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
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Lampros LamprinakisAbstract
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Habtamu AlemAbstract
The lecture provides practical guidance on research proposal development and international funding opportunities, with a particular focus on Horizon Europe. It covers how to identify suitable funding calls, understand call requirements and eligibility, develop competitive research ideas, formulate clear and measurable objectives, and design a sound research methodology. It also explains the main components of a Horizon Europe proposal, including Part A and Part B, and provides step-by-step guidance on writing the Excellence, Impact, and Implementation sections. The lecture further addresses international partnership and consortium building, work packages, deliverables, milestones, risks, staff effort, budget allocation, dissemination, exploitation, communication, and proposal evaluation criteria. Real and illustrative project examples, including collaborations between Norwegian and African universities, are used to connect proposal-writing principles with practical research funding experience.
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Habtamu AlemAbstract
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L. Llonch M. Ruiz-Colmenero Matthias Koesling M. Terré J. Cucurull M. Verdú C. Medinyà J. Riera S. Martí M DevantAbstract
Palm oil is a commonly used dietary fat source in European dairy beef production, but concerns about deforestation associated with its cultivation have stimulated interest in alternative fat sources with the potential to mitigate enteric methane emissions and reduce the carbon footprint of beef production. Oils richer in polyunsaturated fatty acids have been suggested as promising alternatives due to their potential to modify rumen fermentation. The present study aimed to evaluate the dietary substitution of palm oil with sunflower oil, which is rich in polyunsaturated fatty acids, on animal performance, enteric methane emissions, and environmental footprint. Ninety-four crossbred bulls (399 ± 9.3 kg BW and 271 ± 4.0 days of age) were allocated into six pens and fed concentrate and straw separately, both ad libitum. Treatments were based on the fat source of the concentrates: (1) CTR (n = 46), with 3.50% palm oil on a weight basis as control; and (2) SUN (n = 48), with 3.50% sunflower oil. The study lasted 42 days. Individual feed intake was recorded daily. Bulls were weighed every two weeks. Enteric emissions (CH4, CO2, and O2) were recorded daily with a “sniffer” measurement system installed in each concentrate feeder per pen. Daily CH4 production (L/day) was estimated based on Madsen et al. (2010). Data were analysed using a mixed-effects model. The environmental impact of the two diets was evaluated with the life cycle assessment methodology. No differences in average daily weight gain were observed between dietary oil sources (1.83 vs 1.85 ± 0.141 kg/day, P = 0.87; CTR vs SUN, respectively) and concentrate intake (7.48 vs 7.48 ± 0.420 kg DM/day, P = 1.00; CTR vs SUN). A treatment × period interaction (P < 0.05) with no clear pattern was observed in most of the enteric CH4 emissions data. Nevertheless, in the first 14 days of the study, enteric CH4 emission expressed relative to concentrate intake was lower (P < 0.01) in SUN bulls than in CTR bulls. Carbon footprint was lower in SUN concentrate (0.75 kg CO2 eq/kg concentrate feed) compared with the CTR concentrate (0.89 kg CO2 eq/kg concentrate feed). Thus, the substitution of palm oil with sunflower oil reduces enteric methane emissions expressed relative to concentrate intake during the first days after dietary substitution without detrimental effects on animal performance and reduces the carbon footprint of the dietary concentrate, which is the main contributor to meat’s carbon footprint.
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Giovanna Ottaviani AalmoAbstract
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María Bustamante Liria Concetta Maria Messina Simona Manuguerra Andrea Santulli Giovanna Ottaviani AalmoAbstract
Despite significant progress in understanding the complexity of food systems’ challenges, there remains a lack of clarity around how these systems can undergo transformative change and what is required to enable such shifts. This paper focuses on social innovation as a promising mechanism for addressing structural challenges and reconfiguring food systems toward sustainability, equity, justice, and resilience. Our main contribution is the design of an actionable guidance that helps practitioners and researchers explore and leverage the transformative potential of social innovations. This guidance emerges from a dialogue between a key framework for analyzing social-ecological transformations and recent advancements in Transformative Social Innovation (TSI) theory. By incorporating insights on adaptive, bottom-linked governance and relational dynamics into the SES processual framework, we strengthen its applicability to TSI analysis and make it more adaptable for real-world contexts. We apply this guidance to a case study in Western Sicily’s aquatic food systems, where a long-standing social innovation – rooted in Living Lab methodologies and collaborative governance – demonstrates the potential to challenge dominant values by reconfiguring dominant social relations, empower local actors, and engage with both deep and shallow leverage points, contributing to systemic transformations. By combining cross-field dialogue with empirical application, this paper offers both practical tools for analyzing and designing transformative social innovation processes and practical insights into how social innovations can contribute to more sustainable, resilient, and equitable futures. It directly speaks to practitioners, scholars, and policy makers interested in enabling systemic transformation through socially embedded innovation processes.
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A. Terskaia R. Oucheikh Junxiang Peng J. Oliveira D. Parsons J. Morel A.M. Abdi Z. Cai S. Thapa N. Boke-Olen P. Olsson H.G. Smith J. Ekroos L. EklundhAbstract
Grassland management practices are critical both for maintaining resilient food production and for preserving biodiversity, yet spatially explicit information on them is often limited. Recent advances in freely available, remotely sensed land surface phenology and productivity datasets raise the question of whether phenological metrics provide additional, biologically interpretable information for characterising and mapping grassland management beyond conventional spectral features. In this study, we evaluated the information content and added value of freely available, ready-to-use Sentinel-2–derived High-Resolution Vegetation Phenology and Productivity (HR-VPP) data for distinguishing broad grassland management types (conventional leys, organic leys, and semi-natural grasslands) across multiple spatial scales. We analyzed 1455 grasslands across the intensively farmed Skåne province (southern Sweden) over 2018–2022 at provincial and local scales. We further tested whether phenological variables improve grassland management classification when combined with multi-temporal Sentinel-2 spectral data using state-of-the-art machine learning (ML) methods, and applied a conformal prediction framework to manage classification uncertainty. Relationships between HR-VPP metrics and field-measured biomass were additionally assessed using independent field data from northern Sweden. Field-level analyses showed weak (R2 = 0.28) to moderate (R2 = 0.47) relationships between HR-VPP productivity metrics and measured biomass. HR-VPP metrics captured statistically significant but subtle differences among management types, with strong overlap between categories, particularly at the provincial scale. Differences were more pronounced at local scales. Consequently, HR-VPP variables alone had limited predictive power and ML model accuracy was low (0.53). However, combining HR-VPP with Sentinel-2 spectral time-series data substantially improved classification performance, achieving an accuracy of 0.70. Feature importance analysis indicated that spectral indices dominated classification, with limited direct contribution from phenological parameters. These findings highlight both the potential and current limitations of continental-scale phenology and productivity products for grassland management monitoring and point to the need for improved models and complementary data sources to better capture management practices.