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Detecting oestrus by monitoring sows’ visits to a boar
- T. Ostersen, C. Cornou, A.R. Kristensen
Introduction
K.N. Dominiak, AQMHM 2015
Department of Large Animal Sciences
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Detecting oestrus by monitoring sows visits to a boar T. Ostersen, - - PowerPoint PPT Presentation
Detecting oestrus by monitoring sows visits to a boar T. Ostersen, - - PowerPoint PPT Presentation
Detecting oestrus by monitoring sows visits to a boar T. Ostersen, C. Cornou, A.R. Kristensen Katarina Nielsen Dominiak Department of Large Animal Sciences Department of Large Animal Sciences Introduction 5 25 % return to oestrus K.N.
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Introduction
Department of Large Animal Sciences
K.N. Dominiak, AQMHM 2015 Slide 3
When in oestrus the sow seeks the boar more often and stays longer Oestrus can be monitored by:
- Duration of visits
- Frequency of visits
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Department of Large Animal Sciences
K.N. Dominiak, AQMHM 2015 Slide 4
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Variation between sows – visiting patterns
Department of Large Animal Sciences
K.N. Dominiak, AQMHM 2015 Slide 5
Individual variation in both level and variance Diurnal pattern Oestrus:
- Change in level of
duration
- Change in frequency
- Increase in variance
Outliers does not necessarily indicate oestrus
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Model characteristics
Department of Large Animal Sciences
K.N. Dominiak, AQMHM 2015 Slide 6
Duration model:
- Response variable sec/hour
- Adapt to individual level of
duration and variance
- Ignore outliers
- Detect oestrus based on level shift
and increase in variance Multiproces dynamic linear model MP-DLM Dynamic generalized linear model DGLM Frequency model:
- Response variable visits/6 hours
- Two diurnal periods (5 am - 5 pm)
- Adapt to individual level
- Handle Poisson distributed data
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Duration model I
Department of Large Animal Sciences
K.N. Dominiak, AQMHM 2015 Slide 7
More DLMs = Multiproces DLM Connected by First order Markov prior probabilities First six observations reset to ‘Normal’ > 6 hours between two observations resets to ‘Normal’
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Duration model II
Department of Large Animal Sciences
K.N. Dominiak, AQMHM 2015 Slide 8
Normal Level shift Oestrus Outliers P(MOE) = Indicator for oestrus
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Frequency model I
Department of Large Animal Sciences
K.N. Dominiak, AQMHM 2015 Slide 9
Remember from raw data: Two diurnal periods
- High frequency at day
- Low frequency at night
- 1-step forecast of the mean
- Comparison with actual
- bservations
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Frequency model II
Department of Large Animal Sciences
K.N. Dominiak, AQMHM 2015 Slide 10
Relative deviation from expected = oestrus indicator Threshold value 3.3 Normal Oestrus Change in frequency
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Combined probability of oestrus given the result from the frequency model is positive or negative
Combining the two models
P(MOE) from the duration model Test results of frequency model
Department of Large Animal Sciences
K.N. Dominiak, AQMHM 2015 Slide 11
Combined by Bayes Theorem SE of frequency model 1 - SE of frequency model P(MOE)
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Results I
Department of Large Animal Sciences
K.N. Dominiak, AQMHM 2015 Slide 12
SP: Ability to identify non-oestrus correctly SE: Ability to identify oestrus correctly LR+: Reliability of positive test result Error rate: Proportion of alarms being false
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Discussion
Extremely high SP – important when rare condition High error rates – too many false alarms Duration output communicated as a probability of oestrus
- how do you think a farmer would embrace such an output?
Department of Large Animal Sciences
K.N. Dominiak, AQMHM 2015 Slide 13
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Thank you
Department of Large Animal Sciences
K.N. Dominiak, AQMHM 2015 Slide 14