An effective way to incorporate temperature–humidity index to study effect of heat stress on milk yield by an XGBoost machine learning model

The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. Commercial dairy farms face major challenges in safeguarding animal welfare and overall farm sustainability from environmental heat stressors. As climate ch...

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Bibliographic Details
Published in:	Journal of dairy science Vol. 108; no. 12; pp. 13995 - 14017
Main Authors:	Hasan, M.F., Celik, N., Williams, Y., Williams, S.R.O., Marett, L.C.
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.12.2025 Elsevier
Subjects:	Animals Australia Boosting Machine Learning Algorithms Cattle Climate Change Dairying days in milk Female heat stress Heat-Shock Response Hot Temperature Humidity Lactation Machine Learning Milk - metabolism Temperature temperature–humidity index XGBoost Australia heat stress days in milk machine learning temperature–humidity index XGBoost
ISSN:	0022-0302, 1525-3198, 1525-3198
Online Access:	Get full text
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