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
Nicolas Valette Arnaud Legout Barry Goodell Gry Alfredsen Lucas Auer Eric Gelhaye Delphine DerrienAbstract
In forest ecosystems, fungi are the key actors in wood decay. They have the capability to degrade lignified substrates and the woody biomass of coniferous forests, with brown rot fungi being common colonizers. Brown rots are typically involved in the earliest phase of lignocellulose breakdown, which therefore influences colonization by other microorganisms. However, few studies have focused on the impact of introducing decayed wood into forest environments to gauge successional colonization by natural bacterial and fungal communities following partial decay. This study aimed to address this issue by investigating the bacterial and fungal colonization of Norway spruce (Picea abies) wood, after intermediate and advanced laboratory-based, pre-decay, by the brown rot fungus Gloeophyllum trabeum. Using Illumina metabarcoding, the in situ colonization of the wood blocks was monitored 70 days after the blocks were placed on the forest floor and covered with litter. We observed significant changes in the bacterial and fungal communities associated with the pre-decayed stage. Further, the wood substrate condition acted as a gatekeeper by reducing richness for both microbial communities and diversity of fungal communities. Our data also suggest that the growth of some fungal and bacterial species was driven by similar environmental conditions.
Authors
Habtamu AlemAbstract
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Authors
Sjur Sandgrind Xueyuan Li Emelie Ivarson Eu Sheng Wang Rui Guan Selvaraju Kanagarajan Li-Hua ZhuAbstract
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Authors
Jiangsan ZhaoAbstract
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The aim of this study was to investigate the potential of traditional apple cultivars from Bosnia and Herzegovina to improve the aroma of the less aromatic international cultivar “Idared” in the production of spirits. Two flavor improvement approaches were used: joint fermentation of traditional and “Idared” apples and the maceration of traditional apples in raw “Idared” spirits, followed by redistillation. Minor aroma volatile compounds in the obtained spirits were measured by gas chromatography-mass spectroscopy techniques after enrichment via solid-phase microextraction. Overall, 36 minor volatile compounds were found in spirits. The share of detected compounds varied greatly among samples due to the flavoring approach and used cultivars. Flavor improvement during fermentation proved a more efficient approach. Even 10% share of a traditional apple is enough to improve the positive sensory attributes of the spirits. The obtained results encourage the future use of traditional apple cultivars in the production and flavor improvement of fruit spirits.
Abstract
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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.