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.
2026
Sammendrag
Deadwood carbon pool is a crucial component of forest ecosystems and the global carbon cycle. Assessing of deadwood carbon is challenging due to variability in decay status, species and disturbances in tropical forests. Quantifying the magnitude of uncertainty is essential for improving the accuracy of carbon stock estimations. This study aimed to estimate deadwood carbon pool by considering deadwood decay status and different vegetation types as well as the associated uncertainty in carbon stock estimates. Based on the National Forestry Resources Monitoring and Assessment of Tanzania (NAFORMA) sampling design, we analysed 21,946 data points from 1,798 plots. A two-way Analysis of Variance (ANOVA) was used to examine the variation in deadwood carbon stock (rotten and solid) between the primary vegetation types. Tukey’s Honest Significant Difference (HSD), post-hoc test was applied to determine which vegetation types significantly differ in carbon stock while a paired samples t -test was used to compare carbon stock of solid and rotten deadwood. Uncertainty was calculated using Equation 10 of 2006 IPCC Guidelines with 95% confidence interval. The estimated deadwood carbon stock ranged from 0.11 to 1.01 t C ha −1 , with solid deadwood having higher carbon stocks than rotten deadwood, accounting for 0.79% of total estimated carbon stocks. Carbon uncertainty values ranged from 0.0008 to 0.28%, with the highest and lowest uncertainty values from rotten deadwood in cultivated land and woodland, respectively. However, these variations among vegetation types did not significantly impact the deadwood carbon stock. In contrast, decay status had a significant effect on deadwood carbon stock. These findings are crucial for national climate policies, land use contributions to national carbon accounting, REDD+ mechanisms and sustainable management of natural ecosystems.
Forfattere
Belachew Gizachew ZelekeSammendrag
Tropical forests, despite their critical environmental and socio-economic roles, remain highly vulnerable to deforestation, forest degradation, and climate-related disturbances. There is a growing demand for robust and transparent forest monitoring systems, particularly under REDD+, the Paris Agreement’s Enhanced Transparency Framework (ETF), and emerging climate-finance mechanisms. Conventional approaches based on field inventories and traditional remote sensing are often constrained by limited or uneven field data, persistent cloud cover, complex forest conditions, and limited institutional and technical capacity. This review examines how artificial intelligence (AI) and machine learning (ML) are being integrated into remote sensing–based tropical forest monitoring to address these structural constraints. Using a semi-systematic synthesis of peer-reviewed studies, complemented by operational platforms and grey literature, the review assesses AI/ML approaches, remote sensing datasets, and applications relevant to national and large-scale monitoring. Evidence is synthesized across five analytical dimensions: AI/ML model families and workflows, multi-sensor datasets and training resources, operational monitoring platforms, application domains (including deforestation, degradation, and biomass/carbon estimation), and cross-cutting technical, institutional, and governance barriers. The review finds that AI/ML-enabled remote sensing, particularly those combining optical, radar, and LiDAR time series within cloud-based platforms, has substantially improved the automation, scalability, and speed of tropical forest monitoring. However, effective and equitable adoption remains constrained by limitations in training and validation data, dependence on proprietary platforms and data, uneven technical capacity, and unresolved governance and ethical challenges. Emerging solutions, including open and representative training datasets, platform-agnostic processing infrastructures, long-term capacity building, and inclusive data-governance frameworks, are identified as critical enablers of credible and nationally owned AI/ML-enabled forest-monitoring systems. The review highlights that AI/ML can play a transformative role in supporting climate mitigation, biodiversity conservation, and informed decision-making. This potential, however, depends on transparent data governance arrangements, long-term capacity building, and platform-agnostic infrastructures that support national ownership.
Redaktører
Siv Karin Paulsen RyeSammendrag
Driftsgranskingane er eit forvaltningsoppdrag som NIBIO utfører for Landbruks- og matdepartementet. Den årlege undersøkinga viser status og utvikling for økonomien i landbruket og får fram verknadene av politikk og rammevilkår. Ho er såleis eit viktig verktøy for forvaltninga. Analysar av granskingsmaterialet gir dessutan grunnlag for rådgiving, forsking og undervisning.
Sammendrag
I denne rapporten vert resultat frå oppdaterte verdiskapingsberekningar for landbruk og landbruksbasert industri i Møre og Romsdal basert på tal frå 2024 presenterte. Bruttoprodukt frå jordbruk, skogbruk og landbruksbasert tilleggsnæring er berekna til 2,1 mrd. kr, medan den landbruksbaserte industrien bidreg med 1,1 mrd. kr. Totalt utgjer bruttoproduktet frå landbruk og landbruksbasert industri 2 prosent av den totale verdiskapinga i Møre og Romsdal i 2024.
Forfattere
Magnhild Garte HøibergSammendrag
Tilgjengelig litteratur om beiteoverlapp mellom rein og sau er innhentet og sammenstilt i denne rapporten. Undersøkelsene viser at det er beiteoverlapp mellom rein og sau, men det er stor variasjon i beregnede nisjeoverlappsindekser. Samlet sett kan studiene tyde på moderat til høy beiteoverlapp, men det er mange ulike variabler som spiller inn i undersøkelsene som er gjort. Resultatene tyder på at rein og sau beiter flere av de samme vegetasjonstypene og plantene, men at beitearealene benyttes til ulik tid når det er rom for det. Et begrenset antall studier på beiteoverlapp gir usikkerhet, og flere undersøkelser vil gi mer kunnskap om overlappende valg av beite mellom rein og sau.
Forfattere
Karen Ane Frøyland Skjennum Jan Mulder Gijsbert Dirk Breedveld Thomas Hartnik Nicolas Estoppey Erlend Grenager SørmoSammendrag
Det er ikke registrert sammendrag
Forfattere
Ulrika Jansson Asplund Damian Petkovic Karlsen Anne Krag Brysting Rune Halvorsen Håvard Kauserud O. Janne Kjønaas Johan AsplundSammendrag
Det er ikke registrert sammendrag
Forfattere
Ming Yu Sebastian Kepfer-Rojas Yamina Micaela Rosas Teresa Gómez de la Bárcena Inger Kappel Schmidt Per Gundersen Ludovica D'Imperio Carsten W. Mueller Lars VesterdalSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag