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DOI: 10.14569/IJACSA.2018.090715
PDF

Time Series Analysis for Shortened Labor Mean Interval of Dairy Cattle with the Data of BCS, RFS, Weight, Amount of Milk and Outlook

Author 1: Kohei Arai
Author 2: Osamu Fukuda
Author 3: Hiroshi Okumura
Author 4: Kenji Endo
Author 5: Kenichi Yamashita

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 7, 2018.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: MTime series analysis for shortened labor mean interval of dairy cattle with the data of Body Condition Score (BCS), Rumen Fill Score (RFS), Weight, Amount of Milk and Outlook is conducted. Method for shortened the labor mean internal of Japanese dairy cattle based on time-series analysis with the data of visual index of BCS, RFS, Weight, Amount of Milk and Outlook is proposed. In order to shortened the labor mean interval of dairy cattle is the purpose of the research. Through the experiments with 17 Japanese dairy cattle of the 17 Japanese anestrus Holstein dairy cattle, it is found that the combination of weight, BCS and amount of milk is a good indicator for identification of productive cattle. Therefore, the cattle which need hormone treatments can be identified.

Keywords: Body Condition Score (BCS); Rumen Fill Score (RFS); Dairy cattle; Time-Series Analysis; Cattle Productivity

Kohei Arai, Osamu Fukuda, Hiroshi Okumura, Kenji Endo and Kenichi Yamashita, “Time Series Analysis for Shortened Labor Mean Interval of Dairy Cattle with the Data of BCS, RFS, Weight, Amount of Milk and Outlook” International Journal of Advanced Computer Science and Applications(IJACSA), 9(7), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090715

@article{Arai2018,
title = {Time Series Analysis for Shortened Labor Mean Interval of Dairy Cattle with the Data of BCS, RFS, Weight, Amount of Milk and Outlook},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090715},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090715},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {7},
author = {Kohei Arai and Osamu Fukuda and Hiroshi Okumura and Kenji Endo and Kenichi Yamashita}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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