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.
2025
Authors
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 CunniffeAbstract
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.
Abstract
No abstract has been registered
Authors
Stian FolvikAbstract
No abstract has been registered
Authors
Åshild Ergon Signe Bråtelund Randi Berland Frøseth Jon Arne Dieseth Anders Borgen Paolo AnnicchiaricoAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Sabine Huber Marie-Cecile Gruselle Katharina Keiblinger Ingrid Lubbers Sónia Rodrigues Hanne Ugstad Jannes Stolte Nafiseh Taghizadeh Kerman Frederik Bøe Franziska FischerAbstract
No abstract has been registered
Authors
Tobias Modrow Konstantin Ziegler Patrick L. Pyttel Christian Kuehne Ulrich Kohnle Jürgen BauhusAbstract
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.
Abstract
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.
Authors
Frank Thomas Ndjomatchoua Richard Olaf James Hamilton Stutt Ritter Atoundem Guimapi Luca Rossini Christopher A GilliganAbstract
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.
Authors
Wiesław Olek Waldemar Perdoch Andreas Treu Jerzy Majka Łukasz Czajkowski Bartłomiej Mazela Jerzy WeresAbstract
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.