Shelemia Nyamuryekung'e
Research Scientist
(+47) 477 64 707
shelemia.nyamuryekunge@nibio.no
Place
Tjøtta
Visiting address
Parkveien, 8861 Tjøtta
Abstract
The birth process in animals, much like in humans, can encounter complications that pose significant risks to both offspring and mothers. Monitoring these events can provide essential nursing support, but human monitoring is expensive. Although there are commercial monitoring systems for large ruminants, there are no effective solutions for small ruminants, despite various attempts documented in the literature. Inertial sensors are very convenient given their low cost, low impact on animal life, and their flexibility for monitoring animal behavior. This study offers a systematic review of the literature on detecting parturition in small ruminants using inertial sensors. The review analyzed the specifics of published research, including data management and monitoring processes, behaviors indicative of parturition, processing techniques, detection algorithms, and the main results achieved in each study. The results indicated that some methods for detecting birth concentrate on classifying unique animal behaviors, employing diverse processing techniques, and developing detection algorithms. Furthermore, this study emphasized that employing techniques that include analyzing animal activity peaks, specifically recurrent lying down and getting up occurrences, could result in improved detection precision. Although none of the studies provided a completely valid detection algorithm, most results were promising, showing significant behavioral changes in the hours preceding delivery.
Authors
Shelemia Nyamuryekung'eAbstract
On the Ground: -Precision livestock management through sensor technology using the Internet of Things offers enhanced surveillance and monitoring of the ranching operations. -At the ranch scale, the integration of sensor technology, including on-animal sensors, environmental monitoring equipment, and remote sensing can shift livestock operations from a solely reactive, traditional, knowledge-based approach toward a proactive, data-driven, decision-making process. -Leveraging data from sensors at the ranch scale can address logistical challenges and create efficiency in decision-making processes concerning resource management.
Abstract
This study investigates cow behaviour when visiting two GreenFeed Emission Monitoring (GEM) units within a Part-Time Grazing (PTG) system. Two separate PTG systems were assessed in Sweden and Norway, involving Nordic Red and Norwegian Red dairy cows, respectively. In Sweden, 24 cows were allocated to treatments with restricted access to pasture, either daytime or nighttime grazing. Meanwhile, the Norwegian PTG involved 33 cows with free pasture access, categorized by varying training levels (Partially or Fully). In both PTG systems, cows were exposed to GEM units positioned indoors (Indoor) and in the grazing pastures (Pasture), with individual visitations recorded. Significant variations in visitation patterns were observed. In the restricted access PTG, Nighttime grazing access cows exhibited reduced visits to the Indoor GEM unit but increased visits to the Pasture GEM unit compared to Daytime grazing. Conversely, within the free access PTG, fully trained cows demonstrated elevated visits to the pasture GEM unit and total visits compared to their partially trained counterparts. These findings highlight the influence of temporal conditions and training levels on cow-visiting behaviour within PTG systems.