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

ABSTRACT: Interest in reducing eructed CH4 is growing, but measuring CH4 emissions is expensive and difficult in large populations. In this study, we investigated the effectiveness of milk mid-infrared spectroscopy (MIRS) data to predict CH4 emission in lactating Canadian Holstein cows. A total of 1...

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主要な著者:	Saeed Shadpour, Tatiane C.S. Chud, Dagnachew Hailemariam, Graham Plastow, Hinayah R. Oliveira, Paul Stothard, Jan Lassen, Filippo Miglior, Christine F. Baes, Dan Tulpan, Flavio S. Schenkel
フォーマット:	Artigo
言語:	Inglês
出版事項:	Elsevier 2022-10-01
シリーズ:	Journal of Dairy Science
主題:	Holstein cows machine learning methane mid-infrared spectroscopy
オンライン･アクセス:	http://www.sciencedirect.com/science/article/pii/S0022030222004805
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