414 – The relationship between transition period diseases and lameness , feeding time and body condition during the dry period (Research paper – Daros – 2020)

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414 Research paper – Daros – 2020 – The relationship between transition period diseases and lameness

414 Research paper
The relationship between transition period diseases and lameness,feeding time, and body condition during the dry period by Daros, R.R., Eriksson, H.K., Weary, D.M. and M.A.G. von Keyserlingk 2020 Journal of Dairy Science 103: 649-665
In Significant Impact Groups: Precision Livestock Farming & Early detection \ Sensor technology
Species targeted: Dairy;
Age: Adult;
Summary:
This research did record feeding time by placing cameras by the feed bunk and using different digital systems. Lameness was scored as well as body conditions score. The results support the hypothesis that lameness during the dry period is associated with transition diseases. Lameness identified 2 mo before calving was associated with increased risk of transition diseases, highlighting the importance of screening cows for lameness around dry-off. One of the mechanisms through which lameness may be associated with TD is through decreased feeding time; throughout the dry period, lame cows spent less time feeding than sound cows, and lower feeding time was in turn associated with higher odds of transition diseases. These results suggest that reducing lameness during the dry period and avoiding over conditioning at dry-off may improve transition health.
414 Research paper – Daros – 2020 – The relationship between transition period diseases and lameness, feeding time and body condition during the dry period
Where to find the original material: https://doi.org/10.3168/jds.2019-16975; https://doi.org/10.3168/jds.2019-16975
Country: Canada

Best Practice Guide: Precision Livestock Technologies

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400 – Predicting Disease in Transition Dairy Cattle (Research paper -Sahar – 2020)

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400 Research paper -Sahar – 2020 – Predicting Disease in Transition Dairy Cattle

.l M. Weary 2020 Animals 10: 15p paper
In Significant Impact Groups: Precision Livestock Farming & Early detection \ Sensor technology
Species targeted: Dairy;
Age: Adult;
Summary:
Dairy cattle often become ill after calving. This article is about models designed to predict which cows are likely to become ill based upon measures of the cows’ feeding and competitive behaviors before calving. The models had high sensitivity (73–71%), specificity (80–84%), positive predictive values (73–77%), and negative predictive values (80–80%) for both cows that had previously calved and for those calving for the first time. So they concluded that behaviors at the feed bunk before calving can predict cows at risk of becoming sick in the weeks after calving.

Where to find the original material: https://www.mdpi.com/2076-2615/10/6/928/htm; https://doi.org/10.3390/ani10060928
Country: British Columbia, Canada

399 – Machine learning based fog computing assisted data driven approach for early lameness detection in dairy cattle (Research paper – Taneja – 2020)

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399 Research paper – Taneja – 2020-Machine learning based fog computing assisted data driven approach for early lameness detection in dairy cattle

In Significant Impact Groups: Precision Livestock Farming & Early detection \ Sensor technology
Species targeted: Dairy;
Age: Not stated;
Summary:
Timely lameness detection is one of the major and costliest health problems in dairy cattle. This poses a concern with increasing herd sizes, as prolonged or undetected lameness severely compromises cows’ health and welfare, and ultimately affects the milk productivity of the farm. To tackle this, an end-to-end IoT application that leverages advanced machine learning and data analytics techniques to monitor the cattle in real-time and identify lame cattle at an early stage has been developed. The proposed approach has been validated on a real world smart dairy farm setup consisting of a dairy herd of 150 cows in Waterford, Ireland. The detected lameness anomalies are further sent to farmer’s mobile device. The results indicate that lameness can be detected 3 days before it can be visually captured by the farmer with an overall accuracy of 87%. This means that the animal can either be isolated or treated immediately.

Where to find the original material: https://www.researchgate.net/publication/339914936_Machine_learning_based_fog_computing_assisted_data-driven_approach_for_early_lameness_detection_in_dairy_cattle; https://doi.org/10.1016/j.compag.2020.105286
Country: IE

398 -Technological tools for infection detection Case studies with the SOMO respiratory distress monitor in Belgian pig farms (Research paper – Cui – 2019)

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398 Research paper – Cui – 2019 -Technological tools for infection detection Case studies with the SOMO respiratory distress monitor in Belgian pig farms

In Significant Impact Groups: Precision Livestock Farming & Early detection \ Sensor technology
Species targeted: Pigs;
Age: Not stated;
Summary:
In a demonstration project in Flanders (Belgium), the SOMO Respiratory Distress Monitor of SoundTalks, was installed in 10 commercial fattening pig houses showing an automatic alarm when respiratory problems occurred. The warnings of the SOMO-system were analysed against the observations of the farmer. In most cases (74%) the alert situation was confirmed by the farmers inspection, and in 17% of the cases farmers started a medical treatment based on the alerts. At the time of the alert the number of sick animals was still low and the behaviour (activity, feed intake) of the animals still normal in most cases (86%). It was confirmed by the farmers that the use of the SOMO system helped to reduce the amount of medication, because treatments were done in an early stage of infection.

Where to find the original material: http://www.eaplf.eu/wp-content/uploads/ECPLF_19_book.pdf; ISBN 978-1-84170-654-2
Country: BE

397 – Field trial to demonstrate the intelligent dairy assistantIDA system on dairy farms (Research paper – Rutten 2019)

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397 Research paper – Rutten 2019 – Field trial to demonstrate the intelligent dairy assistantIDA system on dairy farms

In Significant Impact Groups: Precision Livestock Farming & Early detection \ Sensor technology
Species targeted: Dairy;
Age: Adult;
Summary:
Connecterra’s Intelligent Dairy Assistant (IDA) is a novel Internet of Things based on a management support system for dairy farms. IDA uses sensor technology, cloud computing and artificial intelligence to support dairy farmers with insights on oestrus and health management. The IDA system uses feedback on historic data to improve its underlying models and farmers may learn from using the system. The experiences indicate that oestrus detection can be improved, and health monitoring can help to start early treatment and thereby reduce the use of antibiotics. For milk production the results are inconclusive as the groups with and without IDA were not balanced on milk yield before the field trial started. Based on the limited size of the experiment it could not be proven significant effects or causal relationships.

Where to find the original material: http://www.eaplf.eu/wp-content/uploads/ECPLF_19_book.pdf; ISBN 978-1-84170-654-2
Country: BE; NL

396 – Precision livestock farming for pigs (Research report – Vranken – 2017)

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396 Research report – Vranken – 2017 – Precision livestock farming for pigs

In Significant Impact Groups: Precision Livestock Farming & Early detection \ Sensor technology
Species targeted: Pigs;
Age: Not stated;
Summary:
In the precision livestock farming (PLF) concept, sensors and algorithms translate the measured animal responses into key indicators for optimal performance, improved animal welfare, and farm sustainability. The output of the sensors (e.g., activity measures with a camera or sound measures with a microphone) is related to animal-based welfare and health indicators such as aggression or respiratory diseases. When sensor signals start to deviate from their expected values, alerts are given to the farmer. In this way the farmer can take an immediate action before the detected change in animal response negatively affects the production performance. These actions range from solving technical problems such as a blocked feeding line, adjusting control settings in the climate and feed controller, etc. In most cases, a preventive medical treatment prevents the further spreading of respiratory diseases in the pen, and the use of antibiotics can be reduced or even precluded.

Where to find the original material: https://academic.oup.com/af/article/7/1/32/4638771; https://doi.org/10.2527/af.2017.0106
Country: BE; NL

320 – Animal Sound Talks Real-time Sound Analysis for Health Monitoring in Livestock (Research paper – Berckmans – 2015)

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320 Research paper – Berckmans – 2015 – Animal Sound Talks Real-time Sound Analysis for Health Monitoring in Livestock

In Significant Impact Groups: Precision Livestock Farming & Early detection \ Sensor technology
Species targeted: Pigs; Beef;
Age: Young;
Summary:
Precision livestock farming (PLF) is a livestock management technology. Sound-based PLF techniques have significant advantages over other technologies such as cameras. Besides the fact that microphones are contactless and relatively cheap, there is no need for a direct line of sight, while large groups of animals can be monitored with a single sensor in a room. This paper presents an example of a PLF product, the respiratory distress monitor, which automatically monitors the respiratory health status of a group of pigs. Results of five different use cases are discussed to show the effectiveness of the respiratory distress monitor as an early warning tool for respiratory problems in a pig house. It is demonstrated that the tool works for the early detection of animal responses due to technical issues (ventilation problems) and health issues in a wide range of different conditions in commercial European pig houses.

Where to find the original material: https://limo.libis.be/primo-explore/fulldisplay?docid=LIRIAS1673361&context=L&vid=Lirias&search_scope=Lirias&tab=default_tab&lang=en_US&fromSitemap=1;
Country: BE

240 Farm Innovation – Interoperable pig health tracking by HOPU; CSEM; SLU; DIGITANIMAL SL (Farm Innovation)

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240 Farm Innovation – Interoperable pig health tracking by HOPU; CSEM; SLU; DIGITANIMAL SL

In Significant Impact Groups: Precision Livestock Farming & Early detection \ Sensor technology
Species targeted: Pigs;
Age: Not stated;
Summary:
Improving animal welfare and sustainability of livestock production by monitoring physiological parameters through IoT sensors.

Where to find the original material: https://www.iof2020.eu/trials/meat/interoperable-pig-tracking;
Country: ES