Comparison of four methods for detection of oestrus in dairy cows with resynchronised oestrous cycles

Cavalieri, J; Eagles, V E; Ryan, Michael P.; Macmillan, K.L.

doi:10.1111/j.1751-0813.2003.tb11553.x

Cited by 34 publications

(32 citation statements)

References 15 publications

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“…Different technologies such as heat mount detectors and activity monitoring systems have been commercialized and validated (At‐Taras and Spahr ; Cavalieri et al. ,b). Activity meters such as pedometers or accelerometers for automated oestrous detection have been reported as useful (Kamphuis et al.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Oestrous Detection in Dairy Cattle Comparing an Automated Activity Monitoring System to Visual Observation

Michaelis

Burfeind

Heuwieser

2014

Reprod Domestic Animals

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The objective of the study was to evaluate an automated activity monitoring (AAM) system for oestrous detection in dairy cows. Specifically, we set out to determine the oestrous detection efficiency and accuracy of the AAM system and to compare reproductive performance of cows detected either by AAM, by visual observation (VO) or by at least 1 of the 2 methods (EOM). A total of 1004 potential cow-periods from 348 cows were analysed. Oestrous detection rates (EDR) were calculated for 21 day (VWP+21), 42 day (VWP+42), and 63 day (VWP+63) after voluntary waiting period (VWP). Oestrous detection rate did not differ between AAM (42.1%) and VO (37.3%) during VWP+21 (p > 0.05) but was significantly higher in EOM (56.3%, p < 0.05). Oestrous detection by AAM achieved an efficiency (number of correctly detected oestruses/total number of cow-periods*100) of 35.6% and an accuracy (number of correctly detected oestruses/(number of true+ false oestruses)*100) of 83.8%. Visual observation resulted in an efficiency of 34.3% and accuracy of 75.1%. Pregnancy rate at 200 DIM was higher in AAM (66.8%) and EOM (68.8%) than in VO (57.1%, p < 0.05). Cows detected by AAM (p < 0.05) showed a 1.37 times greater risk to conceive than cows detected by VO as the reference (p < 0.05). The results show that an automated activity monitoring system can support oestrous detection in dairy cattle. The exclusive use of such a system, however, cannot be recommended.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Oestrous Detection in Dairy Cattle Comparing an Automated Activity Monitoring System to Visual Observation

Michaelis

Burfeind

Heuwieser

2014

Reprod Domestic Animals

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“…Therefore, the reported performances for such sensor systems should be considered with caution. Progesterone measurements can be considered to be the gold standard for estrus sensors (Cavalieri et al, 2003a;Friggens et al, 2008), so progesterone sensors seem to be promising sensor systems, although not much has been published on the performance of such systems. Furthermore, progesterone measurements in laboratories are expensive, so an on-farm progesterone sensor system will be costly; whether or not such a system is profitable remains unknown.…”

Section: Fertilitymentioning

confidence: 99%

Invited review: Sensors to support health management on dairy farms

Rutten

Velthuis

Steeneveld

et al. 2013

Journal of Dairy Science

403

318

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Since the 1980s, efforts have been made to develop sensors that measure a parameter from an individual cow. The development started with individual cow recognition and was followed by sensors that measure the electrical conductivity of milk and pedometers that measure activity. The aim of this review is to provide a structured overview of the published sensor systems for dairy health management. The development of sensor systems can be described by the following 4 levels: (I) techniques that measure something about the cow (e.g., activity); (II) interpretations that summarize changes in the sensor data (e.g., increase in activity) to produce information about the cow's status (e.g., estrus); (III) integration of information where sensor information is supplemented with other information (e.g., economic information) to produce advice (e.g., whether to inseminate a cow or not); and (IV) the farmer makes a decision or the sensor system makes the decision autonomously (e.g., the inseminator is called). This review has structured a total of 126 publications describing 139 sensor systems and compared them based on the 4 levels. The publications were published in the Thomson Reuters (formerly ISI) Web of Science database from January 2002 until June 2012 or in the proceedings of 3 conferences on precision (dairy) farming in 2009, 2010, and 2011. Most studies concerned the detection of mastitis (25%), fertility (33%), and locomotion problems (30%), with fewer studies (16%) related to the detection of metabolic problems. Many studies presented sensor systems at levels I and II, but none did so at levels III and IV. Most of the work for mastitis (92%) and fertility (75%) is done at level II. For locomotion (53%) and metabolism (69%), more than half of the work is done at level I. The performance of sensor systems varies based on the choice of gold standards, algorithms, and test sizes (number of farms and cows). Studies on sensor systems for mastitis and estrus have shown that sensor systems are brought to a higher level; however, the need to improve detection performance still exists. Studies on sensor systems for locomotion problems have shown that the search continues for the most appropriate indicators, sensor techniques, and gold standards. Studies on metabolic problems show that it is still unclear which indicator reflects best the metabolic problems that should be detected. No systems with integrated decision support models have been found.

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“…Also, accuracy of estrus detection (based on changes in progesterone concentration) varied widely, from 29% to 95%. Cavalieri, J., et al [32] reported that tail-paint, heat mount detectors, pedometers and heat wacth provide a high sensitivity (> 80%) and positive predictive value (> 85%) of detecting oestrus.…”

Section: Automated Heat Detection Systemsmentioning

confidence: 99%

New Technology Usage for Sustaniable Dairy Cow Reproductive Perfomances

Göncü¹,

Anitaş²,

Güngör³

et al. 2017

JESE-B

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Technology gives many oppurtunites for cattle farmers for easier and effective herd management possibilities. New era provides many realistic applications using smart applications for farmers. Especially milking is very popular for engineering applications. Reproductive technologies can be seen as frontier applications. Reproductive performances of the dairy farms can be defined as number of calves for cows in a year. It means new material for meat and milk production. Because new female calf means new cow for her while male calf means new material for beef. If meat and milk production are to be increased, cow productivity, i.e. the number of calves produced lifetime must be improved and increased. Although varying among herds, annual average herd incidences of reproductive disorders and reproductive performance were similar to those reported. Managerial practices influenced incidences of retained placenta and uterine infection, days open of cows not bred and of all cows, services per conception and percentages of herd open more than 100 days and culled for low production. Good heat detection programs can have a major impact on overall herd reproductive performance. The best heat detection programs start with careful timing, good observation and the effective use of detection aids. Being able to distinguish and interpret cow behaviour and other signs is critical, so are good record keeping and training for the people responsible for heat detection. Generaly, farmers with the best heat detection results use a combination of observation and heat detection aids. Several options are available to aid heat detection and increase heat detection rates. Although a rich variety of methods have been introduced for the detection of oestrus, a more accurate and practical method is still required.

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Comparison of four methods for detection of oestrus in dairy cows with resynchronised oestrous cycles

Abstract: Tail-paint, heatmount detectors, pedometers and HW provide a high sensitivity (> 80%) and positive predictive value (> 85%) of detecting oestrus in dairy cows with resynchronized oestrous cycles.

Cited by 34 publications

References 15 publications

Evaluation of Oestrous Detection in Dairy Cattle Comparing an Automated Activity Monitoring System to Visual Observation

Evaluation of Oestrous Detection in Dairy Cattle Comparing an Automated Activity Monitoring System to Visual Observation

Invited review: Sensors to support health management on dairy farms

New Technology Usage for Sustaniable Dairy Cow Reproductive Perfomances

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