Siltumnīcefekta gāzu emisiju ietekmējošie faktori eitrofos purvaiņos un kūdreņos = Greenhouse gas emissions and affecting factors in forests with undrained and drained nutrient-rich organic soils
Authors:   ,   Latvia University of Life Sciences and Technologies, Butlers Aldis ,   Latvian State Forest Research Institute “Silava”
Published: November 2023

Greenhouse gas emissions and affecting factors in forests with undrained and drained nutrient-rich organic soil. LSFRI “Silava” and Latvia University of Life Sciences and Technologies, 2023. Butlers A., supervisor Dr. silv. A. Lazdiņš. The volume of thesis: 105 pages, 19 tables, 39 figures, 5 annexes, and 296 references. The doctoral thesis has been elaborated at the Latvian State Forest Research Institute “Silava” and Latvia University of Life Sciences and Technologies, Forest Faculty, Department of forestry from 2019 to 2023. The topicality of this study is determined by the Paris Agreement and related international regulatory acts, which stipulate that after 2050, the land use, land use change, and forestry (LULUCF) sector must compensate for Latvia's total greenhouse gas (GHG) emissions. Organic forest soils, particularly peat and peaty soils in Latvia, are a significant source of GHG emissions in the country, and one of the most effective climate change mitigation measures in the LULUCF sector is related to their management. However, there is currently a lack of knowledge on the potential contribution of forests with different nutrient availability organic soil management scenarios to mitigating climate change. In the national GHG inventory, a single carbon dioxide (CO2) emission factor (EF) obtained from national studies is applied to calculate the CO2 emissions from drained organic soil, regardless of its nutrient availability. For the calculation of methane (CH4) and nitrous oxide (N2O) emissions, unverified EFs developed in studies in a temperate climate zone are used in the national inventory. This study aims to develop GHG EFs for drained and undrained nutrient-rich organic forest soils and to estimate the net GHG emissions of the forest ecosystem with such soils. The acquired knowledge can be used to improve the national GHG inventory methodology and to plan climate change mitigation measures. Empirical material for characterizing soil GHG emissions and soil C input was collected during a 12-month monitoring period in 31 forest compartments with clearcuts and forest stands in various stages of development. Measurements of soil CO2, CH4, and N2O emissions, as well as soil C input by foliar litter, were carried out in five replicates in each plot with an interval of four weeks. Simultaneously with the GHG measurements, soil and air temperature, as well as groundwater level, were also determined. Soil C input by ground vegetation and fine roots of trees was estimated by biomass measurements at the end of the growing season. Changes in soil C stock were calculated by summing the estimated annual cumulative soil CO2-C emissions and C input. The evaluated relationships between soil GHG emissions, C input, and affecting factors were used to quantify the dynamics of the ecosystem’s annual net GHG emissions in managed forests, by taking into account also the annual C sequestration in living biomass and deadwood, harvested wood products, and the biofuel replacement effect. The estimated annual gross soil CO2 emissions in clearcuts (7.70 ± 0.53 t C ha–1 year–1) are significantly higher than in forest stands (6.14 ± 0.15 t C ha–1 year–1). During the forest management cycle, the annual net CO2 sequestration by nutrient-rich drained and undrained forest soils is on average 0.28 ± 0.66 t C ha–1 year–1 and 0.42 ± 0.43 t C ha–1 year–1, respectively. In forest stands, the main sources of soil C input are ground vegetation and foliar litter, providing an average of 41 ± 8 % and 43 ± 6 % of the total soil C input estimated in the study, respectively. Managed forests with undrained and drained nutrient-rich soil sequester an average of 0.2 ± 9.7 and 2.9 ± 14.4 t CO2 eq year–1, respectively.