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
Abstract
The successful introduction of new cultivars depends on the evaluation of complex parameters essential for the consumers, market, and fruit producers. A new scab-resistant apple cultivar, ‘Wuranda’ (SQ159/Natyra®/Magic Star® × Honeycrisp), recently introduced in Norway and managed under the name Fryd©, is prone to biennial bearing. Therefore, one of the first tasks, investigated in Southwestern Norway by the Norwegian Institute of Bioeconomy Research, NIBIO-Ullensvang in 2021–2024, was the establishment of optimal crop load level based on the combination of productivity, fruit quality, and return bloom. The apple cultivar Fryd (‘Wuranda’) was propagated on ‘M.9’ rootstock and planted in 2019. The trial was performed in the same orchard for four consecutive years, starting three years after planting. Crop load level affected average fruit mass but had no impact on cv. Fryd fruit quality parameters at harvest such as blush, ground color, firmness, soluble solid content, or starch degradation. Fruit size variation was diminished by crop load regulation, and most fruits fell into 2–3 grading classes. Crop load, not the yield per tree, was the determining factor for the return bloom. The optimal crop load level depended on the orchard age. To guarantee a regular bearing mode of cv. Fryd planted on M.9 rootstock at a 3.5 × 1 m distance and trained as slender spindle, crop load of 5.5–6 fruits cm−2 TCSA (trunk cross-sectional area) in the 3rd year, 7.5–8 fruits cm−2 TCSA in the 4th year, and 6.5–7 fruits cm−2 TCSA in the 5th year should be maintained.
Abstract
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Authors
Emma Slone Jessica Green Navneet Kaur Darrin L. Walenta Nicole Anderson Casey Cruse Seth J. DormanAbstract
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Authors
Kristian Hansen Håvard Steinshamn Sissel Hansen Matthias Koesling Tommy Dalgaard Bjørn Gunnar HansenAbstract
To evaluate the environmental impact across multiple dairy farms cost-effectively, the methodological frame- work for environmental assessments may be redefined. This article aims to assess the ability of various statistical tools to predict impact assessment made from a Life Cyle Assessment (LCA). The different models predicted estimates of Greenhouse Gas (GHG) emissions, Energy (E) and Nitrogen (N) intensity. The functional unit in the study was defined as 2.78 MJMM human-edible energy from milk and meat. This amount is equivalent to the edible energy in one kg of energy-corrected milk but includes energy from milk and meat. The GHG emissions (GWP100) were calculated as kg CO2-eq per number of FU delivered, E intensity as fossil and renewable energy used divided by number of FU delivered, and N intensity as kg N imported and produced divided by kg N delivered in milk or meat (kg N/kg N). These predictions were based on 24 independent variables describing farm characteristics, management, use of external inputs, and dairy herd characteristics. All models were able to moderately estimate the results from the LCA calculations. However, their precision was low. Artificial Neural Network (ANN) was best for predicting GHG emissions on the test dataset, (RMSE = 0.50, R2 = 0.86), followed by Multiple Linear Regression (MLR) (RMSE = 0.68, R2 = 0.74). For E intensity, the Supported Vector Machine (SVM) model was performing best, (RMSE = 0.68, R2 = 0.73), followed by ANN (RMSE = 0.55, R2 = 0.71,) and Gradient Boosting Machine (GBM) (RMSE = 0.55, R2 = 0.71). For N intensity predictions the Multiple Linear Regression (MLR) (RMSE = 0.36, R2 = 0.89) and Lasso regression (RMSE = 0.36, R2 = 0.88), followed by the ANN (RMSE = 0.41, R2 = 0.86,). In this study, machine learning provided some benefits in prediction of GHG emission, over simpler models like Multiple Linear Regressions with backward selection. This benefit was limited for N and E intensity. The precision of predictions improved most when including the variables “fertiliser import nitrogen” (kg N/ha) and “proportion of milking cows” (number of dairy cows/number of all cattle) for predicting GHG emission across the different models. The inclusion of “fertiliser import nitrogen” was also important across the different models and prediction of E and N intensity.
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.
Authors
Ingrid Marie Garfelt Paulsen Isabell Eischeid Åshild Ønvik Pedersen Jakob J. Assmann Nigel Yoccoz Jesper Bruun Mosbacher Eeva M Soininen Virve RavolainenAbstract
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Authors
Wendy Wuyts Nhat Strøm-Andersen Shumaila Khatri Arild Eriksen Per F. Jørgensen Arild Øvergaard Emil Rygh Angelica Kveen Alexander Mertens Jannicke Stadaas Inger Gamme Veronique Vasseur Anders Q. Nyrud Kristine NoreAbstract
No abstract has been registered
Authors
Laurie C. Hofmann Janina Brakel Inka Bartsch Gabriel Montecinos Arismendi Ricardo Bermejo Manuela I Parentef Emeline Creis Olivier De Clerck Bertrand Jacquemin Jessica Knoop Maike Lorenz Levi Pompermayer Machado Neusa Martinsk Sotiris Orfanidis Ian Probert Cecilia Rad-Menéndez Michael Ross Ralf Rautenberger Jessica Schiller Ester A. Serrao Sophie Steinhagen Ronan Sulpice Myriam Valero Thomas WichardAbstract
Biobanking (also known as germplasm banking) of genetic material is a well-established concept for preserving plant genetic diversity and also contributes to food security, conservation and restoration. Macroalgae currently represent a very small percentage of the strains in publicly accessible European germplasm banks, despite the increasing recognition of their contribution to achieving several of the United Nations Sustainable Development Goals. There is no strategic coordination of existing macroalgal strains, which could have severe ecological and economic implications as species and their genetic diversity disappear rapidly due to local and global environmental stressors. In this opinion paper, we stress the importance of a coordinated European effort for preserving macroalgal genetic diversity and suggest the development of a three-pillared system to safeguard European macroalgal genetic material consisting of (1) a European Board of Macroalgal Genetic Resources (EBMGR) to provide supervision, support and coordination, (2) a network of germplasm banks consisting of currently existing and newly established infrastructures and (3) an interoperable databank integrating existing databanks. While it will be the task of the EBMGR to identify and coordinate priorities, we offer initial recommendations for preserving macroalgal genetic material, discuss the risks of inaction, and highlight the challenges that must be overcome. Highlights • A coordinated European effort is crucial to preserve macroalgal genetic diversity, addressing rapid species and genetic loss due to environmental stressors. • The initiative should include a European Board of Macroalgal Genetic Resources for oversight, a network of existing and new germplasm banks and an interoperable databank integrating current resources. • The effort supports the United Nations Sustainable Development Goals.