Browsing by Subject "biomass"

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  • Kauppi, P.E.; Selkäinaho, J.; Puttonen, P. (Finnish Zoological and Botanical Publishing Board, 1983)
  • Vuorinne, Ilja (Helsingin yliopisto, 2020)
    Biomass is an important parameter for crop monitoring and management, as well as for assessing carbon cycle. In the field, allometric models can be used for non-destructive biomass assessment, whereas remote sensing is a convenient method for upscaling the biomass estimations over large areas. This study assessed the dry leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre and biofuel production in tropical and subtropical regions. First, an allometric model was developed for predicting the leaf biomass. Then, Sentinel-2 multispectral satellite imagery was used to model the leaf biomass at 8851 ha plantation in South-Eastern Kenya. For the allometric model 38 leaves were sampled and measured. Plant height and leaf maximum diameter were combined into a volume approximation and the relation to biomass was formalised with linear regression. A strong log-log linear relation was found and leave-one-out cross-validation for the model showed good prediction accuracy (R2 = 0.96, RMSE = 7.69g). The model was used to predict biomass for 58 field plots, which constituted a sample for modelling the biomass with Sentinel-2 data. Generalised additive models were then used to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (D2 = 74%, RMSE = 4.96 Mg/ha) was achieved with VIs based on the red-edge (R740 and R783), near-infrared (R865) and green (R560) spectral bands. Highly heterogeneous growing conditions, mainly variation in the understory vegetation seemed to be the main factor limiting the model performance. The best performing VI (R740/R783) was used to predict the biomass at plantation level. The leaf biomass ranged from 0 to 45.1 Mg/ha, with mean at 9.9 Mg/ha. This research resulted a newly established allometric equation that can be used as an accurate tool for predicting the leaf biomass of sisal. Further research is required to account for other parts of the plant, such as the stem and the roots. The biomass-VI modelling results showed that multispectral data is suitable for assessing sisal leaf biomass over large areas, but the heterogeneity of the understory vegetation limits the model performance. Future research should address this by investigating the background effects of understory and by looking into complementary data sources. The carbon stored in the leaf biomass at the plantation corresponds to that in the woody aboveground biomass of natural bushlands in the area. Future research is needed on soil carbon sequestration and soil and plant carbon fluxes, to fully understand the carbon cycle at sisal plantation.
  • Afrane, Yaw (Helsingin yliopisto, 2020)
    The world population is growing and is expected to reach over 9 billion in about 30 years. Climate change is also widely expected to worsen famines in certain regions of the world. This will drastically increase global food demand. Food security efforts should be therefore be geared towards promoting food crops that can thrive in these regions and can withstand the condition likely to be brought about by changing climate. Cassava is a typical example of such a crop. This study investigated the use of digital images to estimate growth parameters of young cassava plants. Cassava was cultivated in pots at the University of Helsinki greenhouse at Viikki. The plants were given different water level (100%, 60% and 30% saturation) and potassium (0.1, 1.0, 4.0, 16.0 and 32.0mM) treatments. Digital red-green-blue (RGB) and multispectral images were taken every other week for 5 consecutive times. The images were processed to obtain leaf area, Normalized Difference Vegetation Index (NDVI), and Crop Senescence Index (CSI) and correlated with directly measured growth parameters of the young cassava crops. It was observed that leaf area that was computed from images, and NDVI which was computed from the multispectral images have significant positive correlations with the growth parameters, ie, actual leaf area, chlorophyll content, and plant biomass. CSI however showed weak a correlation between the growth parameters of the young cassava plants. Images leaf area and NDVI were then used to identify the changes in the effects of the water and potassium treatments.
  • Ahtila, Olli (Helsingfors universitet, 2011)
    In recent times climate change, decrease of fossil fuels and increase of their price have greatly increased worldwide interest in renewable energy sources. In Finland, there has been a lot of concentration towards forest industry’s secondary produced wood basis biomass, that forest industry uses for its energy production. Forest industry’s waste water cleaning process creates different kinds of sludge, which are either reused or destroyed by burning or transporting to waste treatment plant. Especially reuse of bio sludge is difficult, and waste area placing in the future is impossible or at least economically too expensive. In practice, sludge is treated by burning, and by drying it becomes a bio fuel. The energy use is the best way to destroy waste sludge. Because of the high water consist of the sludge it must be dried before burning. Drying the sludge with secondary energy flow with waste heat from forest industry processes increases energy income from the burning process and replaces the use of fossil fuels. The goal of this research was to find out the most optimal mixture of bark and sludge by changing different drying parameters. The experimental work was started by building a laboratory size fixed bed dryer for the energy technology experiment hall, where drying was studied by blowing heated air through the fuel layer. The dried fuel material was a mixture of bark and sludge, or just bark or sludge at different masses, different percentage mixtures and different temperatures. Making the drying curves was based on weight changes. In the test rig were probes for controlling and setting the temperature as the experiment expected. The temperature and weight changes were recorded to computer during the experiment. The drying experiments showed that sludge-bark mixture dries well, when the percentage of the sludge mass doesn’t increase over 50 %. When the share of sludge is higher, drying is no longer effective, which is due to channelling of the air through the dried fuel material in the fixed bed dryer. When drying the bark, increase of the temperature from 50 °C to 70 °C was much more effective than from 70 °C to 90 °C, the difference in drying time was about doubled.
  • Salmi, Pauliina; Mäki, Anita; Mikkonen, Anu; Pupponen, Veli-Mikko; Vuorio, Kristiina; Tiirola, Marja (Suomen ympäristökeskus, 2021)
    Boreal Environment Research 26: 17-27
    The smaller the phytoplankton, the greater effort is required to distinguish individual cells by optics-based methods. Flow cytometry is widely applied in marine picophytoplankton research, but in freshwater research its role has remained minor. We compared epifluorescence microscopy and flow cytometry in assessing the composition, abundance and cell sizes of autofluorescent picophytoplankton in epilimnia of 46 Finnish lakes. Phycocyaninrich picocyanobacteria were the most dominant. The two methods yielded comparable total picophytoplankton abundances, but the determination of cell sizes, and thus total biomasses, were on average an order of magnitude higher in the microscopy results. However, flow cytometry yielded higher cell sizes when applied on small-celled cultured algae. Our study demonstrated that both epifluorescence microscopy and flow cytometry are useful methods in assessing abundances of phycocyanin-rich and phycoerythrin-rich picocyanobacteria and eukaryotic picophytoplankton in lakes. However, accurate determination of cell size and biomass remain challenges for microscopy and especially for flow cytometry.
  • Neumann, Mathias; Moreno, Adam; Thurnher, Christopher; Mues, Volker; Härkönen, Sanna; Mura, Matteo; Bouriaud, Olivier; Lang, Mait; Cardellini, Giuseppe; Thivolle-Cazat, Alain; Bronisz, Karol; Merganic, Jan; Alberdi, Iciar; Astrup, Rasmus; Mohren, Frits; Zhao, Maosheng; Hasenauer, Hubert (2016)
    Net primary production (NPP) is an important ecological metric for studying forest ecosystems and their carbon sequestration, for assessing the potential supply of food or timber and quantifying the impacts of climate change on ecosystems. The global MODIS NPP dataset using the MOD17 algorithm provides valuable information for monitoring NPP at 1-km resolution. Since coarse-resolution global climate data are used, the global dataset may contain uncertainties for Europe. We used a 1-km daily gridded European climate data set with the MOD17 algorithm to create the regional NPP dataset MODIS EURO. For evaluation of this new dataset, we compare MODIS EURO with terrestrial driven NPP from analyzing and harmonizing forest inventory data (NFI) from 196,434 plots in 12 European countries as well as the global MODIS NPP dataset for the years 2000 to 2012. Comparing these three NPP datasets, we found that the global MODIS NPP dataset differs from NFI NPP by 26%, while MODIS EURO only differs by 7%. MODIS EURO also agrees with NFI NPP across scales (from continental, regional to country) and gradients (elevation, location, tree age, dominant species, etc.). The agreement is particularly good for elevation, dominant species or tree height. This suggests that using improved climate data allows the MOD17 algorithm to provide realistic NPP estimates for Europe. Local discrepancies between MODIS EURO and NFI NPP can be related to differences in stand density due to forest management and the national carbon estimation methods. With this study, we provide a consistent, temporally continuous and spatially explicit productivity dataset for the years 2000 to 2012 on a 1-km resolution, which can be used to assess climate change impacts on ecosystems or the potential biomass supply of the European forests for an increasing bio-based economy. MODIS EURO data are made freely available at ftp://palantir.boku.ac.at/Public/MODIS_EURO.
  • Epie, Kenedy E.; Artigas, Olga M.; Santanen, Arja; Mäkelä, Pirjo S. A.; Stoddard, Frederick L. (2018)
    The biomass potential of eight high yielding maize cultivars was studied in the sub-boreal climate of southern Finland. The effects of harvest date on lignin and sugar production, biomass yield, mineral element composition, bioenergy potential and soil nutrient management were determined in two years. The eight maize cultivars produced 17.6-33.3 t ha(-1) of biomass. The ear fraction contained 50-60% of the biomass, and ash and mineral element composition of the plant fractions were significantly different (p <0.001), with more ash, Ca and S in the above-ear fractions of the plants than in the mid-stalk portions, whereas the C:N ratio was highest in the lower stalk. Cultivars with less lignin content produced more fermetable sugars. Despite the relatively cool growing conditions and short season of the sub-boreal region, maize has potential for use as biomass, for biofuel or other uses. The crop can be fractioned into ear and stalk, with the lower 20 cm of stalk left in the field to maintain soil organic matter content.
  • Laine, Jere (Helsingin yliopisto, 2022)
    Cyanobacteria are an important part of the phytoplankton community and aquatic ecosystems. Cyanobacteria can form large mass occurrences, i.e. blooms, which can be toxic or cause other harm. Research and monitoring of cyanobacteria has been based on microscopy analysis. However, molecular-based methods, such as 16S rRNA sequencing are replacing microscopy analyses in the near future. The Finnish Environment Institute has stated that molecular methods are part of environmental monitoring before 2030. In this Master’s thesis the aim was to determine whether conventional microscopy analyses and 16S rRNA sequencing differ when comparing nano- and micro-sized cyanobacteria. The material was collected from a laboratory experiment of the Finnish Environment Institute’s (SYKE) MiDAS project, which was conducted in the summer of 2020. The results of the microscopy and 16S rRNA analyses differed from each other. The relative abundances of the cyanobacteria genera differed between sample types. Microscopy analyses estimated that the alpha diversity was higher compared to the results of the sequencing analyses. The main reason for the difference between the types of analyses was due to the differences in cyanobacteria belonging to the order of Synechococcales. Some of the Synechococcales species were observed only by the sequencing analyses, e.g. Snowella and some of the Synechococcales species were only observed by the microscopy analyses, e.g. Romeria and Woronichinia. It was observed that both methods are prone to identification errors. The differences between the 16S rRNA sequencing and the microscopy analyses are vastly different. It may affect on the review of long-term data of the phytoplankton community. Therefore, it is important to examine the differences between the types of analyses. Studying the dissimilarities between the types of analyses should be focused on the research of the small cell-sized colonial cyanobacteria, i.e. the species of Chroococcales and Synechococcales.
  • Kellomäki, Seppo; Hari, Pertti (Suomen metsätieteellinen seura, 1980)
  • Kellomäki, Seppo; Hari, Pertti; Kanninen, Markku; Ilonen, Pirkko (Suomen metsätieteellinen seura, 1980)
  • Mäkelä, Annikki; Kellomäki, Seppo; Hari, Pertti (Suomen metsätieteellinen seura, 1980)
  • Forsblom, Louise; Engström-öst, Jonna; Lehtinen, Sirpa; Lips, Inga; Lindén, Andreas (2019)
    Journal of Plankton Research 41 (6), 925–938
    Abiotic variables subject to global change are known to affect plankton biomasses, and these effects can be species-specific. Here, we investigate the environmental drivers of annual biomass using plankton data from the Gulf of Finland in the northern Baltic Sea, spanning years 1993–2016. We estimated annual biomass time-series of 31 nanoplankton and microplankton species and genera from day-level data, accounting for the average phenology and wind. We found wind effects on day-level biomass in 16 taxa. We subsequently used state-space models to connect the annual biomass changes with potential environmental drivers (temperature, salinity, stratification, ice cover and inorganic nutrients), simultaneously accounting for temporal trends. We found clear environmental effects influencing the annual biomasses of Dinobryon faculiferum, Eutreptiella spp., Protoperidinium bipes, Pseudopedinella spp., Snowella spp. and Thalassiosira baltica and indicative effects in 10 additional taxa. These effects mostly concerned temperature, salinity or stratification. Together, these 16 taxa represent two-thirds of the summer biomass in the sampled community. The inter-annual variability observed in salinity and temperature is relatively low compared to scenarios of predicted change in these variables. Therefore, the potential impacts of the presented effects on plankton biomasses are considerable.
  • Rodil, I. F.; Attard, K. M.; Norkko, J.; Glud, R. N.; Norkko, A. (2020)
    A central goal of benthic ecology is to describe the pathways and quantities of energy and material flow in seafloor communities over different spatial and temporal scales. We examined the relative macrobenthic contribution to the seafloor metabolism by estimating respiration and secondary production based on seasonal measurements of macrofauna biomass across key coastal habitats of the Baltic Sea archipelago. Then, we compared the macrofauna estimates with estimates of overall seafloor gross primary production and respiration obtained from the same habitats using the aquatic eddy covariance technique. Estimates of macrobenthic respiration rates suggest habitat-specific macrofauna contribution (%) to the overall seafloor respiration ranked as follows: blue mussel reef (44.5) > seagrass meadow (25.6) > mixed meadow (24.1) > bare sand (17.8) > Fucus-bed (11.1). In terms of secondary production (g C m−2 y−1), our estimates suggest ranking of habitat value as follows: blue mussel reef (493.4) > seagrass meadow (278.5) > Fucus-bed (102.2) > mixed meadow (94.2) > bare sand (52.1). Our results suggest that approximately 12 and 10% of the overall soft-sediment metabolism translated into macrofauna respiration and secondary production, respectively. The hard-bottoms exemplified two end-points of the coastal metabolism, with the Fucus-bed as a high producer and active exporter of organic C (that is, net autotrophy), and the mussel reef as a high consumer and active recycler of organic C (that is, net heterotrophy). Using a combination of metrics of ecosystem functioning, such as respiration rates and secondary production, in combination with direct habitat-scale measurements of O2 fluxes, our study provides a quantitative assessment of the role of macrofauna for ecosystem functioning across heterogeneous coastal seascapes.
  • Peltonen-Sainio, Pirjo; Jauhiainen, Lauri; Känkänen, Hannu; Joona, Juuso; Hydén, Tony; Mattila, Tuomas J. (MDPI AG, 2022)
    Agronomy
  • Epie, Kenedy E.; Santanen, Arja; Makela, Pirjo S. A.; Stoddard, Frederick L. (2018)
    Jerusalem artichoke (Helianthus tuberosus L.) produces substantial shoots not used as food. To test its potential as a sustainable bioenergy crop, we studied the effects of synthetic fertilizer and intercropped legumes as nitrogen (N) sources on the growth, aboveground biomass dry matter yield and energy qualities of this crop. Plant height, leaf area index (LAI), SPAD-value, biomass yield, ash content and mineral element composition were determined. Mean aboveground biomass yields were not significantly affected by N source (legume intercrops and synthetic fertilizer) and ranged from 13 to 17 t ha(-1). Remarkably, plants given no fertilizer yielded equally to plants given 90 N kg ha(-1). These results confirm that Jerusalem artichoke, compared to other energy crops, have less need for N and can potentially be sustained by N fixing legumes in an intercropped system. This could reduce or eliminate production and environmental cost in cultivation of biomass feedstock for energy use.
  • Kraft, Kaisa; Seppälä, Jukka; Hällfors, Heidi; Suikkanen, Sanna; Ylöstalo, Pasi; Anglès, Sílvia; Kielosto, Sami; Kuosa, Harri; Laakso, Lauri; Honkanen, Martti; Lehtinen, Sirpa; Oja, Johanna; Tamminen, Timo (Frontiers Media S.A., 2021)
    Frontiers in Marine Science 8: 594144
    Cyanobacteria are an important part of phytoplankton communities, however, they are also known for forming massive blooms with potentially deleterious effects on recreational use, human and animal health, and ecosystem functioning. Emerging high-frequency imaging flow cytometry applications, such as Imaging FlowCytobot (IFCB), are crucial in furthering our understanding of the factors driving bloom dynamics, since these applications provide community composition information at frequencies impossible to attain using conventional monitoring methods. However, the proof of applicability of automated imaging applications for studying dynamics of filamentous cyanobacteria is still scarce. In this study we present the first results of IFCB applied to a Baltic Sea cyanobacterial bloom community using a continuous flow-through setup. Our main aim was to demonstrate the pros and cons of the IFCB in identifying filamentous cyanobacterial taxa and in estimating their biomass. Selected environmental parameters (water temperature, wind speed and salinity) were included, in order to demonstrate the dynamics of the system the cyanobacteria occur in and the possibilities for analyzing high-frequency phytoplankton observations against changes in the environment. In order to compare the IFCB results with conventional monitoring methods, filamentous cyanobacteria were enumerated from water samples using light microscopical analysis. Two common bloom forming filamentous cyanobacteria in the Baltic Sea, Aphanizomenon flosaquae and Dolichospermum spp. dominated the bloom, followed by an increase in Oscillatoriales abundance. The IFCB results compared well with the results of the light microscopical analysis, especially in the case of Dolichospermum. Aphanizomenon biomass varied slightly between the methods and the Oscillatoriales results deviated the most. Bloom formation was initiated as water temperature increased to over 15°C and terminated as the wind speed increased, dispersing the bloom. Community shifts were closely related to movements of the water mass. We demonstrate how using a high-frequency imaging flow cytometry application can help understand the development of cyanobacteria summer blooms.
  • Nurmi, Juha (The Society of Forestry in Finland, 1997)
    The effective heating values of the above and below ground biomass components of mature Scots pine (Pinus sylvestris), Norway spruce (Picea abies), downy birch (Betula pubescens), silver birch (Betula pendula), grey alder (Alnus incana), black alder (Alnus glutinosa) and trembling aspen (Populus tremula) were studied. Each sample tree was divided into wood, bark and foliage components. Bomb calorimetry was used to determine the calorimetric heating values. The species is a significant factor in the heating value of individual tree components. The heating value of the wood proper is highest in conifers. Broad-leaved species have a higher heating value of bark than conifers. The species factor diminishes when the weighted heating value of crown, whole stems or stump-root-system are considered. The crown material has a higher heating value per unit weight in comparison with fuelwood from small-sized stems or wholetrees. The additional advantages of coniferous crown material are that it is a non-industrial biomass resource and is readily available. The variability of both the chemical composition and the heating value is small in any given tree component of any species. However, lignin, carbohydrate and extractive content were found to vary from one part of the tree to another and to correlate with the heating value.
  • Jenal, Alexander; Hueging, Hubert; Ahrends, Hella Ellen; Bolten, Andreas; Bongartz, Jens; Bareth, Georg (2021)
    UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R-2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R-2: 0.75 to 0.84). These results indicate the imaging system's potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed.
  • Larsson, Aron (Helsingin yliopisto, 2021)
    The science of fish stock assessment is one that is very resource and labor intensive, with stock assessment models historically being based on data that causes a model to overestimate the strength of a population, sometimes with drastic consequences. The need of cost-effective assessment models and approaches increases, which is why I looked into using Bayesian modeling and networks as an approach not often used in fisheries science. I wanted to determine if it could be used to predict both recruitment and spawning stock biomass of four fish species in the north Atlantic, cod, haddock, pollock and capelin, based on no other evidence other than the recruitment or biomass data of the other species and if these results could be used to lower the uncertanties of fish stock models. I used data available on the RAM legacy database to produce four different models with the statistical software R, based on four different Bayes algorithms found in the R-package bnlearn, two based on continuous data and two based on discrete data. What I found was that there is much potential in the Bayesian approach to stock prediction and forecasting, as our prediction error percentage ranged between 1 and 40 percent. The best predictions were made when the species used as evidence had a high correlation coefficient with the target species, which was the case with cod and haddock biomass, which had a unusually high correlation of 0.96. As such, this approach could be used to make preliminary models of interactions between a high amount of species in a specific area, where there is data abundantly available and these models could be used to lower the uncertanties of the stock assessments. However, more research into the applicability for this approach to other species and areas needs to be conducted.
  • Larjavaara, Markku; Davenport, Tim R.B.; Gangga, Adi; Holm, Saara; Kanninen, Markku; Nguyen, Dinh Tien (2019)