Ligneous amendments increase soil organic carbon content in fine-textured boreal soils and modulate N2O emissions.

Organic soil amendments are used to improve soil quality and mitigate climate change. However, their effects on soil structure, nutrient and water retention as well as greenhouse gas (GHG) emissions are still poorly understood. The purpose of this study was to determine the residual effects of a single field application of four ligneous soil amendments on soil structure and GHG emissions. We conducted a laboratory incubation experiment using soil samples collected from an ongoing soil-amendment field experiment at Qvidja Farm in south-west Finland, two years after a single application of four ligneous biomasses. Specifically, two biochars (willow and spruce) produced via slow pyrolysis, and two mixed pulp sludges from paper industry side-streams were applied at a rate of 9-22 Mg ha-1 mixed in the top 0.1 m soil layer. An unamended fertilized soil was used as a control. The laboratory incubation lasted for 33 days, during which the samples were kept at room temperature (21°C) and at 20%, 40%, 70% or 100% water holding capacity. Carbon dioxide (CO2), nitrous oxide (N2O) and methane (CH4) fluxes were measured periodically after 1, 5, 12, 20 and 33 days of incubation. The application of ligneous soil amendments increased the pH of the sampled soils by 0.4-0.8 units, whereas the effects on soil organic carbon content and soil structure varied between treatments. The GHG exchange was dominated by CO2 emissions, which were mainly unaffected by the soil amendment treatments. The contribution of soil CH4 exchange was negligible (nearly no emissions) compared to soil CO2 and N2O emissions. The soil N2O emissions exhibited a positive exponential relationship with soil moisture. Overall, the soil amendments reduced N2O emissions on average by 13%, 64%, 28%, and 37%, at the four soil moisture levels, respectively. Furthermore, the variation in N2O emissions between the amendments correlated positively with their liming effect. More specifically, the potential for the pulp sludge treatments to modulate N2O emissions was evident only in response to high water contents. This tendency to modulate N2O emissions was attributed to their capacity to increase soil pH and influence soil processes by persisting in the soil long after their application.

Estimating biomass and soil carbon change at the level of forest stands using repeated forest surveys assisted by airborne laser scanner data.

BACKGROUND: Under the growing pressure to implement mitigation actions, the focus of forest management is shifting from a traditional resource centric view to incorporate more forest ecosystem services objectives such as carbon sequestration. Estimating the above-ground biomass in forests using airborne laser scanning (ALS) is now an operational practice in Northern Europe and is being adopted in many parts of the world. In the boreal forests, however, most of the carbon (85%) is stored in the soil organic (SO) matter. While this very important carbon pool is "invisible" to ALS, it is closely connected and feeds from the growing forest stocks. We propose an integrated methodology to estimate the changes in forest carbon pools at the level of forest stands by combining field measurements and ALS data. RESULTS: ALS-based models of dominant height, mean diameter, and biomass were fitted using the field observations and were used to predict mean tree biophysical properties across the entire study area (50 km2) which was in turn used to estimate the biomass carbon stocks and the litter production that feeds into the soil. For the soil carbon pool estimation, we used the Yasso15 model. The methodology was based on (1) approximating the initial soil carbon stocks using simulations; (2) predicting the annual litter input based on the predicted growing stocks in each cell; (3) predicting the soil carbon dynamics of the annual litter using the Yasso15 soil carbon model. The estimated total carbon change (standard errors in parenthesis) for the entire area was 0.741 (0.14) Mg ha-1 yr-1. The biomass carbon change was 0.405 (0.13) Mg ha-1 yr-1, the litter carbon change (e.g., deadwood and leaves) was 0.346 (0.027) Mg ha-1 yr-1, and the change in SO carbon was - 0.01 (0.003) Mg ha-1 yr-1. CONCLUSIONS: Our results show that ALS data can be used indirectly through a chain of models to estimate soil carbon changes in addition to changes in biomass at the primary level of forest management, namely the forest stands. Having control of the errors contributed by each model, the stand-level uncertainty can be estimated under a model-based inferential approach.

A widely-used eddy covariance gap-filling method creates systematic bias in carbon balance estimates.

Climate change mitigation requires, besides reductions in greenhouse gas emissions, actions to increase carbon sinks in terrestrial ecosystems. A key measurement method for quantifying such sinks and calibrating models is the eddy covariance technique, but it requires imputation, or gap-filling, of missing data for determination of annual carbon balances of ecosystems. Previous comparisons of gap-filling methods have concluded that commonly used methods, such as marginal distribution sampling (MDS), do not have a significant impact on the carbon balance estimate. By analyzing an extensive, global data set, we show that MDS causes significant carbon balance errors for northern (latitude [Formula: see text]) sites. MDS systematically overestimates the carbon dioxide (CO[Formula: see text]) emissions of carbon sources and underestimates the CO[Formula: see text] sequestration of carbon sinks. We also reveal reasons for these biases and show how a machine learning method called extreme gradient boosting or a modified implementation of MDS can be used to substantially reduce the northern site bias.

Mycelium chemistry differs markedly between ectomycorrhizal and arbuscular mycorrhizal fungi.

The chemical quality of soil carbon (C) inputs is a major factor controlling litter decomposition and soil C dynamics. Mycorrhizal fungi constitute one of the dominant pools of soil microbial C, while their litter quality (chemical proxies of litter decomposability) is understood poorly, leading to major uncertainties in estimating soil C dynamics. We examined litter decomposability of arbuscular mycorrhizal (AM) and ectomycorrhizal (EM) fungal species using samples obtained from in vitro cultivation. We showed that the chemical composition of AM and EM fungal mycelium differs significantly: EM fungi have higher concentrations of labile (water-soluble, ethanol-soluble) and recalcitrant (non-extractable) chemical components, while AM fungi have higher concentrations of acid-hydrolysable components. Our results imply that differences in decomposability traits among mycorrhizal fungal guilds represent a critically important driver of the soil C cycle, which could be as vital as is recognized for differences among aboveground plant litter.

Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils.

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.

Comparing soil inventory with modelling: Carbon balance in central European forest soils varies among forest types.

Forest soils represent a large carbon pool and already small changes in this pool may have an important effect on the global carbon cycle. To predict the future development of the soil organic carbon (SOC) pool, well-validated models are needed. We applied the litter and soil carbon model Yasso15 to 1838 plots of the German national forest soil inventory (NFSI) for the period between 1985 and 2014 to enables a direct comparison to the NFSI measurements. In addition, to provide data for the German Greenhouse Gas Inventory, we simulated the development of SOC with Yasso15 applying a climate projection based on the RCP8.5 scenario. The initial model-calculated SOC stocks were adjusted to the measured ones in the NFSI. On average, there were no significant differences between the simulated SOC changes (0.25 ±â€¯0.10 Mg C ha-1 a-1) and the NFSI data (0.39 ±â€¯0.11 Mg C ha-1 a-1). Comparing regional soil-unit-specific aggregates of the SOC changes, the correlation between both methods was significant (r2 = 0.49) although the NFSI values had a wider range and more negative values. In the majority of forest types, representing 75% of plots, both methods produced similar estimates of the SOC balance. Opposite trends were found in mountainous coniferous forests on acidic soils. These soils had lost carbon according to the NFSI (-0.89 ±â€¯0.30 Mg C ha-1 a-1) whereas they had gained it according to Yasso15 (0.21 ±â€¯0.10 Mg C ha-1 a-1). In oligotrophic pine forests, the NFSI indicated high SOC gains (1.36 ±â€¯0.17 Mg C ha-1 a-1) and Yasso15 much smaller (0.29 ±â€¯0.10 Mg C ha-1 a-1). According to our results, German forest soils are a large carbon sink. The application of the Yasso15 model supports the results of the NFSI. The sink strength differs between forest types possibly because of differences in organic matter stabilisation.

Ecosystem services of boreal forests - Carbon budget mapping at high resolution.

The carbon (C) cycle of forests produces ecosystem services (ES) such as climate regulation and timber production. Mapping these ES using simple land cover -based proxies might add remarkable inaccuracy to the estimates. A framework to map the current status of the C budget of boreal forested landscapes was developed. The C stocks of biomass and soil and the annual change in these stocks were quantified in a 20 × 20 m resolution at the regional level on mineral soils in southern Finland. The fine-scale variation of the estimates was analyzed geo-statistically. The reliability of the estimates was evaluated by comparing them to measurements from the national multi-source forest inventory. The C stocks of forests increased slightly from the south coast to inland whereas the changes in these stocks were more uniform. The spatial patches of C stocks were larger than those of C stock changes. The patch size of the C stocks reflected the spatial variation in the environmental conditions, and that of the C stock changes the typical area of forest management compartments. The simulated estimates agreed well with the measurements indicating a good mapping framework performance. The mapping framework is the basis for evaluating the effects of forest management alternatives on C budget at high resolution across large spatial scales. It will be coupled with the assessment of other ES and biodiversity to study their relationships. The framework integrated a wide suite of simulation models and extensive inventory data. It provided reliable estimates of the human influence on C cycle in forested landscapes.

Modeling Soil Carbon Dynamics in Northern Forests: Effects of Spatial and Temporal Aggregation of Climatic Input Data.

Boreal forests contain 30% of the global forest carbon with the majority residing in soils. While challenging to quantify, soil carbon changes comprise a significant, and potentially increasing, part of the terrestrial carbon cycle. Thus, their estimation is important when designing forest-based climate change mitigation strategies and soil carbon change estimates are required for the reporting of greenhouse gas emissions. Organic matter decomposition varies with climate in complex nonlinear ways, rendering data aggregation nontrivial. Here, we explored the effects of temporal and spatial aggregation of climatic and litter input data on regional estimates of soil organic carbon stocks and changes for upland forests. We used the soil carbon and decomposition model Yasso07 with input from the Norwegian National Forest Inventory (11275 plots, 1960-2012). Estimates were produced at three spatial and three temporal scales. Results showed that a national level average soil carbon stock estimate varied by 10% depending on the applied spatial and temporal scale of aggregation. Higher stocks were found when applying plot-level input compared to country-level input and when long-term climate was used as compared to annual or 5-year mean values. A national level estimate for soil carbon change was similar across spatial scales, but was considerably (60-70%) lower when applying annual or 5-year mean climate compared to long-term mean climate reflecting the recent climatic changes in Norway. This was particularly evident for the forest-dominated districts in the southeastern and central parts of Norway and in the far north. We concluded that the sensitivity of model estimates to spatial aggregation will depend on the region of interest. Further, that using long-term climate averages during periods with strong climatic trends results in large differences in soil carbon estimates. The largest differences in this study were observed in central and northern regions with strongly increasing temperatures.

Temperature sensitivity of soil carbon fractions in boreal forest soil.

Feedback to climate warming from the carbon balance of terrestrial ecosystems depends critically on the temperature sensitivity of soil organic carbon (SOC) decomposition. Still, the temperature sensitivity is not known for the majority of the SOC, which is tens or hundreds of years old. This old fraction is paradoxically concluded to be more, less, or equally sensitive compared to the younger fraction. Here, we present results that explain these inconsistencies. We show that the temperature sensitivity of decomposition increases remarkably from the youngest annually cycling fraction (Q10 < 2) to a decadally cycling one (Q10 = 4.2-6.9) but decreases again to a centennially cycling fraction (Q10 = 2.4-2.8) in boreal forest soil. Compared to the method used for current global estimates (temperature sensitivity of all SOC equal to that of the total heterotrophic soil respiration), the soils studied will lose 30-45% more carbon in response to climate warming during the next few decades, if there is no change in carbon input. Carbon input, derivative of plant productivity, would have to increase by 100-120%, as compared to the earlier estimated 70-80%, in order to compensate for the accelerated decomposition.

The importance of age-related decline in forest NPP for modeling regional carbon balances.

We show the implications of the commonly observed age-related decline in aboveground productivity of forests, and hence forest age structure, on the carbon dynamics of European forests in response to historical changes in environmental conditions. Size-dependent carbon allocation in trees to counteract increasing hydraulic resistance with tree height has been hypothesized to be responsible for this decline. Incorporated into a global terrestrial biosphere model (the Lund-Potsdam-Jena model, LPJ), this hypothesis improves the simulated increase in biomass with stand age. Application of the advanced model, including a generic representation of forest management in even-aged stands, for 77 European provinces shows that model-based estimates of biomass development with age compare favorably with inventory-based estimates for different tree species. Model estimates of biomass densities on province and country levels, and trends in growth increment along an annual mean temperature gradient are in broad agreement with inventory data. However, the level of agreement between modeled and inventory-based estimates varies markedly between countries and provinces. The model is able to reproduce the present-day age structure of forests and the ratio of biomass removals to increment on a European scale based on observed changes in climate, atmospheric CO2 concentration, forest area, and wood demand between 1948 and 2000. Vegetation in European forests is modeled to sequester carbon at a rate of 100 Tg C/yr, which corresponds well to forest inventory-based estimates.