Best Practice Guide: Water Quality
/in Best Practice Guide, Cattle, Pigs, Poultry, Sheep, Water /by Laura PalczynskiBest Practice Guide: Potential of Breeding and Genetics for Robust and Resilient animals
/in Best Practice Guide, Breeding for Resilience, Cattle, Pigs, Poultry, Sheep /by Laura PalczynskiBest Practice Guide: Precision Livestock Technologies
/in (Data) Monitoring & surveillance, Best Practice Guide, Cattle, Pigs, Poultry, Precision Livestock Farming, Sensor Technology, Sheep /by Laura PalczynskiBest Practice Guide: Optimal Housing
/in Best Practice Guide, Cattle, Environment, Group Management, Housing and Welfare, Housing Systems, Pigs, Poultry, Sheep /by Laura PalczynskiBest Practice Guide: External biosecurity
/in Best Practice Guide, Biosecurity, Cattle, Pigs, Poultry, Sheep /by Laura PalczynskiBest Practice Guide: Internal Biosecurity
/in Best Practice Guide, Biosecurity, Cattle, Pigs, Poultry, Sheep /by Laura Palczynski400 – Predicting Disease in Transition Dairy Cattle (Research paper -Sahar – 2020)
/in Cattle, Database record, Other country, Precision Livestock Farming, Research report/paper, Sensor Technology /by GeorgetaANGST
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)
/in Cattle, Database record, Other country, Precision Livestock Farming, Research report/paper, Sensor Technology /by GeorgetaANGST
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)
/in Belgium, Database record, Pigs, Precision Livestock Farming, Research report/paper, Sensor Technology /by GeorgetaANGST
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
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 817591
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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 817591
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