Functions for estimating belowground and whole tree biomass of birch in Norway

Obtaining accurate estimates of national belowground and whole tree biomass is important to better understand the global carbon cycle and to quantify biomass stocks and changes. However, the availability of individual tree belowground biomass functions is generally low due to the difficulty of extracting roots. Allometric birch (Betula pubescens Ehrh. and Betula pendula Roth) biomass functions were derived from 67 trees for belowground and whole tree biomass using diameter at breast height (dbh) and height as the independent variables. The sampled trees spanned a dbh range from 4.0 to 45.5?cm and the functions provided a good fit to the data (RMSE?=?14.2?kg for BG and 40.7?kg for whole tree with dbh as predictor). Belowground, total stem, live crown, and dead branch biomass comprised 29.2%, 52.2%, 18.1%, and 0.5% of the whole tree biomass, respectively. Observed root-to-shoot ratios were between 0.21 and 0.88 with a mean of 0.42. Comparisons with existing belowground birch biomass functions from Fennoscandia indicated considerable differences in estimates between existing functions. The derived data-set for belowground birch biomass is the largest in Fennoscandia and the developed functions are likely the best available for estimating national birch biomass stock and stock change in Norway.     

Remote sensing and forest inventories in Nordic countries – roadmap for the future

The Nordic countries have long traditions in forest inventory and remote sensing (RS). In sample-based national forest inventories (NFIs), utilization of aerial photographs started during the 1960s, satellite images during the 1980s, laser scanning during the 2000s, and photogrammetric point clouds during the 2010s. In forest management inventories (FMI), utilization of aerial photos started during the 1940s and laser scanning during the 2000s. However, so far, RS has mostly been used for map production and research rather than for estimation of regional parameters or inference on their accuracy. In recent years, the RS technology has been developing very fast. At the same time, the needs for information are constantly increasing. New technologies have created possibilities for cost-efficient production of accurate, large area forest data sets, which also will change the way forest inventories are done in the future. In this study, we analyse the state-of-the-art both in the NFIs and FMIs in the Nordic countries. We identify the benefits and drawbacks of different RS materials and data acquisition approaches with different user perspectives. Based on the analysis, we identify the needs for further development and emerging research questions. We also discuss alternatives for ownership of the data and cost-sharing between different actors in the field.     

Analysis of pregerminated barley using hyperspectral image analysis

Pregermination is one of many serious degradations to barley when used for malting. A pregerminated barley kernel can under certain conditions not regerminate and is reduced to animal feed of lower quality. Identifying pregermination at an early stage is therefore essential in order to segregate the barley kernels into low or high quality. Current standard methods to quantify pregerminated barley include visual approaches, e.g. to identify the root sprout, or using an embryo staining method, which use a time-consuming procedure. We present an approach using a near-infrared (NIR) hyperspectral imaging system in a mathematical modeling framework to identify pregerminated barley at an early stage of approximately 12 h of pregermination. Our model only assigns pregermination as the cause for a single kernel's lack of germination and is unable to identify dormancy, kernel damage etc. The analysis is based on more than 750 Rosalina barley kernels being pregerminated at 8 different durations between 0 and 60 h based on the BRF method. Regerminating the kernels reveals a grouping of the pregerminated kernels into three categories: normal, delayed and limited germination. Our model employs a supervised classification framework based on a set of extracted features insensitive to the kernel orientation. An out-of-sample classification error of 32% (CI(95%): 29-35%) is obtained for single kernels when grouped into the three categories, and an error of 3% (CI(95%): 0-15%) is achieved on a bulk kernel level. The model provides class probabilities for each kernel, which can assist in achieving homogeneous germination profiles. This research can further be developed to establish an automated and faster procedure as an alternative to the standard procedures for pregerminated barley.     

Finding the appropriate level of complexity for a simulation model: An example with a forest growth model

The topic of model complexity is fundamental to model developers and model users. In this study, we investigate how over- and under-fitting of a driving function in a simulation model influences the predictive ability of the model. Secondly, we investigate whether model selection approaches succeed in selecting driving functions with the best predictive ability. We address these issues through an example with the forest simulator SORTIE-ND. Utilizing maximum likelihood methods and individual tree growth data we parameterize five growth functions of increasing complexity. We then incorporate each growth function into the simulation model SORTIE-ND and test predicted growth against independent data. Compared to the independent data, the simplest and the most complex growth functions had the poorest predictive ability while functions of intermediate complexity had the best predictive ability. The poor predictive ability of the simplest model is caused by poor approximation of the system while the poor predictive ability of the most complex model is caused by biased parameter estimates. A growth function of intermediate complexity was the most parsimonious model where error due to approximation and error due to estimation were simultaneously minimized. The model selection criteria AIC and BIC were found to select complex functions that were over-fitted according to the independent data comparison. BIC was closer to choosing the model that minimized prediction error than AIC. In this example, BIC is the more appropriate model selection criterion. It is important that both model developers and models users remember that more complex models do not always result in better predictive models.     

Large scale reforestation of farmlands on sloping hills in South China karst

Landscape Ecology - South China karst is undergoing large scale land-use conversions driven by reforestation projects aiming at combating land degradation. However, the spatial extent of these...     

Solar nebula heterogeneity in p-process samarium and neodymium isotopes

Bulk carbonaceous chondrites display a deficit of approximately 100 parts per million (ppm) in 144Sm with respect to other meteorites and terrestrial standards, leading to a decrease in their 142Nd/144Nd ratios by approximately 11 ppm. The data require that samarium and neodymium isotopes produced by the p process associated with photodisintegration reactions in supernovae were heterogeneously distributed in the solar nebula. Other samarium and neodymium isotopes produced by rapid neutron capture (r process) in supernovae and by slow neutron capture (s process) in red giants were homogeneously distributed. The supernovae sources supplying the p- and r-process nuclides to the solar nebula were thus disconnected or only weakly connected.     

Erratum: Chemical composition of cabbage (Brassica oleracea L. var. capitata) grown on acid sulfate soils

The purpose of this study was to investigate the influence of soil geochemistry on the concentrations of Ca, K, Mg, P, Co, Ni, Zn, Mn, Cu, and Fe in cabbage (Brassica oleracea L. var. capitata) grown on acid sulfate (AS) soils in Western Finland. A total of 11 topsoil (0–20 cm) and corresponding cabbage samples and three whole‐soil profiles (≈ 0–260 cm) were collected on three agricultural fields. The concentrations of Co and Zn in cabbage were correlated with the NH₄Ac‐extractable (easily available) concentrations in the topsoil, indicating that the uptake of these elements in cabbage is largely governed by soil geochemistry. Yet, the concentrations of Co and Zn in cabbage were not in general elevated relative to that of Finnish average values, although some AS soils showed enriched concentrations of these metals in the soil and cabbage. Significant geochemical differences (e.g., oxidation depth, organic‐matter and S content, pH) were observed among the studied AS soils, while, on the other hand, the concentrations of Ca, K, Mg, P, Ni, Mn, Cu, and Fe in cabbage were relatively similar. The hydroxylamine‐extractable concentrations of these elements in the topsoil were not correlated to those in cabbage, suggesting that uptake is not governed by the oxide‐bound fraction of these elements in the soil. Similarly, the easily available concentrations of Ca, P, Ni, Mn, Cu, and Fe in the topsoil were not correlated to those in cabbage, indicating that uptake is independent of the easily available concentrations in the soil. Hence, it is suggested that cabbage can regulate and thus optimize its concentrations of Ca, P, Ni, Mn, Cu, and Fe. Oxidation depth affected neither the easily available concentrations of Co, Ni, Zn, and Mn in the topsoil nor the concentrations in cabbage. However, the subsoil with a lower oxidation depth, which is to a smaller extent affected by leaching, may partly be enriched in these metals. Nevertheless, these showed no increased concentrations in cabbage. Based on these findings, it is suggested that the large amounts of metals mobilized in AS soils are easily lost to drains, subsequently contaminating nearby waterways and estuaries whereas they are only partly enriched in cabbage and other previously studied crops (oat).