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
Gunnhild JaastadSammendrag
Det er ikke registrert sammendrag
Sammendrag
Since the 1950s, the use of plastics in agriculture has helped solving many challenges related to food production, while its persistence and mismanagement has led to the plastic pollution we face today. A variety of biodegradable plastic products have thus been marketed, with the aim to solve plastic pollution through complete degradation after use. But the environmental conditions for rapid and complete degradation are not necessarily fulfilled, and the possibility that biodegradable plastics may also contribute to plastic pollution must be evaluated. A two-year field experiment with biodegradable mulches (BDMs) based on polybutylene adipate terephthalate (PBAT/starch and PBAT/polylactic acid) buried in several agricultural soils in mesh bags showed that also under colder climatic conditions does degradation occur, involving fragmentation after two months and depolymerization by hydrolysis, as shown by Fourier-transform infrared spectroscopy. The phytopathogenic fungus Rhizoctonia solani was found to be associated with BDM degradation, and the formation of biodegradable microplastics was observed throughout the experimental period. Between 52 and 93 % of the original BDM mass was recovered after two years, suggesting that accumulation is likely to happen in cold climatic regions when BDM is repeatedly used every year. Mass loss followed negative quadratic functions, implying increasing mass loss rates over time. Despite the range of climatic and edaphic factors, with various agricultural practices and vegetable productions at the study locations, the parameters that significantly favored in situ BDM degradation were higher soil organic matter content and temperatures.
Forfattere
Payel Bhattacharjee Mari Talgø Syvertsen Igor A. Yakovlev Marcos Viejo Somoano Torgeir Rhoden Hvidsten Jorunn Elisabeth Olsen Carl Gunnar FossdalSammendrag
Det er ikke registrert sammendrag
Forfattere
Payel Bhattacharjee Mari Talgø Syvertsen Igor A. Yakovlev Marcos Viejo Torgeir Rhoden Hvidsten Mallikarjuna Rao Kovi Carl Gunnar Fossdal Jorunn Elisabeth OlsenSammendrag
Det er ikke registrert sammendrag
Forfattere
Payel Bhattacharjee Mari Talgø Syvertsen Igor A. Yakovlev Torgeir Rhoden Hvidsten Torstein Tengs Mallikarjuna Rao Kovi Marcos Viejo Carl Gunnar Fossdal Jorunn Elisabeth OlsenSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
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.
Forfattere
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 AtzmuellerSammendrag
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.
Forfattere
Andres Perea Sajidur Rahman Huiying Chen Andrew Cox Shelemia Nyamuryekung'e Mehmet Bakir Huiping Cao Richard Estell Brandon Bestelmeyer Andres F. Cibils Santiago A. UtsumiSammendrag
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].