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
Forfattere
Camille S. Delavaux Thomas W. Crowther Constantin M. Zohner Niamh M. Robmann Thomas Lauber Johan van den Hoogen Sara Kuebbing Jingjing Liang Sergio de-Miguel Gert-Jan Nabuurs Peter B. Reich Meinrad Abegg Yves C. Adou Yao Giorgio Alberti Angelica M. Almeyda Zambrano Braulio Vilchez Alvarado Esteban Alvarez-Dávila Patricia Alvarez-Loayza Luciana F. Alves Christian Ammer Clara Antón Fernandéz Alejandro Araujo-Murakami Luzmila Arroyo Valerio Avitabile Gerardo A. Aymard Timothy R. Baker Radomir Bałazy Olaf Banki Jorcely G. Barroso Meredith L. Bastian Jean-Francois Bastin Luca Birigazzi Philippe Birnbaum Robert Bitariho Pascal Boeckx Frans Bongers Olivier Bouriaud Pedro H. S. Brancalion Susanne Brandl Roel Brienen Eben N. Broadbent Helge Bruelheide Filippo Bussotti Roberto Cazzolla Gatti Ricardo G. César Goran Cesljar Robin Chazdon Han Y. H. Chen Chelsea Chisholm Hyunkook Cho Emil Cienciala Connie Clark David Clark Gabriel D. Colletta David A. Coomes Fernando Cornejo Valverde José J. Corral-Rivas Philip M. Crim Jonathan R. Cumming Selvadurai Dayanandan André L. de Gasper Mathieu Decuyper Géraldine Derroire Ben DeVries Ilija Djordjevic Jiri Dolezal Aurélie Dourdain Nestor Laurier Engone Obiang Brian J. Enquist Teresa J. Eyre Adandé Belarmain Fandohan Tom M. Fayle Ted R. Feldpausch Leandro V. Ferreira Markus Fischer Christine Fletcher Lorenzo Frizzera Javier G. P. Gamarra Damiano Gianelle Henry B. Glick David J. Harris Andrew Hector Andreas Hemp Geerten Hengeveld Bruno Hérault John L. Herbohn Martin Herold Annika Hillers Eurídice N. Honorio Coronado Cang Hui Thomas T. Ibanez Iêda Amaral Nobuo Imai Andrzej M. Jagodziński Bogdan Jaroszewicz Vivian Kvist Johannsen Carlos A. Joly Tommaso Jucker Ilbin Jung Viktor Karminov Kuswata Kartawinata Elizabeth Kearsley David Kenfack Deborah K. Kennard Sebastian Kepfer-Rojas Gunnar Keppel Mohammed Latif Khan Timothy J. Killeen Hyun Seok Kim Kanehiro Kitayama Michael Köhl Henn Korjus Florian Kraxner Diana Laarmann Mait Lang Simon L. Lewis Huicui Lu Natalia V. Lukina Brian S. Maitner Yadvinder Malhi Eric Marcon Beatriz Schwantes Marimon Ben Hur Marimon-Junior Andrew R. Marshall Emanuel H. Martin Olga Martynenko Jorge A. Meave Omar Melo-Cruz Casimiro Mendoza Cory Merow Abel Monteagudo Mendoza Vanessa S. Moreno Sharif A. Mukul Philip Mundhenk María Guadalupe Nava-Miranda David Neill Victor J. Neldner Radovan V. Nevenic Michael R. Ngugi Pascal A. Niklaus Jacek Oleksyn Petr Ontikov Edgar Ortiz-Malavasi Yude Pan Alain Paquette Alexander Parada-Gutierrez Elena I. Parfenova Minjee Park Marc Parren Narayanaswamy Parthasarathy Pablo L. Peri Sebastian Pfautsch Oliver L. Phillips Nicolas Picard Maria Teresa T. F. Piedade Daniel Piotto Nigel C. A. Pitman Irina Polo Lourens Poorter Axel Dalberg Poulsen Hans Pretzsch Freddy Ramirez Arevalo Zorayda Restrepo-Correa Mirco Rodeghiero Samir G. Rolim Anand Roopsind Francesco Rovero Ervan Rutishauser Purabi Saikia Christian Salas-Eljatib Philippe Saner Peter Schall Dmitry Schepaschenko Michael Scherer-Lorenzen Bernhard Schmid Jochen Schöngart Eric B. Searle Vladimír Seben Josep M. Serra-Diaz Douglas Sheil Anatoly Z. Shvidenko Javier E. Silva-Espejo Marcos Silveira James Singh Plinio Sist Ferry Slik Bonaventure Sonké Alexandre F. Souza Stanislaw Miscicki Krzysztof J. Stereńczak Jens-Christian Svenning Miroslav Svoboda Ben Swanepoel Natalia Targhetta Nadja Tchebakova Hans ter Steege Raquel Thomas Elena Tikhonova Peter M. Umunay Vladimir A. Usoltsev Renato Valencia Fernando Valladares Fons van der Plas Tran Van Do Michael E. van Nuland Rodolfo M. Vasquez Hans Verbeeck Helder Viana Alexander C. Vibrans Simone Vieira Klaus von Gadow Hua-Feng Wang James V. Watson Gijsbert D. A. Werner Susan K. Wiser Florian Wittmann Hannsjoerg Woell Verginia Wortel Roderik Zagt Tomasz Zawiła-Niedźwiecki Chunyu Zhang Xiuhai Zhao Mo Zhou Zhi-Xin Zhu Irie C. Zo-Bi Daniel S. MaynardSammendrag
Det er ikke registrert sammendrag
Sammendrag
Nutrient uptake and transport depend on the root system of a tree. Various apple rootstock genotypes may interact fruit tree nutrition. In 2017, two multi-location apple rootstock trials were established at 16 sites in 12 European countries. The evaluations are performed by members of the EUFRIN (European Fruit Research Institute Network) Apple & Pear Variety & Rootstock Testing Working Group. Following rootstocks are included in the tests: G.11, G.41, G.202 and G.935 (US), EM_01, EM_02, EM_03, EM_04, EM_05 and EM_06 (UK), 62-396-B10® (Russia), P 67 (Poland), NZ-A, NZ-B, NZ-C and NZ-D (New Zealand) and Cepiland-Pajam®2 as control. The effect of rootstocks on the mineral content of leaf and fruit was studied at the Institute of Horticulture, Lithuanian Research Centre for Agriculture and Forestry in 2019-2020. The leaf and fruit mineral concentration of nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and leaf mineral content of copper (Cu), zinc (Zn), iron (Fe), manganese (Mn) and boron (B) were measured. Significant rootstock effect was established on leaf P, Mg, Zn, Mn, B, and fruit Ca and Mg content. Rootstocks EM_01 and G.41 were the most efficient in leaf mineral uptake, while G.935 had the lowest content of all leaf macro nutrients. Rootstocks EM_06 and P 67 were the most efficient in fruit mineral uptake, while EM_02 had the lowest content of three nutrients. Current research reveals differences among rootstocks and their capacity to absorb separate minerals and enables creation of rootstock specific nutrition management.
Forfattere
Yi Zhang Zhangmu Jing Yijing Feng Shuo Chen Yeqing Li Yongming Han Lu Feng Junting Pan Mahmoud Mazarji Hongjun Zhou Xiaonan Wang Chunming XuSammendrag
Exploring key factors has important guidance for understanding complex anaerobic digestion (AD) systems. This study proposed a multi-layer automated machine learning framework to understand the complex interactions in AD systems and explore key factors at the environmental factor, microorganisms and system levels. The first layer of the framework identified hydraulic residence time (HRT) as the most important environmental factor, with an optimal range of 33–45 d. In the second layer of the framework, Methanocelleus (optimal relative abundance (ORA) = 3.0%) and Candidatus_Caldatribacterium (ORA = 1.7%) were found to be the key archaea and bacteria, respectively. Furthermore, the prediction of key microorganisms based on environmental factors and remaining microbial data showed the essential roles of Methanothermobacter and Acetomicrobium. The third layer for finding the optimal combination of data variables for predicting biogas production demonstrated that combined Archaea genera and environmental factors should be achieved for the most accurate prediction (root mean square error (RMSE) = 84.21). GBM had the best model performance and prediction accuracy among all the built-in models. Based on the optimal GBM model, the analysis at the system level showed that HRT was the most important variable. However the most important microorganism, Methanocelleus, within the appropriate survival range is also essential to achieve optimal biogas production. This research explores key parameters at various levels through automated machine learning techniques, which are expected to provide guidance in understanding the complex architecture of industrial and laboratory AD systems.
Forfattere
Damaris Marina Matten Ida Marielle Mienna Vanessa Carina Bieker Brent D. Mishler Victoria Stornes Moen Malene Østreng Nygård Katariina Elsa M Vuorinen Mika Bendiksby Michael David Martin James David Mervyn SpeedSammendrag
Paid open access
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Simo Maduna Ólöf Dóra Bartels Jónsdóttir Albert Kjartan Dagbjartarson Imsland Davíð Gíslason Patrick Reynolds Lauri Kapari Thor Arne Hangstad Kristian Meier Snorre HagenSammendrag
Aquaculture of the lumpfish (Cyclopterus lumpus L.) has become a large, lucrative industry owing to the escalating demand for “cleaner fish” to minimise sea lice infestations in Atlantic salmon mariculture farms. We used over 10K genome-wide single nucleotide polymorphisms (SNPs) to investigate the spatial patterns of genomic variation in the lumpfish along the coast of Norway and across the North Atlantic. Moreover, we applied three genome scans for outliers and two genotype–environment association tests to assess the signatures and patterns of local adaptation under extensive gene flow. With our ‘global’ sampling regime, we found two major genetic groups of lumpfish, i.e., the western and eastern Atlantic. Regionally in Norway, we found marginal evidence of population structure, where the population genomic analysis revealed a small portion of individuals with a different genetic ancestry. Nevertheless, we found strong support for local adaption under high gene flow in the Norwegian lumpfish and identified over 380 high-confidence environment-associated loci linked to gene sets with a key role in biological processes associated with environmental pressures and embryonic development. Our results bridge population genetic/genomics studies with seascape genomics studies and will facilitate genome-enabled monitoring of the genetic impacts of escapees and allow for genetic-informed broodstock selection and management in Norway.
Forfattere
Albert Kjartan Dagbjartarson Imsland Patrick Reynolds Lauri Teemu Kapari Simo Maduna Snorre Hagen Anna Hanssen Ólöf Dóra Bartels JónsdóttirSammendrag
Det er ikke registrert sammendrag
Forfattere
Abirami Ramu Ganesan Philipp Hoellrigl Hannah Mayr Demian Martini Loesch Noemi Tocci Elena Venir Lorenza ConternoSammendrag
This study aimed to evaluate the rheological properties of doughs with 50% brewers’ spent grain (BSG) derived from a rye-based (RBSG) and barley-based (BBSG) beer added, and the textural profile of the related baked products. Simple model systems using BSG flour mixed with water were studied. Two bakery products, focaccia and cookies, were made as food systems using BSG in a 1:1 ratio with wheat flour (WF). Their rheological properties and texture after baking were characterized. BSG-added dough exhibited viscoelastic properties with a solid gel-like behavior. The addition of BSG increased G′ > G″ and decreased the dough flexibility. BSG addition in baked RBSG focaccia increased the hardness, gumminess, and chewiness by 10%, 9%, and 12%, respectively. BBSG cookies had a 20% increase in fracturability. A positive correlation was found between the rheological metrics of the dough and the textural parameters of BBSG-added cookies. PCA analysis revealed that complex viscosity, G′, G″, and cohesiveness separated BBSG focaccia from RBSG focaccia and the control. Therefore, the rheological properties of BSG dough will have industrial relevance for 3D-printed customized food products with fiber. Adding RBSG and BBSG to selected foods will increase the up-cycling potential by combining techno-functional properties.
Sammendrag
This chapter describes the work performed within the Sinograin II project on implementation of new precision nitrogen management technologies in three regions of North China. Each of the analyzed regions represents a different crop and scale of a farming system: large-scale rice farming system in Heilongjiang province, medium-scale maize farming system in Jilin province, and small-scale wheat farming system in the North China Plain. A village was selected in each region to represent the agricultural practices and current nutrient and crop management strategies of the tested region. Moreover, the initial regional optimum crop management, the current agricultural extension, as well as the precision nitrogen technologies implemented in the respective regions are described. During the course of the project, a number of novel tools and strategies for precision nitrogen management were developed for the respective regions and published in scientific papers. This chapter reviews and discusses the selected findings and indicates directions of the upcoming research.
Sammendrag
Soil management is important for sustainable agriculture, playing a vital role in food production and maintaining ecological functions in the agroecosystem. Effective soil management depends on highly accurate soil property estimation. Machine learning (ML) is an effective tool for data mining, selection of key soil properties, modeling the non-linear relationship between different soil properties. Through coupling with spectral imaging, ML algorithms have been extensively used to estimate physical, chemical, and biological properties quickly and accurately for more effective soil management. Most of the soil properties are estimated by either near infrared (NIR), Vis-NIR, or mid-infrared (MIR) in combination with different ML algorithms. Spectroscopy is widely used in estimation of chemical properties of soil samples. Spectral imaging from both UAV and satellite platforms should be taken to improve the spatial resolution of different soil properties. Spectral image super-resolution should be taken to generate spectral images in high spatial, spectral, and temporal resolutions; more advanced algorithms, especially deep learning (DL) should be taken for soil properties’ estimation based on the generated ‘super’ images. Using hyperspectral modeling, soil water content, soil organic matter, total N, total K, total P, clay and sand were found to be successfully predicted. Generally, MIR produced better predictions than Vis-NIR, but Vis-NIR outperformed MIR for a number of properties. An advantage of Vis-NIR is instrument portability although a new range of MIR portable devices is becoming available. In-field predictions for water, total organic C, extractable phosphorus, and total N appear similar to laboratory methods, but there are issues regarding, for example, sample heterogeneity, moisture content, and surface roughness. More precise and detailed soil property estimation will facilitate future soil management.