Haas, Y., de

Hoorneman, J.N.

Nauta, W.J.

Smolders, E.A.A.

Veerkamp, R.F.

English

Wageningen Yield 

Effect of Crossbreeding on Milk Production, Udder 
Health and Fertility on Dutch Organic Dairy Farms 
Y. de Haas*, J.N. Hoorneman*,#, E.A.A. Smolders*, W.J. Nauta‡ and         
R.F. Veerkamp* 
Introduction 
Organic farming in Europe has developed into a small (about 3% of total agricultural area) 
but important factor in agricultural production. In the Netherlands, organic dairy farming 
grew rapidly in the late 1990s. Farmers who converted to organic production had to undergo 
some major changes in their farm management. The most important changes were no use of 
chemical fertilizers, restricted use of concentrates, and limited use of antibiotics. Because of 
these restrictions, the cows have a lower energy intake and do have to combat diseases 
themselves. This is expected to affect especially the high-producing cows (Padel, (2000)), 
usually Holsteins. Ten years ago most organic dairy farmers in the Netherlands milked 
Holstein cows (Nauta et al. (2006)). After conversion many organic farmers started crossing 
with other breeds, but without clear insight on the effects of crossbreeding in an organic 
system. Within organic farms there is a large variation in management. To some extent this 
variation can be captured by soil type and housing system. Both aspects might influence the 
performance of cows. The aim of this study was to analyze an unique dataset with 24 
different breeds and their crosses and to estimate the effects of crossbreeding of the five most 
prominent breeds with Holstein Friesian cows for milk production, udder health and fertility 
and to investigate if these effects differ according to soil type and housing systems. 
Material and methods 
Available data.  Organic farms in The Netherlands were approached and 113 out of 325 
gave permission to use their data for this study. Data was collected between January 1st, 2003 
and February 1st, 2009. Farms had on average a herd size of 50 cows each year. Primiparous 
cows represented 28.3% of lactations, 23.1% were 2nd lactations. In total, there were 33,788 
lactations available on 15,015 individual cows. For these cows, data were collected on breed 
composition, birth date, age at calving, parity, stage of lactation, calving interval, kg milk 
production in 305 days, kg fat and protein corrected milk yield (FPCM = (0.337+0.116* 
%FAT+0.06*%PROT)*KGMILK), log transformed lactation average somatic cell score up 
to 350 days (SCS = 1000+(1000*(log(SCC)/log(2)))) and calving interval in days (CI). 
 
In total 24 different breeds were present,  however, Brown Swiss (BS), Dutch Friesian (FH), 
Groningen White Headed (GWH), Holstein-Friesian (HF), Jersey (J), and Meuse-Rhine-
Yssel (MRY) cattle were presented mostly and had a large spread of crosses (e.g. Purebreds, 
                                                 
*
 Animal Breeding and Genomics Centre, Wageningen UR Livestock Research,. PO Box 65. NL-8200 AB Lelystad 
‡
  Louis Bolk Instituut. Hoofdweg 24. NL-3972 LA  Driebergen 
#
  Current address: Hendrix Genetics, PO Box 114. NL-5830 AC  Boxmeer 
F1-crosses, and backcrosses) present. Holstein-Friesian was the most important breed, with 
74.5% of the cows carrying at least 50% Holstein genes.  
 
Farm systems. Farms were separated based on their farming system: (a) soil type of a farm 
(sand or no sand) and (b) the housing system of a farm (cubicle barn or not). In the data, 49 
out of 113 farms farmed on sand, with in total 13,203 lactations. The soil type of the other 64 
farms was anything other than sand, like peat meadows, clay, or loess. With regard to the 
housing system, 84 farms had cubicle barns, corresponding with 26,341 lactations. 
 
Statistical analyses. The data with all breeds and their crosses were analyzed using 
ASREML (Gilmour et al. (2006)), including a regression on all breed fractions (scaled in 
classes of 12.5%, from 1 to 8) and the expected heterosis and recombination. Subsequently, 
least square means were predicted for animals of the five most prominent breeds and crosses 
with Holstein (F1-offspring), and both between purebred parents and a F1-parent 
(backcross). The model was the same for all traits (milk, FPCM, SCS and CI): 
Y ~ µ + fixed effects + ∑ bi*breedi + b2*heterosis + b3*recombination  
 + animal + error 
 
In this model, Y = observation on the performance of a production traits on a cow and µ = 
overall mean. Fixed effects include parity (with four classes, where the last class contains all 
parities ≥4), herd (with 113 classes), year (with 7 classes), season (based on 4 seasons; Jan.–
Mar., Apr.–Jun., Jul.–Sep., and Oct.–Dec.). Linear regressions were included for the breed 
proportions (bi, with i ranging from 1 to 24), for heterosis (b2) and for recombination (b3). A 
random effect was included for animal, to account for multiple parities of a cow in the 
dataset, and for the residual term. The interactions with soil types and housing systems were 
fitted by including an interaction for these factors with all regression coefficients. Because 
herd effect was already included as a fixed effect separately, the single effects of soil type 
and housing systems were not included in the model as those traits are entangled with herd. 
Results and discussion 
Raw average milk production was 6858 kg in 305 days, and 300 kg fat and 235 kg protein. 
The lactation-average SCS was 1730 (this coincides with appr. 266,000 cells/ml). Predicted 
milk production, while correcting for effects of parity, herd and the interaction between year 
and season of calving, is highest for cows that carry 100% HF-genes and lowest for cows 
that carry 100% GWH-genes (Figure 1). The higher the proportion of Holstein-genes, the 
higher the predicted milk production, as expected.  
 
When analyzing the overall data, heterosis had significant effect (p<0.10) on milk, FPCM 
and CI in the favourable direction (i.e. more milk, shorter CI), but unfavourably for SCS 
(higher cell count). Recombination was unfavourable for the milk traits, but favourable for 
fertility and udder health. Regression coefficients differed per breed; i.e. the only positive 
regression coefficient for milk was for HF, but when milk was corrected for fat and protein 
content, Jerseys gave a positive regression as well. Regression coefficient for udder health 
(SCS) were favourable for BS, but unfavourable for GWH and Jerseys. Regression 
coefficient for fertility (CI) was favourable for GWH and unfavourable for HF and BS. 
5200
5400
5600
5800
6000
6200
6400
6600
6800
7000
7200
100% 75% 50% 25% 0%
Percentage of Holstein-genes
30
5d
 
m
ilk
 
pr
o
du
ct
io
n
 
(kg
)
Brown Swiss
Dutch Friesian
White Headed
Jersey
MRY
 
Figure 1: Predicted 305d milk production (kg) per percentage of Holstein-genes and 
Brown Swiss, Dutch Friesian, Jersey, Groningen White Headed and Meuse Rhine Yssel 
 
 
The regression coefficients for milk, FPCM, SCS and CI are shown in Table 1 separated per 
soil type. The soil type of a farm determined directly the composition of the cow’s diet. On 
sandy soil more maize silage was fed to the cows, which has a high energy content, whereas 
diets on farms with non-sandy soil consisted mostly out of grass silage. Some of the breed 
effects differed significantly depending on the soil type: regression coefficients of milk on 
BS, HF, J, and MRY were significantly higher (p<0.10) on farms farming on sand than on 
farms with other soil types (Table 1). On the other hand, the regression coefficient of SCS on 
GWH was higher on farms with soil types other than sand, implying a higher cell count and 
worse udder health. Regression coefficient of CI on Jersey was higher in farms on sand, 
indicating a longer calving interval. 
 
 
Table 1: Regression coefficients for milk, fat-protein corrected milk (FPCM), somatic 
cell score (SCS) and calving interval (CI) based on soil type of the farm (sand vs. no 
sand (No S.)) 
 Milk FPCM SCS CI 
 Sand No S Sand No S Sand No S Sand No S 
Heterosis 104.9 123.9 124.5 134.3 4.5 3.6 -2.2 -3.1 
Recombination -526.6 -516.5 -312.7 -420.2 -41.9* -8.9 -3.7 -3.7 
Brown Swiss -6.2* -75.0 -7.2* -50.7 -5.2 -4.0 3.2 2.6 
Dutch Friesian -28.3 -25.2 -14.8 -39.3 -1.2 -1.0 -2.0 1.6 
White Headed -113.9 -161.8 -137.4 -160.7 12.1* 4.0 -2.0 -1.4 
Holstein 114.7* 79.3 46.5* 74.0 1.0 1.2 3.7 3.7 
Jersey -62.3* -135.7 -14.3 -59.2 4.1 4.3 2.9* -0.4 
MRY -7.0* -66.6 -20.4* -59.5 1.8 1.0 0.3 -0.9 
*
 p-value <0.10 
  
The farms in this study had either a barn with cubicles or another housing system. Compared 
to the other breeds, the regression coefficient of milk on HF and MRY (and also FPCM) was 
higher in a cubicle barn than in barns without cubicles (Table 2). The regression coefficient 
of udder health on FH or Jersey was significantly higher in non cubicle barns. This might be 
due to environmental pathogens in the bedding of non cubicle barns causing more mastitis 
and higher cell counts. Regression of fertility on HF is worse in cubicle barns resulting in 
higher CI, which might be because of the slippery floor and the cows show their heat less.  
 
 
Table 2: Regression coefficients for milk, fat-protein corrected milk (FPCM), somatic 
cell score (SCS) and calving interval (CI) based on housing system of the farm (cubicles 
(cub.)  vs. no cubicles (No C.)) 
 Milk FPCM SCS CI 
 Cub No C Cub No C Cub No C Cub No C 
Heterosis 69.0 183.5 121.5 199.7 1.9 13.7 -1.7 -5.3 
Recombination -583.9 -365.5 -434.9 -267.7 -19.7 -35.6 -8.5 7.6 
Brown Swiss -43.7 -88.7 -17.7* 78.6 -5.5 2.2 3.4 0.7 
Dutch Friesian 1.6 -120.1 -5.3 -105.5 -3.5* 6.9 0.4 -2.2 
White Headed -155.5* -142.9 -162.9 -139.8 6.1* 3.9 -1.6 -1.9 
Holstein 107.0* 54.1 97.0* 49.9 0.5 3.0 4.5* 1.4 
Jersey -83.6 -149.2 -20.1 -86.5 1.6* 8.2 1.4 1.2 
MRY -21.5* -86.7 -24.3* -90.5 1.3 0.7 0.5 -2.4 
*
 p-value <0.10 
 
Conclusion 
Crossbreeding Holstein dairy cows with other breeds (BS, FH, GWH, J, or MRY) decreased 
milk production (also when corrected for fat and protein content (FPCM)), but improved 
fertility. Udder health was only improved in some crosses, and not when crossed with GWH 
or J. Farm management systems (depending on soil type or housing system) affected the 
regression coefficients on breed components significantly for some breeds. For example, 
the effect of HF on milk and CI was twice as large in cubicle housing than in other housing 
systems, and FH had a unfavourable effect on SCS in cubicles, but a favourable effect in 
other systems. Jersey had a negative effect on fertility only on farms on sandy soil. Hence, 
breed effects differ across farming systems, also within the organic systems. 
References 
Gilmour A.R., Gogel, B.J., Cullis, B.R. et al. (2006). ASREML User Guide 
Nauta, W.J., Veerkamp, R.F., Brascamp, E.W. et al. (2006). J. Dairy Sci., 89: 2729-2737. 
Padel, S. (2000). In Proc. 3rd Network for Animal Health and Welfare in Organic 
Agriculture (NAHWOA) Workshop, pages 121-135. 


Effect of Crossbreeding on Milk Production, Udder Health and Fertility on Dutch Organic Dairy Farms

Haas, de, Y.

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Effect of Crossbreeding on Milk Production, Udder Health and Fertility on Dutch Organic Dairy Farms

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