Browsing by Subject "Precision livestock farming"

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Measuring grazing behaviour of dairy cows : validation of sensor technologies and assessing application potential in intensive pasture-based milk production systems
(2019) Werner, Jessica; Schick, Matthias
Grazing is the natural feed intake behaviour of a cow. However, in the last century, intensive confinement systems with silage feeding and concentrate supplementation have replaced many extensive pasture-based milk production systems. Grazed grass is now acknowledged as the cheapest feed available as a consequence of rising machinery, labour and feeding costs. Thus there is a renewed interest in intensive pasture-based milking systems. In addition, policy objectives, societal expectations and environmental concerns have all supported reconsiderations for pasture-based milk production. Novel technology to aid measuring and managing grassland and cow grazing behaviour have the potential to facilitate improved performance. Until recently, sensor technologies for dairy farms were mainly developed for measuring feeding behaviour of housed cows. Adapting and calibrating these technologies to grazing context would therefore further support improved pasture-based dairying. In this thesis, two sensor technologies were validated against visual observation. The RumiWatch noseband sensor (Itin+Hoch, Switzerland) is a high precision technology designed for research applications. It can measure detailed grazing behaviour such as grazing bites, rumination chews, time spent grazing and time spent ruminating. The MooMonitor+ (Dairymaster, Ireland) is the second technology assessed in this thesis. It is a collar based accelerometer and is primarily designed for use on commercial farms. The initial development was for oestrus detection. It can now monitor grazing and rumination times. The results of the studies reported in this thesis revealed that both sensors were highly accurate compared to visual observation. The implementation of sensor technology on commercial dairy farms is still slow. This is especially true on pasture-based dairy systems. The management of grazing cows is thus largely not supported by technology. With increasing herd sizes and skilled labour shortages, sensor technology to support grazing management will likely improve some major dairy farm management challenges. A key factor in pasture-based milk production is the correct grass allocation to maximize the grass utilization per cow. Cow behaviour is indicative of the quantity and quality of feed available as well as animal performance, health and welfare. Thus, the measurement of cow grazing behaviour is an important management indicator. A further study of detailed individual grazing behaviour aimed to identify behavioural indicators of restricted versus sufficient availability of grass. Such objective measurement has potential since currently grass allocation is based on subjective eye measurements and calculations per herd. To identify behavioural indicators, a group of 30 cows in total were allocated a restricted pasture allowance of 60 % of their intake capacity. Their behavioural characteristics were compared to those of 10 cows with pasture allowance of 100 % of their intake capacity. The grazing behaviour and activity of cows was measured using the RumiWatchSystem, consisting of the noseband sensor and pedometer. The results showed that bite frequency was continuously higher for cows with a restricted grass allocation, but also rumination behaviour was affected by the restriction. This study contributes vital information towards developing a decision support tool for automated allocation of grass based on feedback from individual cows rather than herd based measurements. Further research activities should focus on identification of significant changes in grazing behaviour of cows at individual animal and herd level. This would allow implementation of specific thresholds to be used in decision support tools. After developing and validating the decision support tools, the application of automated solutions for grazing management can improve efficiency and productivity of pasture-based milk production systems.
RumiWatch - Development and assessment of a sensor-based behavior monitoring system for ruminants
(2018) Zehner, Nils; Schick, Matthias
Sustainable and competitive milk production is highly dependent on securing the performance potential, health and fertility of dairy cows. Therefore, farmers can benefit from sensor data of animal monitoring systems to improve health management and work processes in dairy farming. The research during this PhD thesis aimed to contribute to the development and evaluation of a scientifically validated, sensor-based animal monitoring system that comprises a device for measurement of ingestive behavior and a device for measurement of movement behavior in cattle that interact as a system with system-specific software. Further aim of this thesis was to evaluate application potentials for this animal monitoring system by means of calving prediction in dairy cows and measurement of chewing activity in horses. The underlying experimental work was structured into four separate studies. The aim of the first study was to develop and validate a novel scientific monitoring device for automated measurement of rumination and eating behavior in dairy cows. Research works for this study aimed to provide a complete and detailed technical specification of the functionality of this device and to perform a validation under field conditions in stable-fed cows. The objective of the second study was to develop and validate a novel algorithm to monitor lying, standing, and walking behavior based on the output of a triaxial accelerometer collected from loose-housed dairy cows. The third study aimed to use automated measurements of ingestive behavior obtained from the developed sensor device to develop and validate a predictive model for calving in dairy cows. The aim of the fourth study was to investigate the suitability and validity of the developed sensor system for automated measurement of chewing activity in horses. In conclusion, the RumiWatch noseband sensor and pedometer that were developed and validated in the current project represent a suitable measuring instrument for automated recording of ingestive and locomotor behavior in dairy cows. The system-specific software is suitable for research purposes and shows a high performance for classification of extended parameters of rumination, eating, lying, standing, and walking behavior. The achieved validation results indicate that the measuring performance satisfies scientific requirements. Further application potentials were demonstrated by means of automated calving prediction in dairy cows and automated measurement of chewing activity in horses. The development and validation of a predictive model for calving time using measurements of the RumiWatch noseband sensor revealed a high amount of false positive alerts that was prohibitive for application of the model in farming practice. However, the analyses showed that particularly parameters of ruminating behavior have predictive value and should be taken into consideration for future research on calving prediction models. Furthermore, it was successfully demonstrated that it is feasible to apply the RumiWatch noseband sensor to horses. The results of direct observation compared with the automatic measurement showed a very high overall agreement of the observed and automatically measured data and, after minor refinements, this measuring device has the potential to become a valuable and easy-to-use tool for equine research and management.