Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2023
Sammendrag
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Sammendrag
Sustainable forest management systems require operational measures to preserve the functional design of forest roads. Frequent road data collection and analysis are essential to support target-oriented and efficient maintenance planning and operations. This study demonstrates an automated solution for monitoring forest road surface deterioration using consumer-grade optical sensors. A YOLOv5 model with StrongSORT tracking was adapted and trained to detect and track potholes in the videos captured by vehicle-mounted cameras. For model training, datasets recorded in diverse geographical regions under different weather conditions were used. The model shows a detection and tracking performance of up to a precision and recall level of 0.79 and 0.58, respectively, with 0.70 mean average precision at an intersection over union (IoU) of at least 0.5. We applied the trained model to a forest road in southern Norway, recorded with a Global Navigation Satellite System (GNSS)−fitted dashcam. GNSS-delivered geographical coordinates at 10 Hz rate were used to geolocate the detected potholes. The geolocation performance over this exemple road stretch of 1 km exhibited a root mean square deviation of about 9.7 m compared to OpenStreetMap. Finally, an exemple road deterioration map was compiled, which can be used for scheduling road maintenance operations.
Sammendrag
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Forfattere
Mette ThomsenSammendrag
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Therese With BergeSammendrag
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El Houssein Chouaib HarikSammendrag
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Forfattere
Peter Zubkov Barry Gardiner Bjørn Egil Kringlebotn Nygaard Sigmund Guttu Svein Solberg Tron Haakon EidSammendrag
Forest damage caused by heavy wet snow accumulation in the canopy is the second most important abiotic forest disturbance agent in Nordic conifer stands after wind. The extent and frequency of snow damage in the future climate in the Nordic region is a major uncertainty. Few mechanistic models of snow damage risk to trees exist that could support forest management scenario analysis and decision making. We propose a snow damage risk model consisting of a numerical weather prediction-based snow accumulation model for forest canopies and a mechanistic critical snow load model. Snow damage probability predictions were validated on snow breakage data from the winters of 2016 and 2018 covering 3.5 million individual trees in south-eastern Norway derived from pre- and post-damage aerial laser scanning campaigns. The proposed model demonstrated satisfactory damage and no-damage class separation with an AUC of 0.72 and 0.77 in Norway spruce and Scots pine, respectively, and an F1 score of 0.7 in conifers taller than 10 m that suffered moderate stem breakage. The model achieved a classification accuracy that is comparable to that of statistical models but is simpler and requires fewer inputs.
Forfattere
Jaime Candelas Bielza Lennart Noordermeer Erik Næsset Terje Gobakken Johannes Breidenbach Hans Ole ØrkaSammendrag
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Alejandro Belanche Alexander N. Hristov Henk J. van Lingen Stuart E. Denman Ermias Kebreab Angela Dagmar Schwarm Michael Kreuzer Mutian Niu Maguy Eugène Vincent Niderkorn Cécile Martin Harry Archimede Mark McGee Christopher K. Reynolds Les A. Crompton Ali Reza Bayat Zhongtang Yu André Bannink Jan Dijkstra Alex V. Chaves Harry Clark Stefan Muetzel Vibeke Lind Jon M. Moorby John A. Rooke Aurelie Aubry Walter Antezana Min Wang Roger Hegarty V. Hutton Oddy Julian Hill Philip E Vercoe Jean Victor Savian Adibe Luiz Abdalla Yosra A. Soltan Alda Lucia Gomes Monteiro Juan Carlos Ku-Vera Gustavo Jaurena Carlos A. Gomez-Bravo Olga L. Mayorga Guilhermo F.S. Congio David R. Yáñez-RuízSammendrag
Enteric methane (CH4) emissions from sheep contribute to global greenhouse gas emissions from livestock. However, as already available for dairy and beef cattle, empirical models are needed to predict CH4 emissions from sheep for accounting purposes. The objectives of this study were to: 1) collate an intercontinental database of enteric CH4 emissions from individual sheep; 2) identify the key variables for predicting enteric sheep CH4 absolute production (g/d per animal) and yield [g/kg dry matter intake (DMI)] and their respective relationships; and 3) develop and cross-validate global equations as well as the potential need for age-, diet-, or climatic region-specific equations. The refined intercontinental database included 2,135 individual animal data from 13 countries. Linear CH4 prediction models were developed by incrementally adding variables. A universal CH4 production equation using only DMI led to a root mean square prediction error (RMSPE, % of observed mean) of 25.4% and an RMSPE-standard deviation ratio (RSR) of 0.69. Universal equations that, in addition to DMI, also included body weight (DMI + BW), and organic matter digestibility (DMI + OMD + BW) improved the prediction performance further (RSR, 0.62 and 0.60), whereas diet composition variables had negligible effects. These universal equations had lower prediction error than the extant IPCC 2019 equations. Developing age-specific models for adult sheep (>1-year-old) including DMI alone (RSR = 0.66) or in combination with rumen propionate molar proportion (for research of more refined purposes) substantially improved prediction performance (RSR = 0.57) on a smaller dataset. On the contrary, for young sheep (<1-year-old), the universal models could be applied, instead of age-specific models, if DMI and BW were included. Universal models showed similar prediction performances to the diet- and region-specific models. However, optimal prediction equations led to different regression coefficients (i.e. intercepts and slopes) for universal, age-specific, diet-specific, and region-specific models with predictive implications. Equations for CH4 yield led to low prediction performances, with DMI being negatively and BW and OMD positively correlated with CH4 yield. In conclusion, predicting sheep CH4 production requires information on DMI and prediction accuracy will improve national and global inventories if separate equations for young and adult sheep are used with the additional variables BW, OMD and rumen propionate proportion. Appropriate universal equations can be used to predict CH4 production from sheep across different diets and climatic conditions.
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