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.
2024
Abstract
No abstract has been registered
Authors
Christian Wilhelm Mohr Johannes Breidenbach Gunnhild Søgaard Oliver Moen Snoksrud Rune EriksenAbstract
No abstract has been registered
Authors
Lingwei Dong Yuxin Miao Xinbing Wang Krzysztof Kusnierek Hainie Zha Min Pan William D. BatchelorAbstract
Efficient and accurate in-season diagnosis of crop nitrogen (N) status is crucially important for precision N management. The main objective of this study was to develop a strategy for in-season dynamic diagnosis of maize (Zea mays L.) N status across the growing season by integrating proximal sensing and crop growth modeling. In this study, we integrated plant N concentration (PNC) derived from leaf fluorescence sensor data and aboveground biomass (AGB) based on the best-performing spectral index calculated from active canopy reflectance sensor data with simulated PNC and AGB using a crop growth model, DSSAT-CERES-Maize, for dynamic in-season maize N status diagnosis across the growing season. The results confirmed the applicability of leaf fluorescence sensing for PNC estimation and active canopy reflectance sensing for AGB estimation, respectively. The calibrated DSSAT CERES-Maize model performed well for simulating AGB (R2 = 0.96), which could be used for calculating the N status indicator, N nutrition index (NNI). However, the model did not perform satisfactorily for PNC simulation, with significant discrepancies between the simulated and measured PNC values. The data integration method using both proximal sensing and crop growth modeling produced accurate predictions of NNI (R2 = 0.95) and N status diagnostic outcomes (Kappa statistics = 0.64) for key growth stages in this study and could be used to simulate maize N status across the growing season, showing the potential for in-season dynamic N status diagnosis and management decision support. More studies are needed to further improve this approach by multi-sensor and multi-source data fusion using machine learning models.
Abstract
Raspberry (Rubus idaeus L.) is susceptible to aphid-borne viruses. We studied the incidence of four of them – black raspberry necrosis virus (BRNV), raspberry leaf mottle virus (RLMV), raspberry vein chlorosis virus (RVCV), and Rubus yellow net virus (RYNV) – in raspberry plants and aphids in and around Norwegian raspberry crops for three years (2019, 2021, and 2022). Most of the samples were from symptomatic plants. Applying RT-PCR, 274 leaf samples and 107 aphid samples were analyzed. All four viruses were found, but BRNV dominated: it was detected in 93% of the 178 leaf samples with virus and was the only virus that occurred more frequently as a single infection than in co-infections with the other viruses. The old cv. Veten had the highest virus incidence (97%) among the sampled plants, followed by uncultivated raspberry in the boundary vegetation (82%). All aphids identified were Amphorophora idaei and Aphis idaei. BRNV and/or RLMV was detected in 27% of the aphid samples. Notably, BRNV was detected in 30% of A. idaei samples, a species not known as a BRNV vector. In subsequent transmission experiments we found that although A. idaei can acquire BRNV within one hour, it did not transmit the virus to healthy raspberry plants. In contrast, Am. idaei, a known BRNV vector, was able to acquire the virus within one minute and transmit it within one hour of inoculation. Our study will improve the identification and management of BRNV.
Authors
Darius KviklysAbstract
In the frame of EUFRIN apple rootstock trials, seven apple rootstocks are being tested for their resistance to ARD (apple replant disease) in several European countries. Current paper focus on the rootstock and soil type (ARD vs. fresh soil) effect on the accumulation of phenolic compounds in apple fruit. This research was performed at the Lithuanian trial site. Accumulation of phenolics compounds in fruit tissues was enhanced at replant soil. On the average of all rootstocks, total phenol content in fruit flesh increased by 25%, and in fruit peel by 31%. Hyperoside and rutin in fruit flesh and hyperoside, reynoutrin, phloridzin and procyanidin C1 were the most variable among detected phenolic compounds and their content in fruits from ARD soil was by 50 – 77 % higher than in fruits from the fresh soil. Content of (-) epicatechin in fruit flesh and (+) catechin and procyanidin B1 in fruit peel was similar in both ARD and fresh soil. Rootstock had a significant effect on the accumulation of phenolic compounds, but this effect was modified by soil conditions. Soil type had no effect on total phenol accumulation in fruits (flesh and peel) grown on Pajam 2 rootstock. Also, a stable phenol content in fruit flesh was on G.11 and M200 rootstocks, and in fruit peel on G.41. The highest increase of total phenol content at replant conditions was recorded on B.10 (by 66% in flesh and 60% in peel) and on G.935 (by 68% in flesh and 47% in peel) rootstocks.
Authors
Annika M. Felton Robert Spitzer David Raubenheimer Per-Ola Hedwall Adam Felton Ruth V. Nichols Brendan L. O'Connell Jonas Malmsten Erik Löfmarck Hilde Karine WamAbstract
Animals representing a wide range of taxonomic groups are known to select specific food combinations to achieve a nutritionally balanced diet. The nutrient balancing hypothesis suggests that, when given the opportunity, animals select foods to achieve a particular target nutrient balance, and that balancing occurs between meals and between days. For wild ruminants who inhabit landscapes dominated by human land use, nutritionally imbalanced diets can result from ingesting agricultural crops rich in starch and sugar (nonstructural carbohydrates [NCs]), which can be provided to them by people as supplementary feeds. Here, we test the nutrient balancing hypothesis by assessing potential effects that the ingestion of such crops by Alces alces (moose) may have on forage intake. We predicted that moose compensate for an imbalanced intake of excess NC by selecting tree forage with macro-nutritional content better suited for their rumen microbiome during wintertime. We applied DNA metabarcoding to identify plants in fecal and rumen content from the same moose during winter in Sweden. We found that the concentration of NC-rich crops in feces predicted the presence of Picea abies (Norway spruce) in rumen samples. The finding is consistent with the prediction that moose use tree forage as a nutritionally complementary resource to balance their intake of NC-rich foods, and that they ingested P. abies in particular (normally a forage rarely eaten by moose) because it was the most readily available tree. Our finding sheds new light on the foraging behavior of a model species in herbivore ecology, and on how habitat alterations by humans may change the behavior of wildlife.
Authors
de Díaz de Otálora B. Amon L. Balaine F. Dragoni F. Estellés G. Ragaglini M. Kieronczyk Grete H. M. Jørgensen Prado del PradoAbstract
CONTEXT European dairy cattle production systems (DPS) are facing multiple challenges that threaten their social, economic, and environmental sustainability. In this context, it is crucial to implement options to promote the reconnection between crop and livestock systems as a way to reduce emissions and enhance nutrient circularity. However, given the sector's diversity, the successful implementation of these options lacks an evaluation framework that jointly considers the climatic conditions, farm characteristics, manure management and mineral fertilisation practices of DPS across Europe. OBJECTIVE This study aims to develop a modelling and statistical framework to assess the effect of climatic conditions, farm characteristics, manure management and mineral fertilisation practices on the on-farm sources of greenhouse gas (GHG) emissions and nitrogen (N) losses from ten contrasting case studies for dairy production across Europe, identifying options for emissions mitigation and nutrient circularity. METHODS Using the SIMSDAIRY deterministic whole-farm modelling approach, we estimated the GHG emissions and N losses from the ten case studies. SIMSDAIRY captures the effect of different farm management choices and site-specific conditions on nutrient cycling and emissions from different components of a dairy farm. In addition, we applied the Factor Analysis for Mixed Data multivariate statistical approach to quantitative and qualitative variables and identified relationships among emissions, nutrient losses, and the particular characteristics of the case studies assessed. RESULTS AND CONCLUSIONS The results showed how intensive case study farms in temperate climates were associated with lower enteric emissions but higher emissions from manure management (e.g. housing). In contrast, semi-extensive case study farms in cooler climates exhibited higher N losses and GHG emissions, directly linked to increased mineral fertilisation, excreta during grazing, and slurry application using broadcast. Furthermore, the results indicated opportunities to improve nutrient circularity and crop-livestock integration by including high-quality forages instead of concentrates and substituting mineral fertilisers with organic fertilisers. SIGNIFICANCE The presented framework provides valuable insights for designing, implementing, and monitoring context-specific emission mitigation options and nutrient circularity practices. By combining whole-farm modelling approaches and multivariate statistical methods, we enhance the understanding of the interactions between sources of N losses and GHG emissions. We expect our findings to inform the adoption of emissions reduction and circularity practices by fostering the recoupling between crop and livestock systems.
Abstract
Climate change is and will continue to alter plant responses to their environment. This is especially prominent concerning the adaptive tracking in reproductive phenology. For wind pollinated plants, this will substantially influence their pollen seasonality, yet there are gaps in knowledge about how environmental variation influences pollen seasonality. To investigate this, we monitored daily atmospheric pollen concentrations of seven pollen types from ecologically, economically and allergenically important plants (alder, hazel, willow, birch, pine, grass and mugwort) in twelve Norwegian locations spanning the entire country for up to 28 years. Six daily meteorological variables (maximum temperature, precipitation, wind speed, relative humidity, solar radiation and atmospheric pressure) was obtained from the MET Nordic dataset with full data cover. The pollen seasonality was then modelled using four spatial, three temporal and the six meteorological variables in a generalized linear model approach with a negative binomial distribution to investigate how each variable group thematically and individually contribute to variation in pollen seasonality. We found that the full models explained the most variation, ranging from R2 = 20.3 % to 59.5 %. The models were also highly accurate, being able to predict 54.5 % to 99.1 % of daily pollen concentrations to within 20.1 pollen grains/m3. Overall, the temporal variables were able to explain more variation than spatial and meteorological variables for most pollen types. Month, altitude and maximum temperature were the most important single variables for each category. The importance of each variable could be traced back to their individual effects of reproductive phenology, plant metabolism, species distributions and pollen release processes. We further emphasise the importance of source maps and atmospheric regional transport models in further model improvements. By understanding the relevance of environmental variation to pollen seasonality we can make better predictions regarding the consequences of climate change on plant populations.
Abstract
No abstract has been registered
Abstract
No abstract has been registered