[Objective] The paper was to increase utilization rate of roughage to solve current shortage of forage and improve production performance of Simmental cow.[Method] Fruit-flavored agent and green pigment were added in ...[Objective] The paper was to increase utilization rate of roughage to solve current shortage of forage and improve production performance of Simmental cow.[Method] Fruit-flavored agent and green pigment were added in roughage of Simmental cow respectively,and their effects on feed intake,milk production and milk quality of Simmental cow were studied.[Result] After adding fruit-flavored agent and green pigment,the feed intakes of Simmental cow were increased by 30.69% and 12.27%,while the milk productions were increased by 1.74 and 2.25 kg,respectively,and the differences were all significant（P 〈 0.05）.[Conclusion]Adding 0.3% and 0.1% fruit-flavored agent and green pigment in roughage of Simmental cow could significantly improve feed intake and milk production,which can also delay the decline of milk production during late lactation stage and improve milk quality.展开更多
Feed intake is an important indicator to reflect the production performance and disease risk of dairy cows,which can also evaluate the utilization rate of pasture feed.To achieve an automatic and non-contact measureme...Feed intake is an important indicator to reflect the production performance and disease risk of dairy cows,which can also evaluate the utilization rate of pasture feed.To achieve an automatic and non-contact measurement of feed intake,this paper proposes a method for measuring the feed intake of cows based on computer vision technology with a Siamese network and depth images.An automated data acquisition system was first designed to collect depth images of feed piles and constructed a dataset with 24150 samples.A deep learning model based on the Siamese network was then constructed to implement non-contact measurement of feed intake for dairy cows by training with collected data.The experimental results show that the mean absolute error(MAE)and the root mean square error(RMSE)of this method are 0.100 kg and 0.128 kg in the range of 0-8.2 kg respectively,which outperformed existing works.This work provides a new idea and technology for the intelligent measuring of dairy cow feed intake.展开更多
文摘[Objective] The paper was to increase utilization rate of roughage to solve current shortage of forage and improve production performance of Simmental cow.[Method] Fruit-flavored agent and green pigment were added in roughage of Simmental cow respectively,and their effects on feed intake,milk production and milk quality of Simmental cow were studied.[Result] After adding fruit-flavored agent and green pigment,the feed intakes of Simmental cow were increased by 30.69% and 12.27%,while the milk productions were increased by 1.74 and 2.25 kg,respectively,and the differences were all significant（P 〈 0.05）.[Conclusion]Adding 0.3% and 0.1% fruit-flavored agent and green pigment in roughage of Simmental cow could significantly improve feed intake and milk production,which can also delay the decline of milk production during late lactation stage and improve milk quality.
基金This work was supported in part by the National Natural Science Foundation of China(Grant No.32072788,31902210)the National Key Research and Development Program of China(Grant No.2019YFE0125600)the Postdoctoral Research Start-up Fund of Heilongjiang Province(Grant No.LBH-Q21062)and the Earmarked Fund for CARS36.
文摘Feed intake is an important indicator to reflect the production performance and disease risk of dairy cows,which can also evaluate the utilization rate of pasture feed.To achieve an automatic and non-contact measurement of feed intake,this paper proposes a method for measuring the feed intake of cows based on computer vision technology with a Siamese network and depth images.An automated data acquisition system was first designed to collect depth images of feed piles and constructed a dataset with 24150 samples.A deep learning model based on the Siamese network was then constructed to implement non-contact measurement of feed intake for dairy cows by training with collected data.The experimental results show that the mean absolute error(MAE)and the root mean square error(RMSE)of this method are 0.100 kg and 0.128 kg in the range of 0-8.2 kg respectively,which outperformed existing works.This work provides a new idea and technology for the intelligent measuring of dairy cow feed intake.
