AN EVALUATION OF PRECISION DAIRY FARMING TECHNOLOGY ADOPTION, PERCEPTION, EFFECTIVENESS, AND USE

Precision dairy farming technologies provide a variety of functions to dairy farmers. Little is known about dairy producer perception of these technologies. A study was performed to understand dairy producer perception of parameters monitored by precision dairy farming technologies. Calving has potential to be predicted using these same parameters and technologies. A second study was performed using two commercially marketed technologies in calving prediction. In order for these technologies to generate accurate and useful information for dairy farm use, they must accurately quantify these parameters. The final study evaluated the accuracy of five commercially marketed technologies in monitoring feeding, rumination, and lying behaviors

Using animal-mounted sensor technology and machine learning to predict time-to-calving in beef and dairy cows

Worldwide, there is a trend towards increased herd sizes, and the animal-to-stockman ratio is increasing within the beef and dairy sectors; thus, the time available to monitoring individual animals is reducing. The behaviour of cows is known to change in the hours prior to parturition, for example, less time ruminating and eating and increased activity level and tail-raise events. These behaviours can be monitored non-invasively using animal-mounted sensors. Thus, behavioural traits are ideal variables for the prediction of calving. This study explored the potential of two sensor technologies for their capabilities in predicting when calf expulsion should be expected. Two trials were conducted at separate locations: (i) beef cows (n = 144) and (ii) dairy cows (n = 110). Two sensors were deployed on each cow: (1) Afimilk Silent Herdsman (SHM) collars monitoring time spent ruminating (RUM), eating (EAT) and the relative activity level (ACT) of the cow, and (2) tail-mounted Axivity accelerometers to detect tail-raise events (TAIL). The exact time the calf was expelled from the cow was determined by viewing closed-circuit television camera footage. Machine learning random forest algorithms were developed to predict when calf expulsion should be expected using single-sensor variables and by integrating multiple-sensor data-streams. The performance of the models was tested using the Matthew’s correlation coefficient (MCC), the area under the curve, and the sensitivity and specificity of predictions. The TAIL model was slightly better at predicting calving within a 5-h window for beef cows (MCC = 0.31) than for dairy cows (MCC = 0.29). The TAIL + RUM + EAT models were equally as good at predicting calving within a 5-h window for beef and dairy cows (MCC = 0.32 for both models). Combining data-streams from SHM and tail sensors did not substantially improve model performance over tail sensors alone; therefore, hour-by-hour algorithms for the prediction of time of calf expulsion were developed using tail sensor data. Optimal classification occurred at 2 h prior to calving for both beef (MCC = 0.29) and dairy cows (MCC = 0.25). This study showed that tail sensors alone are adequate for the prediction of parturition and that the optimal time for prediction is 2 h before expulsion of the calf

Calving and estrus detection in dairy cattle using a combination of indoor localization and accelerometer sensors

Accelerometers (neck- and leg-mounted) and ultra-wide band (UWB) indoor localization sensors were combined for the detection of calving and estrus in dairy cattle. In total, 13 pregnant cows and 12 cows with successful insemination were used in this study. Data were collected two weeks before and two weeks after delivery for calving. Similarly, data were collected two weeks before and two weeks after artificial insemination (AI) for estrus. Different cow variables were extracted from the raw data (e.g., lying time, number of steps, ruminating time, travelled distance) and used to build and test the detection models. Logistic regression models were developed for each individual sensor as well as for each combination of sensors (two or three) for both calving and estrus. Moreover, the detection performance within different time intervals (24 h, 12 h, 8 h, 4 h, and 2 h) before calving and AI was investigated. In general, for both calving and estrus, the performance of the detection within 2-4 h was lower than for 8 h24 h. However, the use of a combination of sensors increased the performance for all investigated detection time intervals. For calving, similar results were obtained for the detection within 24 h, 12 h, and 8 h. When one sensor was used for calving detection within 24-8 h, the localization sensor performed best (Precision (Pr) 73-77%, Sensitivity (Se) 57-58%, Area under curve (AUC) 90-91%), followed by the leg-mounted accelerometer (Pr 67-77%, Se 54-55%, AUC = 88-90%) and the neck-mounted accelerometer (Pr 50-53%, Se 47-48%, AUC = 86-88%). As for calving, the results of estrus were similar for the time intervals 24 h-8 h. In this case, similar results were obtained when using any of the three sensors separately as when combining a neck- and a leg-mounted accelerometers (Pr 86-89%, Se 73-77%). For both calving and estrus, the performance improved when localization was combined with either the neck- or leg-mounted accelerometer, especially for the sensitivity (73-91%). Finally, for the detection with one sensor within a time interval of 4 h or 2 h, the Pr and Se decreased to 55-65% and 42-62% for estrus and to 40-63% and 33-40% for calving. However, the combination of localization with either leg or neck-mounted accelerometer as well as the combination of the three sensors improved the Pr and Se compared to one sensor (Pr 72-87%, Se 63-85%). This study demonstrates the potential of combining different sensors in order to develop a multi-functional monitoring system for dairy cattle

Precision dairy technologies for organic and low-input dairy production systems

University of Minnesota M.S. thesis. February 2018. Major: Animal Sciences. Advisors: Bradley Heins, Marcia Endres. 1 computer file (PDF); v, 94 pages.The use of precision dairy technologies for the management of confinement dairy cattle has been well documented. Less work has been conducted on evaluating precision dairy technologies within pasture-based dairy herds in the United States. The potential of precision dairy technologies to be utilized in pasture-based dairy herds was evaluated at the West Central Outreach and Research Center in Morris, MN, organic grazing and low-input conventional dairy herds. An ear-attached accelerometer was validated for accuracy of recording rumination, eating and activity behaviors in pasture-based dairy crossbred cows. Activity and rumination were recorded by an activity and rumination collar system from January 2014 to December 2017, and purebred Holsteins were compared with crossbreds. Because activity and rumination monitoring collars have improved estrus detection in confinement dairy herds, the estrus detection performance of a collar system was evaluated in an organic grazing dairy herd and a low-input conventional herd by breeding season

Evaluation of novel strategies for improving prevention and early diagnosis of health disorders in organic dairy cattle

2018 Summer.Includes bibliographical references.To view the abstract, please see the full text of the document

Localization and accelerometer sensors for the detection of oestrus in dairy cattle

The aim of this work was to combine ultra-wide band (UWB) localisation tracking, a neck-mounted accelerometer and a leg-mounted accelerometer for the detection of oestrus in dairy cows. Twelve Holstein cows with successful artificial insemination (AI) were used in this study. The sensors were attached two weeks before the expected day of oestrus and removed after AI. Different cow variables (e.g. lying time, number of steps, ruminating time, travelled distance) were extracted from the raw sensor data and used to build and test the detection models. Logistic regression models were developed for each individual sensor as well as for each combination of sensors (two or three). The performances were similar when one sensor was used only as when combining the neck- and leg-mounted accelerometer (sensitivity (Se) =75-78%, area under curve (AUC) =93-94%). The performance increased when localisation was combined with either the neck- or leg-mounted accelerometer, especially for the sensitivity (80% for leg accelerometer + localisation and 88% for neck accelerometer + localisation). The AUC were nearly the same (97%). The best performance was obtained with the combination of all three sensors (Se = 90%, AUC = 99%). Future work will consist of expanding this research to other herds with larger sample size as well as considering cows’ anomalies (e.g. mastitis, lameness) and other sensors (e.g. bolus or eartag to measure the temperature)

Dairy cow behaviour around calving: Its relationship with management practices and environmental conditions

Calving is one of the most challenging and painful experiences for dairy cattle and a process that involves coping with physical and physiological changes, as well as environmental and management-related stressors. In recent years, it has been argued that the application of cow behaviour knowledge might facilitate their efficacious management during calving. This review aims to summarise and discuss current knowledge regarding the behavioural changes that occur around calving time. The relationship between calving behaviour, management practices, and environmental conditions in dairy cattle raised in intensive indoor production systems, as well as pasture-based systems, is also discussed. First, we briefly outline the process of parturition and the concept of maternal behaviour. We then describe behavioural changes that occur around parturition in normal and dystocic births and how variations in these behaviours can be used to predict normal or assisted calving in dairy cattle; particular emphasis is placed on the role of feeding, rumination, and lying behaviour. Finally, we review how management practices and environmental conditions can influence cow’s behaviour at calving and discuss the importance of providing an environment that accommodates the behaviour they are motivated to perform. This review presents evidence that the time a cow is moved to the calving area, the type of group housing and the provision of a secluded area to calve, can impact the behavioral responses of dairy cows at calving. Evidence regarding the effects of exposure to environmental conditions such as heat during summer, and/or cold, wet and mud during winter can also have a negative impact on behaviour, suggesting potential benefits of providing cows with a protected area to calve. We conclude that a better understanding of the behaviour of parturient cows may help producers improve the care and management around calving time

Gyr cows (Bos taurus indicus) in the peripartum period: assessment of calving prediction devices and factors affecting the maternal behavior and defensiveness

The aim of this study was to evaluate the potential use of the reticulo-rumen temperature and activity pattern of Gyr heifers as calving predictors and characterize the defensiveness and maternal care of primiparous and multiparous Gyr cows, evaluating the possible effects of parity and training protocol to the first milking prior calving toward these behaviors. Fifty-two Gir Leiteiro cows from the Empresa de Pesauissa Agropecuária de Minas Gerais (Epamig Oeste, Uberaba, Brazil) were used. All samples came from the same herd divided into three experimental groups, one for each chapter. (Chapter I): Forty pregnant Gir heifers received an intra-ruminal bolus that recorded reticulo-rumen temperature (Trr) and activity (Act). The animals had Trr and Act monitored during the prepartum period. We observed a decrease in Trr and an increase in Act in the days before calving. Differences in Trr and Act were most evident during the last 21 and 11 hours before parturition, respectively. There was a drop of 0.20°C in Trr. The analyzes revealed that both characteristics have the potential to predict parturition, however particularities in the thermal physiology of Zebu cattle must be considered when using devices validated only for European breeds. (Chapter II): Thirty-one Gir cows, among primiparous (n = 16) and multiparous (n = 15) were allocated in a maternity paddock monitored by video cameras. The behavior of the animals was collected in four periods: Pre-calving, Post-calving, First handling of calf and Post-handling. Primiparous cows showed a longer duration of standing with an arched spine and tended to move more than multiparous cows in the pre-calving period, which can be considered an indicator of pain and discomfort in these animals. Both primiparous and multiparous cows were protective of their calves, but only multiparous females were aggressive towards the handlers in the first calf handling. Furthermore, more protective cows spent more time eating before calving, while less attentive cows spent more time lying down before calving. (Chapter III): Thirty-seven primiparous dairy Gyr cows were allocated into two groups: Training Group (n = 16) was submitted to a protocol for the first milking, involving tactile stimulation; Control group (n = 21) was submitted to the common management of the farm, without interactions and/or additional handling. Animal behavior was recorded in three periods: Post-calving, First handling of calf and Post-handling. Calf latency to stand up, weight, and sex influenced cow-calf interactions, whereas training group cows touched less and spent more time not interacting with their calves. Both Training and Control groups had protective dams, but a higher percentage of Trained group dams were calmer toward calf handling. In conclusion, Trr and Act had potential to calving prediction in Gyr Heifers; Multiparous Gyr cows tended be more aggressive with their calves’ defense than primiparous; Training protocol to the first milking involving tactile stimulation reduced maternal care and defensiveness in primiparous Gyr cows.O objetivo desta tese foi avaliar o potencial uso dos padrões de temperatura retículo-ruminal e atividade de novilhas Gir como preditores de parto e caracterizar a defesa e cuidado materno de vacas Gir, avaliando os possíveis efeitos da paridade e do protocolo de treinamento para a primeira ordenha nestes comportamentos. Foram utilizadas cinquenta e duas vacas Gir Leiteiro da Empresa de Pesquisa Agropecuária d Minas Gerais (Epamig Oeste, Uberaba, Brasil). Todas as amostras foram oriundas de um mesmo rebanho dividido em três grupos experimentais, um para cada capítulo. (Capítulo I): Quarenta novilhas Gir prenhes receberam um bolus intra-ruminal que registrou a temperatura retículo-rúmen (Trr) e atividade (Act). Os animais tiveram Trr e Act monitorados durante o período pré-parto. Observamos diminuição do Trr e aumento do Act nos dias que antecederam o parto. As diferenças em Trr e Act foram mais evidentes durante as últimas 21 e 11 horas antes do parto, respectivamente. Houve queda de 0,20°C na Trr. As análises revelaram que ambas as características têm potencial para predizer o parto, porém particularidades na fisiologia térmica de bovinos zebuínos devem ser consideradas quando se utilizam dispositivos validados apenas para raças europeias. (Capítulo II): Trinta e uma vacas Gir, dentre primíparas (n = 16) e multíparas (n = 15) foram alocadas em um piquete de maternidade monitorado por câmeras de vídeo. Os comportamentos dos animais foram coletados em quatro períodos: Pré-parto, Pós-parto, Primeiro manejo do bezerro e Pós-manejo. As vacas primíparas apresentaram maior duração dos comportamentos de ficar em pé com a coluna arqueada e tenderam a se movimentar mais do que as multíparas no período pré-parto, o que pode ser considerado indicador de dor e desconforto nesses animais. Tanto as primíparas quanto as multíparas foram protetoras de seus bezerros, mas apenas as multíparas foram agressivas com os tratadores no primeiro manejo do bezerro. Além disso, vacas mais protetoras passaram mais tempo comendo antes do parto, enquanto vacas menos atentas passaram mais tempo deitadas antes do parto. (Capítulo III): Trinta e sete vacas Gir leiteiras primíparas foram alocadas em dois grupos: O Grupo Treinamento (n = 16) foi submetido a um protocolo para a primeira ordenha, envolvendo estimulação tátil; O Grupo de controle (n = 21) foi submetido ao manejo comum da fazenda, sem interações e/ou manejos adicionais. Os comportamentos dos animais foram registrados em três períodos: pós-parto, primeiro manejo do bezerro e pós-manejo. A latência do bezerro para se levantar, o peso e o sexo influenciaram as interações vaca-bezerro. Vacas do Grupo Treinamento tocaram menos e passaram mais tempo sem interagir com seus bezerros. Ambos os grupos de treinamento e controle tinham mães protetoras, mas uma porcentagem maior de mães do Grupo Treinamento foram mais calmas em relação ao manejo dos bezerros. Em conclusão, Trr e Act apresentaram potencial para predição de parto em Novilhas Gir; Vacas Gir multíparas tendem a ser mais agressivas na proteção de seus bezerros do que primíparas; Protocolo de treinamento para a primeira ordenha reduziu o cuidado e defesa materna nas vacas Gir primíparas

Oestrus and ovulation detection in pasture-based dairy herds: the role of new technologies

Automatic milking systems (AMS) are becoming increasingly popular due to the growing cost of labour and reduced labour availability. The voluntary cow traffic and resultant distribution of milkings throughout the day and night affects most aspects of herd and farm management in AMS. The literature review (Chapter 1) highlighted a need to evaluate the effects of milk yield and milking frequency during early lactation on reproductive performance. The analysis of a 5-year historic database from Australia’s first AMS research farm (Chapter 2) found no significant association of average milk yield and milking frequency during 100 days in milk with any of the reproductive measures. However, the interval from calving to first oestrus increased gradually within the study period and consequently influenced other reproductive outcomes. As a result, a series of studies were conducted with a multidisciplinary approach (both physiological and technological) to investigate the potential to improve oestrus detection on pasture-based AMS farms. A field study (Chapter 3) was conducted to allow for the development and application of an algorithm to assess the application accuracy of an infrared thermography (IRT) device when used to detect oestrus events or pending oestrus events by detecting the time of ovulation. Vulval and muzzle temperatures were measured by IRT in twenty synchronized cows (using a controlled internal drug release and prostaglandin F2α). Whilst the IRT showed some potential as an oestrus detection aid with higher sensitivity than visual observation (67%) and Estrotect activation (67%), the specificity and positive predictive value were lower with the IRT. The vulva and muzzle were the focus areas for the IRT application and some concern was generated with regard to the potential for the IRT data to impacted by faecal contamination, obscuring of the vulva by the tail and time since last drinking (affecting muzzle surface temperature). To address these concerns a further study (Chapter 5) was conducted to test the hypothesis that the specificity of IRT in detecting oestrus (or imminent oestrus) could be improved if other body parts were focused on. In that study (Chapter 5), an additional technology was incorporated to test the hypothesis that the combined activity and rumination data generated by an accelerometer (SCR heat and rumination long distance tags) would provide a more accurate indication of oestrus and/or ovulation than the activity and rumination data alone. Unfortunately the monitoring of eyes and/or ears did not provide the improvement in accuracy of IRT (as an oestrus detection aid) indicating that as an oestrus detection aid there was likely to be limited value in developing this as an automated stand-alone device. Alerts generated by accelerometer based on a lower activity threshold level had high sensitivity and may be able to detect a high proportion of cows in ovulatory periods in pasture-based system; however, the specificities and positive predictive value were lower than the visual assessment of mounting indicators and would still require the herd’s person to filter data to identify the false alerts to ensure that cows are not inseminated unnecessarily. Whilst the use of in-line milk monitoring has already been commercialized for the assessment of milk progesterone, there is potential for other biomarkers to provide further opportunities for the assessment of milk components. Biomarkers of oxidative stress were evaluated in plasma showing that plasma glutathione was lower in ovulated cows compared to those of an-ovulated cows (Chapter 4). Whilst baseline plasma data for oxidative stress biomarkers was a useful starting point, the real value of these biomarkers would be realised if their concentration in milk could be linked with oestrus (and or ovulation). Milk superoxide dismutase activity was shown to be higher in ovulated cows while lipoperoxides, glutathione peroxidase were lower in ovulated cows compared to those in an-ovulated cows (Chapter 6). Further work would be required to determine the accuracy with which these biomarkers could be used to identify oestrus cows but these results are promising and suggest that there may be some potential to develop in-line milk sampling technology to alert the herdsperson to cows that should be inseminated. In summary, this thesis provides very useful, scientifically based information on potential use of technologies for oestrus and ovulation detection in dairy cows, which should serve as a foundation to develop and upgrade automated on-farm technologies and biosensors for better reproductive management of cows in pasture-based AMS. However, it is noted that the most likely success with automated oestrus detection is to require a combination of different indicators that should be incorporated to truly increase the accuracy of detection beyond that which can be achieved by skilled and devoted herd’s people

PRECISION DAIRY FARMING TECHNOLOGY SOLUTIONS FOR DETECTING DAIRY COW DISEASE TO IMPROVE DAIRY COW WELL-BEING

Dairy cow health is multifactorial and complex. High producing dairy cows have been described as metabolic athletes, but metabolic and infectious diseases around calving affect many cows. These diseases have drastic negative effects on dairy cow well-being, milk production, and dairy farm economics. Early disease detection could potentially improve disease management, treatment, and future prevention techniques. The first objective of this research was to evaluate the use of activity, lying behavior, reticulorumen temperature, and rumination time determined by precision dairy farming technologies to detect transition cow diseases including hypocalcemia, ketosis, and metritis. The second objective was to evaluate the ability of activity, body weight, feeding behavior, lying behavior, milking order, milk yield and components, reticulorumen temperature, and rumination time determined by precision dairy farming technologies to predict clinical mastitis cases. The last objective of this research was to evaluate the precision dairy farming technologies used in Objective 3 to predict subclinical cases