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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Vydané v:	Journal of dairy science Ročník 108; číslo 12; s. 13995 - 14017
Hlavní autori:	Hasan, M.F., Celik, N., Williams, Y., Williams, S.R.O., Marett, L.C.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Inc 01.12.2025 Elsevier
Predmet:	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
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