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
Alexey Mikaberidze C. D. Cruz Ayalsew Zerihun Abel Barreto Pieter Beck Rocío Calderón Carlos Camino Rebecca E. Campbell Stephanie Delalieux Frederic Fabre Elin Falla Stuart Fraser Kaitlin Gold Carlos Gongora-Canul Frédéric Hamelin Dalphy Ondine Camira Harteveld Cheng-Fang Hong Melen Leclerc Da-Young Lee Murillo Lobo Jr Anne-Katrin Mahlein Emily McLay Paul Melloy Stephen Parnell Uwe Rascher Jack Rich Irene Salotti Samuel Soubeyrand Susan Sprague Antony Surano Sandhya Takooree Thomas H. Taylor Suzanne Touzeau Pablo Zarco-Tejada Nik CunniffeSammendrag
Plant diseases impair the yield and quality of crops and threaten the health of natural plant communities. Epidemiological models can predict disease and inform management. However, data are scarce, because traditional methods to measure plant diseases are resource intensive, which often limits model performance. Optical sensing offers a methodology to acquire detailed data on plant diseases across various spatial and temporal scales. Key technologies include multispectral, hyperspectral, and thermal imaging, as well as light detection and ranging; the associated sensors can be installed on ground-based platforms, uncrewed aerial vehicles, airplanes, and satellites. However, despite enormous potential for synergy, optical sensing and epidemiological modeling have rarely been integrated. To address this gap, we first review the state of the art to develop a common language accessible to both research communities. We then explore the opportunities and challenges in combining optical sensing with epidemiological modeling. We discuss how optical sensing can inform epidemiological modeling by improving model selection and parameterization and providing accurate maps of host plants. Epidemiological modeling can inform optical sensing by boosting measurement accuracy, improving data interpretation, and optimizing sensor deployment. We consider outstanding challenges in (A) identifying particular diseases; (B) data availability, quality, and resolution; (C) linking optical sensing and epidemiological modeling; and (D) emerging diseases. We conclude with recommendations to motivate and shape research and practice in both fields. Among other suggestions, we propose standardizing methods and protocols for optical sensing of plant health and developing open access databases including both optical sensing data and epidemiological models to foster cross-disciplinary work.
Forfattere
Tobias Modrow Konstantin Ziegler Patrick L. Pyttel Christian Kuehne Ulrich Kohnle Jürgen BauhusSammendrag
Naturally regenerated seedlings of Quercus petraea (Mattuschka) Liebl. are often outcompeted by tree species such as Fagus sylvatica L. and Carpinus betulus L., and understorey species like Rubus subg. Rubus. Since plant growth is fundamentally driven by photosynthetic capacity and efficiency, the competitive dynamics between species are influenced by their ability to adapt to varying light conditions through morphological and physiological plasticity. To explore these adaptations, we measured a number of variables indicating growth performance or potential of 60 12-year-old seedlings of Q. petraea, F. sylvatica, and C. betulus as well as individuals of R. subg. Rubus along a gradient of canopy openness and thus radiation. These variables included: a) key leaf traits, including specific leaf area (SLA) and leaf nitrogen (N) content, b) different photosynthesis measurements under constant and fluctuating light, and c) annual shoot length, total height and root collar diameter. Solar radiation was quantified as total site factor (TSF). In all four species, an increase in leaf N content was observed with increasing TSF, which was accompanied by an increase in maximum photosynthetic rate (A ) and growth. However, while this increase was continuous in C. betulus and R. subg. Rubus, a significant increase in A max and growth in Q. petraea and F. sylvatica occurred only in the radiation ranges between 1 % and 20 % and 50–70 % TSF. Measurements of photosynthesis in relation to simulated lightflecks suggest that leaves of Q. petraea are better adapted to prolonged high photosynthetic photon flux density (PPFD) exposure than to fluctuating light. Under these light conditions, especially at TSF levels > 60 %, Q. petraea showed a higher photosynthetic performance than F. sylvatica and C. betulus, in addition to comparable diameter and height growth. To promote Q. petraea regeneration against F. sylvatica and C. betulus competition and reduce necessary vegetation control interventions, we recommend radiation levels > 60 % TSF after the initial establishment phase, when oak seedlings have reached a height of about 0.8 m.
Sammendrag
Single-class object detection, which focuses on identifying, counting, and tracking a specific animal species, plays a vital role in optimizing farm operations. However, dense occlusion among individuals in group activity scenarios remains a major challenge. To address this, we propose YOLO-SDD, a dense detection network designed for single-class densely populated scenarios. First, we introduce a Wavelet-Enhanced Convolution (WEConv) to improve feature extraction under dense occlusion. Following this, we propose an occlusion perception attention mechanism (OPAM), which further enhances the model’s ability to recognize occluded targets by simultaneously leveraging low-level detailed features and high-level semantic features, helping the model better handle occlusion scenarios. Lastly, a Lightweight Shared Head (LS Head) is incorporated and specifically optimized for single-class dense detection tasks, enhancing efficiency while maintaining high detection accuracy. Experimental results on the ChickenFlow dataset, which we developed specifically for broiler detection, show that the n, s, and m variants of YOLO-SDD achieve AP50:95 improvements of 2.18%, 2.13%, and 1.62% over YOLOv8n, YOLOv8s, and YOLOv8m, respectively. In addition, our model surpasses the detection performance of the latest real-time detector, YOLOv11. YOLO-SDD also achieves state-of-the-art performance on the publicly available GooseDetect and SheepCounter datasets, confirming its superior detection capability in crowded livestock settings. YOLO-SDD’s high efficiency enables automated livestock tracking and counting in dense conditions, providing a robust solution for precision livestock farming.
Forfattere
Frank Thomas Ndjomatchoua Richard Olaf James Hamilton Stutt Ritter Atoundem Guimapi Luca Rossini Christopher A GilliganSammendrag
Empirical field data and simulation models are often used separately to monitor and analyse the dynamics of insect pest populations over time. Greater insight may be achieved when field data are used directly to parametrize population dynamic models. In this paper, we use a differential evolution algorithm to integrate mechanistic physiologicalbased population models and monitoring data to estimate the population density and the physiological age of the first cohort at the start of the field monitoring. We introduce an ad hoc temperature-driven life-cycle model of Bemisia tabaci in conjunction with field monitoring data. The likely date of local whitefly invasion is estimated, with a subsequent improvement of the model’s predictive accuracy. The method allows computation of the likely date of the first field incursion by the pest and demonstrates that the initial physiological age somewhat neglected in prior studies can improve the accuracy of model simulations. Given the increasing availability of monitoring data and models describing terrestrial arthropods, the integration of monitoring data and simulation models to improve model prediction and pioneer invasion date estimate will lead to better decision-making in pest management.
Forfattere
Wiesław Olek Waldemar Perdoch Andreas Treu Jerzy Majka Łukasz Czajkowski Bartłomiej Mazela Jerzy WeresSammendrag
The interaction of cellulose paper with water is a major hindrance to its broader application. This study, which introduces a novel approach to understand water vapor difusion in both untreated and treated paper, aims to identify the difusion coefcient, a crucial property in improving the hydrophobicity of paper. The treatment process utilized an aqueous solution of starch or starch modifed with methyltrimethoxysilane (MTMS). While the initial sorption method is frequently used to determine the difusion coefcient, this study found that it could lead to signifcant errors due to the non-Fickian behavior exhibited by lignocellulosic materials. This behavior causes that the hygroscopic equilibrium is not instantly obtained by surface of paper. It also induces slowing down moisture difusion in its fnal stage due to molecular relaxation. For the frst time, the modifed convective boundary condition was introduced into the moisture difusion model in paper materials. The results from vapor sorption experiments demonstrated this non-Fickian behavior, particularly at high values of air relative humidity. The study also revealed that the commonly applied frst kind boundary condition is not applicable, even for thin paper samples, inhibiting the use of the initial sorption method for determining the difusion coefcient. While the treatment with starch and MTMS signifcantly improved the hydrophobic properties of paper, it didn’t alter substantially its hygroscopic properties, potentially due to not blocking active sorption sites of cellulose fbers. This research underscores the need for further investigation into the chemical modifcation of cellulose fbers to improve the hydrophobicity of paper.
Forfattere
Peter Waldner Katrin Meusburger Bruno de Vos Henning Meesenburg Kai Schwärzel Carmen Iacoban Zoran Galic Arne Verstraeten Andreas Schmitz Aldo Marchetto Nicholas Clarke Heleen Deroo Nathalie Cools Anne Thimonier Vera Fadrhonsovà Holger Sennhenn-Reulen Anna Andreetta Elena Vanguelova Antti-Jussi Lindroos Anita Zolles Tiina M. NieminenSammendrag
Det er ikke registrert sammendrag
Forfattere
Alexander Oliver Jüterbock Hin Hoarau Heemstra Karin Andrea Wigger Bernardo Duarte Christian Guido Bruckner Annelise Chapman Delin Duan Aschwin Engelen Clement Gauci Griffin Goldstein Hill Zi-Min Hu Prabhat Khanal Ananya Khatei Amy Leigh Mackintosh Heidi Meland Ricardo Melo Anne Margrete Leiros Nilsen Leonore Olsen Ralf Rautenberger Henning Reiss Jie ZhangSammendrag
How to build a sustainable seaweed industry is important in Europe’s quest to produce 8 million tons of seaweed by 2030. Based on interviews with industry representatives and an expert-workshop, we developed an interdisciplinary roadmap that addresses sustainable development holistically. We argue that sustainable practices must leverage synergies with existing industries (e.g. IMTA systems, offshore wind farms), as the industry develops beyond experimental cultivation towards economic viability.
Sammendrag
This article presents a novel, ultralight tree planting mechanism for use on an aerial vehicle. Current tree planting operations are typically performed manually, and existing automated solutions use large land-based vehicles or excavators which cause significant site damage and are limited to open, clear-cut plots. Our device uses a high-pressure compressed air power system and a novel double-telescoping design to achieve a weight of only 8 kg: well within the payload capacity of medium to large drones. This article describes the functionality and key components of the device and validates its feasibility through experimental testing. We propose this mechanism as a cost-effective, highly scalable solution that avoids ground damage, produces minimal emissions, and can operate equally well on open clear-cut sites as in denser, selectively-harvested forests.
Forfattere
Lucas K. Johnson Zhiqiang Yang Angela Erb Ryan Bright Grant M. Domke Tracey S. Frescino Crystal B. Schaaf Sean P. HealeySammendrag
Reforestation is generally regarded as having the most substantial climate mitigation potential among a suite of available natural climate solutions which have focused almost exclusively on the benefits of carbon sequestration and storage. However, these reforestation studies have not accounted for the adverse warming impacts resulting from corresponding surface albedo change. A newly available dataset developed with albedo imagery from the Landsat 8 satellite analyzed at field plots from the United States (US) Forest Inventory and Analysis (FIA) program provides non-soil carbon stocks and corresponding carbon-equivalent albedo offsets for 30 distinct forest-type groups indexed by 10-year age bins. In this case study we leverage this new dataset in concert with FIA species distribution data to investigate reforestation scenario planning based on joint carbon-albedo estimates (non-soil carbon stock less a carbon-equivalent albedo offset) instead of just carbon storage estimates alone. Specifically, our analysis informs managers interested in planting optimal forest-type groups for climate change mitigation outcomes approaching the year 2050. We assist in one of the most fundamental steps in any reforestation project: deciding which forest type or tree species mix to plant. We present our results as forest-type group recommendations within 64,000 hectare hexagons as a means to offer localized guidance and to examine the spatial patterns of albedo impacts across the conterminous US. We found that albedo offsets were most impactful on decisions in the Northeastern regions of the US, where optimizing for joint carbon-albedo in the next 25-years implies planting deciduous forest-type groups (Maple/beech/birch) instead of otherwise carbon-optimal coniferous forest-type groups (White/red/jack pine). Although the consideration of albedo did not alter 25-year tree planting decisions in most of the US, it did reduce the expected climate benefit of reforestation in general. We provide a standalone application that ranks all forest-type groups detected by FIA within a given hexagon, allowing managers to evaluate alternatives in light of site-specific constraints. This paper describes a replicable case study for incorporating albedo offsets in reforestation plans. Similar analyses may be performed anywhere Landsat albedo data are available over adequate measurements of forest carbon stocks. Recommendations: • Albedo impacts on 25-year tree planting decisions are concentrated in the Northeastern regions of the United States, where considering albedo offsets together with carbon stocks implies planting the Maple/beech/birch forest-type group in place of the otherwise carbon-optimal White/red/jack pine group. • Our reforestation support application allows managers to explore localized forest-type group rankings on the basis of joint carbon-albedo benefits. • Fine-resolution albedo data, which is not currently a standard data product, provides more comprehensive support for reforestation projects intended to mitigate global climate change.
Forfattere
Andreas Hagenbo Lise Dalsgaard Marius Hauglin Stephanie Eisner Line Tau Strand O. Janne KjønaasSammendrag
Boreal forest soils are a critical terrestrial carbon (C) reservoir, with soil organic carbon (SOC) stocks playing a key role in global C cycling. In this study, we generated high-resolution (16 m) spatial predictions of SOC stocks in Norwegian forests for three depth intervals: (1) soil surface down to 100 cm depth, (2) forest floor (LFH layer), and (3) 0–30 cm into the mineral soil. Our predictions were based on legacy soil data collected between 1988 and 1992 from a subset (n = 1014) of National Forest Inventory plots. We used boosted regression tree models to generate SOC estimates, incorporating environmental predictors such as land cover, site moisture, climate, and remote sensing data. Based on the resulting maps, we estimate total SOC stocks of 1.57–1.87 Pg C down to 100 cm, with 0.55–0.66 Pg C stored in the LFH layer and 0.68–0.80 Pg C in the upper mineral soil. These correspond to average SOC densities of 15.3, 5.4, and 6.6 kg C m−2, respectively. We compared the predictive performance of these models with another set, supplemented by soil chemistry variables. These models showed higher predictive performance (R2 = 0.65–0.71) than those used for mapping (R2 = 0.44–0.58), suggesting that the mapping models did not fully capture environmental variability influencing SOC stock distributions. Within the spatial predictive models, Sentinel-2 Normalized Difference Vegetation Index, depth to water table, and slope contributed strongly, while soil nitrogen and manganese concentrations had major roles in models incorporating soil chemistry. Prediction uncertainties were related to soil depth, soil types, and geographical regions, and we compared the spatial prediction against external SOC data. The generated maps of this offer a valuable starting point for identifying forest areas in Norway where SOC may be vulnerable to climate warming and management-related disturbances, with implications for soil CO2 emissions.