Michael Angeloff
Lead Engineer
(+47) 975 38 594
michael.angeloff@nibio.no
Place
Ås O43
Visiting address
Oluf Thesens vei 43, 1433 Ås
Abstract
Insight from new technology in rangeland grazing systems Inger Hansen, Lise Grøva, Michael Angeloff and Oddbjørn Kaasa Although digital technologies and innovations are increasingly being adopted and accepted in intensive livestock systems, they are poorly developed and implemented in extensive livestock farming systems. On-animal sensors have potential to remotely monitor and identify changes in animal behaviour, such as illness, accidents or depredation. Real time monitoring of livestock may allow farmers and ranchers to respond more rapidly when animals become ill, and by this ensure both production and welfare With the rapid increase in new data from digital technologies from livestock rangeland grazing systems, there is a need to explore the potential for new knowledge and new tools that these data may provide. The potential lies in multisource data analysis to generate new insight on sheep behaviour, interactions and possible criteria for Early Warning Systems (EWS). EWS is in high demand by farmers, authorities and all stakeholders to ensure the sustainable management of our rangeland grazing resources. In Norway we have now started a pilot work to integrate data from GPS tracking collars on free ranging sheep with individual sheep health and production data sources, as well as vegetation maps, weather conditions, human activity and predator killings. Since 2015, Meråker grazing group, consisting of 25 sheep farmers, have used GPS tracking collars on about 2000 ewes from June to September. The data set consists of more than 9 million positions, allowing analysis of the sheep's movement related to numerous environmental and production factors. Integration of these position data with production-, health-, large carnivores- and other explanatory variables, and analysis of these multisource data, has potential to be a game changer for rangeland grazing systems. This presentation will highlight the the potiential for new insight in these farming systems.
Abstract
The Norwegian area frame survey of land cover and outfield land resources (AR18X18), completed in 2014, provided unbiased statistics of land cover in Norway. The article reports the new statistics, discusses implications of the data set, and provides potential value in terms of research, management, and monitoring. A gridded sampling design for 1081 primary statistical units of 0.9 km2 at 18 km intervals was implemented in the survey. The plots were mapped in situ, aided by aerial photos, and all areas were coded following a vegetation type system. The results provide new insights into the cover and distribution of vegetation and land cover types. The statistic for mire and wetlands, which previously covered 5.8%, has since been corrected to 8.9%. The survey results can be used for environmental and agricultural management, and the data can be stratified for regional analyses. The survey data can also serve as training data for remote sensing and distribution modelling. Finally, the survey data can be used to calibrate vegetation perturbations in climate change research that focuses on atmospheric–vegetation feedback. The survey documented novel land cover statistics and revealed that the national cover of wetlands had previously been underestimated.
Authors
Anders Bryn Hans P. Kristoffersen Michael Angeloff Ingvild Nystuen Linda Aune-Lundberg Dag Terje Filip Endresen Christian Svindseth Yngve RekdalAbstract
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