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.
2019
Abstract
Short-day (SD) treatment is used by forest nurseries to induce growth cessation in Picea abies seedlings. SD treatment may however increase the risk of reflushing in autumn and earlier bud break the following spring. When the start of the SD treatment is early in order to control seedling height, the duration of the SD treatment should be longer in order to prevent reflushing in autumn. However, due to the amount of manual work involved in the short-day treatment, increasing the number of days is undesirable from a practical point of view. Splitting the SD treatment could be a way to achieve both early height control and at the same time avoid autumn bud break with less workload. We tested how different starting dates and durations of SD treatment influenced on morphological and phenological traits. Regardless of timing and duration of the SD treatment, height growth was reduced compared to the untreated controls. Seedlings given split SD (7+7 days interrupted with two weeks in long days) had less height growth than all other treatments. Root collar diameter growth was significantly less in control seedlings than in seedlings exposed to early (7 or 14 days) or split (7+7 days) SD treatment. There were also differences in the frequency of reflushing and bud break timing among the SD treated seedlings, dependent on duration and starting date. If the SD treatment started early, a continuous 14-day SD treatment was not sufficient to avoid high frequencies of reflushing. However, by splitting the SD treatment into two periods of 7+7 days these negative effects were largely avoided, although spring bud break occurred earlier than in the controls.
Authors
O. Janne Kjønaas Teresa Gómez de la Bárcena Ryan Bright Mette Hansen Gro Hylen Håvard Kauserud Sunil Mundra Jørn-Frode Nordbakken Tonje Økland Carlo AallAbstract
No abstract has been registered
Authors
O. Janne Kjønaas Teresa Gómez de la Bárcena Ryan Bright Mette Hanssen Gro Hylen Håvard Kauserud Tonje Økland Mundra Sunil Jørn-Frode Nordbakken Carlo AallAbstract
No abstract has been registered
Abstract
Multi-temporal Sentinel 2 optical images and 3D photogrammetric point clouds can be combined to enhance the accuracy of timber volume models on large spatial scale. Information on the proportion of broadleaf and conifer trees improves timber volume models obtained from 3D photogrammetric point clouds. However, the broadleaf-conifer information cannot be obtained from photogrammetric point clouds alone. Furthermore, spectral information of aerial images is too inconsistent to be used for automatic broadleaf-conifer classification over larger areas. In this study we combined multi-temporal Sentinel 2 optical satellite images, 3D photogrammetric point clouds from digital aerial stereo photographs, and forest inventory plots representing an area of 35,751 km2 in south-west Germany for (1) modelling the percentage of broadleaf tree volume (BL%) using Sentinel 2 time series and (2) modelling timber volume per hectare using 3D photogrammetric point clouds. Forest inventory plots were surveyed in the same years and regions as stereo photographs were acquired (2013–2017), resulting in 11,554 plots. Sentinel 2 images from 2016 and 2017 were corrected for topographic and atmospheric influences and combined with the same forest inventory plots. Spectral variables from corrected multi-temporal Sentinel 2 images were calculated, and Support VectorMachine (SVM) regressions were fitted for each Sentinel 2 scene estimating the BL% for corresponding inventory plots. Variables from the photogrammetric point clouds were calculated for each inventory plot and a non-linear regression model predicting timber volume per hectare was fitted. Each SVMregression and the timber volume model were evaluated using ten-fold cross-validation (CV). The SVMregression models estimating the BL% per Sentinel 2 scene achieved overall accuracies of 68%–75% and a Root Mean Squared Error (RMSE) of 21.5–26.1. The timber volumemodel showed a RMSE% of 31.7%, amean bias of 0.2%, and a pseudo-R2 of 0.64. Application of the SVMregressions on Sentinel 2 scenes covering the state of Baden-Württemberg resulted in predictions of broadleaf tree percentages for the entire state. These predicted values were used as additional predictor in the timber volume model, allowing for predictions of timber volume for the same area. Spatially high-resolution information about growing stock is of great practical relevance for forest management planning, especially when the timber volume of a smaller unit is of interest, for example of a forest stand or a forest districtwhere not enough terrestrial inventory plots are available to make reliable estimations. Here, predictions from remote-sensing based models can be used. Furthermore, information about broadleaf and conifer trees improves timber volume models and reduces model errors and, thereby, prediction uncertainties.
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Authors
Mohamed Neji Anais Gorel Dario Isidro Ojeda Alayon Jerome Duminil Chedly Kastally Kathy Steppe Adeline Fayolle Olivier J. HardyAbstract
With the ongoing climate change, African rainforests are expected to experience severe drought events in the future. In Africa, the tropical genus Erythrophleum (Fabaceae) includes two forest sister timber tree species displaying contrasting geographical distributions. Erythrophleum ivorense is adapted to wet evergreen Guineo-Congolian forests, whereas E. suaveolens occurs in a wider range of climates, being found in moist dense forests but also in gallery forests under a relatively drier climate. This geographical distribution pattern suggests that the two species might cope differently to drought at the genomic level. Yet, the genetic basis of tolerance response to drought stress in both species is still uncharacterized. To bridge this gap, we performed an RNA-seq approach on seedlings from each species to monitor their transcriptional responses at different levels of drought stress (0, 2 and 6 weeks after stopping watering seedlings). Monitoring of wilting symptoms revealed that E. suaveolens displayed an earlier phenotypic response to drought stress than E. ivorense. At the transcriptomic level, results revealed 2020 (1204 down-regulated/816 up-regulated) and 1495 differentially expressed genes (DEGs) in response to drought stress from a total of 67,432 and 66,605 contigs assembled in E. ivorense and E. suaveolens, respectively. After identifying 30,374 orthologs between species, we found that only 7 of them were DEGs shared between species, while 587 and 458 were differentially expressed only in E. ivorense or E. suaveolens, respectively. GO and KEGG enrichment analysis revealed that the two species differ in terms of significantly regulated pathways as well as the number and expression profile of DEGs (Up/Down) associated with each pathway in the two stress stages. Our results suggested that the two studied species react differently to drought. E. suaveolens seems displaying a prompt response to drought at its early stage strengthened by the down-regulation of many DEGs encoding for signaling and metabolism-related pathways. A considerable up-regulation of these pathways was also found in E. ivorense at the late stage of drought, suggesting this species may be a late responder. Overall, our data may serve as basis for further understanding the genetic control of drought tolerance in tropical trees and favor the selection of crucial genes for genetically enhancing drought resistance.