The effects of cow introductions on milk production and behaviour of the herd measured with sensors

This research paper addresses the hypothesis that cow introductions in dairy herds affect milk production and behaviour of animals already in the herd. In dairy farms, cows are commonly regrouped or moved. Negative effects of regroupings on the introduced animals are reported in other studies. However, little is known about the effects on lactating cows in the herd. In this research a herd of 53 lactating dairy cows was divided into two groups in a cross-over design study. 25 cows were selected as focal cows for which continuous sensor data were collected. The treatment period consisted of replacing non-focal cows three times a week. Many potentially influencing factors were taken into account in the analysis. Replacement of cows in the treatment period indeed affected the focal animals. During the treatment period these cows showed increased walking and reduced rumination activity and produced less milk compared to the control period. Milk production per milking decreased in the treatment period up to 0.4 kg per milking on certain weekdays. Lying and standing behaviour were similar between the control and the treatment period. The current study suggests that cow introductions affect welfare and milk production of the cows already in the herd

The effects of cow introductions on milk production and behaviour of the herd measured with sensors

This research paper addresses the hypothesis that cow introductions in dairy herds affect milk production and behaviour of animals already in the herd. In dairy farms, cows are commonly regrouped or moved. Negative effects of regroupings on the introduced animals are reported in other studies. However, little is known about the effects on lactating cows in the herd. In this research a herd of 53 lactating dairy cows was divided into two groups in a cross-over design study. 25 cows were selected as focal cows for which continuous sensor data were collected. The treatment period consisted of replacing non-focal cows three times a week. Many potentially influencing factors were taken into account in the analysis. Replacement of cows in the treatment period indeed affected the focal animals. During the treatment period these cows showed increased walking and reduced rumination activity and produced less milk compared to the control period. Milk production per milking decreased in the treatment period up to 0.4 kg per milking on certain weekdays. Lying and standing behaviour were similar between the control and the treatment period. The current study suggests that cow introductions affect welfare and milk production of the cows already in the herd

Pressure measurement in the reticulum to detect different behaviors of healthy cows

The aim of the current study was to investigate the relation between reticulorumen contractions and monitored cow behaviors. A purpose-built pressure measuring device was used and shown to be capable of detecting the known contraction patterns in the reticulorumen of four rumen-fistulated cows. Reticular pressure data was used to build a random forest algorithm, a learning algorithm based on a combination of decision trees, to detect rumination and other cow behaviors. In addition, we developed a peak-detection algorithm for rumination based on visual inspection of patterns in reticular pressure. Cow behaviors, differentiated in ruminating, eating, drinking, sleeping and 'other', as scored from video observation, were used to develop and test the algorithms. The results demonstrated that rumination of a cow can be detected by measuring pressure differences in the reticulum using either the random forest algorithm or the peak-detection algorithm. The random forest algorithm showed very robust performances for detecting rumination with an accuracy of 0.98, a sensitivity of 0.95 and a specificity of 0.99. The peak-detection algorithm could detect rumination robustly, with an accuracy of 0.92, a sensitivity of 0.97 and a specificity of 0.90. In addition, we provide proof of principle that a random forest algorithm can also detect eating, drinking and sleeping behavior from the same data with performances above 0.90 for all measures. The measurement device used in this study needed rumen-fistulated cows, but the results indicate that behavior detection using algorithms based on only measurements in the reticulum is feasible. This is promising as it may allow future wireless sensor techniques in the reticulum to continuously monitor a range of important behaviors of cows

The effects of cow introductions on milk production and behaviour of the herd measured with sensors

This research paper addresses the hypothesis that cow introductions in dairy herds affect milk production and behaviour of animals already in the herd. In dairy farms, cows are commonly regrouped or moved. Negative effects of regroupings on the introduced animals are reported in other studies. However, little is known about the effects on lactating cows in the herd. In this research a herd of 53 lactating dairy cows was divided into two groups in a cross-over design study. 25 cows were selected as focal cows for which continuous sensor data were collected. The treatment period consisted of replacing non-focal cows three times a week. Many potentially influencing factors were taken into account in the analysis. Replacement of cows in the treatment period indeed affected the focal animals. During the treatment period these cows showed increased walking and reduced rumination activity and produced less milk compared to the control period. Milk production per milking decreased in the treatment period up to 0.4 kg per milking on certain weekdays. Lying and standing behaviour were similar between the control and the treatment period. The current study suggests that cow introductions affect welfare and milk production of the cows already in the herd

Pressure measurement in the reticulum to detect different behaviors of healthy cows

The aim of the current study was to investigate the relation between reticulorumen contractions and monitored cow behaviors. A purpose-built pressure measuring device was used and shown to be capable of detecting the known contraction patterns in the reticulorumen of four rumen-fistulated cows. Reticular pressure data was used to build a random forest algorithm, a learning algorithm based on a combination of decision trees, to detect rumination and other cow behaviors. In addition, we developed a peak-detection algorithm for rumination based on visual inspection of patterns in reticular pressure. Cow behaviors, differentiated in ruminating, eating, drinking, sleeping and 'other', as scored from video observation, were used to develop and test the algorithms. The results demonstrated that rumination of a cow can be detected by measuring pressure differences in the reticulum using either the random forest algorithm or the peak-detection algorithm. The random forest algorithm showed very robust performances for detecting rumination with an accuracy of 0.98, a sensitivity of 0.95 and a specificity of 0.99. The peak-detection algorithm could detect rumination robustly, with an accuracy of 0.92, a sensitivity of 0.97 and a specificity of 0.90. In addition, we provide proof of principle that a random forest algorithm can also detect eating, drinking and sleeping behavior from the same data with performances above 0.90 for all measures. The measurement device used in this study needed rumen-fistulated cows, but the results indicate that behavior detection using algorithms based on only measurements in the reticulum is feasible. This is promising as it may allow future wireless sensor techniques in the reticulum to continuously monitor a range of important behaviors of cows