Development of Sow Lameness Classification Trees Using an Embedded Microcomputer-based Force Plate in a Commercial Setting

dc.contributor.author Niemi, Jarad
dc.contributor.author Butters-Johnson, Anna
dc.contributor.author Stalder, Kenneth
dc.contributor.author McNeil, B. M.
dc.contributor.author Calderon Diaz, J. A.
dc.contributor.author Stock, Joseph
dc.contributor.author Parsons, T. D.
dc.contributor.author Stalder, Kenneth
dc.contributor.author Beam, D. L.
dc.contributor.author Johnson, Anna
dc.contributor.author Bruns, C. E.
dc.contributor.author Niemi, Jarad
dc.contributor.department Animal Science
dc.contributor.department Statistics
dc.date 2021-03-31T21:02:06.000
dc.date.accessioned 2021-04-29T23:52:34Z
dc.date.available 2021-04-29T23:52:34Z
dc.date.copyright Tue Jan 01 00:00:00 UTC 2019
dc.date.issued 2019-01-31
dc.description.abstract <p>Background and Objectives: The objectives of this study were: 1) to examine the relationship between forces applied by each leg as measured by the force plate and the degree of visually assessed lameness under conditions applicable to a commercial herd, and 2) to develop an automated lameness detection algorithm based on the force plate output.</p> <p>Methods and Findings: The microcomputer-based embedded force plate system provides an objective approach to lameness detection by measuring the force generated by each individual limb. The force plate device was installed within an Electronic Sow Feeder (ESF) and used to monitor a subset of the 120 multiparous gestating sows housed in a dynamic group over a 21 day period. Each day sows entered the ESF station one at a time to eat. At times when the sow stood squarely and applied pressure to all quadrants of the device, the force applied by each foot was recorded once per second. Sows were visually scored for the presence of lameness using a four-point scale (0=normal to 3=severely lame) on a weekly basis and classified based on this visual assessment as non-lame (score ≤ 1) or lame (score ≥ 2). An ensemble learning method called Random Forest was used to identify the optimal decision tree for classifying the force plate data into similar categories of non-lame and lame. A Kappa Statistics test was used to measure the level of agreement between the visual scoring and force plate results. Changes in lameness status, as well as the first day of lameness identification for each detection method, were also analyzed. Seven variables were included in the classification tree with the most weight given to the difference between the forces applied to the 2 hind legs. The two lameness detection methods assigned the same lameness classification in 95% of cases and had substantial agreement (Kappa Statistic=0.79; P<0.05). However, the classification tree algorithm detected lameness almost 5 days earlier than the visual scoring system (P<0.001). Additionally, comparing lameness of sows from the time of entry into the group, showed an increase in lameness after the first week regardless of the lameness scoring method.</p> <p>Conclusions: Lameness detection typically is based on subjective visual evaluation, which requires time, training, and can be biased between and within individuals. Results demonstrate that under conditions applicable to a commercial herd, the force plate can accurately detect lameness sooner than a weekly visual lameness assessment.</p>
dc.description.comments <p>This article is published as Stalder KJ, McNeil BM, Diaz JAC, Stock JD, Parsons TD, et al. (2019) Development of Sow Lameness Classification Trees Using an Embedded Microcomputer-based Force Plate in a Commercial Setting. J Anim Sci Livest Prod Vol. 2 No.2:06. doi: <a href="http://dx.doi.org/10.21767/2577-0594.10006" target="_blank">10.21767/2577-0594.10006</a>.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/ans_pubs/801/
dc.identifier.articleid 1805
dc.identifier.contextkey 22251678
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ans_pubs/801
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/104555
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/ans_pubs/801/2019_Johnson_DevelopmentSow.pdf|||Sat Jan 15 02:05:08 UTC 2022
dc.source.uri 10.21767/2577-0594.10006
dc.subject.disciplines Agriculture
dc.subject.disciplines Animal Sciences
dc.subject.disciplines Statistical Models
dc.subject.keywords Sow
dc.subject.keywords Lameness detection
dc.subject.keywords Force plate
dc.subject.keywords Weight distribution
dc.title Development of Sow Lameness Classification Trees Using an Embedded Microcomputer-based Force Plate in a Commercial Setting
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 31b412ec-d498-4926-901e-2cb5c2b5a31d
relation.isAuthorOfPublication 9459ddeb-303d-4035-933f-925ec181c7a6
relation.isAuthorOfPublication 0b0a34a3-f123-4f94-a9cf-e730cb2183a6
relation.isOrgUnitOfPublication 85ecce08-311a-441b-9c4d-ee2a3569506f
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
File
Original bundle
Now showing 1 - 1 of 1
Name:
2019_Johnson_DevelopmentSow.pdf
Size:
365.43 KB
Format:
Adobe Portable Document Format
Description:
Collections