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
Authors
Linn Vassvik Anders Nielsen Michael P. D. Garratt Bjørn Arild Hatteland Joseph Chipperfield Jørund Johansen Silje Maria Midthjell Høydal Erik Trond AschehougAbstract
Insect pollinators are important drivers of fruit quality and yield in horticultural systems. The global reduction in wild bee populations has increased the demand for managed honeybees, despite honeybees relatively low pollination efficiency. Here, we assessed how bee communities, bee behaviour, and orchard design in Norwegian apple orchards affects apple pollination success, an important determinant of apple quality. We placed pan and vane traps in 18 apple orchards, in six distinct locations, within the two main apple growing regions in Norway. We also tracked individual bees (honeybees, bumblebees, and solitary bees) throughout the apple flowering season, and recorded their flower handling time, number of flower visits, stigma contact, and movement between apple flowers. Finally, we calculated the seed set rate (ovules developed into seeds / total number of ovules) from 908 harvested apples to estimate pollination success. Our key finding is that pollination success was driven by the abundance of wild bees and overall orchard planting design. We found lower pollination success in block design orchards where a single cultivar is planted continuously over a large area, compared to orchards with an integrated design where compatible cultivars are planted within the orchard. We also found that stigma contact decreased as apple flowering progressed, and that solitary bees visited fewer flowers per foraging event but were potentially more thorough foragers. Our results highlight the importance of promoting wild bees in apple orchards while also ensuring there is compatible pollen in the orchards for optimal pollination.
Authors
Ingunn Øvsthus T. Radovanović Vukajlović M. Martelanc G. Antalick L. Butinar A. Hermes B.T. Grein Mats Carlehög B. Mozetič VodopivecAbstract
No abstract has been registered
Abstract
Greenhouse cultivation can help meet food demand in a growing and increasingly urbanised population. Reliance on fossil-fuel heating and natural ventilation often makes conventional greenhouses energy- and carbon-inefficient. Closed greenhouses address these limitations through resource recycling and energy recovery. While a centralised environmental control system (ECS) integrating climate control and heat harvesting has shown potential to improve greenhouse crop performance at high latitudes, its year-round energy use and energy-related carbon footprint reduction potential remains insufficiently quantified. This study extends an existing dynamic greenhouse climate model to incorporate a novel centralised ECS integrating air recirculation, heating, cooling, and heat harvesting in (semi-)closed greenhouses. The model was validated using experimental data from Norway, reproducing temperature and relative humidity with RMSEs of 1.40–1.63 °C and 7.60–8.55%, respectively. Energy use and tomato yield were predicted with relative errors of 3.8–8.4% and 1.6–4.2%, respectively. Scenario simulations under Norwegian conditions showed that (semi-)closed greenhouses with heat harvesting can reduce fossil fuel use by over 80% while increasing tomato yields by 15–41% relative to open greenhouses, driven by changes in CO2 concentration and temperature following reduced ventilation and heat recovery. The performance of a fully closed greenhouse relying solely on on-site cold storage is constrained by cooling capacity and buffer size, particularly during summer; adding a supplemental cold energy source such as surface water can improve its performance. Despite heat harvesting, a residual boiler heating demand of 3–10% remains. Further gains in energy efficiency and crop performance may be achieved through optimised climate control.
Authors
Lampros LamprinakisAbstract
No abstract has been registered
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No abstract has been registered
Authors
Linghan Huang Tingxuan Zhuang Meiqi Zhang Syed Tahir Ata-Ul-Karim Kang Yu Krzysztof Kusnierek Wei Li Xiaojun Liu Yongchao Tian Yan Zhu Weixing Cao Qiang CaoAbstract
Early-season prediction of winter wheat yield and grain protein content is essential for guiding fertilizer and irrigation decisions and reducing uncertainty in variable agroecosystems, as yield affects profitability and quality affects market value and nutrition. Although multi-source data and both single-task learning (STL) and multi-task learning (MTL) are widely used for predicting grain yield and quality, the conditions under which each approach performs best remain poorly understood, especially when data availability, noise, and measurement or computational constraints vary. To address this gap, we conducted a three-year field experiment in Henan Province, China, compiling environmental, agronomic, and proximal-sensing variables across five growth stages. Seven subsets were constructed, including environmental, agronomic, sensor, and combined subsets, and STL/MTL variants of Multilayer Perceptron (MLP), Transformer, and Random Forest (RF) were benchmarked. SHapley Additive exPlanations (SHAP) analysis quantified feature- and stage-level contributions and guided construction of compact Top-K subsets for accuracy–efficiency trade-offs. Multi-source fusion substantially improved accuracy over single-source inputs, with the combined agronomic-sensor subset providing the best performance (yield R2 = 0.823; GPC R2 = 0.743). Under the current stage-aggregated multi-source representation, MLPs outperformed Transformers and RFs across configurations, indicating that compact nonlinear models were better suited to the present medium-dimensional tabular setting. MTL provided the greatest benefit with sparse feature sets or imbalanced predictive difficulty, whereas STL performed better when information was abundant and signals were strong. SHAP analysis showed that agronomic and sensor features associated with biomass accumulation, nitrogen status, water availability, and canopy light interception were key drivers of model predictions, particularly during erecting and early grain filling. These findings further show that the value of STL versus MTL depends on data-source composition and information richness, and that SHAP can be used not only for interpretation but also for reduced-feature subset design. Within the present plot-scale setting, this study therefore provides a decision-oriented framework for identifying both accuracy-oriented and efficiency-oriented configurations, with efficiency referring to feature parsimony, reduced input and preprocessing burden, and computational time for winter wheat yield and GPC.
Authors
Linn Vassvik Anders Nielsen Michael P. D. Garratt Bjørn Arild Hatteland Joseph Chipperfield Erik Trond AschehougAbstract
Apples are dependent upon pollinators for the transfer of pollen between cultivars to ensure high quality fruit production. Agricultural intensification has reduced the availability of stable floral resources for wild bees, leading to widespread declines of important pollinators of apples. Managed honeybees are commonly used to supplement pollination services in apple orchards, but honeybees are less efficient pollinators compared to wild bees. We investigated whether increased flower abundance in the understory vegetation of apple orchards can increase pollinator activity to apple flowers. We compared bee visitation in five orchards in Eastern Norway: three unmowed orchards, and two mowed orchards. In unmowed orchards, dandelions ( Taraxacum spp.) dominated the understory vegetation. Bee observations were conducted on the understory vegetation and apple trees, via manual observations and time-lapse cameras. Wild bees preferentially visited apple flowers over dandelions, while honeybees did not differ in their visits to apple flowers and dandelion flowers. We also found that the abundance of dandelion flowers in the understory increased visits by wild bees to apple flowers. Taken together, this suggests that within orchard floral resources do not compete for pollinators but instead increase apple visitation and improve pollination success. Our results highlight the importance of managing apple orchards for wild bee populations and the potential short-term benefits of understory floral resources on apple production. Implications for insect conservation Our results show that understory vegetation should be left unmowed to increase pollination of apple flowers and provide pollinators with alternate floral resources before, during, and after apple flowering.
Authors
Anne Linn Hykkerud Meeri Santikko Michel Verheul Baoru Yang Niina Kelanne Dmitry Kechasov Anita SønstebyAbstract
No abstract has been registered
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No abstract has been registered
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No abstract has been registered