Exploring the value of routinely collected herd data for estimating dairy cattle welfare

Vries, M. de; Bokkers, E.A.M.; Schaik, G. van; Engel, B.; Dijkstra, T.; Boer, I.J.M. de

doi:10.3168/jds.2013-6585

Cited by 60 publications

(107 citation statements)

References 43 publications

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“…This related largely to a low prevalence associated with certain health measures, that is, most of the data was 0 as opposed to missing, and this has been reported elsewhere with regard Welfare Quality ® (e.g. de Vries et al, 2013b).…”

Section: Missing Datamentioning

confidence: 67%

Navigating the iceberg: reducing the number of parameters within the Welfare Quality® assessment protocol for dairy cows

Heath

Browne

Mullan

et al. 2014

Animal

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The Welfare Quality ® protocols provide a multidimensional assessment of welfare, which is lengthy, and hence limited in terms of practicality. The aim of this study was to investigate potential 'iceberg indicators' which could reliably predict the overall classification as a means of reducing the length of time for an assessment and so increase the feasibility of the Welfare Quality ® protocol as a multidimensional assessment of welfare. Full Welfare Quality ® assessments were carried out on 92 dairy farms in England and Wales. The farms were all classified as Acceptable or Enhanced. Logistic regression models with cross validation were used to compare model fit for the overall classification on farms. 'Absence of prolonged thirst', on its own, was found to correctly classify farms 88% of the time. More generally, the inclusion of more measures in the models was not associated with greater predictive ability for the overall classification. Absence of prolonged thirst could thus, in theory, be considered to be an iceberg indicator for the Welfare Quality ® protocol, and could reduce the length of time for a farm assessment to 15 min. Previous work has shown that the parameters within the Welfare Quality ® protocol are important and relevant for welfare assessment. However, it is argued that the credibility of the published aggregation system is compromised by the finding that one resource measure (Absence of prolonged thirst) is a major driver for the overall classification. It is therefore suggested that the prominence of Absence of prolonged thirst in this role may be better understood as an unintended consequence of the published measure aggregation system rather than as reflecting a realistic iceberg indicator.

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Section: Missing Datamentioning

confidence: 67%

Navigating the iceberg: reducing the number of parameters within the Welfare Quality® assessment protocol for dairy cows

Heath

Browne

Mullan

et al. 2014

Animal

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“…Currently, in many countries, herd data are routinely collected because of cattle improvement schemes, milk quality control, regulations, and surveillance, although the exact mix of data may differ. Collection of routinely collected data presents increasing opportunities to apply predictive modeling in veterinary medicine (de Vries et al, 2014;McParland et al, 2014;Vergara et al, 2014). The predictive model for the average CMI of all dairy herds accurately estimated the CMI and was not significantly different from the average CMI that was observed and registered by the farmers.…”

Section: Discussionmentioning

confidence: 91%

An estimation of the clinical mastitis incidence per 100 cows per year based on routinely collected herd data

Santman-Berends

Lam

Keurentjes

et al. 2015

Journal of Dairy Science

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The aim of this study was to evaluate whether it was possible to (1) estimate the clinical mastitis incidence rate (CMI) for all Dutch dairy herds and (2) to detect farms with a high CMI based on routinely collected herd data. For this study, 240 dairy farms with a conventional milking system that participated in the milk recording program every 4 to 6 wk were randomly selected and agreed to participate. From the initial 240 herds, data of clinical mastitis (CM) registrations and routinely collected herd data of 227 herds were complete and could be used for analysis. Routinely collected herd data consisted of identification and registration records, antimicrobial usage, test-day records from the milk recording program, bulk tank milk (BTM) somatic cell count data and results of diagnostic tests on BTM samples. For each of the 227 herds, the CMI per 100 cows per year was calculated per quarter of the year and was combined with the available herd data. Two models were developed to predict the CMI for all dairy herds and to detect individual herds that belonged to the 25% herds with the highest CMI. Records of 156 (67%) herds were used for development of the models and the remaining 71 (33%) were used for validation. The model that estimated the CMI in all herds consisted of 11 explanatory variables. The observed and predicted averages of the validation herds were not significantly different. The model estimated a CMI per 100 cows per year of 32.5 cases (95% confidence interval=30.2-34.8), whereas the farmers registered 33.4 cases (95% confidence interval=29.5-37.4). The model that aimed at detecting individual herds with a high CMI contained 6 explanatory variables and could correctly classify 77% of all validation herds at the quarter-year level. The most important variables in the model were antibiotic usage for treating CM and BTM somatic cell count. In conclusion, models based on routinely collected herd data gave an accurate prediction of CMI for all Dutch dairy herds and could detect individual dairy herds with a high CMI. With these models it is possible to periodically monitor CMI both at the herd and at the national level, which is valuable for monitoring purposes and can motivate farmers to continuously improve udder health in their herds.

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“…lameness, vulva discharge, diarrhea, ocular discharge). Routinely collected herd data have been shown to allow a prescreening for the discovery of herds having welfare problems (de Vries et al, 2014). Accordingly, routinely collected herd data can improve the classification of herd welfare (Nyman et al, 2011) and facilitate the time consuming and, therefore, costly on-farm assessment of the welfare indicators.…”

Section: Strategies For Phenotypingmentioning

confidence: 99%

Review: Opportunities and challenges for small populations of dairy cattle in the era of genomics

Schöpke¹,

Swalve

2016

Animal

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In modern dairy cattle breeding, genomic breeding programs have the potential to increase efficiency and genetic gain. At the same time, the requirements and the availability of genotypes and phenotypes present a challenge. The set-up of a large enough reference population for genomic prediction is problematic for numerically small breeds but also for hard to measure traits. The first part of this study is a review of the current literature on strategies to overcome the lack of reference data. One solution is the use of combined reference populations from different breeds, different countries, or different research populations. Results reveal that the level of relationship between the merged populations is the most important factor. Compiling closely related populations facilitates the accurate estimation of marker effects and thus results in high accuracies of genomic prediction. Consequently, mixed reference populations of the same breed, but from different countries are more promising than combining different breeds, especially if those are more distantly related. The use of female reference information has the potential to enlarge the reference population size. Including females is advisable for small populations and difficult traits, and maybe combined with genotyping females and imputing those that are un-genotyped.The efficient use of imputation for un-genotyped individuals requires a set of genotyped related animals and well-considered selection strategies which animals to choose for genotyping and phenotyping. Small populations have to find ways to derive additional advantages from the cost-intensive establishment of genomic breeding schemes. Possible solutions may be the use of genomic information for inbreeding control, parentage verification, within-herd selection, adjusted mating plans or conservation strategies.The second part of the paper deals with the issue of high-quality phenotypes against the background of new, difficult and hard to measure traits. The use of contracted herds for phenotyping is recommended, as additional traits, when compared to standard traits used in dairy cattle breeding can be measured at set moments in time. This can be undertaken even for the recording of health traits, thus resulting in complete contemporary groups for health traits. Future traits to be recorded and used in genomic breeding programs, at least partly will be traits for which traditional selection based on widespread phenotyping is not possible. Enabling phenotyping of sufficient numbers to enable genomic selection will rely on cooperation between scientists from different disciplines and may require multidisciplinary approaches.

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Exploring the value of routinely collected herd data for estimating dairy cattle welfare

Cited by 60 publications

References 43 publications

Navigating the iceberg: reducing the number of parameters within the Welfare Quality® assessment protocol for dairy cows

Navigating the iceberg: reducing the number of parameters within the Welfare Quality® assessment protocol for dairy cows

An estimation of the clinical mastitis incidence per 100 cows per year based on routinely collected herd data

Review: Opportunities and challenges for small populations of dairy cattle in the era of genomics

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