed ration(TMR)), tie stalls v. loose housing or
open cowshed, space per animal for lying down, space in the
manger, length of feeding lane per animal, photoperiod, temperature, humidity. None of the existing food intake models
incorporate individual feeding behavior into the evaluating
food intake formula, that is, time feeding, number of
meals, etc.
The National Research Council (NRC) predictors (BW, MY
and DIM) are frequently applied (Table 1) and were therefore
also applied in this study.
An interesting attempt to introduce data from another
sensor parameter, rumination time, into the DMI prediction
equation was presented by Clément et al. (2014), but they
concluded that rumination time had no significant effect on
accuracy of the DMI model. In a previous unpublished
project, we concluded the same.
As feeding behavior will soon be monitored in commercial
farms, the research question of this study was: What is the
added value of feeding behavior for prediction of individual
DMI in lactating dairy cows? The hypothesis of this study was
that incorporating feeding behavior into the DMI model
might improve the model by at least 10%. This hypothesis
was statistically tested and was not rejected. Hence, the aim
of this study was to develop a DMI model for the individual
dairy cow that includes feeding behavior.
Table 1 Predictors included in several dry matter intake (DMI) models
LFU
NRC
IND
CNCPS
NAAT
DFFS
MY
BW FCM DIM
BW FCM DIM
BW FCM DIM Age Breed, empty body fat, food
additives, ambient temperature,
muddiness of pen
BW MY DIM
Feed NDF content, silage
fermentation quality
MY DIM Parity Race, energy density, legume
content, silage DM, FFL/FU, TMR,
stable type
LFU = feeding recommendations by Lantmännen (Lindgren et al., 2001);
MY = milk yield; NRC = National Research Council (NRC, 2007); FCM = fatcorrected milk, 4%; DIM = days in milking; IND = model proposed by Halachmi
et al. (2004 and 2011); CNCPS = Cornell Net Carbohydrate and Protein System
(Fox et al., 2004); NAAT = Norwegian AAT model (Volden, 2001); DFFS = Danish
Fill Factor System (Hvelplund and Nørgard, 2003); DM = dry matter; FFL/FU =
fullness factor and Danish feed unit; TMR = total mixed ration.
Material and methods
Animals
The experiment was performed at the ARO research farm in Bet
Dagan, with 220 high-yielding Holstein-Friesian cows housed
in an open no-stall cowshed and fed a TMR that is commonly
used in Israel. Average feed intake was 26.5 DMI/day DM; the
diet, TMR, used in this study comprised 21% wheat silage,
8% wheat hay and 2% vetch hay and 69% concentrates. The
concentrate mixture contained (% of TMR DM) 29.4 corn grain,
1.9 rolled barley, 8.0 soybean meal, 1.7 Ca salts of long-chain
fatty acids, 19.2 corn gluten feed, 2.0 non-linted cotton grains,
3.2% whey, 2.3 minerals + vitamins mix. This TMR contained
63.4% DM, 16.5% CP, 5.25% ether extract, 33.3% NDF,
18.0% forage NDF, and it’s in vitro DM digestibility was 75.3%.
Energy level value was 1.68 Mcal/kg. The cows were, on
average 187 days in lactation. Milk production was on average
43 l/day: 3.6% fat; 3.2% protein; 4.67% lactose. The individual
cows’ voluntary feed intake and feeding behavior were monitored at every meal by a 42-stalls feed intake monitoring
system developed by Halachmi et al. (1996 and 1998). The
following parameters: MY, DIM, BW, neck and leg activity,
lying behavior, milk composition and rumination time, were
recorded with the farm’s regular herd management software
(Noa, Afimilk and SCR). The best correlated parameters were
calibrated into a DMI prediction equation and are presented in
the Results section. Data used for model development were not
used for model validation.
Nomenclature
Feeding behavior is frequency (number of meals and distribution along day and night), and meals’ durations, DMIi is
individual cow’s voluntary DMI on day i; FCM (National
Research Council (NRC), 1989) is fat-corrected milk, where
FCM (4%) = 0.4 × kg milk + 15 × kg milk × fat content; BW
is also known as ‘live weight.’
Errori ¼ predicted DMIi observed DMIi
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Feeding behavior and cow voluntary feed intake
ME is the mean error, also known as absolute bias
= Σ(predicted DMIi − observed DMIi)/N = mean(error).
P
½ ðpredicted DMIi observed DMIi Þ=N
P
Relative bias ¼
½ ðobserved DMIi Þ=N
MAE is the mean absolute error [Σabs (predicted DMIi −
observed DMIi)]/N = [Σabs (Errori)]/N = mean (abs (error)).
MSPE is the mean square prediction error = [Σ (predicted
DMIi − observed DMIi)2]/N.
STD stands for standard deviation; DM stands for dry
matter; TMR stands for total mixed ration (% DM of TMR =
0.629); n = 100 cows; t test was applied for significance.
Statistics
The new model was based on the NRC function (parameters
are FCM, DIM, BW), with the additional parameter of feeding
behavior.
NRC equation:
DMIðkg=dayÞ ¼ ð0:372 ´ fat-corrected milk + 0:0968 ´ live weight0:75 Þ
Consequently, incorporating feeding behavior into a DMI
model seems to make sense, but owing to the individuality of
the eating velocity, this cannot be the only parameter in the
DMI-predicting equation. Other descriptors associated with
the individual’s eating velocity should be incorporated.
Based on the aforementioned literature, the correlated
descriptive parameters in this study were FCM, BW, DIM, FCM/
BW and feeding behavior. Table 2 presents the contribution of
each parameter to the DMI model and suggests that addition
of feeding behavior might improve model accuracy. The contribution of feeding behavior was higher than that of BW and
DIM, and similar to the ratio FCM/BW; however, it was less
than the contribution of FCM. Other investigated parameters
(not shown) contributed considerably less.
As already noted, incorporation of feeding behavior into
the DMI model was performed via the NRC equation. The
NRC and NRC + feeding-behavior models are presented in
Table 3. The accuracy of the behavior-based model
was clearly superior. The standard deviations were not
significantly different between the two models.
´ ð1eð0:192 ´ ðweek of lactation + 3:67ÞÞ Þ
Discussion
NRC equation with feeding behavior:
1
0
b1 ´ fat-corrected milk
C
B
DMI ðkg=dayÞ ¼ @ + b2 ´ live weight0:75 A
+ b5 ´ feeding behavior
´ ð1eðb3 ´ ðweek of lactation + b4 ÞÞ Þ
Restating the hypothesis
The hypothesis of this study was that incorporating feeding
behavior into the DMI model might improve it by 10%.
Table 2 Cross-correlation coefficients of feed intake and potential
predictors
NRC parameters
where the coefficients of bi = 0.41, 0.052, −0.29, 6.3, 0.0074.
Results
At the individual cow level
The eating velocity of each individual cow appeared to be
correlated with the cow’s feed intake (Figure 1, R 2 = 0.93 to
0.94). However, each individual cow had its own eating
velocity (Figure 1, 230 v. 150 g/min per individual).
Feed intake
FCM
BW 0.75
DIM
FCM/BW
FCM
BW0.75
DIM
FCM/BW
Feeding behavior
0.49
0.20
0.089
−0.16
−0.37
0.24
0.29
0.78
−0.55
−0.47
0.28
0.14
−0.062
−0.017
0.15
NRC = National Research Council (NRC, 2007); FCM = fat-corrected milk, 4%;
DIM = days in milking.
Figure 1 Mutual relationship between feed intake and time spent in the feeding lane for cow 3103 (R 2 = 0.94) and cow 3122 (R 2 = 0.93). The two
cows’ eating velocities differed as well, being 230 and 150 g/min, correspondingly.
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Halachmi, Ben Meir, Miron and Maltz
Table 3 Accuracy at the individual cow level of NRC v. NRC with feeding behavior model
ME (kg/day)
MAE (kg/day)
MSPE (kg2/day)
SD error
SD model
SD real
Model 1
NRC (kg/cow per day)
Function of FCM, BW and DIM
Model 2
NRC with feeding behavior (kg/cow per day)
Function of FCM, BW,DIM, feeding behavior
2.81a, relative bias 0.11 ( = 11%)
3.42c
16.82e
2.99
2.52
3.66
−0.13b, relatively bias −0.01 ( = −1%)
2.12d
8.56f
2.92
2.57
3.66
NRC = National Research Council (NRC, 2007); FCM = fat-corrected milk, 4%; DIM = days in milk; ME = mean error; MAE = mean absolute
error; MSPE = mean square prediction error; n = 100 cows; % dry matter (DM) of total mixed ration (TMR) = 0.629.
a,b,c,d,e,f
Significant difference at α = 0.05.
Table 4 Comparing the new model (feeding behavior) with earlier published models, modified after (Arnerdal, 2005)
Bias (%)
Bias (kg)
MSPE (kg2/day)
SD error
1
2
3
4
5
6
7
8
Feeding behavior
DFFS
CNCPS
LFU
NAAT
NRC1
NRC2
CNCPS
−1
−0.13
8.56
2.92
3.2
0.59
4.92
2.22
3.6
0.66
3.29
1.81
−9.6
−1.76
6.43
2.54
9.7
1.78
16.96
4.12
11
2.81
16.82
2.99
12.7
2.32
7.82
2.80
14.9
1.76
20.6
4.54
DFFS = Danish Fill Factor System (Hvelplund and Nørgard, 2003); CNCPS = Cornell Net Carbohydrate and Protein System (Fox et al., 2004); LFU = feeding recommendations by Lantmännen (Lindgren et al., 2001); NAAT = Norwegian AAT model (Volden, 2001); NRC = National Research Council (NRC, 2007); NRC1 was run on
data collected in this study; NRC2 was run on data collected by Arnerdal (2005).
The model accuracy, in terms of MAE, was improved by 38%
(2.12 and 3.42 kg/cow per day).
Potential limitations and weaknesses of the implications of
the findings
Knowing the accurate individual cow’s DMI has implications
for diet composition design in computerized self-feeders that
are either located in a milking robot stall or standing alone.
However, a group-level statistical model (v. ‘real-time closedloop controlled dynamic modeling’) cannot accurately handle
the extreme cases. A model prediction is more accurate
around the average cow, in accordance with the database
upon which the model was calibrated. The reason for the
inaccuracy in extreme cases is the model’s ‘built-in’
smoothing, regression or lag functions.
Today’s ‘hot trend’ is to address the individual cow’s feed
efficiency. The MY or FCM are positively correlated with DMI.
Therefore, in the DMI prediction equation, MY or FCM are
commonly written in the numerator. The MY or FCM are
also the dairy cow economic output, therefore if the feedefficiency equation is calculated as follows: input/output =
feed/[MY or FCM], MY or FCM are written in the denominator. Therefore, if one applies a DMI equation for individual
cow efficiency, the MY or FCM will be written in both the
numerator and the denominator. Such an equation is not
stable, and therefore applying the DMI prediction equations
for individual cow feed efficiency is not recommended.
How are the results related to expectations and the literature?
There are quite a few feed intake prediction models in the
professional literature: Table 4 presents the four measures
used in seven studies. Table 5 presents one measure of
performance that was used in the other eight studies.
The bias (% and kg) in previous reports (Table 4) suggests
that the feeding-behavior model does not fall behind any of
the earlier models addressed by Arnerdal (2005). However,
the other published measures of performance: MSPE and STD
(error) are ambivalent, some better (2 to 4) and some worse
(5 to 8). This raises a general debate present in every
research study: how to select representative measures of
performance. Other literature (Table 5) suggests that the
feeding-behavior model’s MSPE (kg2/day) does not fall
behind all earlier models reviewed byRoseler et al. (1997).
However, none of the above studies primarily tackled the
individual cow. They were more focused on group-level
management. In contrast, Halachmi et al. (2004 and 2011)
proposed an individual cow feed intake model for Danish
reds, Jerseys and Holsteins. In their data for Holstein multiparous cows (Halachmi et al., 2011), the MAE was 2.71 kg/
cow per day (Halachmi et al., 2004 and 2011) and the
National Research Council (NRC) (2007) reported 2.98 kg/
cow per day. In this study, the MAE was 2.12 kg/cow per day.
This study, although introducing a simpler model (five
coefficients v. (Halachmi et al., 2004) a few hundred coefficients), gave a comparable or even better prediction.
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Feeding behavior and cow voluntary feed intake
Table 5 Comparing the new model (feeding behavior) with earlier
published models, measures of performance: mean square prediction
error (MSPE (kg2/day)), modified after (Roseler et al., 1997)
Period
1st
1
2
3
4
5
6
7
8
9
Feeding behavior1
Modified NRC2
Weiss3 (restricted)
Kertz4 (single)
CNCPS5
This study NRC6
Rayburn and Fox7
Weiss8
Kertz4 (multiple)
2nd
3rd
4th
5th Mean
SD
8.6
2.9
9.6
4.4
11.9
5.7
14.1
2.8
9.4 13.1 15.4
5.9
16.8
20.6 20.8 8.0 21.5 30.1 20.2
7.9
64.9 75.1 32.9 11.3 13.9 39.6 29.2
71.5 265.2
168.4 137.0
15.6
16.1
12.1
13.9
12.9 7.9
19.4 10.5
16.0
25.3 15.4
5.3
6.5
6.3
7.0
MSPE = mean square prediction error.
Multiparous Holstein cows, control group (not treated with bovine
somatotropin).
1
This study equation: feeding behavior incorporated into NRC (2007).
2
Roseler et al. (1997) modification of National Research Council (NRC, 1989).
3
Equations with or without (restricted) the adjustment factor (Weiss, 1991).
4
Equations for single or multiple equations (Kertz et al., 1991).
5
CNCPS = Cornell Net Carbohydrate and Protein System (Fox et al., 1992).
6
NRC (2007) run on data from this study.
7
Rayburn and Fox (1993).
8
Where the lactation periods were defined by Roseler et al. (1997) as: (1) weeks
1 to 9 of lactation; (2) weeks 10 to 24 of lactation; (3) weeks 25 to 36 of
lactation; (4) weeks 37 to 48 of lactation; (5) weeks 49 to 60 of lactation. The
equations of Kertz et al. (1991) were designed to predict dry matter intake (DMI)
through week 20 of lactation, and were therefore excluded from periods 3,
4 and 5.
Relationships shown by major findings and further research
Table 1 suggests that if feeding behavior is not highly
cross-correlated with the other descriptive parameters (FCM,
BW and DIM) that are already in the model, the value of
the feeding behavior is enhanced. In further research, with
more cows over longer periods, this question should be
addressed.
A sudden divergent feeding behavior also has health,
approach calving and TMR quality implications (Urton et al.,
2005; Gonzalez et al., 2008). Therefore, low-cost monitoring
of feeding behavior will soon be commercially available for
use on farms, if not because of the feed intake estimation
then because of animal health issues. Implementing new
low-cost feeding-behavior sensors is advised in further
research.
As one might say: ‘if a direct measured (sensor) cow
individual feed-intake will be soon commercially low-cost
available on the market, the necessity of a DMI model is
questionable.’ This point is often raised up since the first
edition of the NRC feed intake model back in the 1980s. The
NorFor Scandinavian feed intake model and other models
might facing the same justified criticism. As yet the NRC and
the other models, although less accurate on cow individual
level comparing with direct measurement, are widely applied
everywhere. Halachmi (2015) book discussed and listed
measures that are potentially going to be replaced soon by
sensors, but feed intake sensor is not listed among them.
After a feed intake sensor will be developed and validated, it
will probably not be immediately purchased by all the farms
worldwide at once. Like milking robots, tractors, pedometers, electronic milk recording, rumination tags and
other developments in agriculture, it will get started with few
pioneer farms in advance and intensive production systems
and will take decades before the massive number of farms
will purchase the feed intake sensors. Right now (2014) the
principle author is familiar with only two installation sites
here in Israel on commercial conditions that provide feedingbehavior data on cow individual level, the correlation with
feed intake (or ‘meal size’) is not available yet.
The feed intake change with time (Halachmi, 2004, No
63); the NRC takes it into account by means of cow individual
factors: MY, BW and DIM those are also changing over time.
The feeding behavior does not differ from the other cow
individual factors, it changes over time. This paper does not
develop a new model, it introduces feeding behavior into
existing (NRC) model. In further research, we advise studying
how feeing behavior changes with time (age) and environment (cold or heat challenges).
In this study, incorporation of feeding behavior was
performed via the NRC feed intake equation. In further research,
we advise incorporating feeding behavior into other equations.
Equation forms that do not include smoothing or lag functions
(e.g. the one developed by Halachmi et al. (2004) does not
include the lag function) has the potential to be more positively
influenced by the new introduction of feeding behavior.
Conclusions
At the individual cow level, eating velocity seemed to be
correlated with feed intake (R 2 = 0.93 to 0.94). The eating
velocity coefficient varied among individuals, ranging from
150 to 230 g/min per cow. The contribution of feeding
behavior to the DMI model was higher than the contribution
of BW, similar to the contribution of FCM/BW and not as
large as the contribution of FCM. Incorporating feeding
behavior into the DMI model improved its accuracy by 1.3 kg/
cow per day (38%, 2.12 and 3.42 kg/cow per day, Table 3). In
further DMI model developments, in additional to MY and
BW, feeding behavior should be incorporated.
Acknowledgements
The authors would like to thank the farm personnel (led by
Shamay Yakobi) and the feed intake monitoring systems
research assistants: Mr. Aharon Antler and Mr. Natan Barchilon
for data collection and their engineering help. The research was
funded by the Israeli Ministry of Agriculture's Chief Scientist
Fund, OptiBarn 459-4490-2014; the IDB fund number 0442, and
provide preliminary data for Eu-PLF 459 4465.
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