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
Payel Bhattacharjee Mari Talgø Syvertsen Igor A. Yakovlev Marcos Viejo Somoano Torgeir Rhoden Hvidsten Mallikarjuna Rao Kovi Jorunn Elisabeth Olsen Carl Gunnar FossdalAbstract
No abstract has been registered
Authors
Payel Bhattacharjee Mari Talgø Syvertsen Igor A. Yakovlev Marcos Viejo Somoano Torgeir Rhoden Hvidsten Jorunn Elisabeth Olsen Carl Gunnar FossdalAbstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Frank Thomas Ndjomatchoua Richard Olaf James Hamilton Stutt Ritter Atoundem Guimapi Luca Rossini Christopher A GilliganAbstract
No abstract has been registered
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 physiological-based 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
Komi Mensah Agboka Elfatih M. Abdel-Rahman Daisy Salifu Brian Kanji Frank T. Ndjomatchoua Ritter Atoundem Guimapi Sunday Ekesi Landmann TobiasAbstract
This study introduces a hybrid approach that combines unsupervised self-organizing maps (SOM) with a supervised convolutional neural network (CNN) to enhance model accuracy in vector-borne disease modeling. We applied this method to predict insecticide resistance (IR) status in key malaria vectors across Africa. Our results show that the combined SOM/CNN approach is more robust than a standalone CNN model, achieving higher overall accuracy and Kappa scores among others. This confirms the potential of the SOM/CNN hybrid as an effective and reliable tool for improving model accuracy in public health applications. • The hybrid model, combining SOM and CNN, was implemented to predict IR status in malaria vectors, providing enhanced accuracy across various validation metrics. • Results indicate a notable improvement in robustness and predictive accuracy over traditional CNN models. • The combined SOM/CNN approach demonstrated higher Kappa scores and overall model accuracy.
Abstract
No abstract has been registered
Authors
Alexey Mikaberidze Christian Cruz Ayalsew Zerihun Abel Barreto Pieter S. Beck Rocío Calderón Carlos Camino Rebecca 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 Sarlotti Samuel Soubeyrand Susie Sprague Antony Surano Sandhya Takooree Thomas Taylor Suzanne Touzeau Pablo Zarco-Tejada Nik CunniffeAbstract
Plant diseases impair 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, since traditional methods to measure plant diseases are resource intensive and this 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, and light detection and ranging; the associated sensors can be installed on ground-based platforms, uncrewed aerial vehicles, aeroplanes and satellites. However, despite enormous potential for synergy, optical sensing and epidemiological modelling 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 modelling. We discuss how optical sensing can inform epidemiological modelling by improving model selection and parameterisation and providing accurate maps of host plants. Epidemiological modelling can inform optical sensing by boosting measurement accuracy, improving data interpretation and optimising sensor deployment. We consider outstanding challenges in: A) identifying particular diseases; B) data availability, quality and resolution, C) linking optical sensing and epidemiological modelling, and D) emerging diseases. We conclude with recommendations to motivate and shape research and practice in both fields. Among other suggestions, we propose to standardise methods and protocols for optical sensing of plant health and develop open access databases including both optical sensing data and epidemiological models to foster cross-disciplinary work.
Authors
Anne MuolaAbstract
No abstract has been registered
Authors
Ivan M. De-la-Cruz Femke Batsleer Dries Bonte Carolina Diller Timo Hytönen José Luis Izquierdo Sonia Osorio David Posé Aurora de la Rosa Martijn Lodewijk Vandegehuchte Anne Muola Johan A. StenbergAbstract
Background and Aims Climate change is causing increasing temperatures and drought, creating new environmental conditions, which species must cope with. Plant species can respond to these shifting environments by escaping to more favorable environments, undergoing adaptive evolution, or exhibiting phenotypic plasticity. In this study, we investigate genotype responses to variation in environmental conditions (genotype-by-environment interactions; G × E) over multiple years to gain insights into the plasticity and potential adaptive responses of plants to environmental changes in the face of climate change. Methods We reciprocally transplanted 16 European genotypes of Fragaria vesca (Rosaceae), the woodland strawberry, between four sites along a latitudinal gradient from 40°N (Spain) to 70°N (northern Finland). We examined G × E interactions in plant performance traits (fruit and stolon production and rosette size) under ambient weather conditions and a reduced precipitation treatment (as a proxy for drought), at these sites over two years. Key Results Our findings reveal signals of local adaptation for fruit production at the latitudinal extremes of F. vesca distribution. No clear signals of local adaptation for stolon production were detected. Genotypes from higher European latitudes were generally smaller than genotypes from lower latitudes across almost all sites, years and both treatments, indicating a strong genetic control of plant size in these genotypes. We found mixed responses to reduced precipitation: while several genotypes exhibited poorer performance under the reduced precipitation treatment across most sites and years, with the effect being most pronounced at the driest site, other genotypes responded to reduced precipitation by increasing fruit and/or stolon production and/or growing larger across most sites and years, particularly at the wettest site. Conclusions This study provides insights into the influence of different environments on plant performance at a continental scale. While woodland strawberry seems locally adapted in more extreme environments, reduced precipitation results in winners and losers among its genotypes. This may ultimately reduce genetic variation in the face of increasing drought frequency and severity, with implications for the species’ capacity to adapt.