Applying dairy cow behavior in management practice is an effective way of improving cow health, welfare and performance. This paper first reviewed daily time budget and normal patterns of dairy cow behavior, and then ...Applying dairy cow behavior in management practice is an effective way of improving cow health, welfare and performance. This paper first reviewed daily time budget and normal patterns of dairy cow behavior, and then discussed the influence of major management conditions and practices （such as competitive environments, stocking density, grouping strategies） on cow＇s feeding, lying and social behavior. Finally, new findings of using feeding behavior to predict disorders in transition period were addressed. It was suggested that dairy researchers and farmers should take advantage of related knowledge of dairy cow behavior to improve dairy cow health and welfare. More research is required to further study dairy cow behavior so as to better apply it in practical management and meet the needs of production.展开更多
To address the issue of low recognition accuracy for eight types of behaviors including standing,walking,drinking,lying,eating,mounting,fighting and limping in complex multi-cow farm environments,a multi-target cow be...To address the issue of low recognition accuracy for eight types of behaviors including standing,walking,drinking,lying,eating,mounting,fighting and limping in complex multi-cow farm environments,a multi-target cow behavior recognition method based on an improved YOLOv11n algorithm was proposed.The detection capability for small targets in images was enhanced by incorporating a DASI module into the backbone network and a MDCR module into the neck network,based on YOLOv11.The improved YOLOv11 algorithm increased the mean average precision from the original 89.5%to 93%,with particularly notable improvements of 8.7%and 6.3%in the average precision for recognizing drinking and walking behaviors,respectively.These results fully demonstrate that the proposed method enhances the model s ability to recognize cow behaviors.展开更多
For the rapid and accurate identification of cow reproduction and healthy behavior from mass surveillance video,in this study,400 head of young cows and lactating cows were taken as the research object and analyzed co...For the rapid and accurate identification of cow reproduction and healthy behavior from mass surveillance video,in this study,400 head of young cows and lactating cows were taken as the research object and analyzed cow behavior from the dairy activity area and milk hall ramp.The method of object recognition based on image entropy was proposed,aiming at the identification of motional cow object behavior against a complex background.Calculating a minimum bounding box and contour mapping were used for the real-time capture of rutting span behavior and hoof or back characteristics.Then,by combining the continuous image characteristics and movement of cows for 7 d,the method could quickly distinguish abnormal behavior of dairy cows from healthy reproduction,improving the accuracy of the identification of characteristics of dairy cows.Cow behavior recognition based on image analysis and activities was proposed to capture abnormal behavior that has harmful effects on healthy reproduction and to improve the accuracy of cow behavior identification.The experimental results showed that,through target detection,classification and recognition,the recognition rates of hoof disease and heat in the reproduction and health of dairy cows were greater than 80%,and the false negative rates of oestrus and hoof disease were 3.28%and 5.32%,respectively.This method can enhance the real-time monitoring of cows,save time and improve the management efficiency of large-scale farming.展开更多
[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been propo...[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.展开更多
<span style="font-family:Verdana;">A grazing experiment was undertaken to assess the effects of two levels of herbage mass (HM) on herbage DM intake (DMI), fat and protein corrected milk yield (FPCM), ...<span style="font-family:Verdana;">A grazing experiment was undertaken to assess the effects of two levels of herbage mass (HM) on herbage DM intake (DMI), fat and protein corrected milk yield (FPCM), grazing behaviour, energy expenditure (HP), and methane emissions (CH</span><sub><span style="font-family:Verdana;">4</span></sub><span style="font-family:Verdana;">) of grazing dairy cows in spring. Treatments were a low HM (1447 kg DM/ha;LHM) or a high HM (1859 kg DM/ha;HHM). Pasture was composed mainly </span><span style="font-family:Verdana;">of</span><span style="font-family:;" "=""><span><span style="font-family:Verdana;"> cocksfoot (</span><i></i></span><i><i><span style="font-family:Verdana;">Dactylis glomerata</span></i><span></span></i></span><span><span style="font-family:Verdana;">) and lucerne (</span><i></i></span><i><i><span style="font-family:Verdana;">Medicago sativa</span></i><span></span></i><span style="font-family:Verdana;">), offered at a daily herbage allowance of 30 kg DM/cow, above 5 cm. Eight multiparous Holstein cows were used in a 2</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">×</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">2 Latin Square design in two 10-day periods. Despite the differences in pre-grazing HM between treatments, OM digestibility was not different (P = 0.28). Herbage mass did not affect DMI or FPCM. Grazing time was not different between treatments, but cows had a greater bite rate when grazing on LHM swards. However, HP did not differ between treatments. Daily methane emission (per cow), methane emission intensity (per kg FPCM) and methane yield (as percentage of gross energy intake) were not different. The lack of effect of the amount of pre-grazing HM on energy intake, confirms that the difference between HM treatments w</span><span style="font-family:Verdana;">as</span><span style="font-family:Verdana;"> beyond the limits that impose extra energy expenditure during grazing.</span>展开更多
After the 2011 Tohoku earthquake (EQ), there have been numerous aftershocks in the eastern and Pacific Ocean of Japan, but EQs are still rare in the western part of Japan. In this situation a relatively large (magnitu...After the 2011 Tohoku earthquake (EQ), there have been numerous aftershocks in the eastern and Pacific Ocean of Japan, but EQs are still rare in the western part of Japan. In this situation a relatively large (magnitude (M) ~6) EQ happened on April 12 (UT), 2013 at a place close to the former 1995 Kobe EQ (M~7), so we have tried to find whether there existed any precursors to this EQ, especially abnormal animal behavior (milk yield of cows), observed at Kagawa, Shikoku, near the EQ epicenter. The milk yield of cows has been continuously monitored at Kagawa, and it is found that the milk yield exhibited an abnormal depletion about 10 days before the EQ. This behavior has been extensively compared with the former electromagnetic precursors (ULF radiation, ionos-pheric perturbation). This leads to the discussion on the sensory mechanism of unusual behavior of mild yield of cows, and it may be suggested that ULF radiation among different electromagnetic precursors is a mostly likely driver, at least, for this EQ.展开更多
文摘Applying dairy cow behavior in management practice is an effective way of improving cow health, welfare and performance. This paper first reviewed daily time budget and normal patterns of dairy cow behavior, and then discussed the influence of major management conditions and practices （such as competitive environments, stocking density, grouping strategies） on cow＇s feeding, lying and social behavior. Finally, new findings of using feeding behavior to predict disorders in transition period were addressed. It was suggested that dairy researchers and farmers should take advantage of related knowledge of dairy cow behavior to improve dairy cow health and welfare. More research is required to further study dairy cow behavior so as to better apply it in practical management and meet the needs of production.
基金Supported by The Three Vertical Basic Cultivation Project of Heilongjiang Bayi Agricultural University(ZRCPY202314).
文摘To address the issue of low recognition accuracy for eight types of behaviors including standing,walking,drinking,lying,eating,mounting,fighting and limping in complex multi-cow farm environments,a multi-target cow behavior recognition method based on an improved YOLOv11n algorithm was proposed.The detection capability for small targets in images was enhanced by incorporating a DASI module into the backbone network and a MDCR module into the neck network,based on YOLOv11.The improved YOLOv11 algorithm increased the mean average precision from the original 89.5%to 93%,with particularly notable improvements of 8.7%and 6.3%in the average precision for recognizing drinking and walking behaviors,respectively.These results fully demonstrate that the proposed method enhances the model s ability to recognize cow behaviors.
基金the Natural Science Foundation of Beijing(4172026)Capability Innovation Project of Beijing Academy of Agriculture and Forestry(KJCX20170706).
文摘For the rapid and accurate identification of cow reproduction and healthy behavior from mass surveillance video,in this study,400 head of young cows and lactating cows were taken as the research object and analyzed cow behavior from the dairy activity area and milk hall ramp.The method of object recognition based on image entropy was proposed,aiming at the identification of motional cow object behavior against a complex background.Calculating a minimum bounding box and contour mapping were used for the real-time capture of rutting span behavior and hoof or back characteristics.Then,by combining the continuous image characteristics and movement of cows for 7 d,the method could quickly distinguish abnormal behavior of dairy cows from healthy reproduction,improving the accuracy of the identification of characteristics of dairy cows.Cow behavior recognition based on image analysis and activities was proposed to capture abnormal behavior that has harmful effects on healthy reproduction and to improve the accuracy of cow behavior identification.The experimental results showed that,through target detection,classification and recognition,the recognition rates of hoof disease and heat in the reproduction and health of dairy cows were greater than 80%,and the false negative rates of oestrus and hoof disease were 3.28%and 5.32%,respectively.This method can enhance the real-time monitoring of cows,save time and improve the management efficiency of large-scale farming.
文摘[Objective]Real-time monitoring of cow ruminant behavior is of paramount importance for promptly obtaining relevant information about cow health and predicting cow diseases.Currently,various strategies have been proposed for monitoring cow ruminant behavior,including video surveillance,sound recognition,and sensor monitoring methods.How‐ever,the application of edge device gives rise to the issue of inadequate real-time performance.To reduce the volume of data transmission and cloud computing workload while achieving real-time monitoring of dairy cow rumination behavior,a real-time monitoring method was proposed for cow ruminant behavior based on edge computing.[Methods]Autono‐mously designed edge devices were utilized to collect and process six-axis acceleration signals from cows in real-time.Based on these six-axis data,two distinct strategies,federated edge intelligence and split edge intelligence,were investigat‐ed for the real-time recognition of cow ruminant behavior.Focused on the real-time recognition method for cow ruminant behavior leveraging federated edge intelligence,the CA-MobileNet v3 network was proposed by enhancing the MobileNet v3 network with a collaborative attention mechanism.Additionally,a federated edge intelligence model was designed uti‐lizing the CA-MobileNet v3 network and the FedAvg federated aggregation algorithm.In the study on split edge intelli‐gence,a split edge intelligence model named MobileNet-LSTM was designed by integrating the MobileNet v3 network with a fusion collaborative attention mechanism and the Bi-LSTM network.[Results and Discussions]Through compara‐tive experiments with MobileNet v3 and MobileNet-LSTM,the federated edge intelligence model based on CA-Mo‐bileNet v3 achieved an average Precision rate,Recall rate,F1-Score,Specificity,and Accuracy of 97.1%,97.9%,97.5%,98.3%,and 98.2%,respectively,yielding the best recognition performance.[Conclusions]It is provided a real-time and effective method for monitoring cow ruminant behavior,and the proposed federated edge intelligence model can be ap‐plied in practical settings.
文摘<span style="font-family:Verdana;">A grazing experiment was undertaken to assess the effects of two levels of herbage mass (HM) on herbage DM intake (DMI), fat and protein corrected milk yield (FPCM), grazing behaviour, energy expenditure (HP), and methane emissions (CH</span><sub><span style="font-family:Verdana;">4</span></sub><span style="font-family:Verdana;">) of grazing dairy cows in spring. Treatments were a low HM (1447 kg DM/ha;LHM) or a high HM (1859 kg DM/ha;HHM). Pasture was composed mainly </span><span style="font-family:Verdana;">of</span><span style="font-family:;" "=""><span><span style="font-family:Verdana;"> cocksfoot (</span><i></i></span><i><i><span style="font-family:Verdana;">Dactylis glomerata</span></i><span></span></i></span><span><span style="font-family:Verdana;">) and lucerne (</span><i></i></span><i><i><span style="font-family:Verdana;">Medicago sativa</span></i><span></span></i><span style="font-family:Verdana;">), offered at a daily herbage allowance of 30 kg DM/cow, above 5 cm. Eight multiparous Holstein cows were used in a 2</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">×</span><span style="font-family:;" "=""> </span><span style="font-family:Verdana;">2 Latin Square design in two 10-day periods. Despite the differences in pre-grazing HM between treatments, OM digestibility was not different (P = 0.28). Herbage mass did not affect DMI or FPCM. Grazing time was not different between treatments, but cows had a greater bite rate when grazing on LHM swards. However, HP did not differ between treatments. Daily methane emission (per cow), methane emission intensity (per kg FPCM) and methane yield (as percentage of gross energy intake) were not different. The lack of effect of the amount of pre-grazing HM on energy intake, confirms that the difference between HM treatments w</span><span style="font-family:Verdana;">as</span><span style="font-family:Verdana;"> beyond the limits that impose extra energy expenditure during grazing.</span>
文摘After the 2011 Tohoku earthquake (EQ), there have been numerous aftershocks in the eastern and Pacific Ocean of Japan, but EQs are still rare in the western part of Japan. In this situation a relatively large (magnitude (M) ~6) EQ happened on April 12 (UT), 2013 at a place close to the former 1995 Kobe EQ (M~7), so we have tried to find whether there existed any precursors to this EQ, especially abnormal animal behavior (milk yield of cows), observed at Kagawa, Shikoku, near the EQ epicenter. The milk yield of cows has been continuously monitored at Kagawa, and it is found that the milk yield exhibited an abnormal depletion about 10 days before the EQ. This behavior has been extensively compared with the former electromagnetic precursors (ULF radiation, ionos-pheric perturbation). This leads to the discussion on the sensory mechanism of unusual behavior of mild yield of cows, and it may be suggested that ULF radiation among different electromagnetic precursors is a mostly likely driver, at least, for this EQ.
