Modelling of daily rhythms of behavioural patterns in growing pigs on two commercial farms

Villagrá, Aránzazu; Althaus, R. L.; Lainez, M.; Martinez, Ana Belén; Torres, Álvaro Pastor

doi:10.1080/09291010600950131

Cited by 28 publications

(19 citation statements)

References 30 publications

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“…Pigs have a circadian activity rhythm with one or two peaks during daytime (e.g. Villagra et al, 2007;Andersen et al, 2008). In accordance, the lowest number of visits to the drinker was found at night for both treatments.…”

Section: Discussionsupporting

confidence: 54%

“…Drinking behaviour often mirrors feeding behaviour as 75% of the water intake is associated with the pigs' feed intake (Bigelow and Houpt, 1988). The water intake follows a stable diurnal pattern at a group level Villagra et al, 2007) and systems for automatic detection of changes in the diurnal water use have been developed and are used as animal welfare monitoring tools at a batch level. Pigs' water consumption can be affected by disease (Pijpers et al, 1991) and with the present development in computer technology, automatic real-time monitoring of drinking behaviour of individual pigs might potentially be used to identify individuals suffering from disease.…”

Section: Introductionmentioning

confidence: 99%

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Growing pigs’ drinking behaviour: number of visits, duration, water intake and diurnal variation

Andersen

Dybkjær

Herskin

2014

Animal

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Individual drinking patterns are a potential tool for disease monitoring in pigs. However, to date, individual pig drinking behaviour has not been described, and effects of external factors have not been examined. The aim of this study was to perform detailed quantification of drinking behaviour of growing pigs and to examine effects of period of day and effects of competition for access to the drinking nipple on the drinking behaviour, amount of water used and water wastage. In all, 52 cross-bred castrated male pigs (live weight 20.5 ± 1.7 kg; mean ± s.d.) maintained as either 3 (N3) or 10 (N10) pigs per pen and water nipple (four groups/ treatment) were used. All pigs were fitted with a transponder ear tag. A radio frequency identification reader recorded and time stamped visits at the nipple. In each pen, water flow was logged every second. The drinking behaviour was recorded for 4 consecutive days and analysed using a linear mixed model. Overall, the pigs spent 594 s at the nipple during 24 h distributed among 44 visits. During this period, 5 l of water were used, of which >30% was wasted. Social competition did not affect the drinking behaviour over 24 h, except for the proportion of interrupted visits where pigs, kept with recommended nipple availability (N10), showed an increased proportion of interrupted drinking bouts compared with pigs kept at very low level of competition (N3) (0.18 ± 0.01 v. 0.11 ± 0.01; P < 0.01). However, splitting data into 8-h periods (P1, P2, P3) starting from 0600 h revealed differences between treatments, showing that in N3, water use per visit was lower in P1 than P2 and P3 (110 ± 10 v. 126 ± 7 and 132 ± 7 ml; P < 0.05), whereas in N10, the water used per visit was higher during P3 than during the other periods (P1: 107 ± 14 ml, P2: 112 ± 10 ml v. P3: 151 ± 10 ml; P < 0.001). A similar pattern was found for visit duration. In N3, fewer nipple visits were observed in P2 than P1 (15.6 ± 1.2 v. 22.0 ± 1.2; P < 0.001), whereas no difference was found between P1 and P2 in N10. The results demonstrate that growing pigs at the two levels of competition maintained a comparable level of 24 h water intake by changing behavioural variables involved in drinking. This dynamic characteristic of drinking behaviour means that if individual drinking patterns are to be used as disease monitoring tools, it is important to consider effects of external factors and include data on period level to allow rapid detection of behavioural changes.

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Section: Discussionsupporting

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Growing pigs’ drinking behaviour: number of visits, duration, water intake and diurnal variation

Andersen

Dybkjær

Herskin

2014

Animal

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“…Differences in the number of feeding peaks have been attributed to the photoperiod that pigs are exposed to (Feddes et al, 1989) and the temperature in the fattening unit (Villagrá et al, 2007).…”

mentioning

confidence: 99%

“…According to Villagrá et al (2007) if the indoor temperature is close to the thermo neutral zone (between 14°C and 24°C), pigs are likely to develop a natural behavior with two peaks of feed consumption and activity during the illuminated period. However, Feddes et al (1989) reported that changes in temperature on the farm might be sufficient to modify feeding patterns of growing pigs.…”

mentioning

confidence: 99%

Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring

Stygar

Dolecheck

Kristensen

2018

Animal

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Frequent BW monitoring of growing pigs can be useful for identifying production (e.g. feeding), health and welfare problems. However, in order to construct a tool which will properly recognize abnormalities in pigs' growth a precise description of the growth process should be used. In this study we proposed a new model of pig growth accounting for daily fluctuations in BW. Body weight measurements of 1710 pigs (865 gilts and 843 barrows) originating from five consecutive batches from a Danish commercial farm were collected. Pigs were inserted into a large pen (maximum capacity = 400) between November 2014 and September 2015. On average, each pig was observed for 42 days and weighed 3.6 times a day when passing from the resting to feeding area. Altogether, 243,160 BW measurements were recorded. A multilevel model of pig growth was constructed and fitted to available data. The BW of pigs was modeled as a quadratic function of time. A diurnal pattern was incorporated into the model by a cosine wave with known length (24 h). The model included pig effect which was defined as a random autoregressive process with exponential correlation. Variance of within-pigs error was assumed to increase with time. Because only five batches were observed, it was not possible to obtain the random effect for batch. However, in order to account for the batch effect the model included interactions between batch and fixed parameters: intercept, time, square value of time and cosine wave. The gender effect was not significant and was removed from the final model. For all batches, morning and afternoon peaks in the frequency of visits to the feeding area could be distinguished. According to results, pigs were lighter in the morning and heavier in the evening (minimum BW was reached around 1000 h and maximum around 2200 h). However, the exact time of obtaining maximum and minimum BW during the day differed between batches. Pigs had access to natural light and, therefore, existing differences could be explained by varying daylight level during observations periods. Because the diurnal amplitude for pig growth varied between batches from 0.9 to 1.4 kg, BW monitoring tools based on frequent measurements should account for diurnal variation in BW of pigs. This proposed description of growth will be built into a monitoring tool (a dynamic linear model) and applied to farm data in future studies.

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“…Since most animal behavioural studies require individual identification, our innovation is promising since animals need not be directly observed nor do previous recordings need to be made (Brandl, 1997;Villagra et al, 2007) and thus considerable savings in time are possible.…”

Section: Discussionmentioning

confidence: 99%

An automatic colour-based computer vision algorithm for tracking the position of piglets

Jover

Raya

Martínez-Gómez

et al. 2009

Span. j. agric. res.

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Artificial vision is a powerful observation tool for research in the field of livestock production. So, based on the search and recognition of colour spots in images, a digital image processing system which permits the detection of the position of piglets in a farrowing pen, was developed. To this end, 24,000 images were captured over five takes (days), with a five-second interval between every other image. The nine piglets in a litter were marked on their backs and sides with different coloured spray paints each one, placed at a considerable distance on the RGB space. The programme requires the user to introduce the colour patterns to be found, and the output is an ASCII file with the positions (column X, line Y) for each of these marks within the image analysed. This information may be extremely useful for further applications in the study of animal behaviour and welfare parameters (huddling, activity, suckling, etc.). The software programme initially segments the image in the RGB colour space to separate the colour marks from the rest of the image, and then recognises the colour patterns, using another colour space [B/(R+G+B), (G-R), (B-G)] more suitable for this purpose. This additional colour space was obtained testing different colour combinations derived from R, G and B. The statistical evaluation of the programme's performance revealed an overall 72.5% in piglet detection, 89.1% of this total being correctly detected.Additional key words: animal behaviour, image analysis, pattern recognition, piglets activity, segmentation. ResumenSistema de visión artificial basado en el color, para el seguimiento de la posición de lechones La visión artificial es una potente herramienta de observación al servicio de la investigación en el campo de la producción ganadera. En este sentido, se ha desarrollado un software de tratamiento digital de imagen, basado en la búsqueda y reconocimiento de manchas de color en la imagen, que permite la detección de la posición de los lechones en una corralina de maternidad. Para ello, se capturaron 24.000 imágenes en cinco tomas (días), con un intervalo de 5 segundos entre cada dos imágenes. Los nueve lechones presentes en estas camadas fueron pintados en el lomo y laterales con colores distintos y alejados entre sí en el espacio RGB. El programa precisa la introducción por el usuario de los patrones de color a buscar, devolviendo como salida un fichero ASCII con las posiciones (columna X, fila Y) de cada una de esas manchas en cada imagen analizada. Esta información podría ser de gran utilidad en posteriores aplicaciones para el estudio de paráme-tros de comportamiento y bienestar de los animales (hacinamiento, actividad, amamantamiento, etc.).El software realiza una segmentación previa de la imagen en el espacio RGB, para aislar las manchas de color del resto de la imagen, y posteriormente el reconocimiento de los patrones de color utilizando otro espacio de color [B/(R+G+B), (G-R), (B-G)] más adecuado para tal fin, obtenido a partir de pruebas con diferentes combinaciones ...

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Modelling of daily rhythms of behavioural patterns in growing pigs on two commercial farms

Cited by 28 publications

References 30 publications

Growing pigs’ drinking behaviour: number of visits, duration, water intake and diurnal variation

Growing pigs’ drinking behaviour: number of visits, duration, water intake and diurnal variation

Analyses of body weight patterns in growing pigs: a new view on body weight in pigs for frequent monitoring

An automatic colour-based computer vision algorithm for tracking the position of piglets

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