Efficacy of statistical process control procedures to identify deviations in continuously measured physiologic and behavioral variables in beef steers experimentally challenged with Mannheimia haemolytica

Word, Alyssa B; Carstens, G. E.; Parsons, Ira L; Washburn, Kevin E.; Lawhon, Sara D.; Pinchak, W. E.; Chevaux, E.; Skidmore, A.L.

doi:10.1093/jas/skaa009

Cited by 6 publications

(5 citation statements)

References 38 publications

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“…AFS, such as automated milk dispensers for pre-weaned calves and automatic concentrate bunks for post-weaned calves, and water bins, have been used in studies aimed at automated health monitoring in young calves, hence stage two research. These AFS can measure a wide range of parameters linked to feeding and drinking patterns, including daily feed intake (4, 45, 47, 52-55, 57, 72-74), frequency and duration of rewarded and unrewarded visits (4, 45, 47, 52-55, 57, 58, 61, 62, 72, 74), drinking speed (milk) (47, 52-54), water-drinking behavior (intake, time, and frequency) (62,74), and other feeding behaviors (head-down duration at the AFS) (55, 72), time-to-bunk: time to approach feeding stations following feed-truck delivery (55, 72), and duration of unrewarded visit intervals (55). We did not find validation studies for common systems such as Förster-Technik GmbH (n = 5; Engen, Germany) or GrowSafe Systems (n = 3; GrowSafe Systems Ltd., Airdrie, AB, Canada).…”

Section: Automatic Feeding Stationsmentioning

confidence: 99%

“…For example, in parasitized beef steers (aged between 4 and 11 months) and BRD-infected dairy calves, changes in activity (e.g., lying, standing, and step counts) enabled a better disease detection than feeding behaviors such as frequency and duration of feeding and drinking (66) and feed intake (47). In identifying NCD-infected calves and BRD-infected steers, however, feeding behaviors (e.g., the number of unrewarded visits to an automated milk dispenser, DMI, and bunk visit duration) permitted a more accurate detection of disease compared with activities such as lying and standing duration (4,72). In addition, certain diseases result in behavioral changes that are easier to detect at an earlier stage.…”

Section: Changes In Other Parametersmentioning

confidence: 99%

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A Systematic Review of Automatic Health Monitoring in Calves: Glimpsing the Future From Current Practice

et al. 2021

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Infectious diseases, particularly bovine respiratory disease (BRD) and neonatal calf diarrhea (NCD), are prevalent in calves. Efficient health-monitoring tools to identify such diseases on time are lacking. Common practice (i.e., health checks) often identifies sick calves at a late stage of disease or not at all. Sensor technology enables the automatic and continuous monitoring of calf physiology or behavior, potentially offering timely and precise detection of sick calves. A systematic overview of automated disease detection in calves is still lacking. The objectives of this literature review were hence: to investigate previously applied sensor validation methods used in the context of calf health, to identify sensors used on calves, the parameters these sensors monitor, and the statistical tools applied to identify diseases, to explore potential research gaps and to point to future research opportunities. To achieve these objectives, systematic literature searches were conducted. We defined four stages in the development of health-monitoring systems: (1) sensor technique, (2) data interpretation, (3) information integration, and (4) decision support. Fifty-four articles were included (stage one: 26; stage two: 19; stage three: 9; and stage four: 0). Common parameters that assess the performance of these systems are sensitivity, specificity, accuracy, precision, and negative predictive value. Gold standards that typically assess these parameters include manual measurement and manual health-assessment protocols. At stage one, automatic feeding stations, accelerometers, infrared thermography cameras, microphones, and 3-D cameras are accurate in screening behavior and physiology in calves. At stage two, changes in feeding behaviors, lying, activity, or body temperature corresponded to changes in health status, and point to health issues earlier than manual health checks. At stage three, accelerometers, thermometers, and automatic feeding stations have been integrated into one system that was shown to be able to successfully detect diseases in calves, including BRD and NCD. We discuss these findings, look into potentials at stage four, and touch upon the topic of resilience, whereby health-monitoring system might be used to detect low resilience (i.e., prone to disease but clinically healthy calves), promoting further improvements in calf health and welfare.

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Section: Automatic Feeding Stationsmentioning

confidence: 99%

Section: Changes In Other Parametersmentioning

confidence: 99%

A Systematic Review of Automatic Health Monitoring in Calves: Glimpsing the Future From Current Practice

et al. 2021

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“…2 In another experimental trial, a significant increase of Hp concentration (p < 0.001) was detected on the first day of MH challenge, which was 4 d after BoHV1 inoculation. 22 Other experimental transmission trials conducted with MH challenge also demonstrated significant increases (p < 0.01) of Hp concentration during the early stage of infection, 11,30,52 which indicates the utility of Hp as an early biomarker of BRDC, especially for bacterial infection.…”

Section: Acute-phase Protein Biomarkersmentioning

confidence: 88%

A systematic review of the utility of biomarkers as aids in the early diagnosis and outcome prediction of bovine respiratory disease complex in feedlot cattle

Zhu

Shoemake

et al. 2022

J VET Diagn Invest

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Bovine respiratory disease complex (BRDC) is a common, serious problem in feedlot cattle worldwide. Early diagnosis and outcome prediction are critical for making decisions to prevent economic loss and to limit antimicrobial use. Diagnosing BRDC is commonly based on visual signs and behavioral changes; both assessments are considered to have low diagnostic accuracy. Biomarkers are important for supporting the diagnosis of BRDC, determining the necessity and potential outcomes of treatment, and assisting in research in which differentiating diseased animals is required. There are few reviews summarizing the biomarkers available and utilized. We systematically evaluated the detection and prognostic potential of biomarkers from the literature published between January 1990 and December 2020. We performed a descriptive analysis of 5 biomarker categories: acute-phase proteins, stress-related hormones, other blood biomarkers, omics biomarkers, and non-blood biomarkers. The retrieved articles consisted of studies or trials that assessed the detection value and treatment and/or outcome prediction efficacy of biomarkers for BRDC in feedlot cattle; 23 manuscripts for review and analysis satisfied the selection criteria. Based on our review, we cannot recommend a specific biomarker as the sole method for the early detection or outcome prediction for BRDC, given that the application and efficacy of biomarkers varies in different situations. Our systematic review may serve as a reference for clinical and research investigations of early detection and outcome prediction of BRDC.

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“…In identifying NCD-infected calves and BRD-infected steers, however, feeding behaviours (i.e. the number of unrewarded visits to an automated milk feeder, DMI and bunk visit duration) permitted a more accurate detection of disease compared with activities such as lying and standing duration (Sutherland et al, 2018;Kayser et al, 2020). In addition, certain diseases result in behavioural changes that are easier to detect at an earlier stage.…”

Section: Changes In Other Parametersmentioning

confidence: 99%

“…two or multiple sensors) was applied in a number of studies (n = 9, Fig. 3), in which data from accelerometers, thermometers, and AFS are integrated into one model to identify diseases including BRD (Kayser et al, 2020;Duthie et al, 2021) and NCD (Sutherland et al, 2018;Lowe et al, 2019b). Information integration, however, means more than a multiple-sensor-tool.…”

Section: Stage Three: Information Integration -Outcomes Of Modelsmentioning

confidence: 99%

Decoding calf patterns : Advancing automated health monitoring with algorithmic insights

Sun

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World milk production reached 887 million tonnes in 2020 (FAO, 2022). With such high demand worldwide, dairy cows who produce the milk need to keep giving birth to renew the lactation in order to produce milk. Most female calves will be kept as heifers for the dairy industry, while the male (and some female) calves are not needed. The veal industry was therefore developed (mainly in Europe and North America) to make use of the dairy calves that are not wanted or needed for herd replacement, commonly referred to as 'surplus' calves.Although there are a number of possible routes for surplus dairy calves (Haskell, 2020;Webb et al., 2023), those calves destined for veal production are typically transported from different source dairy farms, sometimes across country borders, at the early age of around two to four weeks to a fattening veal farm (Marcato et al., 2018). At the veal farm, the calves are grouphoused, typically in small groups of five to ten, but sometimes in large groups of 40 to 70 calves, with typically 1.8 m 2 per calf, on wooden slatted floors. Veal calves are fed milk replacer in open troughs or buckets if housed in small groups, or via an automated milk feeders if house in large groups, until slaughter for the 'white' veal calves, and for eight weeks for the 'rosé' veal calves. Next to milk replacer, the calves receive solid feed, typically a mixture of concentrates and roughage (e.g. straw) until slaughter at approximately six (white) to eight (rosé) months of age (EFSA AHAW Panel, 2023). Veal calves do not typically receive enrichment, unless housed in the large groups, and do not typically have access to outdoor spaces. This production system allows veal calves to be raised efficiently, allowing each step of the chain to be considered and controlled (Renaud and Pardon, 2022), to end up with relatively high-quality meat products which fetch a high price with consumers, while using minimal resources.The Dutch veal sector is the biggest veal producer in the EU, accounting for 36% of the total production in 2020 (Berkhout et al., 2021). With around one million veal calves being kept in approximately 1,600 fattening farms 1 , the Dutch veal sector produces 1.6 to 1.7 million calves per year (Berkhout et al., 2021). These calves are transported from their source dairy farms all across Europe to the collection centres first, where they are sorted based on weight, breed, or conformation, before being sent to the fattening farms (Damiaans et al., 2019). This dairyveal chain presents potential concerns for calf welfare, including transportation of young animals, high risk of disease, and barren housing (Webb et al., 2023). One important concern for the farmers in this production system is the high morbidity (Sandelin et al., 2022) and high mortality rates (

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Efficacy of statistical process control procedures to identify deviations in continuously measured physiologic and behavioral variables in beef steers experimentally challenged with Mannheimia haemolytica

Cited by 6 publications

References 38 publications

A Systematic Review of Automatic Health Monitoring in Calves: Glimpsing the Future From Current Practice

A Systematic Review of Automatic Health Monitoring in Calves: Glimpsing the Future From Current Practice

A systematic review of the utility of biomarkers as aids in the early diagnosis and outcome prediction of bovine respiratory disease complex in feedlot cattle

Decoding calf patterns : Advancing automated health monitoring with algorithmic insights

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