Data_Sheet_1_Effect of lameness on feeding behavior of zero grazed Jersey dairy cows.PDF

The dairy industry faces major challenges with high levels of lameness, in parallel to an increased consumer focus on animal welfare. This encourages farmers to consider more robust breeds, such as Jersey cows. As little is known about the behavior of this breed under loose housing conditions, the present study sought to describe the feeding behavior of lame and non-lame Jersey cows in different parities. Such breed-specific information of behavioral changes is needed for breed-specific herd management decisions and may contribute to identifying animals that are susceptible to developing lameness in the future, thus reducing impacts on the welfare and production of cows. Feeding data from 116 Danish Jersey cows were collected using automatic feeders, and lameness status was assessed by technicians every second week. The cows were kept in a loose housing system, with cubicles, a slatted concrete floor, and automatic milking robots. Eating time per visit and per day, the number of visits per day, and intervals between meals were analyzed using generalized linear mixed effects models. The effect of lameness was not significant for any variable. Primiparous Jersey cows had significantly longer eating times per day, shorter meal intervals, and a lower number of visits per day than older Jersey cows. Week in lactation affected the eating time per visit and per day, the number of visits, and between-meal intervals. In conclusion, we found no differences between lame and non-lame Jersey cows but between parities, which disagree with previous research on other breeds, suggesting that Jersey cows not just differ in size and looks but also in their behavioral reaction when lame. Although data from only one herd of a research center were used, this study has demonstrated the need for further research about breed-specific differences and their implications for the health and welfare of the animals.</p

Correlation between metabolite significance, module membership and maximum adjacency ratio for modules of weighted network analysis.

<p>Correlation between metabolite significance, module membership and maximum adjacency ratio for modules of weighted network analysis.</p

Accuracy parameters and number of metabolites with significant variable importance (VI) and maximal VI per trait according to random forest regression.

<p>Accuracy parameters and number of metabolites with significant variable importance (VI) and maximal VI per trait according to random forest regression.</p

Cumulative proportion of explained variance by principal component one to 10.

<p>PC = principal component.</p

Correlation coefficients and corresponding p-values of module-trait relationship.

<p>Correlations of traits drip loss, pH1, pH24 and meat color to modules are characterized by color range from red (‘1’—positive correlation) to green (‘-1’—negative correlation). In parenthesis below correlation coefficients the p-value is given. Drip loss is measured in <i>Musculus longissimus dorsi</i> (LD) 24 h post-mortem (p.m.); pH1 measured in LD 45 minutes p.m.; pH24 measured in LD 24 h p.m.; meat color measured in LD 24 h p.m.; ME = module eigenvalues.</p

Selection of significant modules for meat quality traits in weighted network analysis.

<p>Selection of significant modules for meat quality traits in weighted network analysis.</p

Variable importance boxplot of important metabolites by random forest regression of Strobl et al. (2009) [29].

<p>Drip loss measured in <i>Musculus longissimus dorsi</i> (LD) 24 h post-mortem (p.m.); pH1 measured in LD 45 minutes p.m.; pH24 measured in LD 24 h p.m.; color = meat color measured in LD 24 h p.m.</p

Descriptive statistics and phenotypic correlations between meat quality traits.

<p>Descriptive statistics and phenotypic correlations between meat quality traits.</p

Predictive power of principal component analysis, weighted network analysis and random forest regression in drip loss, pH1, pH24 and meat color based on a multiple regression model.

<p>Predictive power of principal component analysis, weighted network analysis and random forest regression in drip loss, pH1, pH24 and meat color based on a multiple regression model.</p

Selection of metabolites for joint analysis based on their ranking in top 30 metabolites in correlation analysis, metabolite significance of weighted network analysis and variable importance of random forest regression.

<p>Selection of metabolites for joint analysis based on their ranking in top 30 metabolites in correlation analysis, metabolite significance of weighted network analysis and variable importance of random forest regression.</p