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
Joyce Machado Nunes Romeiro Jostein Gohli Paal Krokene Tron Haakon Eid Clara Antón FernándezAbstract
Bark beetle (Ips typographus) outbreaks have the potential to damage large areas of spruce-dominated forests in Scandinavia. To define forest management strategies that will minimize the risk of bark beetle attacks, we need robust models that link forest structure and composition to the risk and potential damage of bark beetle attacks. Since data on bark beetle infestation rates and corresponding damages does not exist in Norway, we implement a previously published meta-model for estimating I. typographus damage probability and intensity. Using both current and projected climatic conditions we used the model to estimate damage inflicted by I. typographus in Norwegian spruce stands. The model produces feasible results for most of Norway’s climate and forest conditions, but a revised model tailored to Norway should be fitted to a dataset that includes older stands and lower temperatures. Based on current climate and forest conditions, the model predicts that approximately nine percent of productive forests within Norway’s main spruce-growing region will experience a loss ranging from 1.7 to 11 m3/ha of spruce over a span of five years. However, climate change is predicted to exacerbate the annual damage caused by I. typographus, potentially leading to a doubling of its detrimental effects.
Abstract
No abstract has been registered
Abstract
Weed harrowing is commonly used to manage weeds in organic farming but is also applied in conventional farming to replace herbicides. Due to its whole-field application, weed harrowing after crop emergence has relatively poor selectivity and may cause crop damage. Weediness generally varies within a field. Therefore, there is a potential to improve the selectivity and consider the within-field variation in weediness. This paper describes a decision model for precision post-emergence weed harrowing in cereals based on experimental data in spring barley and nonlinear regression analysis. The model predicts the optimal weed harrowing intensity in terms of the tine angle of the harrow for a given weediness (in terms of percentage weed cover), a given draft force of tines, and the biological weed damage threshold (in terms of percentage weed cover). Weed cover was measured with near-ground RGB images analyzed with a machine vision algorithm based on deep learning techniques. The draft force of tines was estimated with an electronic load cell. The proposed model is the first that uses a weed damage threshold in addition to site-specific values of weed cover and soil hardness to predict the site-specific optimal weed harrow tine angle. Future field trials should validate the suggested model.
Abstract
The European spruce bark beetle Ips typographus and the North American spruce beetle Dendroctonus rufipennis cause high mortality of spruces on their native continents. Both species have been inadvertently transported beyond their native ranges. With similar climates and the presence of congeneric spruce hosts in Europe and North America, there is a risk that one or both bark beetle species become established into the non-native continent. There are many challenges that an introduced population of bark beetles would face, but an important prerequisite for establishment is the presence of suitable host trees. We tested the suitability of non-native versus native hosts by exposing cut bolts of Norway spruce (Picea abies), black spruce (Picea mariana) and white spruce (Picea glauca) to beetle attacks in the field in Norway and Canada. We quantified attack density, brood density and reproductive success of I. typographus and D. rufipennis in the three host species. We found that I. typographus attacked white and black spruce at comparable densities to its native host, Norway spruce, and with similar reproductive success in all three host species. In contrast, D. rufipennis strongly preferred to attack white spruce (a native host) but performed better in the novel Norway spruce host than it did in black spruce, a suboptimal native host. Our results suggest that I. typographus will find abundant and highly suitable hosts in North America, while D. rufipennis in Europe may experience reduced reproductive success in Norway spruce.
Authors
Dario Isidro Ojeda Alayon Max John Robert L. Hammond Riitta Savolainen Kari Vepsäläinen Torstein KvammeAbstract
The Formicoxenus genus-group comprises six genera within the tribe Crematogastrini. The group is well known for repeated evolution of social parasitism among closely related taxa and cold-adapted species with large distribution ranges in the Nearctic and Palearctic regions. Previous analyses based on nuclear markers (ultraconserved elements, UCEs) and mitochondrial genes suggest close relationship between Formicoxenus Mayr, 1855, Leptothorax Mayr, 1855 and Harpagoxenus Forel, 1893. However, scant sampling has limited phylogenetic assessment of these genera. Also, previous phylogeographic analyses of L. acervorum (Fabricius, 1793) have been limited to its West-Palearctic range of distribution, which has provided a narrow view on recolonization, population structure and existing refugia of the species. Here, we inferred the phylogenenetic history of genera within the Formicoxenus genus-group and reconstructed the phylogeography of L. acervorum with more extensive sampling. We employed three datasets, one data set consisting of whole mitochondrial genomes, and two data sets of sequences of the COI-5P (658 bp) with different number of specimens. The topologies of previous nuclear and our inferences based on mitochondrial genomes were overall congruent. Further, Formicoxenus may not be monophyletic. We found several monophyletic lineages that do not correspond to the current species described within Leptothorax, especially in the Nearctic region. We identified a monophyletic L. acervorum lineage that comprises both Nearctic and Palearctic locations. The most recent expansion within L. acervorum probably occurred within the last 0.5 Ma with isolated populations predating the Last Glacial Maximum (LGM), which are localized in at least two refugial areas (Pyrenean and Northern plateau) in the Iberian Peninsula. The patterns recovered suggest a shared glacial refugium in the Iberian Peninsula with cold-adapted trees that currently share high-altitude environments in this region.
Authors
Linn Vassvik Anders Nielsen Erik Trond Aschehoug Bjørn Arild Hatteland Joseph Chipperfield Michael P. D. GarrattAbstract
No abstract has been registered
Abstract
Pandora neoaphidis is a common entomopathogenic fungus on Sitobion avenae, which is an important aphid pest on cereals in Europe. Pandora neoaphidis is known to cause epizootics (i.e. an unusually high prevalence of infected hosts) and the rapid collapse of aphid populations. We developed a weather-driven mechanistic model of the winter wheat-S. avenae-P. neoaphidis system to simulate the dynamics from spring to harvest. Aphid immigration was fixed at a rate that would lead to a pest outbreak, if not controlled by the fungus. We estimated the biocontrol efficacy by running pair-wise simulations, one with and one without the fungus. Uncertainty in model parameters and variation in weather was included, resulting in a range of simulation outcomes, and a global sensitivity analysis was performed. We identified two key understudied parameters that require more extensive experimental data collection to better assess the fungus biocontrol, namely the fungus transmission efficiency and the decay of cadaver, which defines the time window for possible disease transmission. The parameters with the largest influence on the improvement in yield were the weather, the lethal time of exposed aphids, the fungus transmission efficiency, and the humidity threshold for fungus development, while the fungus inoculum in the chosen range (between 10 and 70% of immigrant aphids carrying the fungus) was less influential. The model suggests that epizootics occurring early, around Zadoks growth stage (GS) 61, would lead to successful biocontrol, while later epizootics (GS 73) were a necessary but insufficient condition for success. These model predictions were based on the prevalence of cadavers only, not of exposed (i.e. infected but yet non-symptomatic) aphids, which in practice would be costly to monitor. The model suggests that practical Integrated Pest Management could thus benefit from including the cadavers prevalence in a monitoring program. We argue for further research to experimentally estimate these cadaver thresholds.
Abstract
Pandora neoaphidis is a common entomopathogenic fungus on Sitobion avenae, which is an important aphid pest on cereals in Europe. Pandora neoaphidis is known to cause epizootics (i.e. an unusually high prevalence of infected hosts) and the rapid collapse of aphid populations. We developed a weather-driven mechanistic model of the winter wheat-S. avenae-P. neoaphidis system to simulate the dynamics from spring to harvest. Aphid immigration was fixed at a rate that would lead to a pest outbreak, if not controlled by the fungus. We estimated the biocontrol efficacy by running pair-wise simulations, one with and one without the fungus. Uncertainty in model parameters and variation in weather was included, resulting in a range of simulation outcomes, and a global sensitivity analysis was performed. We identified two key understudied parameters that require more extensive experimental data collection to better assess the fungus biocontrol, namely the fungus transmission efficiency and the decay of cadaver, which defines the time window for possible disease transmission. The parameters with the largest influence on the improvement in yield were the weather, the lethal time of exposed aphids, the fungus transmission efficiency, and the humidity threshold for fungus development, while the fungus inoculum in the chosen range (between 10 and 70% of immigrant aphids carrying the fungus) was less influential. The model suggests that epizootics occurring early, around Zadoks growth stage (GS) 61, would lead to successful biocontrol, while later epizootics (GS 73) were a necessary but insufficient condition for success. These model predictions were based on the prevalence of cadavers only, not of exposed (i.e. infected but yet non-symptomatic) aphids, which in practice would be costly to monitor. The model suggests that practical Integrated Pest Management could thus benefit from including the cadavers prevalence in a monitoring program. We argue for further research to experimentally estimate these cadaver thresholds.
Abstract
Weeds affect crop yield and quality due to competition for resources. In order to reduce the risk of yield losses due to weeds, herbicides or non-chemical measures are applied. Weeds, especially creeping perennial species, are generally distributed in patches within arable fields. Hence, instead of applying control measures uniformly, precision weeding or site-specific weed management (SSWM) is highly recommended. Unmanned aerial vehicle (UAV) imaging is known for wide area coverage and flexible operation frequency, making it a potential solution to generate weed maps at a reasonable cost. Efficient weed mapping algorithms need to be developed together with UAV imagery to facilitate SSWM. Different machine learning (ML) approaches have been developed for image-based weed mapping, either classical ML models or the more up-to-date deep learning (DL) models taking full advantage of parallel computation on a GPU (graphics processing unit). Attention-based transformer DL models, which have seen a recent boom, are expected to overtake classical convolutional neural network (CNN) DL models. This inspired us to develop a transformer DL model for segmenting weeds, cereal crops, and ‘other’ in low-resolution RGB UAV imagery (about 33 mm ground sampling distance, g.s.d.) captured after the cereal crop had turned yellow. Images were acquired during three years in 15 fields with three cereal species (Triticum aestivum, Hordeum vulgare, and Avena sativa) and various weed flora dominated by creeping perennials (mainly Cirsium arvense and Elymus repens). The performance of our transformer model, 1Dtransformer, was evaluated through comparison with a classical DL model, 1DCNN, and two classical ML methods, i.e., random forest (RF) and k-nearest neighbor (KNN). The transformer model showed the best performance with an overall accuracy of 98.694% on pixels set aside for validation. It also agreed best and relatively well with ground reference data on total weed coverage, R2 = 0.598. In this study, we showed the outstanding performance and robustness of a 1Dtransformer model for weed mapping based on UAV imagery for the first time. The model can be used to obtain weed maps in cereals fields known to be infested by perennial weeds. These maps can be used as basis for the generation of prescription maps for SSWM, either pre-harvest, post-harvest, or in the next crop, by applying herbicides or non-chemical measures.
Authors
Arne Stensvand Katherine Ann Gredvig Nielsen Nina Trandem Vinh Hong Le Lill-Iren Hansen Anita SønstebyAbstract
No abstract has been registered