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Meteorological data and behavioural sensor data, including lying duration, number of steps and number of postural changes, were retrieved from eight dairy farms. Eighteen behavioural variables were extracted using time windows informed by exploratory analysis across three THI categories. Several 24-h pattern variables reflecting behavioural changes for specific periods in a day had a consistent direction of correlation with the daily mean THI across farms on days with and\/or without pasture access. For the daily mean variables, only the daily mean lying duration had a consistent direction of correlation with daily mean THI across farms on days without pasture access, and no daily mean variables had a consistent direction of correlation with daily mean THI across farms on days with pasture access. Generalized additive mixed models indicated that relationships between behavioural variables and daily mean THI were nonlinear. For most behavioural variables, the daily mean THI ranges in which the behavioural variables changed differed between days with and without pasture access. Some of the 24-h pattern behavioural variables exhibited a wider daily mean THI range with significant behavioural change than the daily mean variables. In conclusion, some of the variables derived from the 24-h behavioural pattern were highly sensitive to daily mean THI and could complement daily mean variables in the detection of heat stress. Dairy cows had more behavioural changes in response to increasing THI on days with pasture access than on days without pasture access.<\/jats:p>","DOI":"10.1017\/s0022029926102556","type":"journal-article","created":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T11:14:46Z","timestamp":1786360486000},"page":"1-12","source":"Crossref","is-referenced-by-count":1,"title":["Quantifying the 24-h pattern and daily mean behavioural variables of dairy herds related to the environmental temperature and humidity"],"prefix":"10.1017","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5958-3025","authenticated-orcid":false,"given":"Lianzhe","family":"Shi","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04qw24q55","id-type":"ROR","asserted-by":"publisher"}],"name":"Wageningen University & Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akke","family":"Kok","sequence":"additional","affiliation":[{"name":"Wageningen University & Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bas","family":"Kemp","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04qw24q55","id-type":"ROR","asserted-by":"publisher"}],"name":"Wageningen University & Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ariette","family":"van Knegsel","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04qw24q55","id-type":"ROR","asserted-by":"publisher"}],"name":"Wageningen University & Research"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2026,8,10]]},"reference":[{"key":"S0022029926102556_ref19","doi-asserted-by":"publisher","DOI":"10.1017\/S0022029923000559"},{"key":"S0022029926102556_ref42","doi-asserted-by":"publisher","DOI":"10.1016\/j.theriogenology.2020.02.048"},{"key":"S0022029926102556_ref43","unstructured":"United Nations Environment Programme (2021) Emissions Gap Report 2021 . 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