Oestrus Detection in Dairy Cows Using Likelihood Ratio Tests

Jónsson, Ragnar Ingi; Björgvinsson, Trausti; Blanke, Mogens; Poulsen, Niels Kjølstad; Højsgaard, Søren; Munksgaard, Lene

doi:10.3182/20080706-5-kr-1001.00111

Cited by 6 publications

(4 citation statements)

References 19 publications

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“…However those detections rates were for behavioral oestrus events, whereas our data also contains silent oestruses. The false positive rate of 14% is in line with their and other results (Jonsson et al 2008). The results from our bivariate model indicate that the introduction of progesterone can reduce the number of false positives.…”

Section: Discussionsupporting

confidence: 81%

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Combining Cattle Activity and Progesterone Measurements Using Hidden Semi-Markov Models

O’Connell

Tøgersen

Friggens

et al. 2010

JABES

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Hourly pedometer counts and irregularly measured concentration of the hormone progesterone were available for a large number of dairy cattle. A hidden semi-Markov was applied to this bivariate time-series data for the purposes of monitoring the reproductive status of cattle. In particular, the ability to identify oestrus is investigated as this is of great importance to farm management. Progesterone concentration is a more accurate but more expensive method than pedometer counts, and we evaluate the added benefits of a model that includes this variable. The resulting model is biologically sensible, but validation is difficult. We utilize some auxiliary data to demonstrate the model's performance.

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Section: Discussionsupporting

confidence: 81%

“…Alternative methods for oestrus detection solely using pedometer readings are in active development, see Jonsson et al (2008) and references therein. Roelofs et al (2005) provide a method using simple statistical tests.…”

Section: Activity Indexmentioning

confidence: 99%

Combining Cattle Activity and Progesterone Measurements Using Hidden Semi-Markov Models

O’Connell

Tøgersen

Friggens

et al. 2010

JABES

Self Cite

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“…Moore et al identified 55% of visually observed estrus by comparing the variation of mean daily activity [33]. Jónsson et al achieved a sensitivity of 88.9% using the means from statistical change detection [34]. Machine learning methods can describe multiple complex interaction relationships or nonlinear relationships, and, thus, bring about remarkable predictive accuracy.…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning Techniques Can Enhance Dairy Cow Estrus Detection Using Location and Acceleration Data

Wang

Bell

Liu

et al. 2020

Animals

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The aim of this study was to assess combining location, acceleration and machine learning technologies to detect estrus in dairy cows. Data were obtained from 12 cows, which were monitored continuously for 12 days. A neck mounted device collected 25,684 records for location and acceleration. Four machine-learning approaches were tested (K-nearest neighbor (KNN), back-propagation neural network (BPNN), linear discriminant analysis (LDA), and classification and regression tree (CART)) to automatically identify cows in estrus from estrus indicators determined by principal component analysis (PCA) of twelve behavioral metrics, which were: duration of standing, duration of lying, duration of walking, duration of feeding, duration of drinking, switching times between activity and lying, steps, displacement, average velocity, walking times, feeding times, and drinking times. The study showed that the neck tag had a static and dynamic positioning accuracy of 0.25 ± 0.06 m and 0.45 ± 0.15 m, respectively. In the 0.5-h, 1-h, and 1.5-h time windows, the machine learning approaches ranged from 73.3 to 99.4% for sensitivity, from 50 to 85.7% for specificity, from 77.8 to 95.8% for precision, from 55.6 to 93.7% for negative predictive value (NPV), from 72.7 to 95.4% for accuracy, and from 78.6 to 97.5% for F1 score. We found that the BPNN algorithm with 0.5-h time window was the best predictor of estrus in dairy cows. Based on these results, the integration of location, acceleration, and machine learning methods can improve dairy cow estrus detection.

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“…Eradus et al (1996) worked with activity data, milk yield and milk temperature, and obtained a sensitivity rate of 79% and an error rate of 6.6%. Jønsson et al (2008) used a combination of activity and lying data and gained a sensitivity of 88.9% and an error rate of 5.9%. However, in contrast to these other studies that detected an online estrus event for an improved estrus detection on-farm, this study was developed for the detection of estrus events in the progesterone time series for a following parameter definition out of the progesterone measurements.…”

Section: Discussionmentioning

confidence: 99%

Mathematical approaches to detect low concentrations in progesterone profiles

Leesen

Tetens

Junge

et al. 2013

Animal

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There is a general need for higher objectivity and accuracy in describing the physiological fertility performance of dairy cows. To develop the alternative meaningful starting points for the selection of genetically superior dairy cows, this study focused on the detection of low progesterone concentrations, which are indicative of estrus events. Three mathematical approaches were used: one based on the exponentially weighted moving average control chart, and two threshold methods, which were developed in-house. Data were collected from one data set that included 97 insemination data of first-lactating Holstein dairy cows, and a second set that included 160 inseminations of primiparous and multiparous Holstein dairy cows. On the basis of these 2 data sets, and using a threshold of 1.2 ng progesterone/ml skimmed milk, the sensitivity of the 3 models was high and ranged between 100% and 93.13%, with an error rate between 4% and 22.17%. The specificity varied between 97.92% and 99.93%. The average concentration levels of true-positive-detected progesterone measures were low and ranged between 0.18 and 0.28 ng progesterone/ml skimmed milk (first data set) and 0.21 to 0.26 ng progesterone/ml skimmed milk (second data set). False-positive-detected low progesterone concentrations during estrus events were closely related to progesterone values around the 1.2 ng progesterone/ml skimmed milk threshold and the detecting rules of the control chart. Thus, we suggest that a threshold of 0.8 ng progesterone/ml skimmed milk is indicative for luteal activity in defatted foremilk. By means of the three methods used, the detection of low progesterone concentrations was possible and it can be assumed that this is a good starting point for further studies (such as interval calculation) in this area.

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Oestrus Detection in Dairy Cows Using Likelihood Ratio Tests

Cited by 6 publications

References 19 publications

Combining Cattle Activity and Progesterone Measurements Using Hidden Semi-Markov Models

Combining Cattle Activity and Progesterone Measurements Using Hidden Semi-Markov Models

Machine-Learning Techniques Can Enhance Dairy Cow Estrus Detection Using Location and Acceleration Data

Mathematical approaches to detect low concentrations in progesterone profiles

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