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
Trond HaraldsenSammendrag
Catastrophic floods have large effect on agricultural land both in short and long term. In this chapter, examples of impact of floods of different size in cold regions with glaziers have been presented. The largest floods occur as combination of heavy rainfall and melting and snow and ice in the mountainous areas. Periods of waterlogging by cold running water resulted in decreased yields, but N-fertilization after the soil no longer was water saturated could reduce the yield loss considerably. Although the floods cause severe erosion and sedimentation, results show that it is possible to find measures for reconstruction of the soils with the same productivity as undamaged soils, while the average result was about 85% of the original productivity.
Sammendrag
After fungal decay experiments chemical characterisation of the wood is often a routine and several methodological approaches are available. In this study, we tested if simultaneous thermal analysis (STA) is a valid alternative to traditional wet chemical methods since STA allows significantly smaller sample size and faster analysis. Three model fungi including the brown rot fungi Rhodonia placenta and Gloeophyllum trabeum and the white rot fungus Trametes versicolor were employed in the study using Norway spruce as substrate. The experiment was harvested after 10, 20 and 52 weeks. At each harvest interval, aliquots of the material were characterized by STA and wet chemical methods. The results validated that STA can be effectively used to estimate cell wall composition of brown rot depolymerised wood. However, STA slightly overestimated cellulose at brown rot decay above 50%. The method was not verified for simultaneous white rot because STA only estimated hemicellulose correctly compared to the wet chemical method. Hence, STA is considered suitable for brown rot fungi below 50% mass loss but not for simultaneous white rot because STA did not estimate cellulose and lignin correctly.
Sammendrag
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Forfattere
Xiaoyan Ma Junhua Bao Jinwei Li Xi Cheng Muhammad Mobeen Tahir Meizi Liu Xian Lu Min-Rui Wang Zhibo Hamborg Dong ZhangSammendrag
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Forfattere
Tore Krogstad Valentina Zivanovic Aleksandar Simic Milica Fotiric Aksic Vlado Licina Mekjell MelandSammendrag
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Forfattere
Haozhi Ma Thomas W. Crowther Lidong Mo Daniel S. Maynard Susanne S. Renner Johan van den Hoogen Yibiao Zou Jingjing Liang Sergio de-Miguel Gert-Jan Nabuurs Peter B. Reich Ülo Niinemets 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 Francis Q. Brearley 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 Leena Finér Markus Fischer Christine Fletcher Jonas Fridman 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 Amaral Iêda 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 Dmitry Kucher 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 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 F. Piedade Daniel Piotto Nigel C. A. Pitman Irina Mendoza-Polo Axel Dalberg Poulsen John R. 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 Mart-Jan Schelhaas 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 Miścicki 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 Bertil Westerlund Susan K. Wiser Florian Wittmann Hannsjoerg Woell Verginia Wortel Roderick Zagt Tomasz Zawiła-Niedźwiecki Chunyu Zhang Xiuhai Zhao Mo Zhou Zhi-Xin Zhu Irie C. Zo-Bi Constantin M. ZohnerSammendrag
Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17–34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling.
Forfattere
Noemi Tocci Gian Marco Riccio Abirami Ramu Ganesan Philipp Hoellrigl Peter Robatscher Lorenza ConternoSammendrag
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Sammendrag
Weeds affect crop yield and quality due to competition for resources. In order to reduce the risk of yield losses due to weeds, herbicides or non-chemical measures are applied. Weeds, especially creeping perennial species, are generally distributed in patches within arable fields. Hence, instead of applying control measures uniformly, precision weeding or site-specific weed management (SSWM) is highly recommended. Unmanned aerial vehicle (UAV) imaging is known for wide area coverage and flexible operation frequency, making it a potential solution to generate weed maps at a reasonable cost. Efficient weed mapping algorithms need to be developed together with UAV imagery to facilitate SSWM. Different machine learning (ML) approaches have been developed for image-based weed mapping, either classical ML models or the more up-to-date deep learning (DL) models taking full advantage of parallel computation on a GPU (graphics processing unit). Attention-based transformer DL models, which have seen a recent boom, are expected to overtake classical convolutional neural network (CNN) DL models. This inspired us to develop a transformer DL model for segmenting weeds, cereal crops, and ‘other’ in low-resolution RGB UAV imagery (about 33 mm ground sampling distance, g.s.d.) captured after the cereal crop had turned yellow. Images were acquired during three years in 15 fields with three cereal species (Triticum aestivum, Hordeum vulgare, and Avena sativa) and various weed flora dominated by creeping perennials (mainly Cirsium arvense and Elymus repens). The performance of our transformer model, 1Dtransformer, was evaluated through comparison with a classical DL model, 1DCNN, and two classical ML methods, i.e., random forest (RF) and k-nearest neighbor (KNN). The transformer model showed the best performance with an overall accuracy of 98.694% on pixels set aside for validation. It also agreed best and relatively well with ground reference data on total weed coverage, R2 = 0.598. In this study, we showed the outstanding performance and robustness of a 1Dtransformer model for weed mapping based on UAV imagery for the first time. The model can be used to obtain weed maps in cereals fields known to be infested by perennial weeds. These maps can be used as basis for the generation of prescription maps for SSWM, either pre-harvest, post-harvest, or in the next crop, by applying herbicides or non-chemical measures.