Publications
NIBIOs employees contribute to several hundred scientific articles and research reports every year. You can browse or search in our collection which contains references and links to these publications as well as other research and dissemination activities. The collection is continously updated with new and historical material.
2023
Authors
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. ZohnerAbstract
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.
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Authors
Noemi Tocci Gian Marco Riccio Abirami Ramu Ganesan Philipp Hoellrigl Peter Robatscher Lorenza ConternoAbstract
No abstract has been registered
Abstract
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.
Authors
Amos Samkumar Rajan Premkumar Katja Hannele Karppinen Inger Martinussen Richard V. Espley Laura Elina JaakolaAbstract
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Authors
Heidi Udnes Aamot Hesam Mousavi Jafar Razzaghian Guro Brodal Michael Sulyok Rudolf Krska Simon G. Edwards Ingerd Skow HofgaardAbstract
In Norway, high levels of mycotoxins are occasionally observed in oat grain lots, and this cause problems for growers, livestock producers and the food and feed industries. Mycotoxins of primary concern are deoxynivalenol (DON) produced by Fusarium graminearum and HT2- and T2-toxins (HT2+T2) produced by Fusarium langsethiae. Although effort has been made to understand the epidemiology of F. langsethiae in oats, this is still not fully understood. In the present study, we aimed to increase our understanding of the F. langsethiae – oat interaction. Resistance to F. langsethiae was studied in three oat varieties after inoculation at early (booting, heading, flowering) or late (flowering, milk, dough) growth stages in greenhouse experiments. The oat varieties had previously shown different levels of resistance to F. graminearum: Odal, Vinger (both moderately resistant), and Belinda (susceptible). The levels of F. langsethiae DNA and HT2+T2 in harvested grain were measured, and differences in aggressiveness (measured as the level of F. langsethiae DNA in grain) between F. langsethiae isolates were observed. Substantial levels of F. langsethiae DNA and HT2+T2 were detected in grain harvested from oats that had been spray-inoculated at heading or later growth stages, suggesting that oats are susceptible to F. langsethiae from heading and onwards. Vinger had a moderate resistance to F. langsethiae/HT2+T2, whereas Odal and Belinda were relatively susceptible. We observed that late inoculations resulted in relatively higher levels of trichothecene A metabolites other than HT2+T2 (mostly glycosylated HT-2, and smaller amounts of some other metabolites) in harvested grain, which indicate that infections close to harvest may pose a further risk to food and feed safety.
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No abstract has been registered
Authors
Hadush Tsehaye Beyene Leif Sundheim Arne Tronsmo May Bente Brurberg Dereje Assefa Anne Marte TronsmoAbstract
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Material exiting the harvester is composed of chaff and straw. Chaff is a by-product of grain harvest comprises weed seeds and husk. Harvest Weed Seed Control (HWSC) systems aim at collecting and/or killing weed seeds in the chaff fraction during crop grain harvest. If chaff is removed or processed via impact mills or concentrated in a narrow zone in the field and collected, the overall weed infestation may be reduced in the following years. Chaff may be used as a new biomass feedstock, for example, as a renewable energy source, material for construction ( e.g. , insulating boards, cardboard, bedding), soil improvement ( e.g ., mulch, mushroom compost), and for agricultural purposes ( e.g. , weed growth inhibitor, animal diet). Using chaff directly is unfavorable because of its low bulk density. Therefore, compressing chaff into pellets can improve its handling. In this preliminary study, we assessed how pelletizing would affect the germinability of weed seeds in the chaff pellets. Whole wheat chaff and fine wheat chaff sieved were mixed with seeds of the two weed species scentless mayweed ( Tripleurospermum inodorum (L.) Sch.Bip.) and cornflower ( Centaurea cyanus L.), respectively. While 22% of T. inodorum seeds and 59% of C. cyanus seeds in wheat chaff samples were able to germinate, no weed seeds germinated from moist pelletized original and fine wheat chaff samples. The study indicates a low risk of spreading weed seeds with pelletized chaff probably because the heating during the pelletizing process kills the weed seeds.