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.
2023
Authors
Rui Dong Yuxin Miao Pete Berry Xinbing Wang Fei Yuan Krzysztof Kusnierek Chris Baker Mark SterlingAbstract
Lodging is a major problem in maize (Zea mays L.) production worldwide. An analytical lodging model has previously been established. However, some of the model inputs are time consuming to obtain and require destructive plant sampling. Efficient prediction of lodging risk early in the season would be beneficial for management decision-making to reduce lodging risks and ensure high yield potential. Remote sensing technology provides an alternative method for fast and nondestructive measurements with the potential for efficient prediction of lodging risks. The objective of this study was to explore the potential of using an active canopy sensor for the early prediction of maize stem lodging risk using simple regression and multiple linear regression (MLR) models. The results indicated that the MLR models using active canopy sensor data together with weather and management factors performed better than simple regression models using only sensor data for predicting maize stem lodging indicators. Similar results were achieved either using regression models to predict the maize stem lodging risk indicators directly or using the regression models to predict lodging related plant parameters as inputs to a process-based lodging model to predict lodging risk indicators indirectly, although the latter approach using MLR models performed slightly better. A medium planting density (7.0 plants m-2) and 240 kg ha-1 N rate would be suitable in the study region, and the recommendations may be adjusted according to different weather conditions. It is concluded that maize stem lodging risks can be predicted using active canopy sensor data together with weather and management information at V8 stage, which can be used to guide in-season management decisions. Additional research is needed to evaluate the potential of using unmanned aerial vehicles and satellite remote sensing technologies in conjunction with machine learning methods to improve the prediction of lodging risks for large scale applications.
Abstract
No abstract has been registered
Authors
Nhat Strøm-AndersenAbstract
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Authors
Lone Ross Lizhen Huang Erik Trømborg Roja Modaresi Hanne Kathrine Sjølie Kristina Bringedal GeddeAbstract
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Authors
Nhat Strøm-AndersenAbstract
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Authors
Damaris Marina Matten Ida Marielle Mienna Vanessa Carina Bieker Brent D. Mishler Victoria Stornes Moen Malene Østreng Nygård Katariina Elsa M Vuorinen Mika Bendiksby Michael David Martin James David Mervyn SpeedAbstract
Paid open access
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No abstract has been registered
Abstract
The fungus Neonectria ditissima causes Fruit Tree Canker on apple and pear. In the past years the disease has become a threat for Swedish and Northern European apple production since devastating outbreaks destroy large numbers of trees. To date, no complete genetic resistance to N. ditissima is known in apple but genotypes (scion cultivars and rootstocks) differ greatly in their level of partial resistance. Furthermore, the degree of susceptibility of a scion cultivar may be influenced by the rootstock it is grafted to. Thus, we aimed to improve our understanding of genetically determined differences in resistance among rootstocks and clarify cultivar/rootstock interactions with regards to canker resistance. For that, we evaluated differences in resistance to fruit tree canker in 24 rootstocks (including two M9 clones). We also evaluated differences in resistance of four most widely grown in Sweden scion cultivars grafted to four common rootstocks differing in vigour. The new knowledge will be useful for growers and breeders to minimize canker damages, prevent loss of the fruit-bearing surface in the orchards, save time and money for the growers.
Abstract
No abstract has been registered
Authors
Simo Maduna Ólöf Dóra Bartels Jónsdóttir Albert Kjartan Dagbjartarson Imsland Davíð Gíslason Patrick Reynolds Lauri Kapari Thor Arne Hangstad Kristian Meier Snorre HagenAbstract
Aquaculture of the lumpfish (Cyclopterus lumpus L.) has become a large, lucrative industry owing to the escalating demand for “cleaner fish” to minimise sea lice infestations in Atlantic salmon mariculture farms. We used over 10K genome-wide single nucleotide polymorphisms (SNPs) to investigate the spatial patterns of genomic variation in the lumpfish along the coast of Norway and across the North Atlantic. Moreover, we applied three genome scans for outliers and two genotype–environment association tests to assess the signatures and patterns of local adaptation under extensive gene flow. With our ‘global’ sampling regime, we found two major genetic groups of lumpfish, i.e., the western and eastern Atlantic. Regionally in Norway, we found marginal evidence of population structure, where the population genomic analysis revealed a small portion of individuals with a different genetic ancestry. Nevertheless, we found strong support for local adaption under high gene flow in the Norwegian lumpfish and identified over 380 high-confidence environment-associated loci linked to gene sets with a key role in biological processes associated with environmental pressures and embryonic development. Our results bridge population genetic/genomics studies with seascape genomics studies and will facilitate genome-enabled monitoring of the genetic impacts of escapees and allow for genetic-informed broodstock selection and management in Norway.