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
Randi Berland FrøsethSammendrag
Resultater fra Capture-prosjektet
Sammendrag
The simultaneous improvement of crop yield and nitrogen (N) use efficiency (NUE) can be potentially achieved through precision N management. In this study, crop modeling, remote sensing, and machine learning were combined to develop and assess a precision N recommendation strategy (MSM-PNM) for maize (Zea mays L.) based on twenty-eight site-years of field experiments conducted in Northeast China involving various N rates and planting densities. For the MSM-PNM strategy, a crop growth model was used at the eight-leaf stage of maize to initially predict the in-season optimal side-dressing N rate (EOSN) by combining current season’s available weather data prior to side-dressing with weather data from previous years for the remaining growing period. Maize N status was then estimated using machine learning models based on data collected with an active canopy sensor along with weather, soil, and management information. Finally, the crop model predicted EOSN was adjusted using the estimated maize N status. The prediction accuracy of EOSN (R2 = 0.70, root mean squared error (RMSE) = 18.60 kg ha−1) based on this integrated MSM-PNM strategy was higher than using crop model only (R2 = 0.65, RMSE = 20.10 kg ha−1). The precision maize management system based on the integrated MSM-PNM strategy decreased N rates by 6–16 % and increased NUE by 8–18 % over farmer practice applying 250 kg N ha−1 as basal N fertilizer without side-dress N application and regional optimal management practice applying split N at fixed rate and timing, while maintaining high grain yield and marginal net return. It is concluded that this new integrated precision N management strategy combining the advantages of crop modeling, remote sensing, and machine learning can significantly increase maize NUE while maintaining high crop yield, thus contributing to food security and agricultural sustainability.
Forfattere
Xiaokai Chen Yuxin Miao Krzysztof Kusnierek Fenling Li Chao Wang Botai Shi Fei Wu Qingrui Chang Kang YuSammendrag
Timely and accurate monitoring of crop nitrogen (N) status is essential for precision agriculture. UAV-based hyperspectral remote sensing offers high-resolution data for estimating plant nitrogen concentration (PNC), but its cost and complexity limit large-scale application. This study compares the performance of UAV hyperspectral data (S185 sensor) with simulated multispectral data from DJI Phantom 4 Multispectral (P4M), PlanetScope (PS), and Sentinel-2A (S2) in estimating winter wheat PNC. Spectral data were collected across six growth stages over two seasons and resampled to match the spectral characteristics of the three multispectral sensors. Three variable selection strategies (one-dimensional (1D) spectral reflectance, optimized two-dimensional (2D), and three-dimensional (3D) spectral indices) were combined with Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), and Partial Least Squares Regression (PLSR) to build PNC prediction models. Results showed that, while hyperspectral data yielded slightly higher accuracy, optimized multispectral indices, particularly from PS and S2, achieved comparable performance. Among models, SVM and RFR showed consistent effectiveness across strategies. These findings highlight the potential of low-cost multispectral platforms for practical crop N monitoring. Future work should validate these models using real satellite imagery and explore multi-source data fusion with advanced learning algorithms.
Sammendrag
Interpreting multi-component 1H NMR spectra is difficult due to peak overlap, concentration variability, and low-abundance signals. We cast mixture identification as a single-pass multi-label task. A compact CNN–Transformer (“Hybrid”) model was trained end-to-end on domain-informed and realistically simulated spectra derived from a 13-component flavor library; the model requires no real mixtures for training. On 16 real formulations, the Hybrid attains micro-F1 = 0.990 and exact-match (subset) accuracy = 0.875, outperforming CNN-only and Transformer-only ablations, while remaining efficient (~0.47 M parameters; ~0.68 ms on GPU, V100). The approach supports abstention and shows robustness to simulated outsiders. Although the evaluation set was small, and the macro-ECE (per-class, 15 bins) was inflated by sparse classes (≈0.70), the micro-averaged Brier is low (0.0179), and temperature scaling had negligible effect (T ≈ 1.0), indicating the good overall probability quality. The pipeline is readily extensible to larger libraries and adjacent applications in food authenticity and targeted metabolomics. Classical chemometric baselines trained on simulation failed to transfer to real measurements (subset accuracy 0.00), while the Hybrid model maintained strong performance.
Forfattere
Annbjørg KristoffersenSammendrag
Det er ikke registrert sammendrag
Forfattere
Annbjørg KristoffersenSammendrag
Det er ikke registrert sammendrag
Forfattere
Jostein Beseth NordeideSammendrag
In this study, the modulus of resilience, modulus of elasticity and density of structural timber from Norway Spruce (Picea Abies) from Nordland in Norway were studied. The main objectives were to assess whether structural timber from Nordland meets the requirements specified in the Norwegian standard NS-EN 338 when graded by using visual grading according to the Norwegian standard NS-INSTA 142, and to examine the variations. Timber was collected from 45 trees from five stands in Nordland. The logs were sawn into 411 planks, which were visually strength-graded in accordance with Norwegian standard NS-INSTA 142, and density, modulus of elasticity and modulus of resilience were tested following the standard EN 408. The test results were adjusted in accordance with Norwegian standard NS-EN 384, and characteristic values were calculated in accordance with Norwegian standard NS-EN 14358. The study found that sorting class T1 meets the requirements for strength class C18, sorting class T2 meets the requirements for C24, and class “T2 and better” meets the requirements for C24. However, spruce graded as T3 did not meet the requirements for C30 given in Norwegian standard NS-EN 338. Statistical models were developed, showing that visual and position- related variables such as knot diameter, relative height within the tree and annual ring width can explain the mechanical properties. Some of the models use forest-, tree-, and log-specific variables, requiring traceability of timber from forest to sawmill for these models to be implemented in sorting at the sawmill. The average values for density, modulus of elasticity and modulus of resilience in this study were lower than those found in studies of spruce from southern Norway. Nevertheless, the spruce from this study meets the requirements up to and including strength class C24 when visually strength-graded according to Norwegian standard NS-INSTA 142, approving a potential for using spruce from Nordland as structural timber.
Forfattere
Ivan M De-la-Cruz Femke Batsleer Dries Bonte Carolina Diller José Luis Izquierdo Sonja Still Sonia Osorio David Posé Aurora de la Rosa Martijn L. Vandegehuchte Anne Muola Timo Hytönen Johan A StenbergSammendrag
Climate change creates novel environmental conditions that plant species must adapt to. Since plants are finely tuned to the seasonality of their environments, shifts in their phenology serve as some of the most compelling evidence of climate change’s impact. Understanding how key fitness-related phenological traits, such as flowering onset, respond to novel environments is crucial for assessing species’ plasticity and/or adaptive potential under climate change. Here, we investigated the onset of flowering in Fragaria vesca (woodland strawberry; Rosaceae) by translocating genotypes between four sites along a south–north gradient in Europe, encompassing its entire latitudinal distribution range with varying temperatures, precipitation patterns, and photoperiods. At each site, we included a reduced precipitation treatment using rainout shelters to simulate drought conditions and assess their impact on flowering onset. Our findings revealed that southern and central European genotypes exhibited a delayed onset of flowering when translocated to the northernmost site. In contrast, no difference among genotypes was found in the onset of flowering when grown in more southerly sites. Reduced precipitation accelerated flowering across several sites and all genotypes, irrespective of their latitudinal origin. Overall, northern European genotypes showed a greater capacity to adjust their onset of flowering in response to the different photoperiods and temperatures across the latitudinal gradient compared to southern European genotypes, suggesting that they may be more resilient to shifting environmental conditions. Differences in phenotypic plasticity among genotypes translocated to higher versus lower latitudes highlight the role of photoperiod in evaluating a species’ capacity to cope with climate change.
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag