Browsing by Person "Werner, Jessica"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Publication
    Development of a robust sensor calibration for a commercially available rising platemeter to estimate herbage mass on temperate seminatural pastures
    (2024) Werner, Jessica; Salazar‐Cubillas, Khaterine; Perdana-Decker, Sari; Obermeyer, Kilian; Velasco, Elizabeth; Hart, Leonie; Dickhöfer, Uta
    Rising platemeters are commonly used in Ireland and New Zealand for managing intensive pastures. To assess the applicability of a commercial rising platemeter operating with a microsonic sensor to estimate herbage mass with its own equation, the objectives were (i) to validate the original equation; (ii) to identify possible factors hampering its accuracy and precision; and (iii) to develop a new equation for heterogeneous swards. A comprehensive dataset (n = 1511) was compiled on the pastures of dairy farms. Compressed sward heights were measured by the rising platemeter. Herbage mass was harvested to determine reference herbage availability. The adequacy of estimating herbage mass was assessed using root mean squared error (RMSE) and mean bias. As the adequacy of the original equation was low, a new equation was developed using multiple regression models. The mean bias and the RMSE for the new equation were overall low with 201 kg dry matter/ha and 34.6%, but it tended to overestimate herbage availability at herbage mass < 500 kg dry matter/ha and underestimate it at >2500 kg dry matter/ha. Still, the newly developed equation for the microsonic sensor-based rising platemeter allows for accurate and precise estimation of available herbage mass on pastures.
  • Thumbnail Image
    Publication
    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.