Literature DB >> 28527794

Farmers' preferences for automatic lameness-detection systems in dairy cattle.

T Van De Gucht1, W Saeys2, A Van Nuffel3, L Pluym4, K Piccart4, L Lauwers5, J Vangeyte4, S Van Weyenberg4.   

Abstract

As lameness is a major health problem in dairy herds, a lot of attention goes to the development of automated lameness-detection systems. Few systems have made it to the market, as most are currently still in development. To get these systems ready for practice, developers need to define which system characteristics are important for the farmers as end users. In this study, farmers' preferences for the different characteristics of proposed lameness-detection systems were investigated. In addition, the influence of sociodemographic and farm characteristics on farmers' preferences was assessed. The third aim was to find out if preferences change after the farmer receives extra information on lameness and its consequences. Therefore, a discrete choice experiment was designed with 3 alternative lameness-detection systems: a system attached to the cow, a walkover system, and a camera system. Each system was defined by 4 characteristics: the percentage missed lame cows, the percentage false alarms, the system cost, and the ability to indicate which leg is lame. The choice experiment was embedded in an online survey. After answering general questions and choosing their preferred option in 4 choice sets, extra information on lameness was provided. Consecutively, farmers were shown a second block of 4 choice sets. Results from 135 responses showed that farmers' preferences were influenced by the 4 system characteristics. The importance a farmer attaches to lameness, the interval between calving and first insemination, and the presence of an estrus-detection system contributed significantly to the value a farmer attaches to lameness-detection systems. Farmers who already use an estrus detection system were more willing to use automatic detection systems instead of visual lameness detection. Similarly, farmers who achieve shorter intervals between calving and first insemination and farmers who find lameness highly important had a higher tendency to choose for automatic lameness detection. A sensor attached to the cow was preferred, followed by a walkover system and a camera system. In general, visual lameness detection was preferred over automatic detection systems, but this preference changed after informing farmers about the consequences of lameness. To conclude, the system cost and performance were important features, but dairy farmers should be sensitized on the consequences of lameness and its effect on farm profitability.
Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  automated lameness detection; discrete choice; farmer preference; technology adoption

Mesh:

Year:  2017        PMID: 28527794     DOI: 10.3168/jds.2016-12285

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  4 in total

Review 1.  A Review: Development of Computer Vision-Based Lameness Detection for Dairy Cows and Discussion of the Practical Applications.

Authors:  Xi Kang; Xu Dong Zhang; Gang Liu
Journal:  Sensors (Basel)       Date:  2021-01-22       Impact factor: 3.576

2.  Comparison of Low- and High-Cost Infrared Thermal Imaging Devices for the Detection of Lameness in Dairy Cattle.

Authors:  Aidan Coe; Nicola Blackie
Journal:  Vet Sci       Date:  2022-08-06

Review 3.  Association between Lameness and Indicators of Dairy Cow Welfare Based on Locomotion Scoring, Body and Hock Condition, Leg Hygiene and Lying Behavior.

Authors:  Mohammed B Sadiq; Siti Z Ramanoon; Wan Mastura Shaik Mossadeq; Rozaihan Mansor; Sharifah Salmah Syed-Hussain
Journal:  Animals (Basel)       Date:  2017-11-05       Impact factor: 2.752

4.  Supporting the Development and Adoption of Automatic Lameness Detection Systems in Dairy Cattle: Effect of System Cost and Performance on Potential Market Shares.

Authors:  Tim Van De Gucht; Stephanie Van Weyenberg; Annelies Van Nuffel; Ludwig Lauwers; Jürgen Vangeyte; Wouter Saeys
Journal:  Animals (Basel)       Date:  2017-10-08       Impact factor: 2.752

  4 in total

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