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פותח על ידי קלירמאש פתרונות בע"מ -
Towards practical application of sensors for monitoring animal health: the effect of post-calving health problems on rumination duration, activity and milk yield
Year:
2017
Source of publication :
Journal of Dairy Research
Authors :
אנטלר, אהרון
;
.
הלחמי, אילן
;
.
מלץ, אפרים
;
.
סטנסלס, מכטלד
;
.
Volume :
84
Co-Authors:

Bahr C.; Berckmans D

Facilitators :
From page:
139
To page:
145
(
Total pages:
7
)
Abstract:

The objective of this study was to design and validate a mathematical model to detect post-calving ketosis. The validation was conducted in four commercial dairy farms in Israel, on a total of 706 multiparous Holstein dairy cows: 203 cows clinically diagnosed with ketosis and 503 healthy cows. A logistic binary regression model was developed, where the dependent variable is categorical (healthy/diseased) and a set of explanatory variables were measured with existing commercial sensors: rumination duration, activity and milk yield of each individual cow. In a first validation step (within-farm), the model was calibrated on the database of each farm separately. Two thirds of the sick cows and an equal number of healthy cows were randomly selected for model validation. The remaining one third of the cows, which did not participate in the model validation, were used for model calibration. In order to overcome the random selection effect, this procedure was repeated 100 times. In a second (between-farms) validation step, the model was calibrated on one farm and validated on another farm. Within-farm accuracy, ranging from 74 to 79%, was higher than between-farm accuracy, ranging from 49 to 72%, in all farms. The within-farm sensitivities ranged from 78 to 90%, and specificities ranged from 71 to 74%. The between-farms sensitivities ranged from 65 to 95%. The developed model can be improved in future research, by employing other variables that can be added; or by exploring other models to achieve greater sensitivity and specificity.

Note:
Related Files :
Activity
Dairy cow
Ketosis
Logistic regression model
milk yield
rumination duration
sensors
עוד תגיות
תוכן קשור
More details
DOI :
doi: 10.1017/S0022029917000188
Article number:
0
Affiliations:
Database:
PubMed
Publication Type:
מאמר
;
.
Language:
אנגלית
Editors' remarks:
ID:
35317
Last updated date:
02/03/2022 17:27
Creation date:
18/07/2018 12:43
Scientific Publication
Towards practical application of sensors for monitoring animal health: the effect of post-calving health problems on rumination duration, activity and milk yield
84

Bahr C.; Berckmans D

Towards practical application of sensors for monitoring animal health: the effect of post-calving health problems on rumination duration, activity and milk yield .

The objective of this study was to design and validate a mathematical model to detect post-calving ketosis. The validation was conducted in four commercial dairy farms in Israel, on a total of 706 multiparous Holstein dairy cows: 203 cows clinically diagnosed with ketosis and 503 healthy cows. A logistic binary regression model was developed, where the dependent variable is categorical (healthy/diseased) and a set of explanatory variables were measured with existing commercial sensors: rumination duration, activity and milk yield of each individual cow. In a first validation step (within-farm), the model was calibrated on the database of each farm separately. Two thirds of the sick cows and an equal number of healthy cows were randomly selected for model validation. The remaining one third of the cows, which did not participate in the model validation, were used for model calibration. In order to overcome the random selection effect, this procedure was repeated 100 times. In a second (between-farms) validation step, the model was calibrated on one farm and validated on another farm. Within-farm accuracy, ranging from 74 to 79%, was higher than between-farm accuracy, ranging from 49 to 72%, in all farms. The within-farm sensitivities ranged from 78 to 90%, and specificities ranged from 71 to 74%. The between-farms sensitivities ranged from 65 to 95%. The developed model can be improved in future research, by employing other variables that can be added; or by exploring other models to achieve greater sensitivity and specificity.

Scientific Publication
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