Managing respiratory disease in finisher pigs: Combining quantitative assessments of clinical signs and the prevalence of lung lesions at slaughter

Pessoa, Joana; Costa, Maria Rodrigues da; Manzanilla, Edgar García; Norton, Tomás; McAloon, Conor G.; Boyle, Laura

doi:10.1016/j.prevetmed.2020.105208

Cited by 26 publications

(36 citation statements)

References 14 publications

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“…Overall, all models run with the test dataset were less accurate (0.5 < AUC ≤ 0.7), with the prediction model for pneumonia having the poorest performance (AUC = 0.54). Indeed, other studies showed that coughing may be the best on-farm indicator to predict pneumonia lesions at slaughter [ 16 ]. Dorsocaudal pleurisy, with an AUC of 0.66, was the second best performing model.…”

Section: Discussionmentioning

confidence: 99%

“…Still, other studies found that the presence of pleurisy at slaughter was associated with farmers reporting, through the FCI, the presence of coughing for the last 3 months of the rearing cycle [ 4 , 10 ]. However, we have previously identified strong associations between the presence of coughing and a higher prevalence of different lung lesions at slaughter, but only on the last weeks of the finisher stage in the same farm [ 16 ]. Nevertheless, coughing modelling was done at pen and room level in that study.…”

Section: Discussionmentioning

confidence: 99%

“…Full information regarding animal management is available in [ 16 ]. In short, four batches of pigs (n = 1573, in total) of circa 12 weeks of age and weighing 25 ± 5.3 kg were housed, on arrival at the farm, in eight rooms each divided into six pens (mean number of pigs: 197 ± 5 per room and 33 ± 2 per pen).…”

Section: Methodsmentioning

confidence: 99%

“…Data collection on lung lesions followed the same protocol reported in [ 16 ]. Briefly, for each pig, individual lung lobes were examined for pneumonia lesions according to the method developed by Madec & Derrien (1981).…”

Section: Methodsmentioning

confidence: 99%

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Adding value to food chain information: using data on pig welfare and antimicrobial use on-farm to predict meat inspection outcomes

et al. 2021

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Background Using Food Chain Information data to objectively identify high-risk animals entering abattoirs can represent an important step forward towards improving on-farm animal welfare. We aimed to develop and evaluate the performance of classification models, using Gradient Boosting Machine algorithms that utilise accurate longitudinal on-farm data on pig health and welfare to predict condemnations, pluck lesions and low cold carcass weight at slaughter. Results The accuracy of the models was assessed using the area under the receiver operating characteristics (ROC) curve (AUC). The AUC for the prediction models for pneumonia, dorsocaudal pleurisy, cranial pleurisy, pericarditis, partial and total condemnations, and low cold carcass weight varied from 0.54 for pneumonia and 0.67 for low cold carcass weight. For dorsocaudal pleurisy, ear lesions assessed on pigs aged 12 weeks and antimicrobial treatments (AMT) were the most important prediction variables. Similarly, the most important variable for the prediction of cranial pleurisy was the number of AMT. In the case of pericarditis, ear lesions assessed both at week 12 and 14 were the most important variables and accounted for 33% of the Bernoulli loss reduction. For predicting partial and total condemnations, the presence of hernias on week 18 and lameness on week 12 accounted for 27% and 14% of the Bernoulli loss reduction, respectively. Finally, AMT (37%) and ear lesions assessed on week 12 (15%) were the most important variables for predicting pigs with low cold carcass weight. Conclusions The findings from our study show that on farm assessments of animal-based welfare outcomes and information on antimicrobial treatments have a modest predictive power in relation to the different meat inspection outcomes assessed. New research following the same group of pigs longitudinally from a larger number of farms supplying different slaughterhouses is required to confirm that on farm assessments can add value to Food Chain Information reports.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Adding value to food chain information: using data on pig welfare and antimicrobial use on-farm to predict meat inspection outcomes

et al. 2021

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“…Current literature presents developed equipment for poultry farming using sounds, images, and modeling of signals of force and pecking sounds to monitor animals' growth and other factors related to the welfare, behavior, and feeding of birds [11,16,[19][20][21][22][23]. Although the use of an automatic recording of sounds for animal husbandry and health management (quantitative analysis) in other species, such as swine, show similar results in automatic and manual assessments of the frequency of coughing, the disadvantage of manual assessments is the time spent compared to that of automatic assessments [24].…”

Section: Introductionmentioning

confidence: 99%

Smart Feeding Unit for Measuring the Pecking Force in Farmed Broilers

Seber

Moura

Lima

et al. 2021

Animals

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Feeding is one of the most critical processes in the broiler production cycle. A feeder can collect data of force signals and continuously transform it into information about birds’ feed intake and quickly permit more agile and more precise decision-making concerning the broiler farm’s production process. A smart feeding unit (SFU) prototype was developed to evaluate the broiler pecking force and average feed intake per pecking (g/min). The prototype consisted of a power supply unit with a data acquisition module, management software connected to a computer for data storage, and a video camera to verify the pecking force during signal processing. In the present study, seven male Cobb-500 broilers were raised in an experimental chamber to test and commission the prototype. The prototype consisted of a feeding unit (feeder) with a data acquisition module (amplifier), with real-time integration for testing and intuitive operation with Catman Easy software connected to a computer to obtain and store data from signals. The sampling of average feed intake per pecking per broiler (g) was conducted during the first minute of feeding, subtracting the amount of feed provided per the amount of feed consumed, including the count of pecking in the first minute of feeding. An equation was used for estimating the average feed intake per pecking per broiler (g). The results showed that the average broiler pecking force was 1.39 N, with a minimum value of 0.04 N and a maximum value of 7.29 N. The average feed intake per pecking (FIP) was 0.13 g, with an average of 173 peckings per minute. The acquisition, processing, and classification of signals in the pecking force information were valuable during broilers’ feeding. The smart feeding unit prototype for broilers was efficient in the continuous assessment of feed intake and can generate information for estimating broiler performance.

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