Go to Top Go to Bottom
Anim Biosci > Volume 34(4); 2021 > Article
Environment and Management
Animal Bioscience 2021;34(4): 770-782.
DOI: https://doi.org/10.5713/ajas.19.0939    Published online April 12, 2020.
Lactation milk yield prediction in primiparous cows on a farm using the seasonal auto-regressive integrated moving average model, nonlinear autoregressive exogenous artificial neural networks and Wood’s model
Wilhelm Grzesiak1  , Daniel Zaborski1,*  , Iwona Szatkowska1  , Katarzyna Królaczyk2 
1Department of Ruminants Science, West Pomeranian University of Technology, 71-270 Szczecin, Poland
2Department of Animal Anatomy and Zoology, West Pomeranian University of Technology, 71-466 Szczecin, Poland
Correspondence:  Daniel Zaborski, Tel: +48-914496813, Fax: +48-914496800, Email: daniel.zaborski@zut.edu.pl
Received: 7 December 2019   • Revised: 13 February 2020   • Accepted: 16 March 2020
Abstract
Objective
The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood’s model) to the prediction of milk yield during lactation.
Methods
The dataset comprised monthly test-day records from 965 Polish Holstein-Friesian Black-and-White primiparous cows. The milk yields from cows in their first lactation (from 5 to 305 days in milk) were used. Each lactation was divided into ten lactation stages of approximately 30 days. Two age groups and four calving seasons were distinguished. The records collected between 2009 and 2015 were used for model fitting and those from 2016 for the verification of predictive performance.
Results
No significant differences between the predicted and the real values were found. The predictions generated by SARIMA were slightly more accurate, although they did not differ significantly from those produced by the NARX and Wood’s models. SARIMA had a slightly better performance, especially in the initial periods, whereas the NARX and Wood’s models in the later ones.
Conclusion
The use of SARIMA was more time-consuming than that of NARX and Wood’s model. The application of the SARIMA, NARX and Wood’s models (after their implementation in a user-friendly software) may allow farmers to estimate milk yield of cows that begin production for the first time.
Keywords: Prediction; Heifer; Lactation Curve; Milk Yield; Neural Networks; Statistical Methods
TOOLS
METRICS Graph View
  • 0 Crossref
  •  0 Scopus
  • 574 View
  • 39 Download
Related articles


Editorial Office
Asian-Australasian Association of Animal Production Societies(AAAP)
Room 708 Sammo Sporex, 23, Sillim-ro 59-gil, Gwanak-gu, Seoul 08776, Korea   
TEL : +82-2-888-6558    FAX : +82-2-888-6559   
E-mail : animbiosci@gmail.com               

Copyright © 2021 by Animal Bioscience. All rights reserved.

Developed in M2PI

Close layer
prev next