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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

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Abstract

Soil nutrient contents and stoichiometric ratios are determinants for soil biogeochemical cycling and functions. Variable rock fragment contents (RFC) may shape the soil nutrient status and availability in mountain ecosystems. We need to better understand how and why soil nutrients and stoichiometry shift across the RFC gradients. To investigate patterns of soil nutrient stoichiometry and underlying mechanisms in rocky soils, we conducted a field experiment involving four RFCs gradients (0%, 25%, 50% and 75%, V/V) and five vegetation treatments (four indigenous species, Artemisia vestita, Bauhinia brachycarpa, Cotinus szechuanensis and Sophora davidii, plus a non-planted treatment). Soil total carbon (C), total nitrogen (N), total phosphorus (P) and their molar ratios were measured. The contents of soil C, N and P, and C:N, C:P and N:P decreased with increasing RFC in all treatments, despite their trends were inconsistent in certain soil layers. The averages of soil N content significantly increased by 13.8% and 14.8% in C. szechuanensis and S. davidii, respectively. A. vestita and B. brachycarpa had higher soil C:N than C. szechuanensis and S. davidii. Soil nutrients and stoichiometry were positively related to soil water content (SWC) and soil capillary porosity, and negatively to bulk density and soil non-capillary porosity in all vegetation treatments, but varying relationships with biomass of plant components. These results demonstrated negative effect of RFC and discrepant effects of the plants on soil nutrients and stoichiometry. Soil structure, SWC and vegetation were the main drivers of variations in soil nutrient stoichiometry. We further concluded that soil nutrient stoichiometry in rocky soils is shaped by two influencing paths; effects of RFC on soil physical properties (SWC and soil structure) and effects of different vegetations. Our findings advance knowledge and mechanisms of soil nutrient stoichiometry in rocky soils and provide theoretical support for improving and restoring nutrient status in stony regions.

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Abstract

Thinning treatments along with the establishment of mixed forest stands have been put forward as possible adaptation strategies to cope with climate change, although the effectiveness of combining these two measures has scarcely been studied and may vary depending on stand conditions and the thinning regime employed. The aim of this study was to better understand the effect of commercial thinning and of the different growth behavior of two coexisting species on their inter- and intra-annual cumulative radial increment patterns. For this purpose, we studied radial increment in a Scots pine-Pyrenean oak (Pinus sylvestris L.-Quercus pyrenaica Willd.) Mediterranean mixed forest in north-west Spain over two climatically contrasting years (2016–2017). The data came from a thinning trial consisting of a randomized latin square design with a control and two commercial thinning treatments from below; one moderate and the other heavy, removing 25% and 50 % of initial basal area, respectively, of both species. The radial increment was analyzed based on bi-weekly readings from band dendrometers installed on 90 oak and pine trees. A non-linear mixed model based on double-Richards curve was fitted to explore the differences between thinning treatments and species response in the intra-annual cumulative radial increment patterns. Inter-annual basal area increments for each species at stand level were quantified by aggregating the tree estimates obtained from the model fitted in the first step. Tree and stand level growth were greater in Scots pine, which also showed a greater growth response to early spring droughts than the Pyrenean oak. Heavy thinning increased radial increment in trees of both species at the expense of decreased total stand basal area. At species level, basal area growth in Scots pine decreased through thinning, whereas for Pyrenean oak, the heavy thinning intensity resulted in the same basal area growth as the control. Thus, heavy thinning induced a trade-off between total stand growth and tree-level response to climatic conditions for Scots pine but with no loss in productivity in the case of the Pyrenean oak. Hence, heavy thinning may be an appropriate measure to attain productive stability of the oak coppice in the studied mixed forest as well as to adapt tree growth to future droughts associated with climate change.

Abstract

Normal log lengths in Norway are 3–6 m (NL), but occasionally there is a demand for short timber with a 2.5 m log length (ST). There are concerns that ST could reduce the forwarders' productivity. Six type stands were created based on harvester data. Different assortment distributions, conditions, and forwarders were simulated in each type stand. It was found that an additional ST assortment almost always decreased productivity (from –15.5 to +4%). Increased forwarding distance (m), more difficult driving conditions, and increased log concentration [m3·(100 m strip road)–1] decreased the productivity difference between sites with ST and NL and sites with only NL. Increased forwarder size increased the productivity difference between sites with ST and NL and sites with only NL. It is possible to load two stacks of ST on some forwarders. Such loading was more productive than loading one stack on longer forwarding distances, while the opposite was the case on short distances. However, loading two stacks of ST can lead to overloading.

Abstract

After fungal decay experiments chemical characterisation of the wood is often a routine and several methodological approaches are available. In this study, we tested if simultaneous thermal analysis (STA) is a valid alternative to traditional wet chemical methods since STA allows significantly smaller sample size and faster analysis. Three model fungi including the brown rot fungi Rhodonia placenta and Gloeophyllum trabeum and the white rot fungus Trametes versicolor were employed in the study using Norway spruce as substrate. The experiment was harvested after 10, 20 and 52 weeks. At each harvest interval, aliquots of the material were characterized by STA and wet chemical methods. The results validated that STA can be effectively used to estimate cell wall composition of brown rot depolymerised wood. However, STA slightly overestimated cellulose at brown rot decay above 50%. The method was not verified for simultaneous white rot because STA only estimated hemicellulose correctly compared to the wet chemical method. Hence, STA is considered suitable for brown rot fungi below 50% mass loss but not for simultaneous white rot because STA did not estimate cellulose and lignin correctly.

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Abstract

Yield maps give farmers information about growth conditions and can be a tool for sitespecific crop management. Combine harvesters may provide farmers with detailed yield maps if there is a constant flow of a certain amount of biomass through the yield sensor. This is unachievable for grass seeds because the weight of the intake is generally too small to record the variation. Therefore, there is a need to find another way to make grass seed yield maps. We studied seed yield variation in two red fescue (Festuca rubra) fields with variation in management and soil fertility, respectively. We estimated five vegetation indices (VI) based on RGB images taken from a drone to describe yield variation, and trained prediction models based on relatively few harvested plots. Only results from the VI showing the strongest correlation between the index and the yield are presented (Normalized Excess Green Index (ExG) and Normalized Green/Red Difference Index (NGRDI)). The study indicates that it is possible to predict the yield variation in a grass field based on relatively few harvested plots, provided the plots represent contrasting yield levels. The prediction errors in yield (RMSE) ranged from 171 kg ha-1 to 231 kg ha-1, with no clear influence of the size of the training data set. Using random selection of plots instead of selecting plots representing contrasting yield levels resulted in slightly better predictions when evaluated on an average of ten random selections. However, using random selection of plots came with a risk of poor predictions due to the occasional lack of correlation between yield and VI. The exact timing of unmanned aerial vehicles (UAVs) image capture showed to be unimportant in the weeks before harvest.