Effects of straw bedding and high fibre diets on the behaviour of floor fed group-housed sows

Whittaker, X.; Edwards, Sandra A.; Spoolder, Hans; Lawrence, Alistair B.; Corning, S.

doi:10.1016/s0168-1591(98)00243-3

Cited by 59 publications

(31 citation statements)

References 18 publications

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“…This finding supports the view that the development of oral stereotypies in food restricted sows is largely based on an inability to express foraging behaviour rather than hunger per se (Lawrence and Terlouw 1993). However, provision of straw either on the floor or in racks increased aggression in grouphoused sows (Whittaker et al 1999;Stewart et al 2008). The incorporation of fibre in sow diets may reduce hunger, as assessed by motivation testing (Meunier-Salaun et al 2001).…”

Section: Hungersupporting

confidence: 64%

Scientific assessment of animal welfare

Hemsworth

Mellor

Cronin

et al. 2014

New Zealand Veterinary Journal

181

156

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Animal welfare is a state within the animal and a scientific perspective provides methodologies for evidence-based assessment of an animal's welfare. A simplistic definition of animal welfare might be how the animal feels now. Affective experiences including emotions, are subjective states so cannot be measured directly in animals, but there are informative indirect physiological and behavioural indices that can be cautiously used to interpret such experiences. This review enunciates several key science-based frameworks for understanding animal welfare. The biological functioning and affective state frameworks were initially seen as competing, but a recent more unified approach is that biological functioning is taken to include affective experiences and affective experiences are recognised as products of biological functioning, and knowledge of the dynamic interactions between the two is considered to be fundamental to managing and improving animal welfare. The value of these two frameworks in understanding the welfare of group-housed sows is reviewed. The majority of studies of the welfare of group-housed sows have employed the biological functioning framework to infer compromised sow welfare, on the basis that suboptimal biological functioning accompanies negative affective states such as sow hunger, pain, fear, helplessness, frustration and anger. Group housing facilitates social living, but group housing of gestating sows raises different welfare considerations to stall housing, such as high levels of aggression, injuries and stress, at least for several days after mixing, as well as subordinate sows being underfed due to competition at feeding. This paper highlights the challenges and potential opportunities for the continued improvement in sow management through well-focused research and multidisciplinary assessment of animal welfare. In future the management of sentient animals will require the promotion of positive affective experiences in animals and this is likely to be a major focus for animal welfare science activity in the early twenty-first century.

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Section: Hungersupporting

confidence: 64%

Scientific assessment of animal welfare

Hemsworth

Mellor

Cronin

et al. 2014

New Zealand Veterinary Journal

181

156

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“…The results also showed that over the three monitoring days the sows spent 84.0 (±0.9)% of time lying down, 4.1 (±0.8)% of time sitting, 11.8 (±0.6)% of time standing. The 84% lying time for the sows paralleled the literature report that pigs spend 75-80% of their time lying down (Velarde and Geers, 2007;Rolandsdotter et al, 2009;Whittaker et al, 1999).…”

Section: Resultssupporting

confidence: 65%

Automatic recognition of lactating sow behaviors through depth image processing

Lao

Brown–Brandl

Stinn³

et al. 2016

Computers and Electronics in Agriculture

105

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Manual observation and classification of animal behaviors is laborious, time-consuming, and of limited ability to process large amount of data. A computer vision-based system was developed that automatically recognizes sow behaviors (lying, sitting, standing, kneeling, feeding, drinking, and shifting) in farrowing crate. The system consisted of a low-cost 3D camera that simultaneously acquires digital and depth images and a software program that detects and identifies the sow's behaviors. This paper describes the computational algorithm for the analysis of depth images and presents its performance in recognizing the sow's behaviors as compared to manual recognition. The images were acquired at 6 s intervals on three days of a 21-day lactation period. Based on analysis of the 6 s interval images, the algorithm had the following accuracy of behavioral classification: 99.9% in lying, 96.4% in sitting, 99.2% in standing, 78.1% in kneeling, 97.4% in feeding, 92.7% in drinking, and 63.9% in transitioning between behaviors. The lower classification accuracy for the transitioning category presumably stemmed from insufficient frequency of the image acquisition which can be readily improved. Hence the reported system provides an effective way to automatically process and classify the sow's behavioral images. This tool is conducive to investigating behavioral responses and time budget of lactating sows and their litters to farrowing crate designs and management practices. RightsWorks produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted. b s t r a c tManual observation and classification of animal behaviors is laborious, time-consuming, and of limited ability to process large amount of data. A computer vision-based system was developed that automatically recognizes sow behaviors (lying, sitting, standing, kneeling, feeding, drinking, and shifting) in farrowing crate. The system consisted of a low-cost 3D camera that simultaneously acquires digital and depth images and a software program that detects and identifies the sow's behaviors. This paper describes the computational algorithm for the analysis of depth images and presents its performance in recognizing the sow's behaviors as compared to manual recognition. The images were acquired at 6 s intervals on three days of a 21-day lactation period. Based on analysis of the 6 s interval images, the algorithm had the following accuracy of behavioral classification: 99.9% in lying, 96.4% in sitting, 99.2% in standing, 78.1% in kneeling, 97.4% in feeding, 92.7% in drinking, and 63.9% in transitioning between behaviors. The lower classification accuracy for the transitioning category presumably stemmed from insufficient frequency of the image acquisition which can be readily improved. Hence the reported system provides an effective way to automatically process and classify the sow's behavioral images. This tool is conducive to investigating behavioral responses an...

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“…In the current study, pigs in straw-enriched pens had more skin lesions under stable social conditions, but showed less non-reciprocal biting than pigs in barren pens. Though the number of skin lesions may be underestimated due to skin dirtiness [72], [73], [74], as pigs in the barren pens had a more dirty skin (unpublished results), lesions were clearly visible and were scored when the observer was in close proximity to the animal.…”

Section: Discussionmentioning

confidence: 99%

Indirect Genetic Effects and Housing Conditions in Relation to Aggressive Behaviour in Pigs

et al. 2013

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Indirect Genetic Effects (IGEs), also known as associative effects, are the heritable effects that an individual has on the phenotype of its social partners. Selection for IGEs has been proposed as a method to reduce harmful behaviours, in particular aggression, in livestock and aquaculture. The mechanisms behind IGEs, however, have rarely been studied. The objective was therefore to assess aggression in pigs which were divergently selected for IGEs on growth (IGEg). In a one generation selection experiment, we studied 480 offspring of pigs (Sus scrofa) that were selected for relatively high or low IGEg and housed in homogeneous IGEg groups in either barren or enriched environments. Skin lesion scores, a proxy measure of aggression, and aggressive behaviours were recorded. The two distinct IGEg groups did not differ in number of skin lesions, or in amount of reciprocal fighting, both under stable social conditions and in confrontation with unfamiliar pigs in a 24 h regrouping test. Pigs selected for a positive effect on the growth of their group members, however, performed less non-reciprocal biting and showed considerably less aggression at reunion with familiar group members after they had been separated during a 24 h regrouping test. The enriched environment was associated with more skin lesions but less non-reciprocal biting under stable social conditions. Changes in aggression between pigs selected for IGEg were not influenced by G×E interactions with regard to the level of environmental enrichment. It is likely that selection on IGEg targets a behavioural strategy, rather than a single behavioural trait such as aggressiveness.

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Effects of straw bedding and high fibre diets on the behaviour of floor fed group-housed sows

Cited by 59 publications

References 18 publications

Scientific assessment of animal welfare

Scientific assessment of animal welfare

Automatic recognition of lactating sow behaviors through depth image processing

Indirect Genetic Effects and Housing Conditions in Relation to Aggressive Behaviour in Pigs

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