Welfare in Danish Dairy Herds 3. Health Management and General Routines in 1983 and 1994

Agger, Jens Frederik; Alban, Lis

doi:10.1186/bf03548121

Cited by 11 publications

(1 citation statement)

References 13 publications

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“…Number of cows within a herd has been previously identified as a significant risk factor in the incidence of metabolic events including ketosis and displaced abomasum (Stengärde et al, 2012). This may be indicative of an underlying risk factor, such as less time spent per cow in larger herds (Agger and Alban, 1996). Higher parity has also been identified as a risk factor for metabolic diseases by other authors (Rasmussen et al, 1999).…”

Section: Random Forest-herd Levelmentioning

confidence: 99%

Benchmarking dairy herd health status using routinely recorded herd summary data

Gaddis

Cole

Clay

et al. 2016

Journal of Dairy Science

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Genetic improvement of dairy cattle health through the use of producer-recorded data has been determined to be feasible. Low estimated heritabilities indicate that genetic progress will be slow. Variation observed in lowly heritable traits can largely be attributed to nongenetic factors, such as the environment. More rapid improvement of dairy cattle health may be attainable if herd health programs incorporate environmental and managerial aspects. More than 1,100 herd characteristics are regularly recorded on farm test-days. We combined these data with producer-recorded health event data, and parametric and nonparametric models were used to benchmark herd and cow health status. Health events were grouped into 3 categories for analyses: mastitis, reproductive, and metabolic. Both herd incidence and individual incidence were used as dependent variables. Models implemented included stepwise logistic regression, support vector machines, and random forests. At both the herd and individual levels, random forest models attained the highest accuracy for predicting health status in all health event categories when evaluated with 10-fold cross-validation. Accuracy (SD) ranged from 0.61 (0.04) to 0.63 (0.04) when using random forest models at the herd level. Accuracy of prediction (SD) at the individual cow level ranged from 0.87 (0.06) to 0.93 (0.001) with random forest models. Highly significant variables and key words from logistic regression and random forest models were also investigated. All models identified several of the same key factors for each health event category, including movement out of the herd, size of the herd, and weather-related variables. We concluded that benchmarking health status using routinely collected herd data is feasible. Nonparametric models were better suited to handle this complex data with numerous variables. These data mining techniques were able to perform prediction of health status and could add evidence to personal experience in herd management.

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Section: Random Forest-herd Levelmentioning

confidence: 99%