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

Abstract

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.

Biomarkers and biosensors for the diagnosis of noncompliant pH, dark cutting beef predisposition, and welfare in cattle

“…This approach is similar to an approach suggested by D. Sun et al. (2021), in which predisposition to diseases is seen as a balance between environmental infection pressure and immune system functioning. The authors suggest that the measurement of a suite of influencing factors (both environmental and physiological, including weather, noise, social surroundings, heart rate, and stress biomarkers) can be used to generate a multidimensional prediction model designed to predict animal resilience.…”

“…DC biomarker selection could, for example, be performed by fitting animal effects that influence the substrates and kinetics of postmortem glycogenolysis and the outcome of DC (breed, tempera-ment, and diet) to a prediction model with the aim of identifying biomarkers that may perform well across different animals/cohorts. This approach is similar to an approach suggested by D. Sun et al (2021), in which predisposition to diseases is seen as a balance between environmental infection pressure and immune system functioning. The authors suggest that the measurement of a suite of influencing factors (both environmental and physiological, including weather, noise, social surroundings, heart rate, and stress biomarkers) can be used to generate a multidimensional prediction model designed to predict animal resilience.…”

“…In concert, the gathered information may then allow for a better understanding of why individual animals within a cohort are prone to having lower preslaughter glycogen levels, and interventions can be applied to minimize economic losses for the industry. For instance, prediction algorithms may be built enabling the optimization of animal production systems, meat quality outcomes, and animal welfare (Neethirajan, 2020; D. Sun et al., 2021; Wijffels et al., 2021). Developing portable sensors enabling the rapid on‐site measurement of such compounds will enable the development of such an approach for DC predisposition prediction.…”

Competence to thrive: resilience as an indicator of positive health and positive welfare in animals

“…A type of resilience appears to have been first recognised in farm animals as a distinction between resistance to infestation and resistance to the effects of infestation of sheep with the nematode parasite, Haemonchus contortus (Clunies Ross 1932). Resilience has been assessed in the context of nematode parasite infection (Albers et al 1987;Bisset and Morris 1996;Kelly et al 2013), microbial infection (Mulder and Rashidi 2017;Putz et al 2019;van der Zande et al 2020;Bai and Plastow 2022), high or low temperature exposure (Mengistu et al 2017;Sánchez-Molano et al 2020;Tsartsianidou et al 2021), diet (Steel 2003), feed shortage (María et al 2004), weaning (Hine et al 2019;Revilla et al 2019), routine management within the production environment (Meyer and Colditz 2015;Elgersma et al 2018;Nunes Marsiglio Sarout et al 2018;Nguyen-Ba et al 2020;Poppe et al 2020Poppe et al , 2021bSun et al 2021;Bai and Plastow 2022), and the transition period in the dairy cow (van Dixhoorn et al 2018), among other contexts (e.g. Bushby et al 2018;Brito et al 2020).…”

“…This trend has been enabled by large datasets containing high frequency records of behavioural, physiological and production variables of individual animals (e.g. Neethirajan et al 2017;Brito et al 2020;Sun et al 2021). Assessments at the level of the individual animal of dynamic properties of the trajectory of performance traits such as milk yield, periodicity of physiological and behavioural variables, and complexity of biological functions such as behavioural repertoires are being interpreted as (1) evidence of resilience to environmental perturbations, (2) an indicator of welfare, and (3) an avenue for improving health and welfare through genetic selection (Scheibe et al 1999;Colditz and Hine 2016;Nunes Marsiglio Sarout et al 2018;van Dixhoorn et al 2018;Berghof et al 2019b;Iung et al 2019;Wagner et al 2021;Bai and Plastow 2022).…”