Long-term soil feedback on plant N allocation in defoliated grassland miniecosystems

Defoliation of plants is known to have effects on soil organisms and nutrient availability in grassland communities, but few studies have examined whether changes in soil attributes can further feed back to plant growth and plant nutrient content. To examine defoliation-induced soil feedbacks, we established replicated miniecosystems with a grass Phleum pratense, defoliated half of the systems, collected soil from both defoliated and non-defoliated systems and planted new seedlings into each soil. The two soils did not differ in promoting shoot and root growth. However, seedlings that grew in the soil collected from defoliated systems had higher shoot N content, allocated relatively more N to shoots and had lower root N concentration than those growing in the soil collected from non-defoliated systems. Our study provides novel evidence that defoliation can generate long-lasting changes in grassland soil that in turn can affect plant N allocation.     

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.     

Critical loads of sulfur and nitrogen for lakes II: Regional extent and variability in Finland

The concept of critical loads has been developed to assist in the design of environmentally sound abatement strategies for the emissions of acidifying compounds. In this paper the critical loads of S and N for lakes in Finland are computed and mapped, based on methods presented in an accompanying paper. The employed steady-state mass balance model allows the simultaneous evaluation of the reductions required of S and N deposition exceeding these critical loads. Special emphasis has been put on the presentation of the spatial variability and the uncertainty of the critical loads and their exceedances. The derived critical loads of S and N for lakes in Finland show a substantial spatial variability. The highest exceedance of critical loads is presently estimated in the south-east of the country, where up to 80% of the lakes show an exceedance of the critical loads of S. The evaluation of two emission scenarios shows that only “maximum feasible reductions” would be sufficient for protecting most Finnish lakes from the impacts of acidic deposition. The results of this study form a basis for setting national targets for emission reductions in Finland.     

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.     

Effects of working-memory training on striatal dopamine release

Updating of working memory has been associated with striato-frontal brain regions and phasic dopaminergic neurotransmission. We assessed raclopride binding to striatal dopamine (DA) D2 receptors during a letter-updating task and a control condition before and after 5 weeks of updating training. Results showed that updating affected DA activity before training and that training further increased striatal DA release during updating. These findings highlight the pivotal role of transient neural processes associated with D2 receptor activity in working memory.     

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.     

Estimating the amount of collagen and elastic fibres in bovine teats

The amount of collagen and elastic fibres near the Fürstenberg's rosette in histological sections of bovine teats was estimated using the ImageJ image processing software. This method holds promise for comparing tissue types within and between sections but it was not a reliable way to quantify the absolute amount of tissue types in a sample. The amount of elastic fibres and collagen was similar in cow teats with a history or acute case of mastitis infection and in non-infected cows, but this could not be statistically tested due to limitations in the study material.     

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.     

A linear programming algorithm and software for forest-level planning problems including factories

Combining stand simulation and forest-level optimization is an efficient way to study harvest scenarios of a forest area. A simulator first generates for each treatment unit a number of treatment schedules. Linear programming (LP) can then be used to study how stand-level schedules can be combined at the forest level with respect to alternative goals and constraints. The special structure of the obtained LP problems can be utilized using the generalized upper-bound technique which takes care of the so-called area constraints. JLP software was based on this technique. Later J software was developed to replace JLP. Now J is developed to deal with factory problems where the transportations costs and capacities of factories are included in the problem definition. The generalized upper-bound technique was modified to handle transportation constraints which tell that each timber unit produced is transported to some of the factories. The number of these constraints is very large. This paper describes the basic features of the algorithm and its implementation in the J software.