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 Jorunn Elisabeth Olsen Carl Gunnar FossdalAbstract
No abstract has been registered
Authors
Payel Bhattacharjee Mari Talgø Syvertsen Igor A. Yakovlev Marcos Viejo Torgeir Rhoden Hvidsten Mallikarjuna Rao Kovi Carl Gunnar Fossdal Jorunn Elisabeth OlsenAbstract
No abstract has been registered
Authors
Payel Bhattacharjee Mari Talgø Syvertsen Igor A. Yakovlev Torgeir Rhoden Hvidsten Torstein Tengs Mallikarjuna Rao Kovi Marcos Viejo Carl Gunnar Fossdal Jorunn Elisabeth OlsenAbstract
No abstract has been registered
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No abstract has been registered
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No abstract has been registered
Abstract
Urban agriculture (UA) is increasingly recognized as a key component of sustainable cities. Commercial farmers in urban areas benefit from a large customer base, short transport distances, and access to diverse sales channels. However, high pressure on land resources makes it difficult for farmers and decision makers to find suitable areas for UA. This study ranks urban and peri-urban farmland areas based on their suitability for urban agriculture (UA) and identifies opportunities for extending the area for UA to currently unused farmland. Through collaboration with urban farmers, we identified four key themes and eleven criteria, which were weighted for two sales scenarios: on-farm and off-farm. We performed a GIS-based multi-criteria decision analysis (MCDA) and assessed suitability using the technique of order preference similarity to the ideal solution (TOPSIS) on 1 × 1 km grid cells. By overlaying the suitability maps with presumably unused farmland (PUF), we identified areas with high potential for extending UA. In the City of Bergen, 15.3 % (on-farm; off-farm=14 %) of the total farmland is both unused and highly suitable for UA, compared to only 2.8 % (on-farm; off-farm=2.4 %) in Oslo. Assessing the suitability of agricultural land for UA can support spatial planning, protect agricultural topsoil from urban expansion, and help achieve global, national, and local goals for urban farming and sustainable land use.
Authors
Jonas Schmidinger Sebastian Vogel Viacheslav Barkov Anh-Duy Pham Robin Gebbers Hamed Tavakoli Jose Correa Tiago R. Tavares Patrick Filippi Edward J. Jones Vojtech Lukas Eric Boenecke Joerg Ruehlmann Ingmar Schroeter Eckart Kramer Stefan Paetzold Masakazu Kodaira Alexandre M.J.-C. Wadoux Luca Bragazza Konrad Metzger Jingyi Huang Domingos S.M. Valente Jose L. Safanelli Eduardo L. Bottega Ricardo S.D. Dalmolin Csilla Farkas Alexander Steiger Taciara Z. Horst Leonardo Ramirez-Lopez Thomas Scholten Felix Stumpf Pablo Rosso Marcelo M. Costa Rodrigo S. Zandonadi Johanna Wetterlind Martin AtzmuellerAbstract
Digital soil mapping (DSM) relies on a broad pool of statistical methods, yet determining the optimal method for a given context remains challenging and contentious. Benchmarking studies on multiple datasets are needed to reveal strengths and limitations of commonly used methods. Existing DSM studies usually rely on a single dataset with restricted access, leading to incomplete and potentially misleading conclusions. To address these issues, we introduce an open-access dataset collection called Precision Liming Soil Datasets (LimeSoDa). LimeSoDa consists of 31 field- and farm-scale datasets from various countries. Each dataset has three target soil properties: (1) soil organic matter or soil organic carbon, (2) clay content and (3) pH, alongside a set of features. Features are dataset-specific and were obtained by optical spectroscopy, proximal- and remote soil sensing. All datasets were aligned to a tabular format and are ready-to-use for modeling. We demonstrated the use of LimeSoDa for benchmarking by comparing the predictive performance of four learning algorithms across all datasets. This comparison included multiple linear regression (MLR), support vector regression (SVR), categorical boosting (CatBoost) and random forest (RF). The results showed that although no single algorithm was universally superior, certain algorithms performed better in specific contexts. MLR and SVR performed better on high-dimensional spectral datasets, likely due to better compatibility with principal components. In contrast, CatBoost and RF exhibited considerably better performances when applied to datasets with a moderate number (<20) of features. These benchmarking results illustrate that the performance of statistical methods can be highly context-dependent. LimeSoDa therefore provides an important resource for improving the development and evaluation of statistical methods in DSM.
Authors
Andres Perea Sajidur Rahman Huiying Chen Andrew Cox Shelemia Nyamuryekung'e Mehmet Bakir Huiping Cao Richard Estell Brandon Bestelmeyer Andres F. Cibils Santiago A. UtsumiAbstract
Monitoring cattle on large, often rugged, rangelands is a daunting task that can be improved using Long Range Wide Area Network (LoRaWAN) tracking and monitoring technology. This study tested the performance of five machine learning classifiers to discriminate between active and stationary states, and among walking, grazing, ruminating and resting behaviors of cattle. Models were trained and tested using a single motion index (MI) collected at 1-minute intervals by LoRaWAN cattle collars equipped with a Global Navigation Satellite System (GNSS) receptor and triaxial accelerometer. Twenty-four mature cows of four breeds were monitored across four periods between July and November 2022. Behavioral observations were made using 168 h of video records, which resulted in a dataset of 9222 instances of labeled sensor data. Logistic regression, support vector machine, multilayer perceptron, XGBoost and random forest algorithms were trained and tested. No differences in MI were detected between ruminating and resting; therefore, subsequent model testing considered the combination of rumination and resting as one class. All classifiers correctly differentiated between the two states and among grazing, walking and resting behaviors with an accuracy and macro-F 1 scores of >0.95 and >0.90, respectively. The results suggest satisfactory application of trained models to monitor cattle behavior on desert rangeland. The annotated dataset used in this study is publicly available at Perea et al. [1].
Authors
Olle Anderbrant Hanh Huynh Ann-Kristin Isaksson Line Beate Lersveen Myhre Christer Löfstedt Sigrid Mogan Elisabeth Öberg Marja Rantanen Gunda Thöming Glenn P. SvenssonAbstract
Currant, and in particular blackcurrant, Ribes nigrum, is widely grown in Europe. It is the host of a number of pest insects, but their occurrence and the damage they cause vary geographically. In northern Europe, three lepidopteran species, the currant shoot borer (Lampronia capitella), the currant clearwing (Synanthedon tipuliformis), and the currant bud moth (Euhyponomeutoides albithoracellus), are particularly damaging and sometimes cause decreased plant vigour and drastic yield losses. With fewer insecticides approved for use and with an increased interest in organic production of currants, the need for alternative methods to control these moths is urgent. We here applied pheromone-based mating disruption in small and sometimes well isolated plantations in Finland, Norway and Sweden against the three pests using 15–25 g of active ingredients and 300 dispensers per ha. A strong trap shutdown effect, up to 100%, was recorded for the currant clearwing and the currant bud moth, but no effect on the most widespread species, the currant shoot borer, was noted. After 1 year of treatment, however, it was not possible to detect any significant effect on the damage level or on the future adult population size of the pests. We conclude that for the currant clearwing and the currant bud moth, mating disruption is likely to work with higher pheromone doses or modified dispenser density, whereas the reason behind the lack of effect on the currant shoot borer needs to be addressed by new experiments and observations of behaviour.
Authors
Glenn P. Svensson Hanh Huynh Ann-Kristin Isaksson Line Beate Lersveen Myhre Christer Löfstedt Sigrid Mogan Elisabeth Öberg Marja Rantanen Nina Trandem Olle AnderbrantAbstract
The currant bud moth, Euhyponomeutoides albithoracellus, the currant shoot borer, Lampronia capitella and the currant clearwing, Synanthedon tipuliformis, are destructive pests on currants in the Nordic countries, but detailed information about their relative abundance in commercial crop fields is lacking. We used pheromone-baited monitoring traps to analyse the presence and flight period of the three species in 28 commercial black currant fields in Finland, Norway and Sweden during 4 years. We also estimated moth-induced damage in the same fields and analysed within- and between-generation relationships of catches and damage to find patterns to predict current and future pest pressures. At least two of the species were found at all sites. The shoot borer was the most widespread and abundant species, followed by the clearwing, which was relatively common at all sites except in northern Sweden, whereas the bud moth was not detected at all in Norway and southern Sweden. Geographic variation in flight phenology was observed for both the shoot borer and the clearwing. We found a significant positive correlation in all between-year analyses of damage and in most between-year analyses of catches, but a less consistent pattern when relating catches to damage within and between generations. Combining catch and damage data may be a useful tool to predict future overall infestation levels of the three pests in black currant fields in the Nordic countries.