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
Abstract
The large pine weevil (Hylobius abietis) is a major regeneration pest in commercial forestry. Pesticide application has historically been the preferred control method, but pesticides are now being phased out in several countries for environmental reasons. There is, thus, a need for alternative plant protection strategies. We applied methyl jasmonate (MeJA), salicylic acid (SA) or oxalic acid (OxA) on the stem of 2-year-old Norway spruce (Picea abies) plants to determine effects on inducible defenses and plant growth. Anatomical examination of stem cross-sections 9 weeks after application of 100 mM MeJA revealed massive formation of traumatic resin ducts and greatly reduced sapwood growth. Application of high concentrations of SA or OxA (500 and 200 mM, respectively) induced much weaker physiological responses than 100 mM MeJA. All three treatments reduced plant height growth significantly, but the reduction was larger for MeJA (~55%) than for SA and OxA (34-35%). Lower MeJA concentrations (5-50 mM) induced comparable traumatic resin duct formation as the high MeJA concentration but caused moderate (and non-significant) reductions in plant growth. Two-year-old spruce plants treated with 100 mM MeJA showed reduced mortality after exposure to pine weevils in the field, and this enhanced resistance-effect was statistically significant for three years after treatment.
Authors
Yupeng Zhang Tuomas Toivainen Kathryn Mackenzie Igor A. Yakovlev Paal Krokene Timo Hytönen Paul Grini Carl Gunnar FossdalAbstract
Temperature conditions experienced during embryogenesis and seed development may induce epigenetic changes that increase phenotypic variation in plants. Here we investigate if embryogenesis and seed development at two different temperatures (28 vs. 18°C) result in lasting phenotypic effects and DNA methylation changes in woodland strawberry (Fragaria vesca). Using five European ecotypes from Spain (ES12), Iceland (ICE2), Italy (IT4), and Norway (NOR2 and NOR29), we found statistically significant differences between plants from seeds produced at 18 or 28°C in three of four phenotypic features investigated under common garden conditions. This indicates the establishment of a temperature-induced epigenetic memory-like response during embryogenesis and seed development. The memory effect was significant in two ecotypes: in NOR2 flowering time, number of growth points and petiole length were affected, and in ES12 number of growth points was affected. This indicates that genetic differences between ecotypes in their epigenetic machinery, or other allelic differences, impact this type of plasticity. We observed statistically significant differences between ecotypes in DNA methylation marks in repetitive elements, pseudogenes, and genic elements. Leaf transcriptomes were also affected by embryonic temperature in an ecotype-specific manner. Although we observed significant and lasting phenotypic change in at least some ecotypes, there was considerable variation in DNA methylation between individual plants within each temperature treatment. This within-treatment variability in DNA methylation marks in F. vesca progeny may partly be a result of allelic redistribution from recombination during meiosis and subsequent epigenetic reprogramming during embryogenesis.
Authors
Cornelya KlutschAbstract
No abstract has been registered
Abstract
Tire wear particles (TWP) are a major source of microplastics that are mainly transported by stormwater from roads to the environment. Their risk has not yet been sufficiently evaluated, mainly because of the lack of suitable analytical methods for identifying and measuring their environmental concentrations. Moreover, TWP are persistent in the environment while their generation is increasing, which calls for action to limit their environmental spread. Conversely, stormwater management solutions are becoming a growing fixture in the road environment for their multipurpose role in controlling peak runoff and reducing pollution. However, knowledge of the effect of stormwater management solutions in removing TWP is limited. The overall goal of this Ph.D. study was to introduce a suitable analytical method for detecting and quantifying TWP in the environment and measuring the actual concentrations of TWP in sediments of stormwater management solutions associated with roads. Three study sites and laboratory experiments were used as data sources for the studies included in this thesis (Papers I–IV). Simultaneous thermal analysis (STA) and Fourier transform infrared spectroscopy (FTIR) were used to analyze the samples, and parallel factor analysis (PARAFAC) was used for data analysis. Analysis of variance (ANOVA) and t-tests were used for statistical analysis.
Authors
Thiago Inagaki Angela R. Possinger Steffen A. Schweizer Carsten W. Mueller Carmen Hoeschen Michael J. Zachman Lena F. Kourkoutis Ingrid Kögel-Knabner Johannes LehmannAbstract
The spatial distribution of organic substrates and microscale soil heterogeneity significantly influence organic matter (OM) persistence as constraints on OM accessibility to microorganisms. However, it is unclear how changes in OM spatial heterogeneity driven by factors such as soil depth affect the relative importance of substrate spatial distribution on OM persistence. This work evaluated the decomposition and persistence of 13C and 15N labeled water-extractable OM inputs over 50 days as either hotspot (i.e., pelleted in 1 – 2 mm-size pieces) or distributed (i.e., added as OM < 0.07 µm suspended in water) forms in topsoil (0-0.2 m) and subsoil (0.8-0.9 m) samples of an Andisol. We observed greater persistence of added C in the subsoil with distributed OM inputs relative to hotspot OM, indicated by a 17% reduction in cumulative mineralization of the added C and a 10% higher conversion to mineral-associated OM. A lower substrate availability potentially reduced mineralization due to OM dispersion throughout the soil. NanoSIMS (nanoscale secondary ion mass spectrometry) analysis identified organo-mineral associations on cross-sectioned aggregate interiors in the subsoil. On the other hand, in the topsoil, we did not observe significant differences in the persistence of OM, suggesting that the large amounts of particulate OM already present in the soil outweighed the influence of added OM spatial distribution. Here, we demonstrated under laboratory conditions that the spatial distribution of fresh OM input alone significantly affected the decomposition and persistence of OM inputs in the subsoil. On the other hand, spatial distribution seems to play a lower role in topsoils rich in particulate OM. The divergence in the influence of OM spatial distribution between the top and subsoil is likely driven by differences in soil mineralogy and OM composition.
Abstract
Active canopy sensors (ACSs) are great tools for diagnosing crop nitrogen (N) status and grain yield prediction to support precision N management strategies. Different commercial ACSs are available and their performances in crop N status diagnosis and recommendation may vary. The objective of this study was to determine the potential to minimize the differences of two commonly used ACSs (GreenSeeker and Crop Circle ACS-430) in maize (Zea mays L.) N status diagnosis and recommendation with multi-source data fusion and machine learning. The regression model was based on simple regression or machine learning regression including ancillary information of soil properties, weather conditions, and crop management information. Results of simple regression models indicated that Crop Circle ACS-430 with red-edge based vegetation indices performed better than GreenSeeker in estimating N nutrition index (NNI) (R2 = 0.63 vs. 0.50–0.51) and predicting grain yield (R2 = 0.56–0.57 vs. 0.49). The random forest regression (RFR) models using vegetation indices and ancillary data greatly improved the prediction of NNI (R2 = 0.81–0.82) and grain yield (R2 = 0.87–0.89), regardless of the sensor type or the vegetation index used. Using RFR models, moderate degree of accuracy in N status diagnosis was achieved based on either GreenSeeker or Crop Circle ACS-430. In comparison, using simple regression models based on spectral data only, the accuracy was significantly lower. When these two ACSs were used independently, they performed similarly in N fertilizer recommendation (R2 = 0.57–0.60). Hybrid RFR models were established using vegetation indices from both ACSs and ancillary data, which could be used to diagnose maize N status (moderate accuracy) and make side-dress N recommendations (R2 = 0.62–0.67) using any of the two ACSs. It is concluded that the use of multi-source data fusion with machine learning model could improve the accuracy of ACS-based N status diagnosis and recommendation and minimize the performance differences of different active sensors. The results of this research indicated the potential to develop machine learning models using multi-sensor and multi-source data fusion for more universal applications.
Authors
Silvano Fares Teodoro Georgiadis Arne Sæbø Ben Somers Koenraad Van Meerbeek Eva Beele Roberto Tognetti Giuseppe E. Scarascia-MugnozzaAbstract
In this chapter, we analyse the current state of the art on how green infrastructures mitigate and adapt to climate changes and pollution, how they may improve urban air quality, increase green mobility, and can promote other important ecosystem benefits as water cycle regulation and supply. Relevant case studies will be also described, as gaps and future perspectives will be analyzed towards reaching the full potential of urban forests and other green spaces, for Biocities in Europe and beyond.
Abstract
No abstract has been registered
Authors
Marleen Pallandt Bernhard Ahrens Marion Schrumpf Holger Lange Sönke Zaehle Markus ReichsteinAbstract
Soil organic carbon (SOC) is the largest terrestrial carbon pool, but it is still uncertain how it will respond to climate change. Especially the fate of SOC due to concurrent changes in soil temperature and moisture is uncertain. It is generally accepted that microbially driven SOC decomposition will increase with warming, provided that sufficient soil moisture, and hence enough C substrate, is available for microbial decomposition. We use a mechanistic, microbially explicit SOC decomposition model, the Jena Soil Model (JSM), and focus on the depolymerization of litter and microbial residues by microbes. These model processes are sensitive to temperature and soil moisture content and follow reverse Michaelis-Menten kinetics. Microbial decomposition rate V of the substrate [S] is limited by the microbial biomass [B]: V = Vmax * [S] * [B]/(kMB + [B]). The maximum reaction velocity, Vmax, is temperature sensitive and follows an Arrhenius function. Also, a positive correlation between temperature and kMB-values of different enzymes has been empirically shown, with Q10 values ranging from 0.71-2.80 (Allison et al., 2018). Q10 kMB-values for microbial depolymerization of microbial residues would be low compared to those of a (lignified) litter pool. An increase in kMB leads to a lower reaction velocity (V) and V becomes less temperature sensitive at low substrate concentrations. In this work we focus on the following questions: “how do temperature and soil moisture changes affect modelled heterotrophic respiration through the Michaelis-Menten term? Is there a temperature compensation effect on modelled decomposition rate because of the counteracting temperature sensitivities of Vmax and kMB?” We model these interactions under a mean warming experiment (+3.5 °K) as well as three soil moisture experiments: constant soil moisture, a drought, and a wetting scenario.
Abstract
Recent decades have seen increased temperatures and precipitation in the Nordic countries with long-term projections for reduced frost duration and depth. The consequence of these trends has been a gradual shift of delivery volumes to the frost-free season, requiring more agile management to exploit suitable weather conditions. Bearing capacity and trafficability are dependent on soil moisture state and in this context two satellite missions offer potenially useful information on soil moisture levels; NASA’s SMAP (Soil Moisture Active Passive) and ESA’s Sentinel-1. The goal of this pilot study was to quantify the performance of such satellite-based soil moisture variables for modeling forest road bearing capacity (e-module) during the frost-free season. The study was based on post-transport registrations of 103 forest road segments on the coastal and interior side of the Scandinavian mountain range. The analysis focused on roads of three types of surface deposits. Weekly SMAP soil moisture values better explained the variation in road e-module than soil water index (SWI) derived from Sentinel-1. Soil Water Index (SWI), however, reflected the weather conditions typical for operations on the respective surface deposit types. Regression analysis using (i) SMAP-based soil dryness index and (ii) its interaction with surface deposit types, together with (iii) the ratio between a combined SMAP_SWI dryness index and segment-specific depth to water (DTW) explained over 70% of the variation in road e-module. The results indicate a future potential to monitor road trafficability over large supply areas on a weekly level, given further refinement of study methods and variables for improved prediction.