Impacts of trace amounts of labile C on plant N limitation vary from hour to week timescales

The ability of terrestrial ecosystems to store carbon (C) under rising atmospheric CO₂ will depend on how severely nitrogen (N) will limit plant growth. We tested whether increased C availability in the soil at elevated CO₂ could affect N limitation by inducing N release from soil organic matter (SOM). We established microcosms composed of Holcus lanatus plants, field soil (containing “old” SOM) and ¹⁵?N-labeled plant litter (representing “new” SOM), simulated different levels of root C release by adding a single pulse of 0, 18, 44, or 175?μg glucose C?g^?1 dry soil and recorded the effects on soil microbial biomass, microbial-feeding protozoa and nematodes and plant performance 1, 3, 9, and 32?days after C addition. The effects on H. lanatus growth and N uptake depended on the amount of added C and the time elapsed since addition. Shoot N concentration and N content were higher in pots amended with 44?μg?C g^?1 soil than in other pots 1?day after C addition. Later, 9 and 32?days after C addition, the highest glucose addition reduced the dry mass, N concentration, and N content of H. lanatus shoots in comparison to other treatment levels. Microbial biomass was generally higher in soils subjected to 44?μg glucose C?g^?1 soil than in control soils, and, at the last harvest, the numbers of protozoa were significantly higher in all soils with glucose amendments than in control soils. No effects on microbial-feeding nematodes were found, and plant N uptake from “old” and “new” SOM was equally affected by C addition. Our results seem to suggest that, while a low pulse of labile C can increase plant N uptake temporarily on an hour scale, higher amounts of C will intensify plant N limitation at timescales of days and weeks. Generalization of such dose and time dependent results requires great caution, but if verified in other plant–soil systems as well, they would suggest that plant N availability under elevated C availability may depend on the balance between positive and negative effects operating at different timescales and triggered by additional C pulses of varying size.     

Chemical Characterization of Metal-Contaminated Soil in Two Study Areas in Finland

Water, Air, & Soil Pollution - In the present work we studied the role of humic acids in the heavy metal–soil interaction after the addition of different amounts of humic acids to a soil...     

Frost hardiness of nutrient-loaded two-year-old <Emphasis Type="Italic">Picea abies</Emphasis> seedlings in autumn and at the end of freezer storage

The effects of nutrient loading (NLOAD) on the frost hardening and dehardening of Picea abies (L.) Karst. seedlings were investigated under nursery conditions. Before NLOAD, second-year container seedlings were either short-day (SD) treated for 3 weeks in July or left for the natural photoperiod (CO). By mid-September, after 5 weeks of NLOAD, the fertilization of three foliar nutrient concentration levels (low = L-SD, medium = M-SD, and high = H-SD) for the SD-treated seedlings and one (medium = M-CO) for the CO-seedlings was completed. The NLOAD resulted in foliar nitrogen concentration 10.6, 16.1, 22.3, and 17.5 g kg⁻¹ for L-SD, M-SD, H-SD and M-CO seedlings, respectively. The NLOAD had no effects on the morphology or dry mass variables of the seedlings, while SD-treatment reduced the dry mass of shoots, but not that of roots. The frost hardiness (FH) of different batches of the seedlings was assessed by the visual scoring of damage in their needles, stems and buds after their controlled exposure to freezing during frost hardening and dehardening. The low nutrient concentration in the SD-treated seedlings (L-SD seedlings) resulted in poor FH, to an even lower extent than that of the M-CO seedlings. The NLOAD did not affect the dehardening of the seedlings at the end of the freezer storage in the following spring.     

Tree regeneration in patch cutting in Norway spruce stands in northern Finland

The study focused on the success of regeneration on patch cuts in spruce-dominated stands in terms of stocking and tree height. It was based on an experiment in the Kainuu region in northern Finland. The experimental design included 8 stands with 58 clearcut patches of variable size (0.09–0.37?ha), on which either natural regeneration without site preparation or site preparation and planting of Scots pine was applied. A regeneration survey was conducted 13–15 growing seasons after treatments. Patches without site preparation had been restocked quite well from natural seed sources. The average number of trees was about 11,000?ha^?1, of which 1700?ha^?1 crop trees. With site preparation and planting, the stocking levels were also very high, 25,000 and 2100?ha^?1, respectively. One hundred percent of the planted patches and 75% of the naturally regenerated patches had an acceptable density. Restocking was equally good across the range of patch sizes. Tree growth had been somewhat slower in the smallest patches than in the largest ones in the range. Natural regeneration was capable of yielding good regeneration results in most patches, especially when site preparation was applied. Stocking levels and height development were higher yet in the planted patches.     

Characterization of a common susceptibility locus for asthma-related traits

Susceptibility to asthma depends on variation at an unknown number of genetic loci. To identify susceptibility genes on chromosome 7p, we adopted a hierarchical genotyping design, leading to the identification of a 133-kilobase risk-conferring segment containing two genes. One of these coded for an orphan G protein-coupled receptor named GPRA (G protein-coupled receptor for asthma susceptibility), which showed distinct distribution of protein isoforms between bronchial biopsies from healthy and asthmatic individuals. In three cohorts from Finland and Canada, single nucleotide polymorphism-tagged haplotypes associated with high serum immunoglobulin E or asthma. The murine ortholog of GPRA was up-regulated in a mouse model of ovalbumin-induced inflammation. Together, these data implicate GPRA in the pathogenesis of atopy and asthma.     

Effect of CCC on the morphology and growth potential of containerised silver birch seedlings

The objective of the experiment described here was to assess the effect of the synthetic growth regulator CCC on the quality of containerised silver birch seedlings. The time course of stem elongation during the growing season, the morphology and size of the seedlings at the end of the growing season, and the growth potential after winter storage were measured and the following results obtained: (1) Stem height, root-collar diameter and total dry weight of the seedlings were decreased by CCC. (2) Stem-height:root-collar-diameter ratio was slightly decreased and root weight ratio was increased by CCC. (3) Stem flexibility was not significantly affected by CCC, although it was significantly correlated with total plant dry weight and with the stem-height:root-collar-diameter ratio. (4) Root-growth potential was decreased at high doses of CCC while shoot growth potential was not affected.     

A flexible regression model for diameter prediction

We present a functional regression model for diameter prediction. Usually stem form is estimated from a regression model using dbh and height of the sample tree as predictor. With our model additional diameter observations measured at arbitrary locations within the sample tree can be incorporated in the estimation in order to calibrate a standard prediction based on dbh and height. For this purpose, the stem form of a sample tree is modelled as a smooth random function. The observed diameters are assumed as independent realizations from a sample of possible trajectories of the stem contour. The population average of the stem form within a given dbh and height class is estimated with the taper curves applied in the national forest inventory in Germany. Tree deviation from the population average is modelled with the help of a Karhunen–Loève expansion for the random part of the trajectory. Eigenfunctions and scores of the Karhunen–Loève expansion are estimated through conditional expectations within the methodological framework of functional principal component analysis (FPCA). In addition to a calibrated estimation of the stem form, FPCA provides asymptotic pointwise or simultaneous confidence intervals for the calibrated diameter predictions. For the application of functional principal component analysis modelling the covariance function of the random process is crucial. The main features of the functional regression model are discussed informally and demonstrated by means of practical examples.     

Genetic variation of pedunculate oak (Quercus robur L.) in the urban woodlands of Helsinki

We present a genetic profile of pedunculate oak (Quercus robur L.) in three woodlands of the Helsinki region. We genotyped all mature trees and samples of seedlings to evaluate patterns of genetic variation and gene flow, and evaluate the long-term stability and natural regeneration of these stands. Although five microsatellite loci showed no significant differences in heterozygosity between generations in most locations, significant (p?per se does not interfere with gene flow over the distances represented here. Based on our findings, a minimal-maintenance program reliant upon natural regeneration of oak in the public parks of Helsinki seems plausible.     

Tree species classification from fused active hyperspectral reflectance and LIDAR measurements

A new terrestrial laser system was tested for tree species classification. A dataset consisting of shape parameters of three boreal tree species was collected with Light Detection and Ranging (LIDAR) and integrated with an actively measured reflectance hyperspectra. Tree species were classified using parameters derived from reflectance spectra and point cloud shape distribution. Classification performance was tested with individual, paired, and mixed combinations of both reflectance and shape parameters. The best classification results were obtained with combined datasets consisting of two reflectance and two shape parameters. Of all tested classification parameter combinations, 67.5% were able to classify all trees with over 90% accuracy. The best reflectance spectrum bands for the examined species were located around 550 and 700 nm. The best shape parameters described the upper midsection or the tops of the trees. This study was a successful step in developing classification algorithms for integrated LIDAR and hyperspectral data.     

Modelling feed intake and milk yield responses to different grass ley harvesting strategies

A meta‐analysis of feeding trials using grass silages was conducted to predict production responses for dairy cows fed grass silage. They were divided into two subsets: 69 diets from 11 studies were used for comparison of silages made from primary growth and regrowth grass (harvesting subset), and another 157 diets from 24 studies were used for comparison of digestibility influenced by the maturity of grass ensiled (D‐value, digestible organic matter in dry matter) (maturity subset). The minimum prerequisite for an experiment to be included in the data set was that milk production, feed intake, silage characteristics and concentrate ingredients were reported. Both subsets were analysed using the mixed model procedures of SAS. The mean response in dry‐matter intake (DMI) and silage DMI to improved silage D‐value was 0.0175 and 0.0161 kg per unit D‐value (g/kg DM) respectively. The average increase in milk and energy‐corrected milk yield was 0.30 and 0.37 kg per 10‐unit increase in silage D‐value respectively. Milk protein concentration increased, and fat concentration tended to increase with enhanced silage D‐value. Each 10‐unit increase in D‐value reduced milk yield by 0.092 kg at a given dietary metabolizable energy intake (MEI), suggesting that the ME concentration of high D‐value silages was overestimated. Cows fed regrowth silage produced 0.55 kg/day more energy‐corrected milk than those fed primary growth silage at a given dietary MEI. The prediction models can be used to improve ration formulation systems or incorporated into economic models for optimizing milk production in various farming systems.