An animal welfare assessment framework for helicopter darting: a case study with a newly developed method for feral horses

Context Helicopter darting (chemical immobilisation) is a very useful technique for large wild herbivores, such as feral horses (Equus caballus). There is currently no reliable framework to report on the animal welfare impacts of helicopter darting methods. Aim The aim of this study was to develop an animal welfare assessment framework for helicopter darting methods, using quantifiable parameters, and to test it with a case study using a newly developed feral horse capture technique. Methods Quantifiable animal welfare parameters were recorded for 11 feral horses captured using a traditional helicopter darting method in north-western Australia in October 2014. Welfare parameters chosen focused on quantifying the duration of procedures and the frequency of adverse events. They included chase time (CT; min) before darting, induction time (IT; min) between darting and recumbency, recumbency time (RT; min), total time (TT; CT+IT+RT; min), repeat-darting rate (animals requiring >1 dart; %), target zone accuracy rate (darts striking the intended anatomical area; %) and mortality rate (at time of capture and 14 days post-capture; %). Results Median CT was 2 min, median IT was 19 min, median RT was 16 min and median TT was 38 min. Repeat-darting rate was 45%, target zone accuracy rate was 53% and mortality rates (time of capture and 14 days post-capture) were zero. Conclusions Animal welfare parameters can be quantified for helicopter darting through estimation of the duration of procedures and the frequency of adverse events. Use of this framework will allow the identification of parameters requiring refinement for newly developed helicopter darting techniques. Implications Animal welfare parameters are particularly important for helicopter-based darting methods. Pilot studies, using quantified parameters, should be performed for newly developed capture techniques before they are approved for large-scale programs.

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Animal Welfare Assessment of Fattening Pigs: A Case Study on Sample Validity

Animals ◽

10.3390/ani10030389 ◽

2020 ◽

Vol 10 (3) ◽

pp. 389 ◽

Cited By ~ 1

Author(s):

Mareike Pfeifer ◽

Armin Otto Schmitt ◽

Engel Friederike Hessel

Keyword(s):

Animal Welfare ◽

Random Sample ◽

Mean Squared Error ◽

Sampling Strategies ◽

Welfare Assessment ◽

Welfare Indicators ◽

Squared Error ◽

Animal Welfare Assessment ◽

Fattening Pigs

A guide for animal welfare assessment of fattening pigs recommends recording some of the indicators for a sample of the animals from a herd. However, it is not certain whether the herd’s level of welfare can be correctly judged using a random sample. Therefore, both the true prevalences of welfare indicators in a full census and the estimated prevalences of the indicators based upon simulated samples taken according to five strategies (termed S1 to S5) were determined. Deviations from the true level of animal welfare in the herd due to the sampling were recorded and analyzed. Depending on the strategy, between 12% and 43% of the samples over- or underestimated the true prevalences by more than 50%. The validity of the sampling strategies was evaluated using the normalized root-mean-squared error (NRMSE) and the relative bias (RB). In terms of accuracy, the strategies differed only slightly (between NRMSE = 0.13 for S2 and NRMSE = 0.19 for S4). However, the strategies varied more obviously regarding the bias (between RB = −0.0002 for S1 and RB = −0.0370 for S5). The described results are the outcome of an initial case study on the sample validity of the indicators and have to be verified using the data of more herds.

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

Journal of Animal Science ◽

10.1093/jas/skaa278.815 ◽

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.

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

Journal of Dairy Science ◽

10.3168/jds.2017-13316 ◽

2018 ◽

Vol 101 (3) ◽

pp. 2359-2369 ◽

Cited By ~ 2

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

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Daniel Haybron’s theory of welfare and its implications for animal welfare assessment

Climate change and sustainable development ◽

10.3920/978-90-8686-753-0_69 ◽

2012 ◽

pp. 450-454

Author(s):

T. Višak

Keyword(s):

Animal Welfare ◽

Welfare Assessment ◽

Animal Welfare Assessment

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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 ◽

10.3390/ani9070398 ◽

2019 ◽

Vol 9 (7) ◽

pp. 398 ◽

Cited By ~ 3

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.

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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 ◽

10.3390/ani10091597 ◽

2020 ◽

Vol 10 (9) ◽

pp. 1597 ◽

Cited By ~ 1

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.

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