Mining data from milk infrared spectroscopy to improve feed intake predictions in lactating dairy cows

• Published in: Journal of dairy science Vol. 101; no. 7; pp. 5878 - 5889
• Main Authors: Dórea, J.R.R., Rosa, G.J.M., Weld, K.A., Armentano, L.E.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.07.2018
• Subjects: Animal Feed; Animals; Bayes Theorem; Body Weight; Cattle - metabolism; Cattle - physiology; dairy cows; data collection; Diet - veterinary; dry matter intake; Energy Intake - physiology; feed conversion; Female; infrared spectroscopy; lactating females; lactation; Lactation - metabolism; lactose; least squares; machine learning; milk; Milk - chemistry; milk composition; milk spectra; milk yield; neural networks; prediction; Spectrophotometry, Infrared - methods; wavelengths; machine learning; milk spectra; dry matter intake

Feed intake is one of the most important components of feed efficiency in dairy systems. However, it is a difficult trait to measure in commercial operations for individual cows. Milk spectrum from mid-infrared spectroscopy has been previously used to predict milk traits, and could be an alternative...