The experiment was conducted with twenty-four grazing sheep divided into 3 experimental groups on the basis of body weights,i.e.control,optimal and non-optimal groups to study the effect of optimized supplementation o...The experiment was conducted with twenty-four grazing sheep divided into 3 experimental groups on the basis of body weights,i.e.control,optimal and non-optimal groups to study the effect of optimized supplementation on growth performance and rumen fermentation characteristics of grazing sheep in winter.The sheep in control were grazing only,whereas the grazing sheep in the optimal were supplemented with 250 g of an optimal forage mix and 404 g of concentrate1.The animals in non-optimal were supplemented with 465 g of concentrate 2.Weight gain rate in the optimal increased by 1.2% and 15.3 % as compared to that of the non-optimal and control group,respectively.Ruminal fermentation parameters were not significantly different between the optimal and non-optimal groups,however,they were significantly improved compared to the control.These results indicated that optimized supplementation strategies in winter were beneficial for markedly improving growth performance and rumen fermentation characteristics of grazing sheep.展开更多
[Objective] To study the effects of apple pomace on milk performance of dairy cows. ~Method] A total of 48 Holstein dairy cows at the same parity and with close lactation stage and similar daily milk yield were random...[Objective] To study the effects of apple pomace on milk performance of dairy cows. ~Method] A total of 48 Holstein dairy cows at the same parity and with close lactation stage and similar daily milk yield were randomly assigned to six groups. The supplementary feed was composed of apple pomace, alfalfa meal, com and premix, and it was processed into pellets. In the groups I - V, the percentage of apple pomace was 17.5%, 35.0%, 52.5%, 70.0% and 0%, respectively. Each cow was fed with supplementary feed （3 kg/d）. [ Result] The milk yield in the group II and group V was increased significantly （ P 〈 0.05）, and the fat content in milk of the group III and V was significantly higher than that of the con- trol group （P 〈 0.05）. [ Conclusion] The supplementary apple pomace can improve the milk performance of dairy cows effectively.展开更多
A deep learning approach using long-short term memory(LSTM)networks was implemented in this study to classify the sound of short-term feeding behaviour of sheep,including biting,chewing,bolus regurgitation,and ruminat...A deep learning approach using long-short term memory(LSTM)networks was implemented in this study to classify the sound of short-term feeding behaviour of sheep,including biting,chewing,bolus regurgitation,and rumination chewing.The original acoustic signal was split into sound episodes using an endpoint detection method,where the thresholds of short-term energy and average zero-crossing rate were utilized.A discrete wavelet transform(DWT),Mel-frequency cepstral,and principal-component analysis(PCA)were integrated to extract the dimensionally reduced DWT based Mel-frequency cepstral coefficients(denoted by PW_MFCC)for each sound episode.Then,LSTM networks were employed to train classifiers for sound episode category classification.The performances of the LSTM classifiers with original Mel-frequency cepstral coefficients(MFCC),DWT based MFCC(denoted by W_MFCC),and PW_MFCC as the input feature coefficients were compared.Comparison results demonstrated that the introduction of DWT improved the classifier performance effectively,and PCA reduced the computational overhead without degrading classifier performance.The overall accuracy and comprehensive F1-score of the PW_MFCC based LSTM classifier were 94.97%and 97.41%,respectively.The classifier established in this study provided a foundation for an automatic identification system for sick sheep with abnormal feeding and rumination behaviour pattern.展开更多
文摘The experiment was conducted with twenty-four grazing sheep divided into 3 experimental groups on the basis of body weights,i.e.control,optimal and non-optimal groups to study the effect of optimized supplementation on growth performance and rumen fermentation characteristics of grazing sheep in winter.The sheep in control were grazing only,whereas the grazing sheep in the optimal were supplemented with 250 g of an optimal forage mix and 404 g of concentrate1.The animals in non-optimal were supplemented with 465 g of concentrate 2.Weight gain rate in the optimal increased by 1.2% and 15.3 % as compared to that of the non-optimal and control group,respectively.Ruminal fermentation parameters were not significantly different between the optimal and non-optimal groups,however,they were significantly improved compared to the control.These results indicated that optimized supplementation strategies in winter were beneficial for markedly improving growth performance and rumen fermentation characteristics of grazing sheep.
文摘[Objective] To study the effects of apple pomace on milk performance of dairy cows. ~Method] A total of 48 Holstein dairy cows at the same parity and with close lactation stage and similar daily milk yield were randomly assigned to six groups. The supplementary feed was composed of apple pomace, alfalfa meal, com and premix, and it was processed into pellets. In the groups I - V, the percentage of apple pomace was 17.5%, 35.0%, 52.5%, 70.0% and 0%, respectively. Each cow was fed with supplementary feed （3 kg/d）. [ Result] The milk yield in the group II and group V was increased significantly （ P 〈 0.05）, and the fat content in milk of the group III and V was significantly higher than that of the con- trol group （P 〈 0.05）. [ Conclusion] The supplementary apple pomace can improve the milk performance of dairy cows effectively.
基金This work was supported by the Basic Research Project of the Science and Technology Department of Qinghai province,China(Grant No.2020-ZJ-716)the Key Research and Development Project of the Science and Technology Department of Jiangsu province,China(Grant No.BE2018433)the Key Research and Development Project of the Science and Technology Department of Qinghai Province,China(Grant No.2017-HZ-813).
文摘A deep learning approach using long-short term memory(LSTM)networks was implemented in this study to classify the sound of short-term feeding behaviour of sheep,including biting,chewing,bolus regurgitation,and rumination chewing.The original acoustic signal was split into sound episodes using an endpoint detection method,where the thresholds of short-term energy and average zero-crossing rate were utilized.A discrete wavelet transform(DWT),Mel-frequency cepstral,and principal-component analysis(PCA)were integrated to extract the dimensionally reduced DWT based Mel-frequency cepstral coefficients(denoted by PW_MFCC)for each sound episode.Then,LSTM networks were employed to train classifiers for sound episode category classification.The performances of the LSTM classifiers with original Mel-frequency cepstral coefficients(MFCC),DWT based MFCC(denoted by W_MFCC),and PW_MFCC as the input feature coefficients were compared.Comparison results demonstrated that the introduction of DWT improved the classifier performance effectively,and PCA reduced the computational overhead without degrading classifier performance.The overall accuracy and comprehensive F1-score of the PW_MFCC based LSTM classifier were 94.97%and 97.41%,respectively.The classifier established in this study provided a foundation for an automatic identification system for sick sheep with abnormal feeding and rumination behaviour pattern.
