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פותח על ידי קלירמאש פתרונות בע"מ -
Prediction of body condition scores in dairy cattle from daily measurements of body weights and milk composition
Year:
2013
Authors :
מלץ, אפרים
;
.
Volume :
Co-Authors:
De Vries, A., Department of Animal Sciences, University of Florida, Gainesville, FL 32611, United States
Barbosa, L.F., Department of Animal Sciences, University of Florida, Gainesville, FL 32611, United States
Du, F., Department of Animal Sciences, University of Florida, Gainesville, FL 32611, United States
Gay, K.D., Department of Animal Sciences, University of Florida, Gainesville, FL 32611, United States
Kaniyamattam, K., Department of Animal Sciences, University of Florida, Gainesville, FL 32611, United States
Maltz, E., Department of Animal Sciences, University of Florida, Gainesville, FL 32611, United States, Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, 50250 Bet Dagan, Israel
Facilitators :
From page:
576
To page:
582
(
Total pages:
7
)
Abstract:
Regression equations were developed to predict body condition scores (BCS ) during the course of the lactation for individual dairy cows from automatically measured milk yields, milk components (fat, protein, lactose), and bodyweights. Data were from 321 lactating Holstein cows scored weekly throughout their lactation on a 1 to 5 scale with 0.25 increments at the University of Florida Dairy Unit. The average BCS in parities 1 and 2+ were 3.54 ± 0.49 and 3.30 ± 0.61, respectively. Trends showed the usual decrease in BCS after calving until a few months in lactation after which BCS increased again. Procedure glmslect in SAS was used to find best predicting regression equations from while protecting for overfitting. The best 5-parameter regression equation had an R-squared of 63% and a root mean square (prediction) error of 0.31 BCS units. Bodyweights and milk yields were important for accuracy of BCS prediction, but availability of milk components was not if bodyweights were measured. An actual BCS observed on each cow at day 70 further significantly improved the accuracy of the best equation throughout the lactation. In conclusion, milk yields and bodyweights were useful predictors for individual BCS of dairy cows.
Note:
Related Files :
Dairies
Dairy
Forecasting
Measurements of
Milk component
regression analysis
Regression equation
University of Florida
עוד תגיות
תוכן קשור
More details
DOI :
Article number:
Affiliations:
Database:
סקופוס
Publication Type:
מאמר מתוך כינוס
;
.
Language:
אנגלית
Editors' remarks:
ID:
22650
Last updated date:
02/03/2022 17:27
Creation date:
16/04/2018 23:53
Scientific Publication
Prediction of body condition scores in dairy cattle from daily measurements of body weights and milk composition
De Vries, A., Department of Animal Sciences, University of Florida, Gainesville, FL 32611, United States
Barbosa, L.F., Department of Animal Sciences, University of Florida, Gainesville, FL 32611, United States
Du, F., Department of Animal Sciences, University of Florida, Gainesville, FL 32611, United States
Gay, K.D., Department of Animal Sciences, University of Florida, Gainesville, FL 32611, United States
Kaniyamattam, K., Department of Animal Sciences, University of Florida, Gainesville, FL 32611, United States
Maltz, E., Department of Animal Sciences, University of Florida, Gainesville, FL 32611, United States, Institute of Agricultural Engineering, Agricultural Research Organization, Volcani Center, 50250 Bet Dagan, Israel
Prediction of body condition scores in dairy cattle from daily measurements of body weights and milk composition
Regression equations were developed to predict body condition scores (BCS ) during the course of the lactation for individual dairy cows from automatically measured milk yields, milk components (fat, protein, lactose), and bodyweights. Data were from 321 lactating Holstein cows scored weekly throughout their lactation on a 1 to 5 scale with 0.25 increments at the University of Florida Dairy Unit. The average BCS in parities 1 and 2+ were 3.54 ± 0.49 and 3.30 ± 0.61, respectively. Trends showed the usual decrease in BCS after calving until a few months in lactation after which BCS increased again. Procedure glmslect in SAS was used to find best predicting regression equations from while protecting for overfitting. The best 5-parameter regression equation had an R-squared of 63% and a root mean square (prediction) error of 0.31 BCS units. Bodyweights and milk yields were important for accuracy of BCS prediction, but availability of milk components was not if bodyweights were measured. An actual BCS observed on each cow at day 70 further significantly improved the accuracy of the best equation throughout the lactation. In conclusion, milk yields and bodyweights were useful predictors for individual BCS of dairy cows.
Scientific Publication
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