Wood based ash can be used as a liming material and fertilizer in forest, thus providing a solution for utilization of wood ash and formation of additional increment of trees, especially on organic soils, where lack of potassium and phosphorus is a well-known factor limiting the growth of trees. Yet, many studies have been made with adverse results mostly because of different soil types and species investigated. However, by now the best results on fertilization with wood ash have been found on drained peatlands, where some nutrient deficiencies have emerged. The aim of this study was to find out the growth response of Norway spruce on drained organic soils after spreading of potassium sulphate (K2SO4) and wood ash (WA). The experiment was carried out in three middle aged Norway spruce (Picea abies) stands on drained mineral and peat soil. Experiment consisted of three replications of each treatment in each stand including WA, K2SO4 and the control on 400 square meter sub-sample plots each. The diameter and height of trees were measured. The samples of increment cores were collected 4 years after fertilization to determine the additional volume increment, which varied from 8.5 m**3 haE-1 in WA plots and from 9.7 m**3 ha-1 to 17.2 m**3 haE-1 on K2SO4 plots. Both - the wood ash and potassium application - significantly increased the increment of Norway spruce on drained mineral and peat soil, although no significant differences were found between the treatments (p = 0.82, α = 0.05).

The aim of the study is to evaluate the impact of drainage on soil carbon stock in a transitional mire drained for forestry. The study site is located in the central part of Latvia representing hemiboreal vegetation zone. Site was drained in 1960. It is located in a catchment area of the river Veseta. An undrained site at the same catchment area was chosen for control (ca. 2.5 km between sites). In both sites, the depth of peat is 4 – 4.5 m. Drained site is dominated by coniferous trees. Soil samples collected in 2014 were used to determine bulk density and carbon content, and to calculate soil carbon stock. Samples were collected down to 80 cm depth. Ground surface elevation was measured before and several times after the drainage to determine peat subsidence. Carbon stock has increased by 0.3 tons haE-1 yr**1 after drainage, although peat has subsided on average by 26 cm (13 – 48 cm). Subsidence was mainly caused by physical shrinkage of peat not by organic matter oxidation. Drainage was followed by compaction of aerated soil layer, which has caused most of the subsidence, especially during the first years after drainage. Soil bulk density has increased almost twice at soil surface layer 0 – 10 cm (from 75 kg m**3 to 141 kg m**3). Differences decrease at deeper sampling depths. It is concluded that drainage is not always followed by reduction of carbon stock in soil. Increased above and below ground litter production rates may offset accelerated decomposition of organic matter after drainage.

The aim of the study is to evaluate carbon stock in litter and organic forest soils in Latvia as well as to characterize accumulation of carbon in litter in afforested lands. The study is providing empirically valid information about soil and litter carbon changes for the National greenhouse gas (GHG) inventory by using data from National forest inventory (NFI), forest soil monitoring demonstration project BioSoil and other studies. The study proves significance of organic forest soil carbon pool in Latvia and demonstrates necessity to extend NFI incorporated forest soil monitoring program to improve data on soil density in wet organic soils, as well as to integrate data characterizing water regime in forests. The acquired data also proves that the conservative approach of calculation of carbon stock changes in litter in afforested lands applied in the Latvia’s National GHG inventory avoids overestimation of CO2 removals. The data on litter carbon stock collected in this study is sufficient to estimate total carbon stock for stands dominated by most common tree species and long term impact of changes of species composition. Measurements of organic soil and litter thickness should be continued by NFI and integrated with more detailed soil monitoring to increase accuracy of carbon stock estimates and gather data necessary for verification of modelling data, particularly in afforested lands and due to change of dominant species.

The global population has begun to rise exponentially; therefore, the demand for bioresources including food and fibre is increasing. An increasing demand for food and fibre necessitates more sustainable use of natural resources especially for soilbased ecosystem services. In this context, Functional Land Management was developed to optimize agricultural soilbased ecosystem services to meet both agricultural and environmental targets simultaneously. The aim of the research is to evaluate unmanaged agricultural land use change impact on primary productivity function in three parishes in Latvia by using Functional Land Management framework. Evaluation of primary productivity function was accomplished for both sectors agriculture and forestry by using profit and working hours as a proxyindicators. Production of vegetables and perennial plantations has higher supply of primary productivity function comparing to other land uses. Land use changes affect all soil functions that we expect from our land, especially primary productivity function. After applying land use changes, an increase in profit is higher in Liezere parish for both areas on mineral soils (7.1%) and areas on organic soils (5.2%); while an increase in working hours is higher in Usma parish: 36.6% in areas on mineral soils and 1.0% increase in areas on organic soils. Shortterm benefits are received from agricultural land, while forest land provides long-term return which increases over time but can only be obtained after reaching the age of felling. Before applying land use changes or changes in management practices we have to consider other soil function and national commitments.

Forest drainage is a common management practice, carried out in order to improve tree growth; however, the alterations in hydrological and microclimate dynamics can cause diverse changes in the characteristics of ground cover vegetation. The aim of study was to characterise the composition and richness of ground cover vegetation in drained forest stands, where the groundwater levels have been affected by the reconstruction of drainage diches. Research was conducted in the hemiboreal forests of Latvia. Three study sites were chosen, measurements of groundwater level, stand parameters and projective cover of ground covered vegetation took place in two stands which underwent reconstruction and restoration of the drainage system in 2019, and a control stand, where the drainage system had not been restored. For assessing the relationship of ground cover vegetation in relation to groundwater level and stand factors, DCA analysis was used. The differences between stands, regarding the species projective cover and species composition, were assessed by ANOSIM (Analysis of similarities). Sites, where drainage ditches were fully or partially reconstructed exhibited a greater diversity of ground cover vegetation species compared to the control stand, where no renewal of drainage ditches had occurred. Conversely, the control stand displayed a higher projective cover of the bryophyte layer. The composition of ground cover vegetation species differed amongst all studied stands, the varying stand characteristics and co-dominant tree species in canopy layer had a more pronounced influence on ground cover vegetation, making it complicated to evaluate direct impact of groundwater level.

This study investigates the distribution and carbon content of organic soils in Latvia, leveraging machine learning techniques alongside remote sensing and National Forest Inventory (NFI) data to enhance the precision of organic soil mapping. Our approach integrates data from various sources, including airborne laser scanning (ALS) data, digital elevation models (DEM), depth-to-water (DTW) and wet area maps (WAM), and historical organic soil data. By classifying over 24,000 soil probing measurements across Latvia into distinct peat layer thickness categories, we develop a machine learning model that categorizes the thickness of the organic layer with notable accuracy. Our findings indicate that the model, particularly when employing the xgbTREE algorithm and over-sampling method, successfully identifies areas with peat layers thicker than 40 cm, demonstrating a significant improvement over traditional mapping methods. The study reveals an underestimation of organic soil coverage in Latvia by previous estimates, suggesting a broader distribution than recognized, with the model achieving an accuracy of 0.86 and a kappa value of 0.67. This research not only underscores the efficacy of integrating machine learning and remote sensing for soil mapping but also highlights the critical role of accurate data and models in determining organic soil distribution. The insights gained from this study are vital for policy-making and environmental planning, offering a more detailed understanding of Latviaʼs peatland resources and their conservation needs.

Forests play a significant role in the mitigation of climate change through carbon storage and sequestration. However, a forest’s capacity to absorb carbon is influenced by a number of factors, such as soil characteristics, the selection of tree species, and the application of silvicultural practices. A study in Latvia was conducted to evaluate the carbon stock and sequestration potential of birch, common aspen, black alder and grey alder growing on periodically waterlogged and drained organic soils. Empirical data of forest resources were obtained from the National Forest Inventory (NFI) from 2016 to 2020. The findings indicate that black alder may thrive in both soil types, as it showed the best increase in carbon stock in periodically waterlogged soils, reaching a maximum of 129 t C haE−1 at the age of 61–70 years. Greater carbon sequestration in tree biomass occurs on drained soils compared to periodically waterlogged. Birch, aspen, and black alder stands may store between 106 and 119 t C haE−1 at age of 61 and 70 years, which is similar to grey alder stands at ages of 31–40 (114 ± 0.73 t C haE−1). Therefore, a short rotation for grey alder growing on drained organic soils could maximize carbon accumulation and add substitution value. These results suggest that different types of deciduous trees have varying capacities for carbon storage and sequestration, and that it’s important to consider site-specific factors, rotation age and silvicultural practices when aiming to maximize carbon sequestration in tree biomass.