A.K. Shirley , P.C. Thomson , A. Chlingaryan , C.E.F. Clark
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The optimised model identified drinking events with high accuracy (F-score = 0.99), as predicted when the average reticulorumen temperature declined by at least 0.5°C per 10-minutes, over a 10-, 20-, or 30-minute period. To account for differences in rapidity of decline, smaller reductions of 0.25°C per 10 min were considered valid indicators of a drinking event, provided the 0.5°C per 10-minute threshold was also met in a consecutive observation period. The temporal variability in drinking behaviour for 1,429 lactating dairy cattle across three dairy farms was then determined. Daily drinking events were greater in summer (mean 4.1) than winter (mean 3.3), while the change in reticulorumen temperature with each drinking event was smaller in summer (mean 3.7°C) than winter (mean 4.9°C). Drinking-recovery duration averaged 97.8 min/event. By revealing temporal differences in drinking behaviour for pasture-based dairy cattle, this work provides the basis for an improved understanding of core body temperature diversity.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"235 ","pages":"Article 110357"},"PeriodicalIF":7.7000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The diversity in dairy cattle reticulorumen temperature: Identifying water intake events\",\"authors\":\"A.K. Shirley , P.C. Thomson , A. Chlingaryan , C.E.F. Clark\",\"doi\":\"10.1016/j.compag.2025.110357\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Climate change and associated weather variability across the Australian landscape has lent themselves to an increased incidence of cattle heat stress. Water consumption can have a sizeable, sustained impact on reticulorumen temperature readings, thereby impacting our interpretation of an individual’s underlying physiological response to changing environmental conditions. To distinguish drinking events, we developed a drinking event detection model based on observed drinking events (video recording) from 28 dairy heifers, alongside sensor-derived reticulorumen temperature (smaXtec Animal Care GmbH) profiles. The optimised model identified drinking events with high accuracy (F-score = 0.99), as predicted when the average reticulorumen temperature declined by at least 0.5°C per 10-minutes, over a 10-, 20-, or 30-minute period. To account for differences in rapidity of decline, smaller reductions of 0.25°C per 10 min were considered valid indicators of a drinking event, provided the 0.5°C per 10-minute threshold was also met in a consecutive observation period. The temporal variability in drinking behaviour for 1,429 lactating dairy cattle across three dairy farms was then determined. Daily drinking events were greater in summer (mean 4.1) than winter (mean 3.3), while the change in reticulorumen temperature with each drinking event was smaller in summer (mean 3.7°C) than winter (mean 4.9°C). Drinking-recovery duration averaged 97.8 min/event. 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引用次数: 0
摘要
澳大利亚各地的气候变化和相关的天气变化使牛热应激的发生率增加。水的消耗可以对网状温度读数产生相当大的、持续的影响,从而影响我们对个体对变化的环境条件的潜在生理反应的解释。为了区分饮水事件,我们开发了一个饮水事件检测模型,该模型基于观察到的28头奶牛的饮水事件(视频记录),以及传感器衍生的网状胃温度(smaXtec Animal Care GmbH)剖面。优化后的模型在10分钟、20分钟或30分钟的时间内,当平均网状温度每10分钟下降至少0.5°C时,可以准确地识别出饮酒事件(f值= 0.99)。为了解释下降速度的差异,每10分钟减少0.25°C被认为是饮酒事件的有效指标,前提是在连续观察期内也达到每10分钟0.5°C的阈值。然后确定了三个奶牛场的1429头泌乳奶牛的饮酒行为的时间变化。夏季每日饮水事件(平均4.1)大于冬季(平均3.3),而每次饮水事件的网状腔温度变化在夏季(平均3.7°C)小于冬季(平均4.9°C)。饮酒恢复时间平均为97.8分钟/次。通过揭示放牧奶牛饮酒行为的时间差异,这项工作为更好地理解核心体温多样性提供了基础。
The diversity in dairy cattle reticulorumen temperature: Identifying water intake events
Climate change and associated weather variability across the Australian landscape has lent themselves to an increased incidence of cattle heat stress. Water consumption can have a sizeable, sustained impact on reticulorumen temperature readings, thereby impacting our interpretation of an individual’s underlying physiological response to changing environmental conditions. To distinguish drinking events, we developed a drinking event detection model based on observed drinking events (video recording) from 28 dairy heifers, alongside sensor-derived reticulorumen temperature (smaXtec Animal Care GmbH) profiles. The optimised model identified drinking events with high accuracy (F-score = 0.99), as predicted when the average reticulorumen temperature declined by at least 0.5°C per 10-minutes, over a 10-, 20-, or 30-minute period. To account for differences in rapidity of decline, smaller reductions of 0.25°C per 10 min were considered valid indicators of a drinking event, provided the 0.5°C per 10-minute threshold was also met in a consecutive observation period. The temporal variability in drinking behaviour for 1,429 lactating dairy cattle across three dairy farms was then determined. Daily drinking events were greater in summer (mean 4.1) than winter (mean 3.3), while the change in reticulorumen temperature with each drinking event was smaller in summer (mean 3.7°C) than winter (mean 4.9°C). Drinking-recovery duration averaged 97.8 min/event. By revealing temporal differences in drinking behaviour for pasture-based dairy cattle, this work provides the basis for an improved understanding of core body temperature diversity.
期刊介绍:
Computers and Electronics in Agriculture provides international coverage of advancements in computer hardware, software, electronic instrumentation, and control systems applied to agricultural challenges. Encompassing agronomy, horticulture, forestry, aquaculture, and animal farming, the journal publishes original papers, reviews, and applications notes. It explores the use of computers and electronics in plant or animal agricultural production, covering topics like agricultural soils, water, pests, controlled environments, and waste. The scope extends to on-farm post-harvest operations and relevant technologies, including artificial intelligence, sensors, machine vision, robotics, networking, and simulation modeling. Its companion journal, Smart Agricultural Technology, continues the focus on smart applications in production agriculture.