基于不同行为时间的奶牛健康状况评价
S831.7; 如何高效准确地监测和管理好奶牛是当前规模化奶牛场发展的关键.该文通过对17头奶牛休息时间、反刍时间和采食时间的连续61d监测和行为记录,利用SPSS23.0软件,结合Logistics回归分析法对试验数据进行统计分析,并构建了奶牛健康状况评价模型.研究结果表明:1)与正常行为期间相比,奶牛非正常行为期间的平均休息时间增加25.7%,反刍时间和采食时间分别平均减少12.7%和2.3%.2)荷斯坦泌乳奶牛正常行为时间呈正态分布,每天(24 h)平均休息时间为300~600 min,反刍时间为400~700 min,采食时间为200~400 min.非正常行为时间呈离散状分布,无明显...
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| Published in | 农业工程学报 Vol. 35; no. 19; pp. 238 - 244 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
中国农业大学农业农村部设施农业工程重点实验室,北京100083
01.10.2019
北京市畜禽健康养殖环境工程技术研究中心,北京100083 中国农业大学水利与土木工程学院,北京100083%中国农业大学农业农村部设施农业工程重点实验室,北京100083 中国农业大学水利与土木工程学院,北京100083 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-6819 |
| DOI | 10.11975/j.issn.1002-6819.2019.19.029 |
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| Abstract | S831.7; 如何高效准确地监测和管理好奶牛是当前规模化奶牛场发展的关键.该文通过对17头奶牛休息时间、反刍时间和采食时间的连续61d监测和行为记录,利用SPSS23.0软件,结合Logistics回归分析法对试验数据进行统计分析,并构建了奶牛健康状况评价模型.研究结果表明:1)与正常行为期间相比,奶牛非正常行为期间的平均休息时间增加25.7%,反刍时间和采食时间分别平均减少12.7%和2.3%.2)荷斯坦泌乳奶牛正常行为时间呈正态分布,每天(24 h)平均休息时间为300~600 min,反刍时间为400~700 min,采食时间为200~400 min.非正常行为时间呈离散状分布,无明显分布规律.3)休息时间和采食时间是预测模型的主要影响因素,其中采食时间对模型预测概率的影响力较休息时间大,在其他条件不变的情况下,采食时间水平每增加1个单位,奶牛非正常行为预测概率变化扩大4.2倍.奶牛非正常行为预测模型预测结果与人工目视观察结果比较,模型预测准确率为91%.该研究可为现代规模化奶牛场科学、精准化管理提供参考. |
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| AbstractList | S831.7; 如何高效准确地监测和管理好奶牛是当前规模化奶牛场发展的关键.该文通过对17头奶牛休息时间、反刍时间和采食时间的连续61d监测和行为记录,利用SPSS23.0软件,结合Logistics回归分析法对试验数据进行统计分析,并构建了奶牛健康状况评价模型.研究结果表明:1)与正常行为期间相比,奶牛非正常行为期间的平均休息时间增加25.7%,反刍时间和采食时间分别平均减少12.7%和2.3%.2)荷斯坦泌乳奶牛正常行为时间呈正态分布,每天(24 h)平均休息时间为300~600 min,反刍时间为400~700 min,采食时间为200~400 min.非正常行为时间呈离散状分布,无明显分布规律.3)休息时间和采食时间是预测模型的主要影响因素,其中采食时间对模型预测概率的影响力较休息时间大,在其他条件不变的情况下,采食时间水平每增加1个单位,奶牛非正常行为预测概率变化扩大4.2倍.奶牛非正常行为预测模型预测结果与人工目视观察结果比较,模型预测准确率为91%.该研究可为现代规模化奶牛场科学、精准化管理提供参考. |
| Abstract_FL | Cows’health is the foundation of large scale dairy farm’development. How to monitor cows accurately and manage-ment efficiently is important to the development of scale dairy farm. In the spring of 2018, 17 high-yielding Holstein lactation cows were selected from the same group of Anxing dairy farm in Hulin city for experimental study. The sample cows’average weight (500±50) kg and lactation age (203±83) days. The average daily milk yield of each cow was (30±2) kg. Materials selected for this experiment include lactating cows, computers, cow collars (MooMonitor+, Dairymaster, Ireland), data base stations, and amazon cloud storage terminal. The cows and computer were provided by Anxing dairy farm. The cow collar and data base sta-tion were provided by Dairy Master company in Ireland, and the cow collar is equipped with the MEMS (micro electro-mechani-cal system) accelerometer, according to the principle of accelerometer sensor technology, big data clustering analysis was carried out for the time of acceleration changes in different directions, and the position of cows was tracked through RFID tags to moni-tor the activity behavior of cows. Refer to the number of samples selected by domestic and foreign scholars related to cow feeding behavior, ruminant behavior, reclining and resting behavior and estrus behavior. From April 1, 2018 to May 31, 2018, Dairymas-ter’s Moonmonitor + information collection system was used to monitor and collect test data of 17 cows’behavior time every day (24h) in Anxing dairy farm, and physical health conditions of the cows were recorded the worker at the same time, such as mastitis, lameness, diarrhea and trauma. During the test period, test data were downloaded from the cloud data storage server through the monitor system at 08: 00 am every day for 61 days. The data collection interval was 24 h, the downloaded data in-cludes the rest time (min/24 h), rumination time (min/24 h) and feeding time (min/24 h) of the cows, and 1 037 data records were obtained. And 937 data were randomly selected as training data set, one hundred data were randomly selected as validation data to verify the model prediction. Then, binary logistic regression analysis method and statistical analysis software SPSS23.0 were used to study the collected data. In order to meet the binary logistic regression analysis conditions, the different behaviors time were converted into classification variables. The independent variables of binomial Logistic regression model were entered by force method. The entry criteria of variables was α<0.05, and the exclusion standard was α>0.1. The results showed that: 1) The behav-ior time changed differently in different cows, and the average rest time changed greatly. The average rest time during abnormal behavior period increased by 25.7% compared with that during normal behavior period. Compared with the normal behavior peri-od, the ruminant time and feeding time during abnormal behavior period decreased by 12.7% and 2.3%, respectively. 2) The be-havior time of healthy Holstein lactation cows was normally distributed, with an average rest time of 300-600 min, ruminant time of 400-700 min and feeding time of 200-400 min per day (24 h). The behavior time of abnormal cows was distributed discreetly without obvious distribution rule. 3) Rest time and feeding time are the main influencing factors of the prediction model, among which feeding time had a greater influence on the prediction probability of the model than rest time. When other conditions re-main unchanged, the prediction probability change of abnormal behavior of cows increased by 4.2 times for one additional unit of feeding time. Compared with the results of human visual observation, the prediction accuracy of the model was 91%. Therefore, the paper can provide a reference for scientific and accurate management of modern large-scale dairy farms. |
| Author | 施正香 郑国生 滕光辉 |
| AuthorAffiliation | 中国农业大学农业农村部设施农业工程重点实验室,北京100083;中国农业大学水利与土木工程学院,北京100083%中国农业大学农业农村部设施农业工程重点实验室,北京100083;中国农业大学水利与土木工程学院,北京100083;北京市畜禽健康养殖环境工程技术研究中心,北京100083 |
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| Author_FL | Zheng Guosheng Shi Zhengxiang Teng Guanghui |
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| DocumentTitle_FL | Health assessment of cows based on different behavior time |
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| Keywords | health assessment 算法 algorithms logistic regression analysis 监测 逻辑回归分析 健康评价 cows behavior monitoring 行为 奶牛 |
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| Title | 基于不同行为时间的奶牛健康状况评价 |
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