The subject ‘young stock rearing’ is important for future dairy farming. So far there has been too little attention for young stock rearing. Proper care by monitoring health and growth of the young animals will lengthen life expectancy and increase milk production of the adult dairy cow. Due to the increasing farm sizes it becomes harder for the farmer to pay sufficient attention to all individual animals. Facilities like automatic milk feeders and water drinkers combined with weighing systems are used to gather information about the status of all individuals. The information consists of daily milk and water intake, body weight and other behavioural data. Models are developed to detect deviations in the normal pattern of this information. Only these anomalies are reported with instructions for standardized actions to be undertaken, the standard operating procedures. This paper provides the background for the alerts for health problems and for lagging growth in young stock till the age of 4 month

Hogewerf, P.H.

Ipema, A.H.

Mol, R.M., de

Wageningen Yield 

 
 
 
Proceedings International Conference of Agricultural Engineering, Zurich, 06-10.07.2014 – www.eurageng.eu    1/6 
Ref: C0275 
Young stock rearing using automatically recorded data  
A.H. Ipema, R.M. de Mol and P.H. Hogewerf, Wageningen UR Livestock Research, P.O. Box 
65, 8200 AB Lelystad, The Netherlands  
Abstract  
The subject ‘young stock rearing’ is important for future dairy farming. So far there has been  
too little attention for young stock rearing. Proper care by monitoring health and growth of the 
young animals will lengthen life expectancy and increase milk production of the adult dairy 
cow. Due to the increasing farm sizes it becomes harder for the farmer to pay sufficient at-
tention to all individual animals. Facilities like automatic milk feeders and water drinkers 
combined with weighing systems are used to gather information about the status of all indi-
viduals. The information consists of daily milk and water intake, body weight and other be-
havioural data.  Models are developed to detect deviations in the normal pattern of this in-
formation. Only these anomalies are reported with instructions for standardized actions to be 
undertaken, the standard operating procedures.  This paper provides the background for the 
alerts for health problems and for lagging growth in young stock till the age of 4 months. 
Keywords: young stock, data collection, milk intake, body weight, detection model 
1 Introduction 
In 2012  started in the Netherlands the project ‘Smart Dairy Farming (SDF)’, in which compa-
nies in the dairy chain cooperate in the development of sensor technology and decision sup-
port models for operational on farm management and exchange of relevant information be-
tween the dairy chain partners.  
Dairy farmers are supported in the care of individual animals, with the goal to provide a good 
health, a longer life and a longer production time of the cow. Sensors and other technological 
tools, decision models and advice products are developed and applied to achieve these 
goals. At the end this project should provide a contribution to the sustainability of the dairy 
chain.  
The project has three focus areas, animal health, fertility and feeding. Within the focus area 
‘animal health’ is worked on optimizing young stock-rearing as a basis for long-lived cows. In 
dairy farming is about 30% of the dairy herd annually replaced. Lowering the replacement 
rate has a significant positive impact on the financial result. Less replacement means a high-
er life span and thereby raise living production per cow. A proper course of development dur-
ing the rearing turns out to have a positive effect on milk production (Soberon et al., 2012). 
 
Calves weigh at birth 35-45 kg. There are several possible strategies to achieve desired de-
velopment of calf to heifer. The weight of the heifer after calving should be between 510 and 
550 kg. To achieve this bodyweight in a two-year-old heifer a certain growth course is de-
sired. It is important that the animal is fed the right nutrition at the right time and that health 
problems can be prevented. The rearing period can be divided in a number of phases. The 
first three days after birth is called the colostrum period. Colostrum contains antibodies, mak-
ing the first resistance against all kinds of diseases. After the colostrum period follows the 
suckling period, which lasts until the age of about 2 months. For a good development ade-
quate amounts of feed (calf milk replacer, calf grain and roughage) of the right composition 
 
 
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should be consumed. If the calf tweighs about 80 kg she can be weaned. This weaning peri-
od runs from about 8 weeks to 16 weeks. A smooth transition from milk to concentrate and 
roughage is important. In the adolescent period, from 4 to 13 months of age, the young stock 
needs less energy for maximum development. In the fertility period, in the age from 13 to15 
months,  the young stock can be inseminated in order to give birth at an age of about 24 
months. In the last phase, the pregnant phase, the yearling has to develop further into a ro-
bust heifer, which is giving birth at the age of 24 months and subsequently has a trouble-free 
start of lactation.  
This project focuses on recognizing and properly addressing the problems that have arisen in 
the young stock rearing. In this paper we will only address the rearing during the first 4 
months of life and particularly the suckling and weaning period. 
Building the ICT infrastructure for collecting real time information about milk, water intake and 
body weight gathered in these periods and the development of decision support tools will be 
discussed. The main objectives for this young stock rearing project are realizing state of the 
art tools that support the farmer to optimize growth and development of young stock. These 
tools will give alerts in cases of potential health problems and lagging growth. 
2 Materials and methods  
In the framework of the Smart Dairy Farming project on two farms, ‘Dairy Campus’ and 
‘Bruunshoge’, data are collected that are important for proper rearing of young stock. On 
these farms automatic milk feeders (AMF) are used for providing milk to individual calves 
(Figure 1).  
                     
 
 
 
 
 
 
 
 
 
 
 
 
These feeders are equipped with a weighing floor. In addition, the animals can drink water in 
stations also equipped with a weighing floor (Figure 2). Each visit to AMF or water drinking 
station produces the following information: animal ID, date and time, milk or water intake, 
duration of visit and bodyweight. This information is automatically stored in an external data-
base and made available via an info broker (Figure 3).  
 
 
Figure 3. Schematic information architecture. 
 
Figure 1. Automatic milk feeder with  
               weighing floor. 
 
Figure 2. Station equipped with water 
               drinker and weighing floor. 
 
 
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Firstly, the information in the database is used to develop software to generate alerts when 
anomalies in the data streams of an individual animal are present.  In a later stage,  this 
software will run at scheduled times each day. In an internet application the farmer has ac-
cess to the output of the models through a dashboard. In the dashboard the output is trans-
lated into practical management advices.  
 
3 Results and discussion  
3.1 Collected data 
Examples of daily collected data will be given to provide an overview of the information that 
is available from each individual calf. Figure 4 gives the average daily bodyweight of a calf 
from birth till the age of about 100 days.  
 
 
Figure 4. Bodyweights of calf 4284 during first 100 days.  
 
The birth weight of the calf in figure 4 was 38 kg. In the first weeks, when the calves are 
housed individually, there is hardly any growth. After two weeks the calves are housed in 
groups and receive milk at the AMF. At 60 days of age the weight of this calf was 68 kg. 
Some deviation of the bodyweight from the trend line is clearly visible. The rough data meas-
ured with the weighing platform in the AMF or water drinker need some filtering to exclude 
values which deviate to much from the expected weight. Deviating values occurred when a 
calf was only with the front legs on the weighing platform (values too low) or when a second 
calf stepped with her front legs on the weighing platform (values too high). When the filtered 
bodyweight as shown in Figure 4 is on two consecutive days lower than the day before an 
alert is given for a possible health problem. For calf 4284 such a case was not found. 
 
In Figures 5 and 6 the daily intake of milk and water respectively the daily number of visits to 
the AMF and water drinker are given. 
 
 
 
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Figure 5. Daily milk and water intake of calf 4284 during first 100 days. 
 
At the age of 16 days calf 4284 has got access to the AMF. Until the age of 75 days a fixed 
amount of milk according a pre-set scheme is available for the calf in the AMF. The daily 
amount starts with 5 litres on the first day, increases to 6 litres in 10 days, stays at 6 litres 
during 25 days, then decreases to 2 litres in 10 days and finally decrease to 0 litres in 5 days. 
The dotted line in Figure 5  shows the quantities of milk the calf has drunk daily. The profile 
of the drunk quantities follows reasonably well the profile of the quantities that were potential-
ly available. Only at the age of 55 days the consumed amount was much lower than availa-
ble. The detection model will then generate an alert (red triangle). At the age of 49 days we 
see the calf drink water for the first time. During the period the available amount of milk de-
creases the water intake starts increasing till the level of about 15-20 litres daily in the period 
the calf is weaned completely and has switched to nutrients intake from concentrates and 
forage. 
 
 
Figure 6. Daily AMF and water drinker visits of calf 4284 during first 100 days. 
 
The milk ration is offered to the calf in meals of 1.5 kg. As shown with the dotted line in Fig-
ure 6, this means that the number of rewarded visits to the AMF will usually be at the level of 
4. When the number of rewarded visits is much lower than expected an alerts is given. In this 
 
 
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calf an alert (red triangle in graph) based on the rewarded AMF visits was produced on day 
55. In the period of decreased availability of milk, after the age of 50 days, the number of 
rewarded visits is also decreasing. 
The total number of visits to the ACF is much higher; the calf is more times daily interested 
whether there is already a new meal of milk available. The total number of visits is in that 
respect a sign for the eagerness of the calf. Svensson & Jensen (2007) found that the total 
number of visits might therefore be of interest as a parameter that shows the activity of the 
calf. A decreasing number of visits to the ACF is detected and alerted by the model (red dia-
mond). A remarkable phenomenon  is the lower number and lower variation of daily visits to 
the water trough after the calf have been weaned. Obviously the AMF creates some unrest. 
3.2 Alerting deviations and standard operation procedures 
As explained in paragraph 3.1 possible health problems are alerted if the consumed amounts 
of milk are considerably less than the available amounts (one case in Figure 5), the visiting  
behaviour to the AMF shows a reduced activity (more cases in Figure 6) and/or the body-
weight on two consecutive days is lower than the day before (no cases detected in Figure 4). 
If one or more of these parameters produce an attention than the farmer is advised to check 
and possibly to treat the animal. In severe cases, the veterinarian should be consulted. The 
message the farmer gets on the dashboard is ‘Health problem calf 4284’. The underlying 
instruction is ‘Check health status of calf, make diagnosis and possibly carry out an action’. 
On request the farmer can be informed about the reason(s) the message is generated. 
 
An important parameter for the development of young stock is the growth rate in the different 
phases of the rearing period. Soberon et al. (2012) concluded that nutrition and management 
of the pre-weaned calf are the major environmental factors influencing the expression of the 
genetic capacity of the animal for milk yield; increased growth rate before weaning had posi-
tive effects on future lactation milk yield. We compared the growth rate of the calves from 
birth till 60 days of age on the two test farms (Table 1). 
 
Table 1. Growth rate of calves from birth till 60 days of age on two test farms. 
Test farm Number of 
calves 
Weight at 
birth (kg) 
Weight at 60 days of 
age (kg) 
Growth rate 
(g.d-1) 
Dairy Campus 51 41.2 77.8 610 
Bruunshoge 40 38.3 67.8 492 
     
Desired  44.6 79.3 579 
 
Table 1 shows that there is a rather large difference in the growth of the calves between the 
two test farms. The results of the calves on farm ‘Dairy Campus’ correspond rather well with 
the desired normative values; on farm ‘Bruunshoge’ the figures were lower. The differences 
were probably caused by a lower amount of milk that was fed to the calves on farm ‘Bruun-
shoge’. For this reason we developed an alert that warns the farmer when the development 
of a certain calf is lagging behind in comparison with the normative (desired) growth. When 
at an age of 42 days the weight and growth of a calf is more than 15% lower than desired the 
message ‘Adapt scheme milk ration’ is given on the dashboard. The underlying instruction is 
‘Switch to a milk scheme in which more milk comes available for the calf’. Also for this mes-
sage the farmer can ask for background information why it was generated.   
4 Conclusions 
The project has shown that it is feasible:  
- to collect automatically various relevant data at individual animals on a farm,  
- to make this data available in real time for detection models using the internet, 
- to report the abnormalities also via an internet application (dashboard) to the farmer 
and  
 
 
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- to include management advices in the reports in order to solve the identified prob-
lems. 
5 Acknowledgements 
This research was supported by the Dutch research program Smart Dairy Farming, the 
Netherlands), Investment and Development Agency for the Northern Netherlands (Groning-
en, the Netherlands), the Dutch Dairy Board (Zoetermeer, the Netherlands) and the Ministry 
of Economic Affairs, Agriculture and Innovation, Pieken in de Delta (Den Haag, the Nether-
lands). 
6 References 
Soberon, F., Raffrenato, E., Everett, R. W. & Van Amburgh, M. E. (2012). Preweaning milk 
replacer intake and effects on long-term productivity of dairy calves. Journal of Dairy Sci-
ence, 95, 783-793.  
 
Borderas,T. F., Rushen, J.,  Von Keyserlingk, M. A. G. & De Passillé, A. M. B. (2009). Auto-
mated measurement of changes in feeding behavior of milk-fed calves associated with ill-
ness. Journal of Dairy Science, 92, 4549-4554. 
 
Svensson, C. & Jensen, M.B. (2007). Identification of diseased calves by use of data from 
automatic milk feeders. Journal of Dairy Science, 90, 994-997.  


Young stock rearing using automatically recorded data

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