Improving oestrus detection in dairy cows by combination of different traits using fuzzy logic

J. Krieter; R. Firk; E. Stamer; W. Junge

Jump to Content

Improving oestrus detection in dairy cows by combination of different traits using fuzzy logic

In: Precision livestock farming

Authors:

J. Krieter

J. Krieter jkrieter@tierzucht.uni-kiel.de

Search for other papers by J. Krieter in
Current site
Google Scholar
PubMed

R. Firk

R. Firk
Search for other papers by R. Firk in
Current site
Google Scholar
PubMed

E. Stamer

E. Stamer
Search for other papers by E. Stamer in
Current site
Google Scholar
PubMed

, and

W. Junge

W. Junge
Search for other papers by W. Junge in
Current site
Google Scholar
PubMed

Type:: Chapter

Pages:: 99–104

DOI:: https://doi.org/10.3920/9789086865154_015

Purchase instant access (PDF download and unlimited online access):

€42.00€35.00 excl. VAT

Add to Cart

Redeem Access Token

The present study investigates oestrus detection in dairy cows using serial data from management information systems. Data recording was performed on a commercial farm (n=862 cows). Activity, milk yield, milk flow rate and electrical conductivity were recorded automatically during each milking period. Additional information about previous oestrus (time interval since last oestrus) was available for 373 cows. Oestrus detection (day of insemination) was based on time series consisting of 15 days before and 15 days after oestrus. The univariate analyses were performed by different smoothing methods (day-to-day comparison, moving average, exponential smoothing). For multivariate analyses a fuzzy logic model was developed. The efficiency of the different models was determined by the parameters sensitivity and error rate.

The comparison between smoothing methods resulted in moderate differences for the different traits. Using the trait activity, sensitivity ranged between 94.2 and 71.0% and error rate was between 53.2 and 21.5% for threshold values between 40 and 120%. Milk yield, milk flow rate and conductivity were not suitable for improving oestrus detection. By considering activity and previous information in a multivariate fuzzy logic model sensitivity was 88.9% and error rate was reduced to 23.8%.

Citation Info

Save
Cite
Email this content

Share link with colleague or librarian

You can email a link to this page to a colleague or librarian:
Email this content
or copy the link directly:

https://brill.edhh.ma/abstract/book/9789086865154/BP000015.xml
The link was not copied. Your current browser may not support copying via this button.

Link copied successfully

Collapse
Expand

Precision livestock farming

E-Book ISBN:: 9789086865154

Publisher:: Wageningen Academic

Print Publication Date:: 10 Oct 2003

Subjects
- Life Sciences
  - General
  - Animal & Veterinary

Metrics

	All Time	Past 365 days	Past 30 Days
Abstract Views	84	28	2
Full Text Views	0	0	0
PDF Views & Downloads	1	0	0

African Studies	Education	Media Studies
American Studies	History	Middle East and Islamic Studies
Ancient Near East and Egypt	Human Rights and Humanitarian Law	Musicology
Art History	International Law	Philosophy
Asian Studies	International Relations	Religious Studies
Biblical Studies	Jewish Studies	Slavic and Eurasian Studies
Biology	Languages and Linguistics	Social Sciences
Book History and Cartography	Life Sciences	Theology and World Christianity
Classical Studies	Literature and Cultural Studies

Subjects

Authors

Open Access

Product Information

Company

Contact

Improving oestrus detection in dairy cows by combination of different traits using fuzzy logic

Citation Info

Share link with colleague or librarian

Precision livestock farming

Table of Contents

Metrics

Metrics