Predicting pen fouling in fattening pigs from pig position

Jensen, Dan Børge; Larsen, Mona Lilian Vestbjerg; Pedersen, Lene Juul

doi:10.1016/j.livsci.2019.103852

Cited by 12 publications

(7 citation statements)

References 24 publications

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“…However, both water flow and pen temperature above the solid floor resulted in around 50% of the no-event days with false alarms, which in this study is around 1000 alarms that the farmer should react on to be able to prevent 14 tail biting events. Using the Bayesian ensemble strategy has previously been proved successful in improving the predictive performance of fouling events [19]. Therefore, it was chosen also to try this strategy in the current study.…”

Section: Discussionmentioning

confidence: 99%

“…Larsen et al [11] saw fewer and fewer pigs lying on the solid floor and more and more pigs lying on the slatted floor the last 3 days prior to an event of pen fouling, although only in pens not provided with straw on the solid floor. Also, Jensen et al [19] investigated the predictive value of the location of finisher pigs in the pen in relation to pen fouling and found AUCs > 0.70. Further, an automatic recording method for pig location in the pen is currently under development using convolutional neural networks.…”

Section: Discussionmentioning

confidence: 99%

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Prediction of Tail Biting Events in Finisher Pigs from Automatically Recorded Sensor Data

Larsen

Pedersen

Jensen

2019

Animals

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Tail biting in pigs is an animal welfare problem, and tail biting should be prevented from developing into tail damage. One strategy could be to predict events of tail biting so that the farmer can make timely interventions in specific pens. In the current investigation, sensor data on water usage (water flow and activation frequency) and pen temperature (above solid and slatted floor) were included in the development of a prediction algorithm for tail biting. Steps in the development included modelling of data sources with dynamic linear models, optimisation and training of artificial neural networks and combining predictions of the single data sources with a Bayesian ensemble strategy. Lastly, the Bayesian ensemble combination was tested on a separate batch of finisher pigs in a real-life setting. The final prediction algorithm had an AUC > 0.80, and thus it does seem possible to predict events of tail biting from already available sensor data. However, around 30% of the no-event days were false alarms, and more event-specific predictors are needed. Thus, it was suggested that farmers could use the alarms to point out pens that need greater attention.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Prediction of Tail Biting Events in Finisher Pigs from Automatically Recorded Sensor Data

Larsen

Pedersen

Jensen

2019

Animals

Self Cite

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“…Comfort around resting 6 Pressure injuries, manure on the body Pen fouling prediction (G) [12,13,19,50,111,112] Thermal comfort 25 Shivering, panting, huddling…”

Section: Good Housing 42mentioning

confidence: 99%

“…Respiration frequency (P), lying posture and location (G, P), cold/heat vocalisation (G, P), pen/room temperature (G), rectal temperature (P, S), pen fouling prediction (G) [12,13,[15][16][17][18][19][22][23][24][25][26][27][28][29][30]50,[58][59][60]83,84,86,111,112] Ease of movement 17 Space allowance, farrowing crates (presence and size)…”

Section: Good Housing 42mentioning

confidence: 99%

“…Within this criterion, only one IT has been developed for sows measuring the surface temperature using infrared thermography and only three ITs have been developed for piglets measuring multiple variables using cameras, microphones and thermometers. This is surprising, as sows are prone to heat stress, especially when housed in crates [120] and as climate is a major conflict in the farrowing unit with piglets needing high temperatures above 30 • C and sows having an upper critical temperature of approximately 25 • C. Instead, the focus within the 'thermal comfort' criterion has so far been on the growing pigs, perhaps because the housing conditions for growing pigs give the animals more opportunity to show thermoregulatory behaviour (change in lying posture and position) and because the thermoregulatory behaviour of growing pigs is related to the undesirable event of pen fouling [121], and thus may be used as an indicator to predict and prevent such an event [50]. ITs developed for prediction and prevention of pen fouling are also the only ones representing the criterion 'Comfort around resting', although not being a direct measure of manure on the body and with no ITs developed to measure pressure injuries.…”

Section: Relevance To the Welfare Quality ® Protocolmentioning

confidence: 99%

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Information Technologies for Welfare Monitoring in Pigs and Their Relation to Welfare Quality®

2021

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The assessment of animal welfare on-farm is important to ensure that current welfare standards are followed. The current manual assessment proposed by Welfare Quality® (WQ), although being an essential tool, is only a point-estimate in time, is very time consuming to perform, only evaluates a subset of the animals, and is performed by the subjective human. Automation of the assessment through information technologies (ITs) could provide a continuous objective assessment in real-time on all animals. The aim of the current systematic review was to identify ITs developed for welfare monitoring within the pig production chain, evaluate the ITs developmental stage and evaluate how these ITs can be related to the WQ assessment protocol. The systematic literature search identified 101 publications investigating the development of ITs for welfare monitoring within the pig production chain. The systematic literature analysis revealed that the research field is still young with 97% being published within the last 20 years, and still growing with 63% being published between 2016 and mid-2020. In addition, most focus is still on the development of ITs (sensors) for the extraction and analysis of variables related to pig welfare; this being the first step in the development of a precision livestock farming system for welfare monitoring. The majority of the studies have used sensor technologies detached from the animals such as cameras and microphones, and most investigated animal biomarkers over environmental biomarkers with a clear focus on behavioural biomarkers over physiological biomarkers. ITs intended for many different welfare issues have been studied, although a high number of publications did not specify a welfare issue and instead studied a general biomarker such as activity, feeding behaviour and drinking behaviour. The ‘good feeding’ principle of the WQ assessment protocol was the best represented with ITs for real-time on-farm welfare assessment, while for the other principles only few of the included WQ measures are so far covered. No ITs have yet been developed for the ‘Comfort around resting’ and the ‘Good human-animal relationship’ criteria. Thus, the potential to develop ITs for welfare assessment within the pig production is high and much work is still needed to end up with a remote solution for welfare assessment on-farm and in real-time.

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Animal Welfare Monitoring

Larsen

2022

Encyclopedia of Smart Agriculture Technologies

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Predicting pen fouling in fattening pigs from pig position

Cited by 12 publications

References 24 publications

Prediction of Tail Biting Events in Finisher Pigs from Automatically Recorded Sensor Data

Prediction of Tail Biting Events in Finisher Pigs from Automatically Recorded Sensor Data

Information Technologies for Welfare Monitoring in Pigs and Their Relation to Welfare Quality®

Animal Welfare Monitoring

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