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
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.
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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.
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Forfattere
Anna Skarin Minna Turunen Svein Eilertsen Heidi Rautiainen Tim Horstkotte Camilla Risvoll Anna-Marja Kaddik Tom Lifjell Majken Paulsen Laura Post Sirpa Rasmus Hans Tømmervik Birgitta ÅhmanSammendrag
Det er ikke registrert sammendrag
Sammendrag
In this chapter, we outline what is known about climatic and stress memory in trees, with examples covering different groups and species of trees (conifers, poplar, oak, ash, and eucalypts). We focus on two broad types of memory: (1) immune memory involved in inducible defenses (defense priming) and (2) climatic memory, whereby trees maintain certain phenological phenotypes in response to environmental conditions experienced during embryogenesis. We outline the epigenetic mechanisms that are thought to be involved in the creation and maintenance of climatic and stress memory in trees. We also give examples of how to study such memories in trees. In these examples, we focus on research protocols that have been proven useful to characterize memories and their mechanistic basis, with an emphasis on molecular techniques that can be used to dissect epigenetic mechanisms.
Sammendrag
Context Dairy farming contributes approximately 2.5 % of annual global anthropogenic greenhouse gas (GHG) emissions, necessitating effective mitigation strategies. Two approaches are often discussed: low-intensity, low-cost production with minimal reliance on purchased inputs; and high-intensity production with higher-yielding cows to reduce land use and reduce methane emissions per unit of milk. Objective The objective was to identify management factors and farm characteristics that explain variations in GHG emissions, environmental, and economic performance. Indicators included were GHG emissions, land use occupation, energy intensity, nitrogen intensity, and gross margin. Methods Life Cycle Assessment (LCA) was used to calculate the environmental impacts for 200 commercial dairy farms in Central Norway based on farm activities, purchased inputs, machinery, and buildings from 2014 to 2016. A multiple regression analysis with backward elimination was conducted to highlight important variables for environmental impact and economic outcome. Results and conclusions A higher share of dairy cows was found to be the most important factor in reducing GHG emissions, energy and nitrogen intensity, and land use but also to decrease gross margin. Additional key factors for reducing environmental impact included less purchased nitrogen fertiliser, and higher forage yield. There were no statistical correlations between GHG emissions and gross margin per MJ of human-edible energy delivered. Significance Conducting LCA for many dairy farms allows to highlight important factors influencing environmental impact and economic outcome. Using the delivery of human-edible energy from milk and meat as a functional unit allows for a combined evaluation of milk and meat production on a farm.