Publikasjoner
NIBIOs ansatte publiserer flere hundre vitenskapelige artikler og forskningsrapporter hvert år. Her finner du referanser og lenker til publikasjoner og andre forsknings- og formidlingsaktiviteter. Samlingen oppdateres løpende med både nytt og historisk materiale. For mer informasjon om NIBIOs publikasjoner, besøk NIBIOs bibliotek.
2019
Forfattere
Trygve S. AamlidSammendrag
Det er ikke registrert sammendrag
Forfattere
Trygve S. AamlidSammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Sammendrag
Det er ikke registrert sammendrag
Forfattere
Grete H. M. JørgensenSammendrag
Det er ikke registrert sammendrag
Sammendrag
Invasive alien species and new plant pests are introduced into new regions at an accelerating rate, due to increasing international trade with soil, plants and plant products. Exotic, plant pathogenic oomycetes in soil from the root zone of imported plants pose a great threat to endemic ecosystems and horticultural production. Detecting them via baiting and isolation, with subsequent identification of the isolated cultures by Sanger sequencing, is labour intensive and may introduce bias due to the selective baiting process. We used metabarcoding to detect and identify oomycetes present in soil samples from imported plants from six different countries. We compared metabarcoding directly from soil both before and after baiting to a traditional approach using Sanger-based barcoding of cultures after baiting. For this, we developed a standardized analysis workflow for Illumina paired-end oomycete ITS metabarcodes that is applicable to future surveillance efforts. In total, 73 soil samples from the rhizosphere of woody plants from 33 genera, in addition to three samples from transport debris, were analysed by metabarcoding the ITS1 region with primers optimized for oomycetes. We detected various Phytophthora and Pythium species, with Pythium spp. being highly abundant in all samples. We also found that the baiting procedure, which included submerging the soil samples in water, resulted in the enrichment of organisms other than oomycetes, compared to non-baited soil samples.
Forfattere
Kaiguang Zhao Michael A. Wulder Tongxi Hu Ryan Bright Qiusheng Wu Haiming Qin Yang Li Elizabeth Toman Bani Mallick Xuesong Zhang Molly BrownSammendrag
Satellite time-series data are bolstering global change research, but their use to elucidate land changes and vegetation dynamics is sensitive to algorithmic choices. Different algorithms often give inconsistent or sometimes conflicting interpretations of the same data. This lack of consensus has adverse implications and can be mitigated via ensemble modeling, an algorithmic paradigm that combines many competing models rather than choosing only a single “best” model. Here we report one such time-series decomposition algorithm for deriving nonlinear ecosystem dynamics across multiple timescales—A Bayesian Estimator of Abrupt change, Seasonal change, and Trend (BEAST). As an ensemble algorithm, BEAST quantifies the relative usefulness of individual decomposition models, leveraging all the models via Bayesian model averaging. We tested it upon simulated, Landsat, and MODIS data. BEAST detected changepoints, seasonality, and trends in the data reliably; it derived realistic nonlinear trends and credible uncertainty measures (e.g., occurrence probability of changepoints over time)—some information difficult to derive by conventional single-best-model algorithms but critical for interpretation of ecosystem dynamics and detection of low-magnitude disturbances. The combination of many models enabled BEAST to alleviate model misspecification, address algorithmic uncertainty, and reduce overfitting. BEAST is generically applicable to time-series data of all kinds. It offers a new analytical option for robust changepoint detection and nonlinear trend analysis and will help exploit environmental time-series data for probing patterns and drivers of ecosystem dynamics.
Sammendrag
Root and butt-rot (RBR) has a significant impact on both the material and economic outcome of timber harvesting, and therewith on the individual forest owner and collectively on the forest and wood processing industries. An accurate recording of the presence of RBR during timber harvesting would enable a mapping of the location and extent of the problem, providing a basis for evaluating spread in a climate anticipated to enhance pathogenic growth in the future. Therefore, a system to automatically identify and detect the presence of RBR would constitute an important contribution in addressing the problem without increasing workload complexity for the machine operator. In this study we developed and evaluated an approach based on RGB images to automatically detect tree-stumps and classify them as to the absence or presence of rot. Furthermore, since knowledge of the extent of RBR is valuable in categorizing logs, we also classify stumps to three classes of infestation; rot = 0%, 0% < rot < 50% and rot >= 50%. In this work we used deep learning approaches and conventional machine learning algorithms for detection and classification tasks. The results showed that tree-stumps were detected with precision rate of 95% and recall of 80%. Using only the correct output (TP) of the stump detector, stumps without and with root and butt-rot were correctly classified with accuracy of 83.5% and 77.5%. Classifying rot to three classes resulted in 79.4%, 72.4% and 74.1% accuracy for stumps with rot = 0%, 0% < rot < 50% and rot >= 50\%, respectively. With some modifications, the algorithm developed could be used either during the harvesting operation to detect RBR regions on the tree-stumps or as a RBR detector for post-harvest assessment of tree-stumps and logs.
Forfattere
Ibrahim Tahir Eivind VangdalSammendrag
Prediction of the optimum harvest date and storability of apples is an important concern for the fruit industry in Scandinavia. Streif index, firmness or only starch conversion are commonly used methods. To replace these with a more practical and non-destructive method, a portable spectrometer (DA meter, chlorophyll absorbance index (IAD)) was used to determine the optimum harvest date and storage potential for five apple cultivars grown in a cool climate. There was a very strong negative correlation between harvest date and IAD value in all cultivars. IAD values also showed a strong negative correlation with fruit respiration. However, the relationship was stronger in ‘Discovery’, ‘Rubinola’ and ‘Santana’ than in ‘Aroma’ and ‘Karin Schneider’. Streif index values showed very close relationships with IAD in all five apple cultivars. Apples harvested with IAD values of 0.8-1.8 had Streif index values of 0.14-0.20, which corresponds to an adequate threshold for harvesting apples for long-term storage. After four months in cold storage, fruits with higher IAD value at harvest showed higher firmness in all cultivars except ‘Rubinola’, and slower softening in ‘Aroma’, ‘Rubinola’ and ‘Santana’. Only in ‘Aroma’ and ‘Karin Schneider’ did IAD show a negative correlation, with a decline in soluble solids content during storage. Negative correlations were also found between IAD values at harvest or after storage and the occurrence of fungal decay. Since fruit respiration rate increases with advanced maturity while Streif index decreases, determination of IAD can be a very promising technique to predict the storage potential of apples and to identify high-quality fruit.
Sammendrag
Large amounts of fruit seeds are discarded yearly in different producing industries, which is a waste of a potentially valuable resource as well as a serious disposal problem. Plum is the most important type of commercial fruit in Serbia and seeds could be obtained as a byproduct of alcoholic beverage processing. Their exploitation should be greater and more information about cultivars’ kernels and their composition is required. Also, consumers’ tendency for “natural foods” arises a need for characterization of genotypes with high phenolic contents which could be used in processed food products. Discarding large amounts of plum seeds is a waste of potentially precious sources of phytochemicals. In order to characterize the phenolic profile of approximately 30 plum cultivars, phenolic acids and flavonoids, as potential antioxidants, were determined by ultra-high-performance liquid chromatography (UHPLC) coupled with hybrid mass spectrometry, which combines the Linear Trap Quadrupole (LTQ) and OrbiTrap MS/MS mass analyzer together with chemometric analysis. The UHPLC–LTQ OrbiTrap MS technique was proven to be reliable for the unambiguous detection of phenolic acids, their derivatives, and flavonoid aglycones based on their molecular masses and fragmentation pattern. The phenolic acids prevail over the flavonoids, with protocatechuic acid, p-hydroxybenzoic acid, ferulic acid, and chlorogenic acid as the most abundant ones. In addition, catechin was the most abundant flavonoid.