Housing and management factors associated with indicators of dairy cattle welfare
Introduction
The level of animal welfare varies considerably among dairy herds. Prevalences of lameness and hock injuries, for example, have been reported ranging from 0% to 100% (Fourichon et al., 2001, Von Keyserlingk et al., 2012, Brenninkmeyer et al., 2013). At the same time, considerable variation between farms exists in housing and management conditions that are expected to affect dairy cattle welfare (Von Keyserlingk et al., 2012). This suggests there is room for improvement of dairy cattle welfare.
Improvement of the level of welfare on a farm by adjusting housing or management factors is complicated because animal welfare is a multidimensional concept (Fraser, 1995). This multidimensionality is illustrated by the fact that animal welfare comprises not only physical (i.e. health and vigor), but also psychological aspects (i.e. sense and feeling; Webster, 2005). Consequently, animal welfare assessment requires the use of multiple indicators. The Welfare Quality protocol for cattle, for example, includes indicators relating to the aspects of feeding, housing, health, and behavior (Welfare Quality, 2009). Welfare indicators relating to similar aspects of animal welfare have been associated with similar housing and management factors. For example, lameness and skin lesions are welfare indicators related to dairy cattle health, and both have been associated with surface of the lying area in free-stalls and with pasturing (e.g. Haskell et al., 2006, Brenninkmeyer et al., 2013, Chapinal et al., 2013). This may be partly due to biological relations between these two indicators. For indicators related to different aspects of animal welfare, however, it is largely unknown whether they are influenced by similar-, or by different housing and management factors, and whether changing a factor has opposite (trade-off) or synergic effects on these indicators. Positioning the neck rail of the free-stall further from the rear curb, for example, was a protective factor for lameness and for hair loss at the hocks (synergy), but it has also been associated with decreased cow cleanliness due to defecation in the stall (trade-off; e.g. Bernardi et al., 2009, Dippel et al., 2009, Fregonesi et al., 2009, Potterton et al., 2011).
Knowing potential synergies and trade-offs of housing and management factors for different indicators of animal welfare is essential for farmers who aim to improve the overall welfare level of their herd. Because housing and management factors associated with dairy cattle welfare can differ across regions, due to, for example, geographical differences in housing design and popular opinions of best practices in the area (Chapinal et al., 2013), identification and comparison of factors associated with dairy cattle welfare is preferably done in the same population. Only in a few studies associations between housing and management factors and indicators relating to different aspects of dairy cattle welfare have been considered simultaneously (e.g. Burow et al., 2012, Husfeldt and Endres, 2012, Coignard et al., 2013). In the present study, we considered four indicators included in the Welfare Quality protocol for dairy cattle (2009) relating to three aspects of animal welfare: prevalence of lameness (relating to health), prevalence of lesions or swellings (health), prevalence of dirty hindquarters (cleanliness), and average frequency of displacements (behavior). A displacement is the act of an animal giving up its present position as a consequence of an agonistic interaction. These indicators were selected because of the data availability of potentially relevant explanatory housing and management factors for each individual indicator, and because they encompassed different aspects of animal welfare (e.g. health and behavior). This allowed us to examine the extent to which indicators relating to the same or different aspects of animal welfare were influenced by the same or different housing and management factors. The aim of this research was to identify and compare housing and management factors associated with prevalence of lameness, prevalence of lesions or swellings, prevalence of dirty hindquarters, and average frequency of displacements in dairy herds with free-stall housing.
Section snippets
Herd selection
In order to evaluate associations between housing and management factors and animal welfare indicators with some confidence, a wide range of the level of animal welfare is required. This cannot be achieved with random sampling of herds when prevalence of herds with poor welfare is low, because the necessary sample sizes would be too large to be covered by the costs of observations. Therefore, herds were selected, in order to obtain a sufficiently wide range of animal welfare. We used several
Results and discussion
The median within-herd prevalence observed was 32.3% lame cows (range 0–97.7%), 35.9% cows with lesions or swellings (range 0–97.6%), and 33.3% cows with dirty hindquarters (range 0–100%). The median number of displacements per cow per hour was 0.43 (range 0–1.85 displacements per cow per hour). Data were complete for prevalence of lameness, prevalence of dirty hindquarters and frequency of displacements. There was a missing value for prevalence of lesions or swellings in one herd. The
Conclusions
Fifteen housing and management factors were significantly associated with four indicators of dairy cattle welfare. Two of these factors, surface of the lying area and access to pasture, were associated with prevalences of lameness, lesions or swellings, and dirty hindquarters. No common housing and management factors were identified for frequency of displacements and prevalence of lameness, lesions or swellings, and dirty hindquarters. Lameness, lesions or swellings, and dirty hindquarters were
Acknowledgements
This research was funded by the Animal Health Service Deventer. The authors gratefully acknowledge farmers for participating in this study, observers for their commitment to collecting the welfare data, and Willem Buist (Biometris, Wageningen University, the Netherlands) for his help with the data analyses.
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