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
Abstract
No abstract has been registered
Abstract
The soil-borne oomycete Phytophthora cactorum causes crown rot, a major disease of the allo-octoploid strawberry (Fragaria × ananassa Duch., 2n = 8× = 56) that limits cultivation worldwide. Resistance to P. cactorum is a highly desirable trait but is typically quantitative and moderately heritable. A better understanding of the genetic basis of resistance to crown rot is essential for developing durable crown rot-resistant cultivars. We conducted a genome-wide association study (GWAS) using multi-locus models on 100 wild strawberry accessions from South and North America. The accessions were genotyped using the Axiom™ 50 K strawberry SNP array and mapped to the F. × ananassa cv. Royal Royce v. 1.0 reference genome. Testing for resistance to P. cactorum revealed a wide range of phenotypes. A single genetic marker, AX-184528282, located on chromosome 7B, was strongly associated with resistance to P. cactorum and explained 53% of the observed phenotypic variation. This marker was present in several highly resistant exotic Fragaria accessions that represent potential donors for introgression of favorable alleles into modern strawberry cultivars. In addition, several strong candidate resistance genes were identified within the 2 Mb genomic region surrounding the significant marker. This study advances understanding of resistance to P. cactorum in strawberry and identifies genetic resources that can accelerate the development of crown rot-resistant cultivars through marker-assisted breeding.
Abstract
No abstract has been registered
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
Authors
Mikarlsen, NoraAbstract
Pest control is a central part of modern strawberry farming. Spider mites are one of the most common pests in strawberries, and can cause significant reduction in yield. In order to properly manage and control spider mite populations, early detection is crucial. This thesis sets out to detect two-spotted spider mites (TSSM) in strawberries using hyperspectral imaging (HSI). A variety of methods have explored including visual inspection of the spectrum and its derivatives, as well as the use of vegetation indices (VIs). In addition, this thesis also explores machine learning (ML) and deep learning (DL) for early detection of TSSM. The mean spectrum from the images was used for classification in combination with Linear Discriminant Analysis (LDA) and Random Forest Classifier. Two separate Random Forest models were trained, one that distinguished between control, drought, and mite-infested strawberry plants, and one five-class with three different infestation levels, in addition to control and drought group. The three-class model achieved an F1-score of 0.86, while the five-class model had an F1-score of 0.845. The images themselves were used for classification by a ResNet18 model. The model was trained for each imaging day separately, and achieved accuracies in the range of 0.7-0.9 and F1-scores between 0.709-0.903. The work presented in this thesis highlights the capabilities of HSI in combination with ML and DL for early detection of TSSM in strawberries.
Authors
Hannah Rivedal Todd N. Temple Robert J. Starchvick Joseph P. Gallagher Dustin W. Herb Jason Crisp Nicole Anderson Tatiana Benedetti Amy B. PeetzAbstract
Oregon’s grass seed industry specialises in producing forage grasses including annual ryegrass (ARG, Lolium multiflorum), a host for the seed gall nematode (SGN, Anguina funesta). SGN causes yieldlimiting seed galls and are strictly regulated in international trade. From 2019 to 2020, over 500 metric tons of Oregon ARG seed were rejected from international ports due to SGN detection. A 2022 field survey of 22 ARG fields in the Willamette Valley of Oregon resulted in SGN detection in 50% of the fields throughout the growing season. Several approaches managing SGN are under evaluation. Previous reports indicate that there may be genetic resistance to SGN in other Lolium species. Therefore, a breeding population of 240 public accessions of L. multiflorum have been seeded with two seed galls and planted in the field. Seed were harvested to evaluate for galls in July 2025 and to identify potential resistant families for future study. To date, no nematicides are labelled for the control of SGN. Varied fluopyram timings and rates, as well as an untreated control, are being evaluated in the field with and without growth regulation for SGN control. Seed yield and galled seed data was collected showing limited differences between treatments. Cultural control methods are also being considered, including seed cleaning and utilizing high energy pulses on seed galls. Preliminary data suggests that these could be viable treatments to reduce SGN inoculum. Successful control options for the SGN in ARG seed production are important to reduce the spread of this nematode globally and maintain healthy forage production.
Authors
Seth Dorman Darrin Walenta Navneet Kaur Hannah Rivedal Nicole AndersonAbstract
Successful mitigation of agricultural insect pests depends on integrated pest management practices incorporating multiple techniques for effective population management below economically damaging levels. Pest surveillance remains the cornerstone of IPM programs, enabling appropriately timed management action. Phenological models using weather data are commonly used as decision support tools to predict the timing of ‘when’ economically important life stages are expected to occur. Furthermore, geostatistical models that consider landscape-level variation in environmental drivers of pest densities may inform ‘where’ outbreaks are expected to occur along spatial gradients of abiotic and biotic risk factors. Thus, predictors of pest populations can be leveraged to generate spatiotemporal risk assessments. Migratory and overwintering moth species in the family Noctuidae (order Lepidoptera) comprise a complex of serious pests that threaten the profitability of grasses grown for seed in the Willamette Valley, Oregon, USA, as well as seed production globally. Noctuid pests, including black cutworm, true armyworm, and winter cutworm, inflict crop damage by direct feeding on plant crowns and roots in the larval developmental stage. Management action with foliar insecticides is most effective when larvae are immature (early instar stage) due to insecticide susceptibility and pest behaviour. For noctuid pests (and other priority pests) in grass seed systems, real-time phenological models can provide field practitioners with information to better allocate pest monitoring and management resources to reduce input costs.