Extending the 'Five Domains' model for animal welfare assessment to incorporate positive welfare states

2015 ◽  
Vol 24 (3) ◽  
pp. 241-253 ◽  
Author(s):  
DJ Mellor ◽  
NJ Beausoleil
Keyword(s):  
Animal Welfare ◽  
Welfare States ◽  
Welfare Assessment ◽  
Animal Welfare Assessment

scholarly journals PSIV-33 The Animal Welfare Assessment Contest® as a Tool to Promote Animal Welfare Education

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 468-468
Author(s):  
Sharon Kuca ◽  
Lindsey McKinney ◽  
Cia Johnson
Keyword(s):  
Graduate Students ◽  
Animal Welfare ◽  
Educational Tool ◽  
Welfare Assessment ◽  
Behavioral Indicators ◽  
Animal Welfare Science ◽  
Computer Based ◽  
Animal Welfare Assessment ◽  
Strongly Agree

Abstract Established in 2001, the Animal Welfare Assessment Contest® (AWJAC®) aims to be an innovative educational tool for enhancing understanding and awareness of welfare issues affecting animals used for human purposes (e.g., research, agriculture, entertainment, companionship). The contest is open to participation by veterinary, undergraduate, and graduate students who may participate as individuals or as part of a team. A limited number of veterinarians are also eligible to compete as non-placing participants. Participation in the contest entails assessment of live and computer-based scenarios encompassing data, photographs, and videos of animals in comparable situations. Students then use the information obtained to rank the welfare of the animals in those situations on the basis of physiologic and behavioral indicators, with attention to facilities and management, and present their analyses orally to expert judges. The species featured change each year of the contest. At the completion of each contest, participants and coaches are asked to anonymously complete a written survey. The quantitative and qualitative results of this survey are used to determine if the contest has achieved its aims and incorporate suggestions for improvement of future contests. The majority of survey respondents from the five contests held between 2014–2018 report they either strongly agree or agree that the AWJAC increased their knowledge of animal welfare science (98%, n = 549) and was an overall valuable experience (99%, n = 547) that they would recommend to their peers (98%, n = 550). Respondents cited networking opportunities and diversity of species featured in the contest as key reasons the contest is valuable. Given these results, the AWJAC is successfully achieving its aims to increase animal welfare knowledge in an innovative way.

scholarly journals Is the profitability of Canadian tiestall farms associated with their performance on an animal welfare assessment?

2018 ◽  
Vol 101 (3) ◽  
pp. 2359-2369 ◽  
Author(s):  
M. Villettaz Robichaud ◽  
J. Rushen ◽  
A.M. de Passillé ◽  
E. Vasseur ◽  
D.B. Haley ◽  
...  
Keyword(s):  
Animal Welfare ◽  
Welfare Assessment ◽  
Animal Welfare Assessment

scholarly journals Test−Retest Reliability of the ‘Welfare Quality® Animal Welfare Assessment Protocol for Sows and Piglets’. Part 1. Assessment of the Welfare Principle of ‘Appropriate Behavior’

Animals ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 398 ◽  
Author(s):  
Friedrich ◽  
Krieter ◽  
Kemper ◽  
Czycholl
Keyword(s):  
Animal Welfare ◽  
Correlation Coefficient ◽  
Retest Reliability ◽  
Welfare Assessment ◽  
Appropriate Behavior ◽  
Assessment Protocol ◽  
Smallest Detectable Change ◽  
Welfare Quality ◽  
Animal Welfare Assessment ◽  
Test Retest Reliability

The present study’s aim was to assess the test−retest reliability (TRR) of the ‘Welfare Quality® animal welfare assessment protocol for sows and piglets’ focusing on the welfare principle ‘appropriate behavior’. TRR was calculated using Spearman’s rank correlation coefficient (RS), intraclass correlation coefficient (ICC), smallest detectable change (SDC), and limits of agreement (LoA). Principal component analysis (PCA) was used for deeper analysis of the Qualitative Behavior Assessment (QBA). The study was conducted on thirteen farms in Northern Germany, which were visited five times by the same observer. Farm visits 1 (F1; day 0) were compared to farm visits 2 to 5 (F2–F5). The QBA indicated no TRR when applying the statistical parameters introduced above (e.g., ‘playful‘ (F1–F4) RS 0.08 ICC 0.00 SDC 0.50 LoA [−0.62, 0.38]). The PCA detected contradictory TRR. Acceptable TRR could be found for parts of the instantaneous scan sampling (e.g., negative social behavior (F1–F3) RS 0.45 ICC 0.37 SDC 0.02 LoA [−0.03, 0.02]). The human−animal relationship test solely achieved poor TRR, whereas scans for stereotypies showed sufficient TRR (e.g., floor licking (F1–F4) RS 0.63 ICC 0.52 SDC 0.05 LoA [−0.08, 0.04]). Concluding, the principle ‘appropriate behavior’ does not represent TRR and further investigation is needed before implementation on-farm.

scholarly journals Developing an Animal Welfare Assessment Protocol for Cows in Extensive Beef Cow–Calf Systems in New Zealand. Part 1: Assessing the Feasibility of Identified Animal Welfare Assessment Measures

Animals ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1597 ◽  
Author(s):  
Y. Baby Kaurivi ◽  
Richard Laven ◽  
Rebecca Hickson ◽  
Tim Parkinson ◽  
Kevin Stafford
Keyword(s):  
New Zealand ◽  
Animal Welfare ◽  
Welfare Assessment ◽  
Assessment Protocol ◽  
Beef Cow ◽  
Cattle Management ◽  
Assessment Measures ◽  
Animal Welfare Assessment ◽  
Cow Calf ◽  
On Farm

Potential measures suitable for assessing welfare in pasture-based beef cow–calf systems in New Zealand were identified from Welfare Quality and UC Davis Cow-Calf protocols. These were trialled on a single farm and a potential protocol of 50 measures created. The aim of this study was to assess the feasibility of the measures included in this protocol on multiple farms in order, to develop a credible animal welfare assessment protocol for pasture-based cow–calf farms systems in New Zealand. The assessment protocol was trialled on 25 farms over two visits and took a total of 2.5 h over both visits for a 100-cow herd. The first visit in autumn included an animal welfare assessment of 3366 cows during pregnancy scanning, while the second visit in winter included a questionnaire-guided interview to assess cattle management and health, and a farm resource evaluation. Through a process of eliminating unsuitable measures, adjustments of modifiable measures and retaining feasible measures, a protocol with 32 measures was created. The application of the protocol on the farms showed that not all measures are feasible for on-farm assessment, and categorisation of identified animal welfare measures into scores that indicate a threshold of acceptable and non-acceptable welfare standards is necessary.

Animal welfare assessment benchmarking as a tool for health and welfare planning in organic dairy herds

2004 ◽  
Vol 155 (8) ◽  
pp. 237-239 ◽  
Author(s):  
J. N. Huxley ◽  
D. C. J. Main ◽  
H. R. Whay ◽  
J. Burke ◽  
S. Roderick
Keyword(s):  
Animal Welfare ◽  
Dairy Herds ◽  
Welfare Assessment ◽  
Organic Dairy ◽  
Animal Welfare Assessment

Animal Welfare Assessment in Farm Assurance Schemes

2001 ◽  
Vol 51 (sup030) ◽  
pp. 108-113 ◽  
Author(s):  
D. C. J. Main ◽  
A. J. F. Webster ◽  
L. E. Green
Keyword(s):  
Animal Welfare ◽  
Welfare Assessment ◽  
Animal Welfare Assessment

scholarly journals Overall animal welfare assessment reviewed. Part 1: Is it possible?

1999 ◽  
pp. 279-291 ◽  
Author(s):  
M.B.M. Bracke ◽  
B.M. Spruijt ◽  
J.H.M. Metz
Keyword(s):  
Decision Making ◽  
Animal Welfare ◽  
Relevant Information ◽  
Fuzzy Information ◽  
Welfare Assessment ◽  
Value Judgements ◽  
Animal Welfare Assessment ◽  
Ethical Stance ◽  
Political Decisions ◽  
Relevant Factors

Several authors have concluded that scientists should not attempt to perform overall animal welfare assessment (OWA). They argue that scientists have continued to fail to make progress in this area and that value judgements are inherently involved in OWA for which science cannot provide answers. We take a more positive attitude toward OWA and argue that scientists should avoid creating a self-fulfilling prophecy. OWA is necessary for making actual moral and political decisions. Science has already accumulated much relevant information about welfare and this information should be applied in decision making. The task of OWA is to assess welfare based on knowledge of the biological needs of animals. Weighting of welfare relevant factors constitutes a problem. However, when scientists cannot provide empirical data to solve weighting issues, this does not mean that rational answers cannot be found, e.g. in the form of procedural rules. OWA is conceived as a problem of multi-criteria decision making with fuzzy information. If focuses on the descriptive aspect of welfare, i.e. on what the welfare status of the animals really is without taking an ethical stance. The welfare status of animals depends on their biology and on the way animals assess their own welfare. It does not depend on how it happens to be perceived by us. Even though OWA necessarily remains a human activity, it is not arbitrary, nor does it allow of multiple 'correct' answers. OWA is a descriptive activity that can achieve more and more accuracy as science proceeds.

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