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.
2025
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Samuel L. Zelinka Samuel V. Glass Natalia Farkas Emil E. Thybring Michael Altgen Lauri Rautkari Simon Curling Jinzhen Cao Yujiao Wang Tina Künniger Gustav Nyström Christopher Hubert Dreimol Ingo Burgert Mark G. Roper Darren P. Broom Matthew Schwarzkopf Arief Yudhanto Mohammad Subah Gilles Lubineau Maria Fredriksson Wiesław Olek Jerzy Majka Nanna Bjerregaard Pedersen Daniel J. Burnett Armando R. Garcia Frieder Dreisbach Louis Waguespack Jennifer Schott Luis G. Esteban Alberto García‑Iruela Thibaut Colinart Romain Rémond Brahim Mazian Patrick Perré Lukas EmmerichSammendrag
Automated sorption balances are widely used for characterizing the interaction of water vapor with hygroscopic materials. This paper is part of an interlaboratory study investigating the stability and performance of automated sorption balances. A previous paper in this study investigated the mass, temperature, and relative humidity (RH) stability of automated sorption balances by looking at the mass change of a non-hygroscopic sample over time. In this study, we examine the mass stability of wood samples held at constant RH for seven to ten days after a step change. The reason for the long hold times was to collect data to “operational equilibrium” where the change in mass is on the order of the inherent operational stability of the instrument. A total of 80 datasets were acquired from 21 laboratories covering absorption with final RH levels ranging from 10 to 95%. During these long hold times, several unusual behaviors were observed in the mass-vs-time curves. Deviations from expected sorption behavior were examined by fitting the data to an empirical sorption kinetics model and calculating the root mean square error (RMSE) between the observed and smoothed behavior. Samples that had a large RMSE relative to the median RMSE of the other datasets often had one of several types of errors: abrupt disturbances, diurnal oscillations, or long-term mass decline during an absorption step. In many cases, mass fluctuations were correlated with changes in the water reservoir temperature of the automated sorption balance. We discuss potential errors in sorption measurements on hygroscopic materials and suggest an acceptable level of RMSE for sorption data.
Forfattere
Radosav Cerović Milica Fotirić Akšić Marko Kitanović Mekjell MelandSammendrag
Apple production in Western Norway faces challenges due to climatic constraints and varying phenology. It is essential for cultivars to adapt to regional ecological factors, while suitable pollinators are necessary for successful cultivation. This study examined the reproductive biology of two newly introduced apple cultivars, ‘Eden’ (Wursixo) and ‘Fryd’ (Wuranda), over two years (2022–2023). Key qualitative and quantitative parameters of reproductive biology were analyzed, including in vitro pollen germination, pollen tube growth within the style and ovary locules, flowering overlap time, and fruit set. The study involved cross-pollination between the pollen recipient cultivars ‘Eden’ and ‘Fryd’, with various pollenizers: ‘Rubinstep’, ‘Red Aroma’, ‘Elstar’, ‘Asfari’ and ‘Professor Sprenger’, as well as self-pollination and open pollination. According to the results from the progamic phase of fertilization and fruit set, the cultivars ‘Rubinstep’, ‘Asfari’, and ‘Fryd’ were the best pollenizers for ‘Eden’. In contrast, ‘Rubinstep’, ‘Eden’, and ‘Elstar’ were the best pollenizers for ‘Fryd’. Looking only at the overlapping of the flowering time between pollen recipient and pollen donor, ‘Professor Sprenger’ and ‘Fryd’ were the best pollenizers for ‘Eden’, while ‘Professor Sprenger’ and ‘Eden’ were good pollenizers for ‘Fryd’.
Forfattere
Eric Watkins Dominic P. Petrella Trygve S. Aamlid Dominic C. Christensen Sigridur Dalmannsdottir Andrew P. Hollman Gary DetersSammendrag
Ice encasement is a major concern for turfgrass managers in cold climates; however, there is a lack of data about both which turfgrasses are best suited for survival under these conditions and the reasons behind the superior recovery of some grasses from long-term ice encasement. In this study, we encased golf course putting greens-height field plots of creeping bentgrass (Agrostis stolonifera L.), velvet bentgrass (Agrostis canina L.), annual bluegrass (Poa annua L. var. reptans Hausskn.), Chewings fescue (Festuca. rubra L. ssp. commutata Gaudin), and slender creeping red fescue (F. rubra L. ssp. littoralis (G. Mey.) Auquier) with ice for 90–120 days with the inclusion of CO2, O2, and temperature sensors at 2.5 and 12.5 cm depth to better understand environmental conditions under ice and factors related to winterkill. Velvet bentgrass had the best overall performance and recovery, while annual bluegrass did not survive. Differences in recovery among turfgrass taxa may have been affected by the length of the ice encasement period, higher CO2 levels (>40,000 ppm), and lower O2 values, particularly in the second experimental run. During the recovery period in both years, photochemical efficiency values began increasing 5–10 days before percent green cover, suggesting that visual performance of the turf surface is a lagging indicator of recovery. Overall, recovery from ice encasement was annual bluegrass < Chewings fescue < creeping bentgrass = slender creeping red fescue = velvet bentgrass. These results can guide turfgrass managers in making species selection decisions in areas where long-duration ice encasement is a risk. Plain Language Summary Turfgrasses on golf course greens in cold climates can be severely damaged or even die from ice encasement. Little is known about this stress, including why certain grasses can survive longer. As a first step to learn more about this problem, we tested five different turfgrasses for their ability to survive under ice. The study was done during two separate winters in Minnesota under field conditions, resulting in 98 days of ice in 2021–2022 and 112 days of ice cover in 2022–2023. Annual bluegrass died completely during both experimental runs, while Chewings fescue suffered some injury in the first year and did poorly in the second year. Velvet bentgrass was the best grass in both years. Under the longer duration of ice cover in the second year, carbon dioxide levels were very high, while oxygen gas levels slowly declined over the course of the ice encasement period.
Forfattere
Attiq Ur Rehman Jahn Davik Petteri Karisto Janne Kaseva Saila Karhu Marja Rantanen Ismo Strandén Timo Hytönen Alan H. Schulman Tuuli HaikonenSammendrag
Key message Multiple QTLs for powdery mildew resistance were identifed in a pre-breeding population derived from the octoploid progenitor species of garden strawberry, including a stable major novel factor on chromosome 3B. Abstract Powdery mildew (PM), caused by the biotrophic fungal pathogen Podosphaera aphanis, poses an increasing threat to garden strawberry (Fragaria×ananassa) production worldwide. While a few commercial cultivars exhibit partial resistance, fungicide application remains essential for managing PM outbreaks. However, breeding ofers a more sustainable approach for controlling PM. A better understanding of the genetics of resistance is required for informed breeding strategies, e.g. through identifying novel resistance factors derived from the progenitor species of garden strawberry, F. chiloensis and F. virginiana. We conducted genome-wide association (GWA) and multivariate analyses in a reconstructed (ReC) strawberry population to investigate PM resistance under natural infection. Leveraging multi-year feld trial data and 20,779 singlenucleotide polymorphism markers, we identifed a novel major quantitative trait locus (QTL) on chromosome 3B, designated as q.LPM.Rec-3B.2, that was consistently associated with high PM resistance in both leaves and fruits. Greenhouse validation with a subset of the ReC population confrmed that this QTL region was stable across feld and greenhouse environments. Promising candidate genes for resistance, including two for MLO and one for EXO70, were identifed within this major QTL. In addition, multi-locus GWA models and non-additive GWA revealed additional resistance QTLs on multiple chromosomes. Despite previous challenges in breeding for robust PM resistance due to its quantitative nature and complex genetic control, our results provide valuable insights into resistance-contributing QTL regions already existing in strawberry, novel wildderived resistance QTLs not previously known, candidate genes, and pre-breeding germplasm carrying resistance traits as resources for future genome-informed breeding eforts.
Forfattere
Ingrid Marie Garfelt Paulsen Isabell Eischeid Åshild Ønvik Pedersen Jakob J. Assmann Nigel Yoccoz Jesper Bruun Mosbacher Eeva M Soininen Virve RavolainenSammendrag
The normalized difference vegetation index (NDVI) is a critical tool for studying Arctic vegetation patterns and changes, but more knowledge is needed about its links with plant biomass and disturbances, especially in sparsely vegetated habitats in the High Arctic. Here, we investigate the relationship between NDVI and vascular plant biomass, summer temperature, goose disturbance, and winter damage in Dryas ridge and moss tundra habitats on Svalbard, all recorded in the corresponding year across a 5-year time series. We test these relationships using mixed-effect models at two spatial resolutions (10 cm and 10 m) and two extents with data from drone and Sentinel-2 imagery. We found that in our plots, an increase in biomass of 100 g m−2 increased NDVI from drone imagery by 0.08 ± 0.03 (95% CI) for Dryas ridge and by 0.04 ± 0.03 for moss tundra. Despite record-warm summers, temperature of the same summer was not associated with NDVI in our time-series. In moss tundra, severe goose disturbance had a negative relationship with drone NDVI in plots, while in Dryas ridge habitat, winter damage had no clear correspondence with NDVI. Our study provides an example of context dependencies highlighted in remote-sensing literature in the Arctic, encouraging future studies to include effects of disturbance on NDVI and to establish habitat-specific relationships with NDVI.
Sammendrag
Soil organic carbon (SOC) is the largest terrestrial carbon pool, but it is still uncertain how it will respond to climate change. Specifically, the fate of SOC due to concurrent changes in soil temperature and moisture is uncertain. It is generally accepted that microbially driven SOC decomposition will increase with warming, provided that sufficient soil moisture (and hence sufficient C substrate) is available for microbial decomposition. We use a mechanistic, microbially explicit SOC decomposition model, the Jena Soil Model (JSM), and focus on the depolymerisation of litter and microbial residues by microbes at different soil depths as well as the sensitivities of the depolymerisation of litter and microbial residues to soil warming and different drought intensities. In a series of model experiments, we test the effects of soil warming and droughts on SOC stocks, in combination with different temperature sensitivities (Q10 values) for the half-saturation constant Km (Q10Km) associated with the breakdown of litter or microbial residues. We find that soil warming can lead to SOC losses at a timescale of a century andthat these losses are highest in the topsoil (compared with the subsoil). Droughts can alleviate the effects of soil warming and reduce SOC losses, by posing strong microbial limitation on the depolymerisation rates, and even lead to SOC accumulation, provided that litter inputs remain unchanged. While absolute SOC losses were highest in the topsoil, we found that the temperature and moisture sensitivities of Km were important drivers of SOC losses in the subsoil– where microbial biomass is low and mineral-associated OC is high. Furthermore, a combination of drought and different Q10Km values associated with different enzymes for the breakdown of litter or microbial residues had counteracting effects on the overall SOC balance. In this study, we show that, while absolute SOC changes driven by soil warming and drought are highest in the topsoil, SOC in the subsoil is more sensitive to warming and drought due to the intricate interplay between Km,temperature, soil moisture, and mineral-associated SOC.
Sammendrag
Simple Summary This study used random test split (RTS) and cross-validation (CV) machine learning data partition methods to test different models to classify cattle behavior, including activity and posture states as well as foraging behaviors, using GPS coupled accelerometer data with 12 h/days continuous recording observation as supporting ground truth. RTS in XGBoost performed best for general activity state classification, while CV in Random Forest excelled in more detailed foraging behaviors and foraging behavior-by-posture classifications. Key movement indicators like speed, Actindex, and sensor values (x, y, and z) were vital in predicting behaviors, suggesting specific sensors for tracking behaviors of interest to ranchers. The results highlight the benefits of continuous monitoring and advanced data analysis for real-time livestock tracking, leading to better grazing management and more sustainable land use. Abstract This study classified cows’ foraging behaviors using machine learning (ML) models evaluated through random test split (RTS) and cross-validation (CV) data partition methods. Models included Perceptron, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Random Forest (RF), and XGBoost (XGB). These models classified activity states (active vs. static), foraging behaviors (grazing (GR), resting (RE), walking (W), ruminating (RU)), posture states (standing up (SU) vs. lying down (LD)), and posture combinations with rumination and resting behaviors (RU_SU, RU_LD, RE_SU, and RE_LD). XGB achieved the highest accuracy for state classification (74.5% RTS, 74.2% CV) and foraging behavior (69.4% CV). RF outperformed XGB in other classifications, including GR, RE, and RU (62.9% CV vs. 56.4% RTS), posture (83.9% CV vs. 79.4% RTS), and behaviors-by-posture (58.8% CV vs. 56.4% RTS). Key predictors varied: speed and Actindex were crucial for GR and W when increasing and for RE and RU when decreasing. X low values were linked to RE_SU and RU_SU, while X and Z influenced RE_LD more. RTS showed higher accuracy in activity states classification while CV in foraging behaviors and by posture classification. These results emphasize CV in RF’s reliability in managing complex behavioral patterns and the importance of continuous recording devices and movement data to monitor cattle behavior accurately.
Forfattere
Jianfeng Gu Xinxin Ma Yiwu Fang Hongmei Li Deliang Peng Xiuhai Gan Xingyue Li Baolin Shao Ricardo Holgado Sergei A. SubbotinSammendrag
The golden cyst nematode, Globodera rostochiensis is a severe quarantine pest of potato, and frstly reported in China in 2022, in Yunnan, Sichuan and Guizhou Provinces. In 2023, a cyst nematode found on roots and rhizosphere soil of potato and circumjacent weeds in the same locations was described as G. vulgaris by Xu et al. (2023). Based on our comparative analysis, including morphological characters, molecular datasets and host range, we conclude on that this cyst nematode is G. rostochiensis. Therefore, we proposed that G. vulgaris described from China should be considered a junior synonym of Globodera rostochiensis.
Forfattere
Dagnew Yebeyen Burru Jayaraman Durai Melaku Anteneh Chinke Gudeta W. Sileshi Yashwant S. Rawat Belachew Gizachew Zeleke Selim Reza Fikremariam Haile Desalegne Kassa Toshe WorassaSammendrag
Highland bamboo (Oldeania alpina) plays a vital role in supporting local livelihoods, fostering biodiversity conservation and sustainable land management. Despite these benefits, its significant potential for carbon sequestration remains underutilized withinEthiopia’s climate mitigation strategies. In this study, we developed site-specific allometric equations to assess the biomass and carbon storage potential of highland bamboo. Datawere collected from the Garamba natural bamboo forest and Hula homestead bamboo stands in the Sidama Regional State, Southern Ethiopia. Data on stand density and structurewere gathered using systematically laid transects and sample plots, while plant samples were analyzed in the laboratory to determine the dry-to-fresh weight ratios. We developedallometric models to estimate the aboveground biomass (AGB) and carbon stock. The study results indicated that homestead bamboo stands exhibited higher biomass accumulationthan natural bamboo stands. The AGB was estimated at 92.3 Mg ha−1in the natural forest and 118.3 Mg ha−1in homestead bamboo stands, with total biomass carbon storage of 52.1 Mg ha−1 and 66.7 Mg ha−1, respectively. The findings highlight the significant potential of highland bamboo for carbon sequestration in both natural stands and homesteads.Sustainable management of natural highland bamboo stands and integrating bamboo into farms can contribute to climate change mitigation, support ecosystem restoration, andenhance the socio-economic development of communities.