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.
2026
Authors
Nhat Strøm-Andersen Julia Szulecka Markus M. Bugge Ellen-Marie ForsbergAbstract
The sustainability transitions literature suggests that individual firms struggle to move toward sustainability unless the broader socio-economic system also evolves. Despite firms' willingness to change, existing systemic challenges often impede their progress. This paper employs paradox theory to address this struggle and examines how firms balance economic and societal concerns in their transition from business thinking to sustainability thinking. Based on a qualitative case study of the food industry's collaboration initiatives on food waste reduction and prevention in Norway, the study identifies the systemic challenges and sustainability paradoxes that the industry faces. We find that the firms' efforts to reduce food waste collide with established food industry agreements, standards, business strategies, regulations, and agricultural policies, impeding a systemic and structural transformation of the industry. The paper discusses how the food industry may navigate these challenges collectively and draws implications for the sustainability transitions literature. Primarily, the conclusions signal a need for governance and incentive structures at the system level beyond the action space of individual firms, and secondarily, illustrate how such governance approaches to sustainability transitions are sector-specific and geographically embedded.
Abstract
No abstract has been registered
Authors
Dafni Foti Stephen Amiandamhen Eleni Voulgaridou Elias Voulgaridis Costas Passialis Stergios AdamopoulosAbstract
Abstract This study investigated the incorporation of various waste materials including wastepaper, Tetra Pak, wood chips and scrap tire fluff into flue gas desulfurization (FGD) gypsum and cement mortar matrices to produce sustainable composite materials. Four distinct composite types based on the waste materials were developed and evaluated for selected properties including thermal and acoustic insulation. The proportion of the waste materials was varied between 10 and 40 vol% of the base matrix. The compressive strength of the filled gypsum composites was in the range of 4.17–10.39 N/mm² while the pure gypsum was 11.38 N/mm². The addition of the wastes in gypsum composites reduced compressive strength by about 10% for the best recipe and as large as 60% for the worst combination. However, the measured strength still exceeds the strength of typical gypsum wallboard with a compressive strength of about 3–4 N/mm² for whole-board crushing tests and it is much lower for point loads. The normal-incidence sound absorption coefficient indicated that the waste-filled samples absorbed around 80% of the incident sound energy between 2000 and 3000 Hz, comparable to some commercial acoustic foams. The results highlight the potential of utilising these waste-based composites in environmentally friendly construction applications. Depending on the waste type and matrix used, the results revealed trade-offs between multi-functional performance and sustainability benefits.
Abstract
Potato field management in Europe is already optimized for high production and tuber quality; however, numerous environmental challenges remain if the industry is to achieve “green economy” targets, such as less resources utilized, and less nitrate leached to the environment. Strategic co-scheduling irrigation and nitrogen (N) fertilization might increase resource use efficiency while minimizing reactive losses such as nitrate leaching. This study aimed to quantify the combined effect of irrigation and N fertilization on potato production, growth, and resource use efficiencies. A field experiment was conducted from 2017 to 2019 on a coarse sandy soil in Denmark, with a drought event occurring in 2018. Full (Ifull, maximized), deficit (Idef, 70–80 % of Ifull) and low irrigation treatments (Ilow, minimized amount to keep crop survival), each under full (Nfull, maximized) and variable (Nvar, variable amount according to the crops’ needs) N fertilization were applied. The analyses results show that Ilow limited potato growth under a drought-heat event; otherwise, potato growth was comparable between Ifull and Idef treatments, with 31–32 % higher irrigation efficiency (IE) under Idef than under Ifull. Nitrate leaching was variable and not significantly different among the treatments, being in general 9–13 % lower under Idef in absolute terms than under Ifull. Unexpectedly, outcomes from Nvar were statistically lower compared to those from Nfull. Radiation use efficiencies (RUEs) from Ilow and Nvar were significantly lower than from Ifull and Idef (14–19 %), and from Nfull (9–11 %). N use efficiencies (NUE) were comparable between N fertilization treatments but significantly different among different irrigation treatments. Overall, this study confirms that Idef is the best irrigation strategy. Future efforts should focus on developing improved approaches for detecting in-season crop N status and further quantifying N requirements, as well as promoting the co-scheduled management of irrigation and N fertilization. Remote sensing approaches have great potential to assist with this.
2025
Abstract
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.
Authors
Xiaokai Chen Yuxin Miao Krzysztof Kusnierek Fenling Li Chao Wang Botai Shi Fei Wu Qingrui Chang Kang YuAbstract
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.
Abstract
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.
Authors
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 StenbergAbstract
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.
Abstract
No abstract has been registered
Abstract
Climate change and human activities are prone to cause the shrinkage of lakes and soil salinization in arid areas, thereby affecting regional ecological security. Biodiversity conservation and ecological restoration in shrinking lake areas have attracted more attention. We have investigated the changes in soil organic carbon (SOC) content and microbial community diversity under different vegetation restoration measures, such as the species of Carex, Salicornia, Tamarisk, reed, and grass restoration in the lakeshore of Dalinor lake in Inner Mongolia. Results showed that the soil pH and water-soluble salt content are relatively high in the Carex and Salicornia restoration areas compared to the bare land, and the changes in SOC and TN content are not significant. Still, the contents of AP (available phosphorus) and AK (available potassium) are significantly increased. For the Tamarisk, reed, and grass restoration areas, the level of soil salinization has significantly decreased. At the same time, the contents of SOC and TN are increased by 23.1% and 116.2% compared with the bare land, respectively. With the different vegetation restoration measures, soil microbial biomass carbon (MBC) content was, on average, 62.4% higher than that of bare land. The high-throughput sequencing data showed that different vegetation restoration measures have significantly changed the composition of soil bacterial communities, the alpha diversity indices of Chao1 and Shannon increased by 73.6% and 19.7%, respectively, and the abundance of microbial species related to soil carbon and nitrogen cycling also showed an enrichment trend. Taken together, our study, built on the joint efforts of Chinese and Norwegian partners, has provided valuable information for the future adaptive management of climate change risks and biodiversity conservation related to the shrinkage of lakes in arid areas.