Hogeveen, H.

Nor, N.M.

Steeneveld, W.

English

NARCIS 

Estimating the youngstock rearing costs in the Netherlands using Monte Carlo simulation

Wageningen Yield 

Nantes, 14-15 January 2010: Farm Animal Health Economics 
 
 
ESTIMATING THE YOUNGSTOCK REARING COSTS IN THE NETHERLANDS 
USING MONTE CARLO SIMULATION 
 
Norhariani Mohd Nor 1 2 , Wilma Steeneveld 2  and Henk Hogeveen 2 3  
1 Department of Veterinary Preclinical studies, Faculty of Veterinary Medicine, University 
Putra Malaysia, 43400 Serdang, Selangor, Malaysia. 
2 Department of Farm Animal Health, Utrecht University, Utrecht, The Netherlands 
3 Chair Group Business Economics, Wageningen University, Wageningen, The Netherlands 
 
 
ABSTRACT 
A dynamic and stochastic Monte Carlo model to calculate the youngstock rearing costs in an 
average Dutch dairy farm which include four important youngstock diseases; Calf Scours 
(CS), Bovine respiratory Diseases (BRD), Subclinical Parasitic Gastroenteritis (SPGE) and 
Bovine Lungworm (BL) was developed.  The uncertainties of disease probabilities, growth 
and reproduction are able to be resolve by different distributions in the model, as well as 
establishing the indirect cost when diseases and mortality occurred.  For Dutch conditions, the 
average total costs of youngstock rearing simulated with all the diseases were €1364.  
 
Keywords; Dynamic and stochastic Monte Carlo, youngstock rearing, average total cost 
 
 
INTRODUCTION 
On average 25% to 35% of dairy cows are culled each year in The Netherlands and have to be 
replaced.  As replacement for culled dairy cows is crucial, this makes the youngstock rearing 
as an essential part in the dairy cows farm management.  However, in The Netherlands 5.6% 
of calves die within the first two years of life (Mourits et al., 2000) especially due to diseases.  
Since youngstock rearing costs are often being overlooked by the dairy farmer (Mourits et al., 
1997, Groenendaal et al., 2004) the estimation of the costs of youngstock rearing must be 
established. Knowing the costs of rearing a youngstock will be an important starting point 
towards optimizing the viability of the youngstock enterprise in the dairy farm. 
 
For an optimal dairy cow replacement management, it is important that the youngstock on the 
farm reach first-calving age at a predetermined time with an optimal growth rate (Place et al., 
1998; Mourits et al., 1997). Any delay in first calving extends the non-productive rearing 
period and cause extra costs (Mourits et al., 1997). The success of youngstock rearing 
depends on the management of youngstock diseases, nutrition, reproduction and environment.  
It is known that youngstock diseases reduce growth and daily weight gain (Hawkins, 1993; 
Virtala et al., 1996; Van der Fels-Klerx et al., 2002; Gulliksen et al., 2009) and affects first 
calving age (Waltner-Toes et al., 1986). Waltner-Toes et al. (1986) mentioned that the 
presence of diarrhea and/or respiratory diseases before 90 days of age affects the performance 
of the animal later in life and is associated with a higher age at calving.  In addition, calf 
diseases increase morbidity and mortality rates, reduce feed conversion, increase the need for 
culling, reduces fertility, increase veterinary expenses, reduce milk production, reduce carcass 
quality and increase the risk of disease later in life (Waltner-Toes et al., 1986; Hawkins, 1993; 
Van der Fels-Klerx et al., 2002; Gulliksen et al., 2009).  Yet, the effects of calf diseases, both 
direct and indirect, have rarely been measured under field conditions (Waltner-Toes et al., 
1986).     
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Nantes, 14-15 January 2010: Farm Animal Health Economics 
 
No study estimated the costs of youngstock rearing which include the uncertainties (e.g. 
incidence of diseases, growth and reproduction probabilities).  In our study, a stochastic 
Monte Carlo model was developed to simulate a calf from birth until first calving age.  Four 
important calf disease (calf scours (CS), bovine respiratory disease (BRD), subclinical 
parasitic gastroenteritis (SPGE) and bovine lungworm (BL) were included, as well as a 
growth function and reproduction probabilities.  Subsequently, this model was used to 
determine the total costs of youngstock rearing for each simulated calf.   
 
MATERIALS AND METHODS 
 
Model development 
 
A dynamic and stochastic Monte Carlo model was built using Microsoft Excel with @Risk 
add-in software (Palisade, 2009) to calculate the costs of the youngstock rearing.  Model 
outcomes were generated in 2 steps.  First, calf life from birth until first calving age which 
include calf disease incidence, growth and reproduction were simulated by using the Monte 
Carlo model.  Subsequently, the total costs were calculated from each simulated calf.  All 
needed inputs were based on literature, expert knowledge and author’s expertise.   
 
Simulating diseases 
 
States were healthy, diseased (CS, BRD, SPGE or BL) or mortality (Table 1).  CS was 
characterized by infectious gastroenteritis caused by bacteria, virus and parasite.  Other 
clinical signs under this state include diarrhoea, enteritis, septicaemia and navel ill.  Next, 
BRD was denoted by respiratory syndrome caused by bacteria, virus and mycoplasma. 
Nematode Ostertagia ostertagi resulted in SPGE infection and caused weight loss, whereas 
BL was denoted as a disease caused by the nematode Dictyocaulus viviparous with coughing 
symptom and weight loss.   
 
Transition matrix 
There were 56 stages, 18 weekly stages from birth until 4 months of age, while stages 19 to 
56 were in 3 weeks intervals from 5 months of age until first calving age.  A transition matrix 
includes probabilities for getting diseased from one stage to the next stage was built at every 
stage in order to adapt for infections variations from birth until first calving age.     
 
Transition matrix provides model stochasticity by using discrete probability function.  Based 
on Table 1, H1 was the probability of the calf remains healthy in next stage and was 
calculated from 1 minus total morbidity.  S1, B1, SP1 and L1 were the probabilities of the 
healthy calf becoming diseased in the next stage.  Cure probabilities from previous diseased 
state were H2, H3, H4 and H5.  The probability a calf will remain diseased from previous 
stage was S2, B3, SP4, and L5 and was calculated from 1 minus the sum of cure rate and 
mortality rate.  M1 and M4 was determined as 0.   
 
Only one state can occur for every stage.  Furthermore, one diseased status did not change to a 
different disease status in the next stage. 
 
 
 
 
 
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Nantes, 14-15 January 2010: Farm Animal Health Economics 
 
Table 1. Example of transition matrix  
                               Period= t+1 
 
 
 
 
 
 
 
Period=t 
 
 
 
 
 
¹ Bovine respiratory disease 
² Parasitic gastroenteritis 
³ The probability depended on previous state at period=t, calf age and season which was based from literatures (CS and BRD) 
and expert opinion (SPGE and BL) 
 
Simulating growth 
 
To calculate the weight for each simulated calf, the model adapted the two phase growth 
function (Koenen and Groen, 1996).  The parameters in the growth function used the normal 
distribution function to provide stochasticity in this model.  In this model, a healthy calf had 
no weight loss.  Weight loss for CS is between 1.57g/day to 2.46g/day while BRD caused 
reduced weight between 13g/day to 86g/day (Virtala et al., 1996; van der Fels-klerx et al., 
2001).  Daily weight loss for SPGE was 150g/day and BL was 110g/day.  The calculation for 
milk replacer quantity, net energy requirement and net energy from feed was adapted from 
PR, 2006 and CVB Table booklet feeding of ruminants, 2008. 
 
Simulating reproduction 
 
Oestrus detection in this model is 80% while conception rates were based from Bage, 2003 
and Brickell et al., 2009.  Both reproduction rates used discrete probability function to 
determine if the calf is pregnant or not.   
 
The input costs in youngstock rearing  
 
The calculation of the average total costs in the youngstock rearing is the sum of healthcare 
costs (Table 3), feed costs, barn costs, and artificial insemination costs (Table 2).  The 
calculation took into account mortality costs and reproductive failure costs (Table 2). 
 
 
 
 
 
 
 
 
 
States  Healthy Calf 
scours 
BRD¹ Subclinical 
PGE² 
Bovine 
lung 
worm 
Mortality 
Healthy H1³ S1³ B1³ P1³ L1³ M1³ 
Calf scours H2³ S2³ B2 P2 L2 M2³ 
BRD¹ H3³ S3 B3³ P3 L3 M3³ 
Subclinical 
PGE² 
H4³ S4 B4 P4³ L4 M4³ 
Bovine 
lungworm 
H5³ S5 B5 P5 L5³ M5³ 
Mortality 0 0 0 0 0 1 
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Nantes, 14-15 January 2010: Farm Animal Health Economics 
 
Table 2. Input prices for estimating the total cost of youngstock rearing in The Netherlands. 
Input variable                                    Price (€ )                            Source 
Farmer labour  18/hour  Huijps et al., 2008 
Veterinarian labour  100/hour Expert 
Milk replacer  0.41/litre Expert 
Milk replacer feeding labour  6/day¹ Expert 
Concentrate  0.174/kg Expert 
Hay  0.15/kg² Expert 
Silage  0.08/kg² Expert 
Fixed feeding labour  1.20/day³ Expert 
Artificial insemination  27/ insemination 4  Expert 
Calf market  110/calf 5  Expert 
Heifer market  960/heifer 6  Expert 
Barn  92/calf/year Expert 
¹  The price was for twice/day feeding with 10 minutes allocated for a milk replacer feeding 
²  The price was calculated by substituting the amount of silage (850NE/kg DM, 0.45DM/kg) or hay (834 NE/kg 
DM, 0.85DM/kg) needed to fulfill the same net energy requirement if the youngstock is fed with concentrate 
(940 NE/kg DM, 0.9 DM/kg).  Therefore, the roughage prices depended on the concentrate price. 
³  The price was for twice/day feeding with 2 minutes time allocated for a feeding session of the roughages and 
concentrate 
4 The price was from the sum of artificial insemination dosage, visit cost and the technician labour cost 
5
 The price was used when mortality occur with the assumption that farmer needed a replacement.  The total 
cost calculation when mortality occurs is the sum of costs to rear until the age of mortality and calf market cost. 
6 The price was used when there is reproductive failure occur.  It is the value of revenue forgone when the 
youngstock was sold as a heifer at 24 months old.  The total cost calculation when reproductive failure occur is 
revenue forgone minus the sum of costs to rear until reproduction failure occurs and calf market cost.   
 
Table 3. The input prices for healthcare costs. 
Healthcare variables Price (€ ) Source 
Preventive treatment  85¹ Expert 
Farmers treatment  
     Calf scours 36² Expert 
     Bovine respiratory disease 5.60² Expert 
     Subclinical Parasitic Gastroenteritis and bovine     
     lungworm 
33³ Expert 
Veterinary treatment 4   
     Calf scours 186 5  Expert 
     Bovine respiratory disease 62.10 5  Expert 
     Subclinical parasitic gastroenteritis and bovine  
     lungworm 30 6  
Expert 
¹ The price was from the sum of BRD vaccination and parasite prevention using bolus anthelmintic  
² The price was from the sum of treatment regime and farmer labour cost for twice/day for 3 days treatment with 
10 minutes/treatment session for CS and 1 minute/treatment session for BRD. 
³ The price was from the sum of pour on anthelmintic price, veterinarian prescription cost and farmer’s labour cost  
4 Veterinary call probability for CS, BRD, SPGE and BL are 0.25, 0.5, 0.05 and 0.15 respectively. 
5 The price was from the sum of treatment regime cost, veterinarian labour cost and farmer labour cost when 
treatment is done for twice/day for 3 days treatment with 45minutes/ treatment session for CS and 15 minutes/ 
treatment session for BRD 
6 The price was from the sum of anthelmintic injection, veterinarian visit and veterinarian labour cost 
 
 
 
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Nantes, 14-15 January 2010: Farm Animal Health Economics 
 
PRELIMINARY RESULTS 
 
From the simulated Monte Carlo model, the non economic output of rearing a youngstock is 
presented in the table 4.  The result showed that on average, a calf had a birth weight of 47kg.  
A calf has less than 1 artificial insemination because there is mortality of the calf in the 
model.   
 
Table 4. Mean non-economic output values of the Monte Carlo model. (5% and 95% 
percentiles are given between brackets) 
 
Output variables Value 
Birth weight (kg) 47 
(40-54) 
Weight (kg) at 10 weeks 80 
(71-90) 
Weight (kg) at 15 months 331 
(314-348) 
Artificial inseminations times 0.8 
(0-3) 
Pregnancy age (month) 18 
(16-19) 
First calving age (month) 26 
(25-28) 
 
The simulations of the Monte Carlo models for the Dutch average farm give the average costs 
of the youngstock rearing and are presented in table 5 below.  The average total cost for 
youngstock raising was estimated as €1364.   
 
Table 5. Economic output values for rearing youngstock (5% and 95% percentiles are given 
between brackets) 
Output variables Mean Costs (€) 
Farmer’s treatment 34 
(0-69) 
Veterinary treatment 46 
(0-62) 
Feed  
 
1005 
(864-1118) 
Artificial insemination  22 
(0-81) 
Barn  
 
178 
(169-191) 
Total  1364 
(1152-1517) 
 
 
CONCLUSIONS 
The average total cost for youngstock raising was estimated as €1364.  The model result 
showed the future use of this model in optimizing the costs in youngstock rearing. 
 
 
 
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Nantes, 14-15 January 2010: Farm Animal Health Economics 
 
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Bage R., 2000. Conception Rates after AI in Swedish Red and white dairy heifers: Relationship with Progesteron 
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CVB, 2008 Table Booklet Feeding of Ruminants; Feeding standards, feeding advices and nutritional values of 
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Gulliksen, S.M., Lie, K.I., and Østerås, O., 2009. Calf health monitoring in Norwegian dairy herds. J. Dairy Sci. 
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26


Costs of mastitis: facts and perception.

Effect of growth and development during the rearing period on the subsequent fertility of nulliparous Holstein-Friesian heifers,

Genetic Analysis of Growth patterns of Black and White dairy heifers,

The effect of early calfhood health status on survivorship and age at first calving,

Estimating the youngstock rearing costs in the Netherlands using Monte Carlo simulation

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