Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks

Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks

ABSTRACT: Dry matter intake (DMI) is a fundamental component of the animal's feed efficiency, but measuring DMI of individual cows is expensive. Mid-infrared reflectance spectroscopy (MIRS) on milk samples could be an inexpensive alternative to predict DMI. The objectives of this study were (1)...

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Bibliographic Details
Main Authors: Saeed Shadpour, Tatiane C.S. Chud, Dagnachew Hailemariam, Hinayah R. Oliveira, Graham Plastow, Paul Stothard, Jan Lassen, Ransom Baldwin, Filippo Miglior, Christine F. Baes, Dan Tulpan, Flavio S. Schenkel
Format: Article
Language:English
Published: Elsevier 2022-10-01
Series:Journal of Dairy Science
Subjects:
dry matter intake
machine learning
mid-infrared reflectance spectroscopy
milk
Online Access:http://www.sciencedirect.com/science/article/pii/S0022030222004817

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http://www.sciencedirect.com/science/article/pii/S0022030222004817

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