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Development and validation of a visual body condition scoring system for dairy goats with picture-based training

2015-09Journal article Restricted access
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dc.contributor.authorVieira, A
dc.contributor.authorBrandão, S
dc.contributor.authorMonteiro, A
dc.contributor.authorAjuda, I
dc.contributor.authorStilwell, G
dc.date.accessioned2015-09-29T10:56:59Z
dc.date.available2015-09-29T10:56:59Z
dc.date.issued2015-09
dc.description.abstractBody condition scoring (BCS) is the most widely used method to assess changes in body fat reserves, which reflects its high potential to be included in on-farm welfare assessment protocols. Currently used scoring systems in dairy goats require animal restraint for body palpation. In this study, the Animal Welfare Indicators project (AWIN) proposes to overcome this constraint by developing a scoring system based only on visual assessment. The AWIN visual body condition scoring system highlights representative animals from 3 categories: very thin, normal, and very fat, and was built from data sets with photographs of animals scored by a commonly used 6-point scoring system that requires palpation in 2 anatomical regions. Development of the AWIN scoring system required 3 steps: (1) identification and validation of a body region of interest; (2) sketching the region from photographs; and (3) creation of training material. The scoring system's reliability was statistically confirmed. An initial study identified features in the rump region from which we could compute a set of body measurements (i.e., measures based on anatomical references of the rump region) that showed a strong correlation with the assigned BCS. To validate the result, we collected a final data set from 171 goats. To account for variability in animal size and camera position, we mapped a subset of features to a standard template and aligned all the rump images before computing the body measurements. Scientific illustrations were created from the aligned images of animals identified as representative of each category to increase clarity and reproducibility. For training material, we created sketches representing the threshold between consecutive categories. Finally, we conducted 2 field reliability studies. In the first test, no training was given to 4 observers, whereas in the second, training using the threshold images was delivered to the same observers. In the first experiment, interobserver results was substantial, showing that the visual scoring system is clear and unambiguous. Moreover, results improved after training, reaching almost perfect agreement for the very fat category. The visual body condition scoring system is not only a practical tool for BCS in dairy goats but also shows potential to be fully automated, which would enhance its use in welfare assessment schemes and farm management.pt_PT
dc.identifier10.3168/jds.2015-9428
dc.identifier.doi10.3168/jds.2015-9428
dc.identifier.urihttp://hdl.handle.net/10400.19/2931
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherAmerican Dairy Science Associationpt_PT
dc.relation.publisherversionhttp://www.journalofdairyscience.org/article/S0022-0302(15)00480-4/fulltextpt_PT
dc.subjectbody condition scoringpt_PT
dc.subjectdairy goatpt_PT
dc.subjectvisual body condition scoring systempt_PT
dc.subjectrepresentative imagept_PT
dc.subjecttemplate alignmentpt_PT
dc.titleDevelopment and validation of a visual body condition scoring system for dairy goats with picture-based trainingpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage6608pt_PT
oaire.citation.issue98pt_PT
oaire.citation.startPage6597pt_PT
oaire.citation.titleJournal of Dairy Sciencept_PT
oaire.citation.volume9pt_PT
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT

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