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.
2025
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No abstract has been registered
Abstract
Context Dairy farming contributes approximately 2.5 % of annual global anthropogenic greenhouse gas (GHG) emissions, necessitating effective mitigation strategies. Two approaches are often discussed: low-intensity, low-cost production with minimal reliance on purchased inputs; and high-intensity production with higher-yielding cows to reduce land use and reduce methane emissions per unit of milk. Objective The objective was to identify management factors and farm characteristics that explain variations in GHG emissions, environmental, and economic performance. Indicators included were GHG emissions, land use occupation, energy intensity, nitrogen intensity, and gross margin. Methods Life Cycle Assessment (LCA) was used to calculate the environmental impacts for 200 commercial dairy farms in Central Norway based on farm activities, purchased inputs, machinery, and buildings from 2014 to 2016. A multiple regression analysis with backward elimination was conducted to highlight important variables for environmental impact and economic outcome. Results and conclusions A higher share of dairy cows was found to be the most important factor in reducing GHG emissions, energy and nitrogen intensity, and land use but also to decrease gross margin. Additional key factors for reducing environmental impact included less purchased nitrogen fertiliser, and higher forage yield. There were no statistical correlations between GHG emissions and gross margin per MJ of human-edible energy delivered. Significance Conducting LCA for many dairy farms allows to highlight important factors influencing environmental impact and economic outcome. Using the delivery of human-edible energy from milk and meat as a functional unit allows for a combined evaluation of milk and meat production on a farm.
2024
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This study investigates cow behaviour when visiting two GreenFeed Emission Monitoring (GEM) units within a Part-Time Grazing (PTG) system. Two separate PTG systems were assessed in Sweden and Norway, involving Nordic Red and Norwegian Red dairy cows, respectively. In Sweden, 24 cows were allocated to treatments with restricted access to pasture, either daytime or nighttime grazing. Meanwhile, the Norwegian PTG involved 33 cows with free pasture access, categorized by varying training levels (Partially or Fully). In both PTG systems, cows were exposed to GEM units positioned indoors (Indoor) and in the grazing pastures (Pasture), with individual visitations recorded. Significant variations in visitation patterns were observed. In the restricted access PTG, Nighttime grazing access cows exhibited reduced visits to the Indoor GEM unit but increased visits to the Pasture GEM unit compared to Daytime grazing. Conversely, within the free access PTG, fully trained cows demonstrated elevated visits to the pasture GEM unit and total visits compared to their partially trained counterparts. These findings highlight the influence of temporal conditions and training levels on cow-visiting behaviour within PTG systems.
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No abstract has been registered
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Presentation of preliminary findings from a feed trial conducted winter 2024, where the effect of feeding lactating dairy cows a 100% ensiled grass pulp diet was measured on production parameters, GHG-emissions, behaviour and metabolic markers, compared to regular whole plant silage from the same ley and harvest dates
Abstract
Context In high-latitude regions, variable weather conditions during the growing season and in winter cause considerable variation in forage grass productivity. Tools for predicting grassland status and yield, such as field measurements, satellite image analysis and process-based simulation models, can be combined in decision support for grassland management. Here, we calibrated and validated the BASic GRAssland (BASGRA) model against dry matter and Leaf area index data from temporary grasslands in northern Norway. Objective The objective of this study was to compare the performance of model versions calibrated against i) only region-specific ground data, ii) both region-specific ground and Sentinel-2 satellite data and, iii) field trial data from other regions. Methods Ground and satellite sensed data including biomass dry matter, leaf area index, and autumn and spring ground cover from 2020 to 2022 were acquired from 13 non-permanent grassland fields at four locations. These data were input to BASGRA calibrations together with soil and daily weather data, and information about cutting and nitrogen fertilizer application regimes. The effect of the winter season was taken into account in simulations by initiating the simulations either in autumn or in early spring. Results Within datasets, initiating the model in spring resulted in higher dry matter prediction accuracy (normalised RMSE 22.3–54.0 %) than initiating the model in autumn (normalised RMSE 41.1–93.4 %). Regional specific calibrations resulted in more accurate biomass predictions than calibrations from other regions while using satellite sensing data in addition to ground data resulted in only minor changes in biomass prediction accuracy. Conclusion All regional calibrations against data from northern Norway changed model parameter values and improved dry matter prediction accuracy compared with the reference calibration parameter values. Including satellite-sensed data in addition to ground data in calibrations did not further increase prediction accuracy compared with using only ground data. Implications Our findings show that regional data from farmers’ fields can substantially improve the performance of the BASGRA model compared to using controlled field trial data from other regions. This emphasises the need to account for regional diversity in non-permanent grassland when estimating grassland production potential and stress impact across geographic regions. Further use of satellite data in grassland model calibrations would probably benefit from more detailed assessments of the effect of grass growth characteristics and light and cloud conditions on estimates of grassland leaf area index and biomass from remote sensing.
Abstract
In high latitude regions, variability in weather and climate conditions during the winter season cause a considerable variation in forage grass productivity and animal feed supply between years and locations. Tools to estimate or predict winter survival and yield, such as ground registrations, satellite image analysis and process-based simulation models, can be combined in decision support for grassland management. In this study, we simulated grassland winter survival using the BAsic GRAssland (BASGRA) model. The model was initialized after the last cut in the autumn. Its performance to simulate ground coverage in the early spring, either assessed by on-site ground registrations or from Sentinel-2 satellite images, was evaluated. Grass fields at Malangen and Målselv in Northern Norway were simulated for the winter seasons 2020–2021 and 2021–2022. Model input including daily air temperature, precipitation, relative humidity and wind speed data were obtained from weather stations nearby the grass fields. The initial values of biomass, leaf area and tiller density in the autumn were based on ground registration in October. Preliminary results show considerable variation in both simulated winter survival and prediction accuracy of observed spring ground coverage between the locations and two winter seasons.
Abstract
With rising temperatures and shifting rainfall patterns driven by climate change, conditions for pathogen-plant interactions will be affected based on the specific pathogen and plant species involved. In general, increased pathogen activity is expected in Norwegian grasslands. Recent breeding efforts in Norway have concentrated primarily on developing varieties resistant to fungal diseases that cause winter damage. However, their resistance against other diseases may fall short, as they have not been targeted in the Norwegian breeding programme. As a result, a comprehensive evaluation of the current situation is essential. This ongoing project aims to identify foliar fungal species and disease distribution in breeding lines and varieties of four prominent meadow species: timothy, perennial ryegrass, meadow fescue and red clover. The study encompasses four locations in Norway, spanning from 60 to 69° N. Observations from the first season indicated relatively good resistance to both winter and growing season-related fungi in the investigated breeding material of timothy. The observations indicated that perennial ryegrass is more susceptible to winter diseases, whereas its resistance to growing-season diseases is relatively good. Conversely, meadow fescue and red clover displayed moderate susceptibility to fungal diseases during the growing season but demonstrated commendable resistance to overwintering fungi.
Abstract
The birth process in animals, much like in humans, can encounter complications that pose significant risks to both offspring and mothers. Monitoring these events can provide essential nursing support, but human monitoring is expensive. Although there are commercial monitoring systems for large ruminants, there are no effective solutions for small ruminants, despite various attempts documented in the literature. Inertial sensors are very convenient given their low cost, low impact on animal life, and their flexibility for monitoring animal behavior. This study offers a systematic review of the literature on detecting parturition in small ruminants using inertial sensors. The review analyzed the specifics of published research, including data management and monitoring processes, behaviors indicative of parturition, processing techniques, detection algorithms, and the main results achieved in each study. The results indicated that some methods for detecting birth concentrate on classifying unique animal behaviors, employing diverse processing techniques, and developing detection algorithms. Furthermore, this study emphasized that employing techniques that include analyzing animal activity peaks, specifically recurrent lying down and getting up occurrences, could result in improved detection precision. Although none of the studies provided a completely valid detection algorithm, most results were promising, showing significant behavioral changes in the hours preceding delivery.
Abstract
Manures are potentially both a source of nutrients for plants and a source of pollution. Manure produced depends on animal densities and type rather than plants need. Over time, this has enriched soils with P and organic N. The challenge is maximal nutrient recycling and minimal pollution from the manure used for plant production. To investigate the optimal seasonal distribution of manure, field experiments were carried out in 2022 and 2023 on grassland in three agricultural regions in Norway. Three distributions of cattle slurry at 30 kg P ha–1 were tested, with or without additional N fertilizer. These were compared with control treatments without slurry: no fertilizer, and compound NPK and NK fertilizers. Different distributions had little effect on grass yield and uptake of P and N. Applying a larger proportion of manure in spring increased grass yield, while additional mineral N fertilizer significantly increased yield but reduced N use efficiency. Slurry alone gave a P surplus, while added mineral N fertilizer allowed a net mining of P. There seems therefore to be a trade-off regarding whether the efficient use of N or P is to be prioritized. The decision should likely depend on required yields as well as local pollution risks.