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.
2020
Sammendrag
The impact of historical and present drivers on biodiversity, particularly species richness and abundance, in afforestation areas concerning non-native tree species is still poorly understood. A better understanding is important to ensure appropriate forest management in the face of climate change and increasing demand for wood products. Here, we have reviewed 75 biodiversity studies in Sitka spruce plantations in NW Europe, forest management recommendations for maintaining biodiversity, timber production and carbon sequestration in Sitka spruce forests in coastal Norway compared to NW Europe. Due to more focus on non-market landscape benefits and protection sites in coastal areas, transformation of spruce plantations is common. Premature cutting of stands and shelterbelts and clearing away saplings has become the dominant management practice in Norway. Based on the extent of use in Norway, and results from biodiversity studies in Sitka spruce plantations in NW Europe, the quality of evidence for the prevailing practice and recommendations in coastal Norway is highly questioned. To reduce conflicts, we propose a more knowledge-based management, a broader perspective underpinning the range of afforestation goals, also including the use of alternative silvicultural methods to increase structural variation in Sitka spruce stands.
Sammendrag
Organic amendments can improve grassland productivity. Timothy and tall fescue were sown on a sandy loam and a coarse sand at Særheim, Norway, in September 2016 and on a loamy sand at Skierniewice, Poland, in April 2017, and cut and fertilised according to normal practices for the two regions from 2017 to 2019. At both sites, 0.75 kg DM m-2 of either digested or undigested manure (the latter with or without 2.9 kg biochar m-2) were incorporated prior to sowing. On the coarse sand at Særheim, total seasonal tall fescue yield in 2018 was 46–60% higher in the organic amendment treatments, and total seasonal timothy yield in the digestate treatment was 97% higher, than in the control treatment for the same species with only mineral fertiliser. On the sandy loam at Særheim and the loamy sand at Skierniewice, none of the amendments resulted in significant yield increments. These results indicate a clear effect on soil type on grassland biomass response to organic amendments.
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Forfattere
Johannes Breidenbach Janis Ivanovs Annika Kangas Mats Nilsson Thomas Nord-Larsen Rasmus AstrupSammendrag
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Forfattere
Johannes BreidenbachSammendrag
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Forfattere
Rui Xia Yalin Yang Xingliang Pan Chenchen Gao Yuanyuan Yao Xuewei Liu Tsegay Teame Fengli Zhang Juan Hu Chao Ran Zhen Zhang Jihong Liu Clarke Zhigang ZhouSammendrag
Quorum quenching (QQ) blocks bacterial cell-to-cell communication (i.e., quorum sensing), and is a promising antipathogenic strategy to control bacterial infection via inhibition of virulence factor expression and biofilm formation. QQ enzyme AiiO-AIO6 from Ochrobactrum sp. M231 has several excellent properties and shows biotherapeutic potential against important bacterial pathogens of aquatic species. AiiO-AIO6 can be secretory expressed in Bacillus subtilis via a non-classical secretion pathway. To improve AiiO-AIO6 production, four intracellular protease-deletion mutants of B. subtilis 1A751 were constructed by individually knocking out the intracellular protease-encoding genes (tepA, ymfH, yrrN and ywpE). The AiiO-AIO6 expression plasmid pWB-AIO6BS was transformed into the B. subtilis 1A751 and its four intracellular protease-deletion derivatives. Results showed that all recombinant intracellular protease-deletion derivatives (BSΔtepA, BSΔymfH, BSΔyrrN and BSΔywpE) had a positive impact on AiiO-AIO6 production. The highest amount of AiiO-AIO6 extracellular production of BSΔywpE in shake flask reached 1416.47 U/mL/OD600, which was about 121% higher than that of the wild-type strain. Furthermore, LC–MS/MS analysis of the degrading products of 3-oxo-C8-HSL by purification of AiiO-AIO6 indicated that AiiO-AIO6 was an AHL-lactonase which hydrolyzes the lactone ring of AHLs. Phylogenetic analysis showed that AiiO-AIO6 was classified as a member of the α/β hydrolase family with a conserved “nucleophile-acid-histidine” catalytic triad. In summary, this study showed that intracellular proteases were responsible for the reduced yields of heterologous proteins and provided an efficient strategy to enhance the extracellular production of AHL lactonase AiiO-AIO6.
Forfattere
Hainie Zha Yuxin Miao Tiantian Wang Yue Li Jing Zhang Weichao Sun Zhengqi Feng Krzysztof KusnierekSammendrag
Optimizing nitrogen (N) management in rice is crucial for China’s food security and sustainable agricultural development. Nondestructive crop growth monitoring based on remote sensing technologies can accurately assess crop N status, which may be used to guide the in-season site-specific N recommendations. The fixed-wing unmanned aerial vehicle (UAV)-based remote sensing is a low-cost, easy-to-operate technology for collecting spectral reflectance imagery, an important data source for precision N management. The relationships between many vegetation indices (VIs) derived from spectral reflectance data and crop parameters are known to be nonlinear. As a result, nonlinear machine learning methods have the potential to improve the estimation accuracy. The objective of this study was to evaluate five different approaches for estimating rice (Oryza sativa L.) aboveground biomass (AGB), plant N uptake (PNU), and N nutrition index (NNI) at stem elongation (SE) and heading (HD) stages in Northeast China: (1) single VI (SVI); (2) stepwise multiple linear regression (SMLR); (3) random forest (RF); (4) support vector machine (SVM); and (5) artificial neural networks (ANN) regression. The results indicated that machine learning methods improved the NNI estimation compared to VI-SLR and SMLR methods. The RF algorithm performed the best for estimating NNI (R2 = 0.94 (SE) and 0.96 (HD) for calibration and 0.61 (SE) and 0.79 (HD) for validation). The root mean square errors (RMSEs) were 0.09, and the relative errors were <10% in all the models. It is concluded that the RF machine learning regression can significantly improve the estimation of rice N status using UAV remote sensing. The application machine learning methods offers a new opportunity to better use remote sensing data for monitoring crop growth conditions and guiding precision crop management. More studies are needed to further improve these machine learning-based models by combining both remote sensing data and other related soil, weather, and management information for applications in precision N and crop management.
Sammendrag
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Forfattere
Tatsiana Espevig Karin Normann Marina Usoltseva Kate Entwistle Jo Anne Crouch Trygve S. AamlidSammendrag
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