Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/22311
Title: Prediction of insemination outcomes in Holstein dairy cattle using alternative machine learning algorithms
Contributor(s): Shahinfar, Saleh  (author); Page, David (author); Guenther, Jerry (author); Cabrera, Victor (author); Fricke, Paul (author); Weigel, Kent (author)
Publication Date: 2014
DOI: 10.3168/jds.2013-6693
Handle Link: https://hdl.handle.net/1959.11/22311
Abstract: When making the decision about whether or not to breed a given cow, knowledge about the expected outcome would have an economic impact on profitability of the breeding program and net income of the farm. The outcome of each breeding can be affected by many management and physiological features that vary between farms and interact with each other. Hence, the ability of machine learning algorithms to accommodate complex relationships in the data and missing values for explanatory variables makes these algorithms well suited for investigation of reproduction performance in dairy cattle. The objective of this study was to develop a user-friendly and intuitive on-farm tool to help farmers make reproduction management decisions. Several different machine learning algorithms were applied to predict the insemination outcomes of individual cows based on phenotypic and genotypic data. Data from 26 dairy farms in the Alta Genetics (Watertown, WI) Advantage Progeny Testing Program were used, representing a 10-yr period from 2000 to 2010. Health, reproduction, and production data were extracted from on-farm dairy management software, and estimated breeding values were downloaded from the US Department of Agriculture Agricultural Research Service Animal Improvement Programs Laboratory (Beltsville, MD) database. The edited data set consisted of 129,245 breeding records from primiparous Holstein cows and 195,128 breeding records from multiparous Holstein cows. Each data point in the final data set included 23 and 25 explanatory variables and 1 binary outcome for of 0.756 ± 0.005 and 0.736 ± 0.005 for primiparous and multiparous cows, respectively. The naïve Bayes algorithm, Bayesian network, and decision tree algorithms showed somewhat poorer classification performance. An information-based variable selection procedure identified herd average conception rate, incidence of ketosis, number of previous (failed) inseminations, days in milk at breeding, and mastitis as the most effective explanatory variables in predicting pregnancy outcome.
Publication Type: Journal Article
Source of Publication: Journal of Dairy Science, 97(2), p. 731-742
Publisher: Elsevier Inc
Place of Publication: United States of America
ISSN: 1525-3198
0022-0302
Field of Research (FOR): 070201 Animal Breeding
070206 Animal Reproduction
070299 Animal Production not elsewhere classified
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Statistics to Oct 2018: Visitors: 11
Views: 19
Downloads: 0
Appears in Collections:Journal Article
School of Environmental and Rural Science
School of Science and Technology

Files in This Item:
4 files
File Description SizeFormat 
Show full item record

SCOPUSTM   
Citations

19
checked on Nov 30, 2018

Page view(s)

64
checked on Mar 7, 2019
Google Media

Google ScholarTM

Check

Altmetric


Items in Research UNE are protected by copyright, with all rights reserved, unless otherwise indicated.