Carbon Balance Model Research Articles

Effects of Wildfire and Logging on Soil CO2 Efflux in Scots Pine Forests of Siberia

Wildfires and logging play an important role in regulating soil carbon fluxes in forest ecosystems. In Siberia, large areas are disturbed by fires and logging annually. Climate change and increasing anthropogenic pressure have resulted in the expansion of disturbed areas in recent decades. However, few studies have focused on the effects of these disturbances on soil CO2 efflux in the vast Siberian areas. The objective of our research was to evaluate differences in CO2 efflux from soils to the atmosphere between undisturbed sites and sites affected by wildfire and logging in Scots pine forests of southern Siberia. We examined 35 plots (undisturbed forest, burned forest, logged plots, and logged and burned plots) on six study sites in the Angara region and four sites in the Zabaikal region. Soil CO2 efflux was measured using an LI-800 infrared gas analyzer. We found that both fire and logging significantly reduced soil efflux in the first years after a disturbance due to a reduction in vegetation biomass and consumption of the forest floor. We found a substantially lower CO2 efflux in forests burned by high-severity fires (74% less compared to undisturbed forests) than in forests burned by moderate-severity (60% less) and low-severity (37% less) fires. Clearcut logging resulted in 6–60% lower soil CO2 efflux at most study sites, while multiple disturbances (logging and fire) had 48–94% lower efflux. The soil efflux rate increased exponentially with increasing soil temperature in undisturbed Scots pine forests (p < 0.001) and on logged plots (p < 0.03), while an inverse relationship to soil temperature was observed in burned forests (p < 0.03). We also found a positive relationship (R = 0.60–0.83, p < 0.001) between ground cover depth and soil CO2 efflux across all the plots studied. Our results demonstrate the importance of disturbance factors in the assessment of regional and global carbon fluxes. The drastic changes in CO2 flux rates following fire and logging should be incorporated into carbon balance models to improve their reliability in a changing environment.

Leveraging long short‐term memory networks and transfer learning for the soft‐measurement of flue gas flowrate from coal‐fired boilers

AbstractThe dynamic operation and deep peak‐shaving of power‐generating units cause significant fluctuations in flue gas flowrate, thus affecting the accuracy of CO2 emissions measured by continuous emission monitoring systems (CEMS). This study established a long short‐term memory network with an attention mechanism (LSTM‐AM) for the soft measurement of the flue gas flowrate in real‐time. First, flue gas flowrate data and continuous operation parameters over 25 days were sampled from a typical 660 MW coal‐fired boiler in China. Then, a carbon balance model was established to verify the data reliability. The LSTM‐AM model was trained and testified at the 660 MW coal‐fired boiler. Results show that the LSTM‐AM model significantly surpassed the pristine LSTM model without attention, the convolutional neural network (CNN) with LSTM, and the static support vector regression (SVR) model in the real‐time prediction of flue gas flowrate. Finally, the LSTM‐AM model was generalized to a 630 MW coal‐fired power unit via transfer learning, which was further demonstrated to outperform the model re‐trained from scratch. This work manifests the feasibility of deep learning for the soft measurement of flue gas flowrate, which is promising to solve data‐lagging issues when measuring CO2 emissions from coal‐fired power plants.

Quantifying the Potential Contribution of Submerged Aquatic Vegetation to Coastal Carbon Capture in a Delta System from Field and Landsat 8/9-Operational Land Imager (OLI) Data with Deep Convolutional Neural Network

Submerged aquatic vegetation (SAV) are highly efficient at carbon sequestration and, despite their relatively small distribution globally, are recognized as a potentially valuable component of climate change mitigation. However, SAV mapping in tidal marshes presents a challenge due to optically complex constituents in the water. The emergence and advancement of deep learning-based techniques in the field of habitat mapping with remote sensing imagery provides an opportunity to address this challenge. In this study, an analytical framework was developed to quantify the carbon sequestration of SAV habitats in the Atchafalaya River Delta Estuary from field and remote sensing observations using deep convolutional neural network (DCNN) techniques. A U-Net-based model, Wetland-SAV Network, was trained to identify the SAV percent cover (high, medium, and low) as well as other estuarine habitat types from Landsat 8/9-OLI data. The areal extent of SAV was up to 8% of the total area (47,000 ha). The habitat areas and habitat-specific carbon fluxes were then used to quantify the net greenhouse gas (GHG) flux of the study area for with/without SAV scenarios in a carbon balance model. The total net GHG flux was in the range of −0.13 ± 0.06 to −0.86 ± 0.37 × 105 tonne CO2e y−1 and increased up to 40% (−0.23 ± 0.10 to −0.90 ± 0.39 × 105 tonne CO2e y−1) when SAV was accounted for within the calculation. At the hectare scale, the inclusion of SAV resulted in an increase of ~60% for the net GHG sink in shallow areas adjacent to the emergent marsh where SAV was abundant. This is the first attempt at remotely mapping SAV in coastal Louisiana as well as a first quantification of net GHG flux at the scale of hectares to thousands of hectares, accounting for SAV within these sub-tropical coastal delta marshes. Remote sensing and deep learning models have high potential for mapping and monitoring SAV in turbid sub-tropical coastal deltas as a component of the increasing accuracy of net GHG flux estimates at small (hectare) and large (coastal basin) scales.

Modeling Climate Regulation of Arable Soils in Northern Saxony under the Influence of Climate Change and Management Practices

One approach to increasing the climate-regulating potential of the agricultural sector is carbon sequestration in agricultural soils. This involves storing atmospheric carbon dioxide in the soil in the form of soil organic carbon (SOC) through agricultural management practices (AMPs). Model simulations of area-specific current and future SOC stocks can be used to test appropriate AMPs. In this study, the CANDY Carbon Balance (CCB) model was used to determine how different AMPs could affect SOC stocks in a study area in northern Saxony, Germany. Specifically, we used scenarios with different intensities of sustainable AMPs to assess the potential effects of reduced tillage, crop cultivation, and fertilizer management, as well as the management of crop residues and by-products. The analysis was carried out for the simulation period 2020–2070, with and without consideration of climate change effects. The results showed an average carbon sequestration potential of 5.13–7.18 t C ha−1 for the whole study area, depending on the intensity of AMP implemented. While higher intensities of sustainable AMP implementation generally had a positive impact on carbon sequestration, the scenario with the highest implementation intensity only led to the second highest gains in SOC stocks. The most important factor in increasing SOC stocks was reduced tillage, which resulted in a carbon sequestration potential of 0.84 t C ha−1 by 2070. However, reduced application rates of fertilizers also proved to be critical, resulting in a reduction in carbon stocks of up to 2.2 t C ha−1 by 2070. Finally, the application of high-intensity sustainable AMPs was shown to be able to offset the negative impacts of an intermediate climate change scenario for most of the simulation period. Overall, the results not only confirmed existing knowledge on the effects of AMPs on carbon sequestration for a specific case study area, but also identified future management scenarios that stress the need for widespread adoption of sustainable management practices under changing climate conditions.

Modeling Soil Organic Carbon Dynamics of Arable Land across Scales: A Simplified Assessment of Alternative Management Practices on the Level of Administrative Units

Regional assessments of soil organic carbon (SOC) trends and the carbon sequestration potential of alternative management practices (AMP) are highly relevant for developing climate change mitigation strategies for the agricultural sector. Such studies could benefit from simplified SOC modeling approaches on the scale of administrative units as this often corresponds to the level of policy-making and data availability. However, there is a risk of systematic errors in such scaling operations. To overcome this problem, we performed a scaling experiment where we simulated the SOC dynamics of the arable soils of the State of Saxony (Germany) across a series of scales using the CANDY Carbon Balance (CCB) model. Specifically, we developed model set-ups on four different administrative levels (NUTS1, NUTS2, NUTS3, and LAU) and evaluated the simulation results of the upscaled models against a 500 m grid-based reference model. Furthermore, we quantified the carbon sequestration potential of selected AMP scenarios (addressing field grass, cover crops, and conservation tillage) across all scales. The upscaled model set-ups adequately simulated the SOC trends of Saxon arable land compared to the grid-based reference simulation (scaling error: 0.8–3.8%), while providing significant benefits for model application, data availability and runtime. The carbon sequestration potential of the AMP scenarios (1.33 Mt C until 2050) was slightly overestimated (+0.07–0.09 Mt C) by the upscaled model set-ups. Regardless of the scale of model set-up, we showed that the use of aggregated statistical input data could lead to a systematic underestimation of SOC trends. LAU and NUTS3 levels were shown to be a suitable compromise for effectively quantifying SOC dynamics and allowed for an acceptable spatial prioritization of AMPs. Such simplified, scale-adapted assessments are valuable for cross-regional comparisons and for communication to and among decision-makers, and might provide a quantitative basis for discussions on the effectiveness of AMPs in various stakeholder processes.

Sources and sequestration rate of organic carbon in sediments of the bare tidal flat ecosystems: A model approach

Humans have been contributing adversely to greenhouse gas emissions by generating a vast amount of CO2, primarily causing climate change. Nature-based climate solutions, consisting of both terrestrial and marine ecosystems, are tremendous potential for sequestering and storing significant amounts of carbon, which can help to slow the progression of climate change. In this study, we use a carbon balance model to simulate the carbon sequestration rate and carbon stored in bare tidal flat (BTF) areas along Korea's west and south coasts from 2018 to 2050. Furthermore, the percentage of potential carbon sources deposited at BTF sites was calculated using a two-terminal mixing model and δ13C data. The carbon deposited on the BTF areas is the result of lateral carbon transport from upslope terrestrial regions as well as marine sources. Based on the δ13C isotope, this study classified potential carbon sources in BTFs sediment into two categories: terrestrial and marine. The results indicate that the proportion of organic carbon contribution from terrestrial sources ranged from 7.63% to 49% in the BTF studied areas. We discuss the validity of projection which was investigated over three years, from 2018 to 2020. A preliminary conclusion is that future carbon storage at BTF sites will increase significantly. Carbon accumulation increases linearly over time in nearly all areas studied, with carbon sequestration rates ranging from 0.053 to 0.623 (MgC ha−1 yr−1). This study found that a significant amount of carbon is sequestered for a long time in the BTF regions based on model simulation results. In addition, it also contributes to projects that seek to promote and conserve these climate benefits by providing estimates of carbon storage in coastal BTFs that can be included in NDCs for the Paris Agreement.

Perennial Crops Can Compensate for Low Soil Carbon Inputs from Maize in Ley-Arable Systems.

(1) Background: Soil organic carbon (SOC) in agricultural soils plays a crucial role in mitigating global climate change but also, and maybe more importantly, in soil fertility and thus food security. Therefore, the influence of contrasting cropping systems on SOC not only in the topsoil, but also in the subsoil, needs to be understood. (2) Methods: In this study, we analyzed SOC content and δ13C values from a crop rotation experiment for biogas production, established in southern Germany in 2004. We compared two crop rotations, differing in their proportions of maize (0 vs. 50%) and perennial legume-grass leys as main crops (75 vs. 25%). Maize was cultivated with an undersown white clover. Both rotations had an unfertilized variant and a variant that was fertilized with biogas digestate according to the nutrient demand of crops. Sixteen years after the experiment was established, the effects of crop rotation, fertilization, and soil depth on SOC were analyzed. Furthermore, we defined a simple carbon balance model to estimate the dynamics of δ13C in soil. Simulations were compared to topsoil data (0-30 cm) from 2009, 2017, and 2020, and to subsoil data (30-60 cm) from 2020. (3) Results: Crop rotation and soil depth had significant effects, but fertilization had no effect on SOC content and δ13C. SOC significantly differed between the two crop rotations regarding δ13C in both depths but not regarding content. Annual enrichment in C4 (maize) carbon was 290, 34, 353, and 70 kg C ha-1 per maize year in the topsoil and subsoil of the unfertilized and fertilized treatments, respectively. These amounts corresponded to carbon turnover rates of 0.8, 0.3, 0.9, and 0.5% per maize year. Despite there being 50% maize in the rotation, maize carbon only accounted for 20% of the observed carbon sequestration in the topsoil. Even with pre-defined parameter values, the simple carbon model reproduced observed δ13C well. The optimization of model parameters decreased the carbon use efficiency of digestate carbon in the soil, as well as the response of belowground carbon allocation to increased aboveground productivity of maize. (4) Conclusions: Two main findings resulted from this combination of measurement and modelling: (i) the retention of digestate carbon in soil was low and its effect on δ13C was negligible, and (ii) soil carbon inputs from maize only responded slightly to increased above-ground productivity. We conclude that SOC stocks in silage maize rotations can be preserved or enhanced if leys with perennial crops are included that compensate for the comparably low maize carbon inputs.

Spatiotemporal evolution of urban carbon balance and its response to new-type urbanization: A case of the middle reaches of the Yangtze River Urban Agglomerations, China

In recent years, low-carbon sustainable development has attracted increasing attention worldwide. However, the spatiotemporal evolution of carbon balance and the impact of various urban development indicators are still uncertain, especially in the current scenario of climate change, rapid urbanization, and ecosystem degradation. This study proposes a flexible framework for analyzing the carbon balance and the factors that drive it. This framework is used to investigate the dynamics and heterogeneity of the regional carbon balance from the perspective of land use. New-type urbanization is introduced to explore the impacts of population, economic, spatial, and ecological urbanization on carbon emission using the PLS–STIRPAT model. The proposed method is applied to analyze the urban carbon balance in the Middle Reaches of the Yangtze River Urban Agglomeration (MRYRUA) in China. The results show that the MRYRUA was in a state of carbon deficit between 2000 and 2020, while the growth rate decreased significantly during 2005–2020. The urban carbon balance model can classify into five categories: the high carbon emissions–high carbon balance index (CBI), the high carbon emissions–low CBI, the medium carbon emissions–high CBI, the low carbon emissions–high CBI, and the low carbon emissions–low CBI. In addition, the indicators of ecological urbanization, population density, and road density in new-type urbanization were found to have a positive effect on curbing the growth in carbon emissions. In conclusion, this study provides appropriate guidelines for carbon reduction policies for cities and is valuable for achieving sustainable development goals.

Double Yields and Negative Emissions? Resource, Climate and Cost Efficiencies in Biofuels With Carbon Capture, Storage and Utilization

As fossil-reliant industries turn to sustainable biomass for energy and material supply, the competition for biogenic carbon is expected to intensify. Using process level carbon and energy balance models, this paper shows how the capture of residual CO2 in conjunction with either permanent storage (CCS) or biofuel production (CCU) benefits fourteen largely residue-based biofuel production pathways. With a few noteworthy exceptions, most pathways have low carbon utilization efficiencies (30–40%) without CCS/U. CCS can double these numbers and deliver negative emission biofuels with GHG footprints below −50 g CO2 eq./MJ for several pathways. Compared to CCS with no revenue from CO2 sequestration, CCU can offer the same efficiency gains at roughly two-third the biofuel production cost (e.g., 99 EUR/MWh vs. 162 EUR/MWh) but the GHG reduction relative to fossil fuels is significantly smaller (18 g CO2 eq./MJ vs. −99 g CO2 eq./MJ). From a combined carbon, cost and climate perspective, although commercial pathways deliver the cheapest biofuels, it is the emerging pathways that provide large-scale carbon-efficient GHG reductions. There is thus some tension between alternatives that are societally best and those that are economically most interesting for investors. Biofuel pathways vent CO2 in both concentrated and dilute streams Capturing both provides the best environomic outcomes. Existing pathways that can deliver low-cost GHG reductions but generate relatively small quantities of CO2 are unlikely to be able to finance the transport infrastructure required for transformative bio-CCS deployment. CCS and CCU are accordingly important tools for simultaneously reducing biogenic carbon wastage and GHG emissions, but to unlock their full benefits in a cost-effective manner, emerging biofuel technology based on the gasification and hydrotreatment of forest residues need to be commercially deployed imminently.

Carbon balance model of groundwater system –A field application

For the wide distribution and huge amount of water, a series of carbon-related water–rock interactions in groundwater recharge, runoff and discharge have an important impact on the global carbon cycle. However, the current research on carbon cycle of groundwater mainly focuses on its emission to surface water. There are few reports on the carbon balance of groundwater system. Hongjiannao basin was taken as an example in this paper. The carbon balance equation of groundwater system was constructed by using mass balance principle and hydrogeochemical simulation method to analyze the carbon budget in groundwater. The result showed that HongJiannao basin was a ‘carbon sink’ with an annual carbon sink of 754.26 t (0.52 t/km2). Dissolution of dolomite and precipitation of calcite were the main hydrochemical reactions affecting carbon balance of groundwater; HCO3– in atmospheric precipitation and carbon dioxide (CO2) in vadose zone were the main sources of carbon in groundwater. The CO2 recharged by the vadose zone to groundwater was 1441.9 tons/a, most of them had hydration and ionization reactions with water. Artificial exploitation and runoff discharge were the main ways of carbon discharge from groundwater in this area. Carbon emissions from artificial exploitation reached 414.16 t/a, accounting for 58 % of the total carbon emissions from runoff.

Understanding Effects of Competition and Shade Tolerance on Carbon Allocation with a Carbon Balance Model

A carbon-balance model based on mechanistic and allometric relationships (CroBas) was used to assess the effects of competition in C allocation in jack pine (Pinus banksiana Lamb.), a shade-intolerant species, and black spruce (Picea mariana (Mill.) B.S.P.), a moderately shade-tolerant species. For both species, model efficiencies ranged from 36 to 99%. The average model bias was lower than 11% and 18% for jack pine and black spruce, respectively. For both jack pine and black spruce, the total tree C increased over the years, with greater increases noted for decreasing competition. When considering a C compartment as a ratio of the total tree C, decreasing competition resulted for both species in decreasing stem C and increasing C in branches and foliage. When considering the amount of C in a given compartment, for jack pine, decreasing competition led to greater C stem, branches, foliage, and roots, whereas, for black spruce, it also increased its stem C but lately shifted at about 20 years, following thinning; thus, the changing C allocation over time results from both “passive plasticity”, reflecting environmentally induced variations in growth, and “ontogenetic plasticity”, referring to variations in the ontogenetic trajectory of a trait. Overall, the C allocation to stem and foliage relative to the total tree C generally decreased as competition decreased, supporting the optimal partitioning theory. These C-allocation patterns were related to the species’ shade tolerance and illustrated how jack pine and black spruce maximize their competitive fitness.

Sustaining maize yields and soil carbon following land clearing in the forest–savannah transition zone of West Africa: Results from a 20-year experiment

Sustainable alternatives to slash-and-burn shifting cultivation in the (sub)humid tropics rely on the use of external nutrient inputs to address soil fertility decline. The use of organic inputs is widely accepted as a practice to improve soil fertility, in particular soil organic carbon (SOC). On the other hand, its combined use with mineral fertilizer has the potential to maintain or increase crop productivity through positive interactive effects between both resources. Few studies have investigated these effects in the long term. Therefore, the objective of this study was to investigate whether maize productivity and soil SOC can be sustained under permanent cropping with sole and combined use of compost and mineral nitrogen (N) fertilizer. Here, we report results from a long-term experiment carried out in Gagnoa, Ivory Coast, from 1971 to 1990. The experiment followed a randomized block design comprising eight replicates of 12 treatments. The two studied factors were compost (0 or 10 t DM ha−1 yr−1) and mineral N (0, 40, 80, 120, 160 or 200 kg N ha−1 yr−1) additions. Average maize grain yields of the first cropping cycles were significantly lower without compost (5.05 ± 1.57 t ha−1) than with compost addition (6.07 ± 1.31 t ha−1). The annual yield variability as shown by the standard deviation of the mean was reduced by 20% with compost addition. Without compost, 53% of the initial SOC stock in the 0–20 cm soil layer was lost, resulting in a SOC loss rate of − 0.62 t C ha−1 yr−1 compared to 21% with compost (−0.27 t C ha−1 yr−1). Compost addition therefore reduced SOC loss with an apparent SOC storage rate of 0.35 t C ha−1 yr−1. The conversion rate of organic carbon (OC) inputs to SOC was about 12%. The Introductory Carbon Balance Model (ICBM) reproduced well SOC dynamics, especially without compost. Without mineral N and without compost, maize grain yield decreased with decreasing SOC concentration until the introduction of leguminous crops in the second cropping cycle. We conclude that combined application of compost with mineral N fertilizers was effective at maintaining maize productivity but inadequate to prevent the decline of SOC stocks, despite large additions. Leguminous crops in the rotation were key for maize productivity, but probably due to effects non-related to supplementary N supply.

Time-dependent climate impact of beef production – can carbon sequestration in soil offset enteric methane emissions?

The time-dependent climate impact of beef production, including changes in soil organic carbon, was examined in this study. A hypothetical suckler cow system located in south-east Sweden was analysed using a time-dependent life cycle assessment method in which yearly fluxes of greenhouse gases were considered and the climate impact in terms of temperature response over time was calculated. The climate impact expressed as carbon dioxide equivalents, i.e. global warming potential in a 100-year time perspective, was also calculated. The Introductory Carbon Balance Model was used for modelling yearly soil organic carbon changes from land use. The results showed an average carbon sequestration rate of 0.2 Mg C ha−1 and yr−1, so carbon sequestration could potentially counteract 15–22% of emissions arising from beef production (enteric fermentation, feed production and manure management), depending on system boundaries and production intensity. The temperature response, which showed a high initial increase due to methane emissions from enteric fermentation, started to level off after around 50 years due to the short atmospheric lifetime of methane. However, sustained production and associated methane emissions would maintain the temperature response and contribute to climate damage. A forage-grain beef system resulted in a lower climate impact than a forage-only beef system (due to higher slaughter age), even though more carbon was sequestered in the forage-only system.

Applying CCB to predict management change affected long‐term SOM turnover of the Extended Static Fertilization Experiment in Bad Lauchstädt

AbstractLong‐term field experiments (LTEs) are invaluable in improving understanding of soil organic matter (SOM) turnover, as some of the involved processes have proceeded over centuries. Prediction of such slow carbon fluxes depends especially on the initialization of slow‐reacting model pools and requires monitoring for a very long time for evaluation. This study reports soil organic matter (SOM) modelling covering more than 100 years for a dataset of 36 treatments of the “Extended Static Experiment” that is based on the classical “Static Experiment Bad Lauchstädt.” The experimental scheme includes different fertilization treatments where the plots are arranged in two field stripes. A special feature of this experiment is the management change for each stripe after seven decades of continuous cultivation that includes a considerable alteration of organic amendments on different SOM levels. The Candy Carbon Balance (CCB) model was calibrated for soil organic carbon (SOC) dynamics regarding initial SOC content and one parameter describing the physical protection of SOM. Calibration was performed for each plot individually, for each stripe, and for the whole experiment, resulting in a relative root mean square error (rRMSE, (−)) of 0.050, 0.058 and 0.064, respectively. Model predictions for soil organic nitrogen (SON) dynamics were satisfactory using the calibration results for SOC and the average C/N ratio from all observations within this dataset (11.8) for model initialization. The model error for SON prediction reached from rRMSE = 0.09 (whole experiment) over rRMSE = 0.085 (each stripe) to rRMSE = 0.084 (each plot). Calibration results suggest some heterogeneity between the two field stripes. Restarting the model after the management change in 1978 using the plot‐specific calculated SOC value from previous simulations for the whole time period provided different results of predicted SOC trends and values, suggesting that the precondition of SOM in steady state was still not fulfilled after seven decades.Highlights SOC and SON were modelled for 36 treatments over more than one century. Model restarts after seven decades indicated that SOM pools were not in steady state. Modelling SON required only an additional input of the average C/N ratio of SOM Effect of serious fertilization changes can be well predicted by the tested model setup

Prospective carbon balance of the wood sector in a tropical forest territory using a temporally-explicit model

Selective logging in tropical forests is often perceived as a source of forest degradation and carbon emissions. Improved practices, such as reduced-impact logging (RIL), and alternative timber production strategies (e.g. plantations) can drastically change the overall carbon impact of the wood production sector. Assessing the carbon balance of timber production is crucial but highly dependent on methodological approaches, especially regarding system boundaries and temporality. We developed a temporally-explicit and territory scale model of carbon balance calibrated with long-term local data using Bayesian inference. The model accounts for carbon fluxes from selective logging in natural forest, timber plantation, first transformation and avoided emissions through energy substitution. We used it to compare prospective scenarios of development for the wood sector in French Guiana. Results show that intensification of practices, through increased logging intensity conducted with RIL and establishment of timber plantations, are promising development strategies to reduce the carbon emissions of the French-Guianese wood sector, as well as the area needed for wood production and hence the pressure on natural forests. By reducing logging damage by nearly 50%, RIL allows increasing logging intensity in natural forest from 20 m3 ha−1 to 30 m3 ha−1 without affecting the carbon balance. The use of logging byproducts as fuelwood also improved the carbon balance of selective logging, when substituted to fossil fuel. Allocating less than 30000 ha to plantation would allow producing 200000 m3 of timber annually, while the same production in natural forest would imply logging more than 400000 ha over 60 years. Timber plantation should be preferentially established on non-forested lands, as converting natural forests to plantation leads to high carbon emission peak over the first three decades.We recommend a mixed-strategy combining selective logging in natural forests and plantations as a way to improve long-term carbon balance while reducing short-term emissions. This strategy can reduce the pressure on natural forests while mitigating the risks of changing practices and allowing a diversified source of timber for a diversity of uses. It requires adaptation of the wood sector and development of technical guidelines. Research and monitoring efforts are also needed to assess the impacts of changing practices on other ecosystem services, especially biodiversity conservation.

Modeling of Individual Fruit-Bearing Capacity of Trees Is Aimed at Optimizing Fruit Quality of Malus x domestica Borkh. 'Gala'.

The capacity of apple trees to produce fruit of a desired diameter, i.e., fruit-bearing capacity (FBC), was investigated by considering the inter-tree variability of leaf area (LA). The LA of 996 trees in a commercial apple orchard was measured by using a terrestrial two-dimensional (2D) light detection and ranging (LiDAR) laser scanner for two consecutive years. The FBC of the trees was simulated in a carbon balance model by utilizing the LiDAR-scanned total LA of the trees, seasonal records of fruit and leaf gas exchanges, fruit growth rates, and weather data. The FBC was compared to the actual fruit size measured in a sorting line on each individual tree. The variance of FBC was similar in both years, whereas each individual tree showed different FBC in both seasons as indicated in the spatially resolved data of FBC. Considering a target mean fruit diameter of 65 mm, FBC ranged from 84 to 168 fruit per tree in 2018 and from 55 to 179 fruit per tree in 2019 depending on the total LA of the trees. The simulated FBC to produce the mean harvest fruit diameter of 65 mm and the actual number of the harvested fruit >65 mm per tree were in good agreement. Fruit quality, indicated by fruit's size and soluble solids content (SSC), showed enhanced percentages of the desired fruit quality according to the seasonally total absorbed photosynthetic energy (TAPE) of the tree per fruit. To achieve a target fruit diameter and reduce the variance in SSC at harvest, the FBC should be considered in crop load management practices. However, achieving this purpose requires annual spatial monitoring of the individual FBC of trees.

Influence mechanism of filling ratio on solid-phase denitrification with polycaprolactone as biofilm carrier

In this study, three up-flow fixed-bed bioreactors were constructed with three different filling ratios (filling volume/effective volume: 30%, 60% and 90%) of polycaprolactone (PCL). Above 98% of nitrate removal efficiency was achieved with low accumulations of nitrite and ammonium for each filling ratio. Low filling ratio of PCL had extensive folds and pores that favored the attachment and growth of microorganisms; however, excessive biomass restrained nitrate specific reduction rate (NaSRR). The most dominant genera were Comamonas (0.80–57.64%), Stenotrophomonas (2.59–54.39%), Acidovorax (7.32–23.55%), Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium (0.30–19.74%) and Thermomonas (0.12–14.58%). Nitrate reductase (EC 1.7.99.4), nitrite reductase (EC 1.7.2.1) and nitric oxide reductase (EC 1.7.2.5) predicted by PICRUSt2 were abundant in high influent nitrate load (NaL). According to the analysis of carbon balance model, the utilization rate (η) of PCL showed a highly positive correlation with influent NaL, indicating reducing filling ratio or HRT might be an effective measure to save cost for nitrate removal.

Sources and sinks of greenhouse gases in the landscape: Approach for spatially explicit estimates

Climate change mitigation is a global response that requires actions at the local level. Quantifying local sources and sinks of greenhouse gases (GHG) facilitate evaluating mitigation options. We present an approach to collate spatially explicit estimated fluxes of GHGs (carbon dioxide, methane and nitrous oxide) for main land use sectors in the landscape, to aggregate, and to calculate the net emissions of an entire region. Our procedure was developed and tested in a large river basin in Finland, providing information from intensively studied eLTER research sites. To evaluate the full GHG balance, fluxes from natural ecosystems (lakes, rivers, and undrained mires) were included together with fluxes from anthropogenic activities, agriculture and forestry. We quantified the fluxes based on calculations with an anthropogenic emissions model (FRES) and a forest growth and carbon balance model (PREBAS), as well as on emission coefficients from the literature regarding emissions from lakes, rivers, undrained mires, peat extraction sites and cropland. Spatial data sources included CORINE land use data, soil map, lake and river shorelines, national forest inventory data, and statistical data on anthropogenic activities. Emission uncertainties were evaluated with Monte Carlo simulations. Artificial surfaces were the most emission intensive land-cover class. Lakes and rivers were about as emission intensive as arable land. Forests were the dominant land cover in the region (66%), and the C sink of the forests decreased the total emissions of the region by 72%. The region's net emissions amounted to 4.37 ± 1.43 Tg CO2-eq yr−1, corresponding to a net emission intensity 0.16 Gg CO2-eq km−2 yr−1, and estimated per capita net emissions of 5.6 Mg CO2-eq yr−1. Our landscape approach opens opportunities to examine the sensitivities of important GHG fluxes to changes in land use and climate, management actions, and mitigation of anthropogenic emissions.

Multi-year eddy covariance measurements of net ecosystem exchange in tropical dry deciduous forest of India

Tropical deciduous forests are unique in terms of their geographical distribution, strong seasonality and their contribution to global carbon dynamics, but yet their carbon sequestration potential is poorly sampled. In the current study, we report the carbon balance of 65-year-old tropical dry deciduous forest in central India using the multi-year eddy covariance measurements from November 2011 to May 2019. Over the study period, the forest site was observed to be a net sink of atmospheric CO2 with a mean annual net ecosystem productivity (NEP) of 524 (± 40; ±1 SD across different years) g C m−2 yr−1 with a 233 (±15) day growing season. The NEP was partitioned into gross primary productivity (GPP) of 3358 (± 167) g C m−2 yr−1 and annual carbon loss due to respiration and decomposition (Reco) of 2834 (± 157) g C m−2 yr−1. The ecosystem showed a strong seasonality as a source of carbon during the leaf-off season (March to June), and as a carbon sink during the rest of the year with significant sequestration during the winter season (October to December). The intra-annual analysis suggested that CO2 flux is primarily controlled by canopy greenness with mean Reco/GPP ratio varying from 1.90 (± 0.12) to 0.79 (± 0.04) during leaf-off to leaf-on seasons. The monthly C fluxes in the growing season are found to be strongly correlated to the environmental variables rather than in the leaf-off season, while variability in monthly ecosystem respiration was better explained by air temperature during the leaf-off season than the growing season. Further, the inter-annual variability of NEP was mainly dependent on growing season length and mean annual temperature. The variations in annual GPP and Reco were directly dependent on the increase in mean annual temperature. The eddy covariance-based NEP was complemented by close independent biometric estimates, imparting confidence in our measurements. In conclusion, this tropical dry deciduous forest site is a substantial carbon sink and would add crucial information regarding carbon budgeting of tropical forests in global carbon balance models.

A derivation error that affects carbon balance models exists in the current implementation of the modified Arrhenius function

Understanding biological temperature responses is crucial to predicting global carbon fluxes. The current approach to modelling temperature responses of photosynthetic capacity in large scale modelling efforts uses a modified Arrhenius equation. We rederived the modified Arrhenius equationfrom the source publication from 1942 and uncovered a missing term that was dropped by 2002. We compare fitted temperature response parameters between the correct and incorrect derivation of the modified Arrhenius equation. We find that most parameters are minimally affected, though activation energy is impacted quite substantially. We then scaled the impact of these small errors to whole plant carbon balance and found that the impact of the rederivation of the modified Arrhenius equationon modelled daily carbon gain causes a meaningful deviation of c.18% day-1 . This suggests that the error in the derivation of the modified Arrhenius equationhas impacted the accuracy of predictions of carbon fluxes at larger scales since >40% of Earth System Models contain the erroneous derivation. We recommend that the derivation error be corrected in modelling efforts moving forward.