Estimating Soil Carbon Research Articles

Divergent Changes in Soil Iron-Bound Organic Carbon Between Distinct Determination Methods

Fe-OC is crucial for SOC preservation in the global ecosystem. However, there is still significant uncertainty in the determination methods of Fe-OC, and these methods are often not calibrated to each other, making the Fe-OC content by different methods impossible to compare. Here, Fe-OC is analyzed by the CBD method and the SD method from 45 soils from different land types (e.g., wetland, grassland, and forest) to compare and analyze the uncertainty and influencing factors between the two methods. Our results showed that the Fe-OC contributions to SOC (fFe-OC) measured by CBD and SD methods were significantly lower in the wetland ecosystem than in grassland and forest ecosystems. The Fe-OC content and fFe-OC in the grassland ecosystem was significantly higher using the CBD method compared to the SD method, with no significant difference between the methods in wetland and forest ecosystems. The random forest model revealed that Fe-OCCBD content was mainly affected by C/N, Clay%, and TC, whereas SOC, total nitrogen, and soil inorganic carbon were the main influences on Fe-OCSD. Taken together, our findings highlight the importance of incorporating ecosystem types and soil properties into soil carbon estimation models when predicting and estimating Fe-OC and its contribution to SOC.

69 The Holos model for estimating greenhouse gases and soil carbon: Characterizing regionalized beef farm model systems

Abstract The objective of this work is to characterize regionally representative beef farm systems that represent dominant or typical surveyed management practices for 11 beef-producing regions across Canada. This work fulfills two further purposes 1) to improve and expand the Holos model interface; and 2) to facilitate the estimation of greenhouse gas (GHG) emissions and soil carbon (C) changes on beef farms in different regions of Canada. Holos version 4 is the whole-farm model of Agriculture and Agri-Food Canada’s to estimate GHG emissions and changes in soil C on Canadian farms in response to shifts in management practices. Holos can be implemented in all 10 Canadian provinces and accounts for GHG emissions from crop and livestock production [enteric and manure methane (CH4), manure and soil N2O emissions], farm machines and infrastructure [on-farm energy carbon dioxide (CO2) emissions], as well as from the upstream production of some farm inputs (synthetic fertilizer and pesticides). The model is designed to utilize data readily available on the farm to answer, ‘What if?’ scenarios, whereby the user can test the effect of changing management practices on their whole-farm GHG budget. To reduce the data input burden on the user, Holos V4 has built-in model livestock systems for beef, dairy, swine and poultry production that characterize the dominant features of these operations in Canada at the national scale based on relevant literature/data and expert opinion. Regarding beef production, we have characterized regionally specific model beef farms for incorporation into Holos, one for each of 11 Canadian beef-producing regions. General characteristics and management practices for each farm were based on the 2011 Beef Farm Survey (Sheppard et al., 2015), which summarizes management information from 1,009 Canadian beef farms, combined with data from the Canadian Cow-Calf Cost of Production Network (Canfax 2023). Each regional farm includes cow-calf, backgrounding in confinement, backgrounding on pasture and finishing components, and considers all the specific feed (e.g., forage, grains, by-products) required for each stage of the beef cycle. These 11 model farms are simulated within the current Holos V4 model to explore the impacts of variation in beef management practices on farm GHG emissions across Canada and on soil C stocks on lands used to produce feed and graze cattle. An overview of the national-level dairy, swine and poultry components in Holos will be presented along with a more detailed perspective of whole-farm GHG budget and multi-decadal soil C dynamics in regionalized beef farms. The impact of management and environmental factors that lead to differences in GHG emissions and soil C stocks in beef farms will also be explored.

Improving soil carbon estimates of Philippine mangroves using localized organic matter to organic carbon equations

BackgroundSoutheast Asian (SEA) mangroves are globally recognized as blue carbon hotspots. Methodologies that measure mangrove soil carbon stock (SCS) are either accurate but costly (i.e., elemental analyzers), or economical but less accurate (i.e., loss-on-ignition [LOI]). Most SEA countries estimate SCS by measuring soil organic matter (OM) through the LOI method then converting it into organic carbon (OC) using a conventional conversion equation (%Corg = 0.415 * % LOI + 2.89, R2 = 0.59, n = 78) developed from Palau mangroves. The local site conditions in Palau does not reflect the wide range of environmental settings and disturbances in the Philippines. Consequently, the conventional conversion equation possibly compounds the inaccuracies of converting OM to OC causing over- or under-estimated SCS. Here, we generated a localized OM-OC conversion equation and tested its accuracy in computing SCS against the conventional equation. The localized equation was generated by plotting % OC (from elemental analyzer) against the % OM (from LOI). The study was conducted in different mangrove stands (natural, restored, and mangrove-recolonized fishponds) in Oriental Mindoro and Sorsogon, Philippines from the West and North Philippine Sea biogeographic regions, respectively. The OM:OC ratios were also statistically tested based on (a) stand types, (b) among natural stands, and (c) across different ages of the restored and recolonized stands. Increasing the accuracy of OM-OC conversion equations will improve SCS estimates that will yield reasonable C emission reduction targets for the country’s commitments on Nationally Determined Contributions (NDC) under the Paris Agreement.ResultsThe localized conversion equation is %OC = 0.36 * % LOI + 2.40 (R2 = 0.67; n = 458). The SOM:OC ratios showed significant differences based on stand types (x2 = 19.24; P = 6.63 × 10–05), among natural stands (F = 23.22; p = 1.17 × 10–08), and among ages of restored (F = 5.14; P = 0.03) and recolonized stands (F = 3.4; P = 0.02). SCS estimates using the localized (5%) and stand-specific equations (7%) were similar with the values derived from an elemental analyzer. In contrast, the conventional equation overestimates SCS by 20%.ConclusionsThe calculated SCS improves as the conversion equation becomes more reflective of localized site conditions. Both localized and stand-specific conversion equations yielded more accurate SCS compared to the conventional equation. While our study explored only two out of the six marine biogeographic regions in the Philippines, we proved that having a localized conversion equation leads to improved SCS measurements. Using our proposed equations will make more realistic SCS targets (and therefore GHG reductions) in designing mangrove restoration programs to achieve the country’s NDC commitments.

Greenhouse gas emissions and nutrient use efficiency assessment of 6 New York organic dairies

Improving nutrient use efficiency and reducing GHG emissions are important environmental priorities for organic-certified dairy operations. The objectives of this research were to quantify annual nutrient use and GHG emissions in 6 organic New York dairy farms. Farm-gate nutrient mass balances (NMB) were estimated with the Cornell NMB calculator. Whole-farm GHG emissions were estimated using the Cool Farm Tool (CFT) and COMET. Farm-gate NMB were low, ranging from -6.5 to 19 kg N/ha for N1 (without legume N fixation), 26 to 71 kg N/ha for N2 (including N fixation), -2.4 to 8.2 kg P/ha for P, and 1.1 to 19.8 kg K/ha for K. Additional nutrient imports, coupled with nutrient management planning, adequate legume stands, and diet balancing may help improve P balances and ensure no N deficiencies in the system. Estimates of annual GHG emissions intensity ranged from 0.98 to 2.10 kg of CO2-equivalents (CO2-eq) per kilogram of fat- and protein-corrected milk (FPCM) estimated by CFT, and from 0.69 to 2.48 kg CO2-eq/kg FPCM estimated by COMET. Enteric fermentation, feed production, and fuel and energy use represented the largest sources of GHG. For farms with liquid manure storages, manure management was also a significant source. Estimates of soil carbon (C) stock changes from CFT were in agreement with or smaller than previous studies, and estimates from COMET were in agreement or greater. Variability and uncertainty in the results for soil C stock change indicate that more research and new protocols are needed. The effects of individual management changes on GHG emissions intensity were small, ranging from -8 to +7% in CFT, and -8% to +8% in COMET. The management changes that resulted in the largest reductions in GHG emissions intensity included increasing individual cow productivity and ratio of milk to total feed, and implementation of manure treatment systems.

Ecosystem restoration can lead to carbon recovery in semi‐arid savanna grasslands in India

Semiarid savanna grasslands (SG) in India deliver enormous benefits to people and nature but are currently undergoing large‐scale degradation. Soil carbon stocks in degraded SGs vary in response to land use and land management changes. Although there is increasing support for restoring grasslands by planting native grass species, its impact on soil carbon recovery is largely unknown. In this study, we undertake a plot‐level investigation of soil and aboveground biomass carbon stocks to provide robust estimates of carbon densities across sites which have undergone restoration over the last 3 years. We compare these restored sites with a no‐intervention control using a space‐for‐time substitution framework and two reference old‐growth grassland sites. We find that SGs store significant amounts of carbon (11.57–26.76 tC/ha across 1‐ to 3‐year restoration sites, respectively), with most of the carbon stored in soils (7.29–15.67 tC/ha across 1‐ to 3‐year restoration sites, respectively). These estimates still remain well below the soil carbon stocks of the reference sites (range of 22.91–39.49 tC/ha). We demonstrate that soil organic carbon (SOC) stocks progressively increase with the age of grass plantings. The 3‐year site shows an increase of 35% in SOC stocks compared to the no‐intervention control and an increase of 30 and 21% in comparison to the 1‐ and 2‐year sites, respectively. Our study demonstrates a robust approach to estimate soil carbon stocks in these ecosystems and highlights that effective conservation and restoration can enable SGs in India to act as natural carbon sinks at scale.

Changes in temperature sensitivity of forest litter during decomposition along an altitudinal gradient in temperate mountains – A reciprocal litter transplantation study

The aim of this study was to assess the effects of the factors controlling the temperature sensitivity of litter decomposition, which is essential for predicting the rate of soil carbon loss in in the context of global climate changes. It based on the translocation of forest litter in a field experiment conducted in the Western Carpathians. Litterbags were used to expose litter originating from different altitudes (i.e., 600, 900, and 1200 m a.s.l.) at the altitude where it was collected and at two other altitudes on five different mountains. Litterbags were collected after 6, 10, and 24 months of exposure. The respiration rate of litter was measured in the laboratory, and Q10 coefficients were evaluated for two temperature ranges: lower (Q10L; 5 °C–15 °C) and higher (Q10H; 15 °C–25 °C). We tested whether litter Q10 values correlated with experimental factors, as well as soil and microbial community properties. After 24 months of exposure, the litter mass decreased to half of its initial mass. The general linear model (GLM) constructed for Q10 (R2adj = 77.3 %; p < 0.0001) indicated, that Q10L values were higher than Q10H values (2.19 ± 0.58 and 1.52 ± 0.31, respectively) and increased strongly with time (1.56 ± 0.36, 1.73 ± 0.45, and 2.36 ± 0.60, consecutively). There was a significant interaction between the temperature range and time, indicating that Q10L increased more over time than Q10H. Other interaction between temperature range and litter origin indicated that Q10L increased with the altitude of the litter origin, whereas Q10H did not change. The proportion of fungi in the microbial biomass correlated positively with Q10. Our results were consistent with the kinetic theory of higher temperature sensitivity for more decomposed organic matter. The conclusion that litter Q10L is more responsive to environmental conditions than Q10H has important implications for estimating soil carbon emissions.

Spatial variation and controls of soil microbial necromass carbon in a tropical montane rainforest

Soil microbial necromass carbon is an important component of the soil organic carbon (SOC) pool which helps to improve soil fertility and texture. However, the spatial pattern and variation mechanisms of fungal- and bacterial-derived necromass carbon at local scales in tropical rainforests are uncertain. This study showed that microbial necromass carbon and its proportion in SOC in tropical montane rainforest exhibited large spatial variation and significant autocorrelation, with significant high-high and low-low clustering patterns. Microbial necromass carbon accounted for approximately one-third of SOC, and the fungal-derived microbial necromass carbon and its proportion in SOC were, on average, approximately five times greater than those of bacterial-derived necromass. Structural equation models indicated that soil properties (SOC, total nitrogen, total phosphorus) and topographic features (elevation, convexity, and aspect) had significant positive effects on microbial necromass carbon concentrations, but negative effects on its proportions in SOC (especially the carbon:nitrogen ratio). Plant biomass also had significant negative effects on the proportion of microbial necromass carbon in SOC, but was not correlated with its concentration. The different spatial variation mechanisms of microbial necromass carbon and their proportions in SOC are possibly related to a slower accumulation rate of microbial necromass carbon than of plant-derived organic carbon. Geographic spatial correlations can significantly improve the microbial necromass carbon model fit, and low sampling resolution may lead to large uncertainties in estimating soil carbon dynamics at specific sites. Our work will be valuable for understanding microbial necromass carbon variation in tropical forests and soil carbon prediction model construction with microbial participation.

Can normalized difference vegetation index and climate data be used to estimate soil carbon, nitrogen, and phosphorus and their ratios in the Xizang grasslands?

Accurately quantifying the relative effects of climate change and human activities on soil carbon, nitrogen, and phosphorus in alpine grasslands and their feedback is an important aspect of global change, and high-precision models are the key to solving this scientific problem with high quality. Therefore, nine models, the random forest model (RFM), generalized boosted regression model (GBRM), multiple linear regression model (MLRM), support vector machine model (SVMM), recursive regression tree model (RRTM), artificial neural network model (ANNM), generalized linear regression model (GLMR), conditional inference tree model (CITM), and eXtreme gradient boosting model (eXGBM), were used for modeling soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), the ratio of SOC to TN (C:N), the ratio of SOC to TP (C:P), and the ratio of TN to TP (N:P) at depths of 0–10, 10–20, and 20–30 cm under non-grazing and free-grazing scenarios in the Xizang grasslands. Annual radiation (ARad), annual precipitation (AP), and annual temperature (AT) were used as independent variables under non-grazing scenarios, whereas ARad, AP, AT, and growing season maximum normalized difference vegetation index (NDVImax) were used as independent variables under free-grazing scenarios. Overall, the RFM and GBRM were more accurate than the other seven models. However, the tree numbers of the GBRM were much larger than those of the RFM, indicating that the GBRM may have a greater model complexity and lower running speed. Therefore, the RFM had the best performance among the nine models in modeling SOC, TN, TP, C:N, C:P, and N:P in the Xizang grasslands. The RFM established in this study can not only help scientists save time and money on massive sampling and analysis, but can also be used to construct a database of SOC, TN, and TP, and their ratios, and further scientific research related to ecological and environmental issues (e.g., examining whether soil systems intensified global warming over the past few decades by exploring whether climate change and human activities altered soil organic carbon) in the grasslands of Xizang Plateau.

Factors controlling the spatial heterogeneity of soil organic carbon concentrations and stocks in a boreal forest

Boreal soils play a crucial role in the global terrestrial carbon cycle, serving as significant carbon reservoirs. However, this region is experiencing rapid climate change which poses a threat to the stability of the soil carbon pool. To understand the carbon balance in the boreal region, it is essential to obtain accurate estimates of soil organic carbon (SOC) stocks. The objective of this study was to investigate factors controlling the spatial heterogeneity of SOC concentrations and stocks in a boreal forested catchment and to quantify spatial variability of SOC concentrations and stocks using a random forest-based regression kriging approach. Soil carbon content, gravel content and bulk density were obtained from the surface soil layer (0–30 cm) of 106 samples across a 390 ha catchment in northwestern Ontario, Canada. Spatial attributes including elevation, wetness index, distance to nearest ridge, relative position index, plan curvature, profile curvature and normalized difference vegetation index were used in the random forest-based regression kriging to estimate spatial heterogeneity of SOC stocks and concentrations across the catchment. Our results showed that shallow soils in well-drained upland areas contained low to moderate SOC concentrations (1.2–16.2%) while depressional wetlands had high concentrations (19.2–54.0%). Topographic variables, including wetness index were the strongest predictors of SOC concentrations, but SOC stocks were very heterogeneous across the study site (12.9–139.0 Mg ha−1) and were not well explained by topographic or vegetation variables. The gravel content was highly variable (0–29.0%), and without accounting for this fraction, SOC stocks were overestimated by 52.5% in this landscape. These results emphasize the substantial catchment scale variability in SOC stocks in boreal landscapes and underscore the significance of considering both landscape-scale drivers and intrinsic soil properties when estimating soil carbon in this region. By understanding these factors, we can better manage and protect the important carbon stores in boreal soils.

Agroecological assessment of spatial variability of carbon content in the conditions of disturbed sod-podzolic soils

Soil carbon is one of the main carbon pools of terrestrial ecosystems. The carbon of arable soils is the most labile. In this regard, rapid and accurate methods for determining soil carbon in different forms are of great interest to researchers. Therefore, in this paper we decided to conduct a comparative analysis of the results of organic carbon determination using the Walkley-Black method (colorimetric method), the Dumas dry combustion method (using the CN Vario El Cube analyzer)) and the remote method using the Valeeva approach (Valeeva, Aleksandrova, Koposov, 2016) according to the values of the red channel of the spectrum, on the images obtained using an RGB camera mounted on a DJI Phantom 4 drone. For laboratory analysis, 100 soil samples of the arable horizon were collected at regular grid points, georeferenced using RTK. The correlation between the Dumas dry combustion method and potassium dichromate oxidation was 0.24 (for n = 100, significant p=0.195), allowing comparison at the trend level. The highest correlation (R = 0.39 was observed between the data obtained using the Dumas method and the Valeeva-Alexandrova approach, indicating that it can be used to estimate soil carbon dynamics from RGB images. Correlation analysis showed relationships (R = 0.63) between total carbon and microrelief, which is reflected by the drone-derived digital elevation model.

Estimating soil carbon change using the web-based Nutrient Tracking Tool (NTT) with APEX

Estimating soil carbon change using the web-based Nutrient Tracking Tool (NTT) with APEX

POTENTIAL OF CARBON SINK IN MANGROVE SUBSTRATES IN LEMBAR BAY, WEST LOMBOK, INDONESIA

Mangroves are one of the coastal vegetation that can mitigate carbon (carbon sink and carbon storage). This study aimed to determine the potential for soil carbon stock found under stands of mangroves in Lembar Bay, West Lombok, and West Nusa Tenggara. The research began with the identification of the species and then proceeded toa sampling of the soil, which was then analyzed using the Walkley and Black method. The results showed that there were ten species of mangroves, namely, Rhizophora stylosa, Avicennia lanata, Avicennia marina, Bruguiera gymnorrhiza, Ceriops decandra, Excoecaria agallocha, Lumnitzera racemosa, Scyphiphora hydrophyllacea, Thespesia populnea, and Xylocarpus maluccensis. The highest soil carbon content percentage was found in the lower soil of the A. lanata (1.43 %C) mangrove, and the lowest was found in the lower-stand soil of E. agallocha (0.21 %C). Meanwhile, the carbon sinks per meter were 0.002-0.066 gC/m2, with an average of 0.020±0.020 gC/m2. The estimated total soil carbon sink in 10 mangrove stands was 0.20-6.60 tons C/ha, with an average of 2.18±2.010 tons C/ha. The average total estimated soil carbon stock found in 20.49 ha of the mangrove area studied was 44.67 tonsC, which is equivalent to 263.69 tonsC in a mangrove area of ​​120.96 ha in Lembar Bay.

Characteristics of soil physical, dust content, and estimation of soil carbon in the reclamation area of PT Semen Bosowa Maros

Mining activities can cause problems with physical properties and decrease soil fertility, air pollution, and increased carbon emissions. Land reclamation is one way to overcome these problems. PT Semen Bosowa Maros (SBM), as a public company engaged in the mining and cement processing industry has succeeded in carrying out post-mining land reclamation and revegetation activities. Post-mining land reclamation and revegetation activities are expected to reduce environmental damage, so some important aspects studied include soil physical quality, dust content, and soil carbon. The study aimed to determine the characteristics of soil physical properties (soil texture, permeability, bulk density, organic matter), dust content, and soil carbon in the reclamation area of PT Semen Bosowa Maros, South Sulawesi. The research was conducted in Barugae Village, Bantimurung District, Maros Regency, through data collection in the form of soil samples, dust content, and soil carbon content in 4 observation plots. The study used a field observation method followed by analyzing sample results in the laboratory. The results showed that the soil texture class was dominated by clay texture, the highest soil permeability data was 6.12 cm/hour, the highest bulk density was 0.71 g/cm3, and the highest organic matter was 2.11%. While the highest dust content value is 0.00251 g/cm2. As well as the highest estimated value of soil carbon at 44.73 g/cm2. Information from this research is important for PT Semen Bosowa Maros. In particular, it can improve land conditions through post-mining land reclamation and revegetation activities.

Preliminary Results in Innovative Solutions for Soil Carbon Estimation: Integrating Remote Sensing, Machine Learning, and Proximal Sensing Spectroscopy

This paper explores the application and advantages of remote sensing, machine learning, and mid-infrared spectroscopy (MIR) as a popular proximal sensing spectroscopy tool in the estimation of soil organic carbon (SOC). It underscores the practical implications and benefits of the integrated approach combining machine learning, remote sensing, and proximal sensing for SOC estimation and prediction across a range of applications, including comprehensive soil health mapping and carbon credit assessment. These advanced technologies offer a promising pathway, reducing costs and resource utilization while improving the precision of SOC estimation. We conducted a comparative analysis between MIR-predicted SOC values and laboratory-measured SOC values using 36 soil samples. The results demonstrate a strong fit (R² = 0.83), underscoring the potential of this integrated approach. While acknowledging that our analysis is based on a limited sample size, these initial findings offer promise and serve as a foundation for future research. We will be providing updates when we obtain more data. Furthermore, this paper explores the potential for commercialising these technologies in Australia, with the aim of helping farmers harness the advantages of carbon markets. Based on our study’s findings, coupled with insights from the existing literature, we suggest that adopting this integrated SOC measurement approach could significantly benefit local economies, enhance farmers’ ability to monitor changes in soil health, and promote sustainable agricultural practices. These outcomes align with global climate change mitigation efforts. Furthermore, our study’s approach, supported by other research, offers a potential template for regions worldwide seeking similar solutions.

Rapid soil harvesting using a novel soil auger system for farm‐scale soil carbon estimates

AbstractSoil sampling at the landscape scale is increasingly in demand for assessing C sequestration projects in agroecosystems. Efforts to decrease sampling costs across large areas have thus far largely ignored improved soil harvesting tools as a means of decreasing sampling time and effort. Rigorous analysis of trade‐offs in speed and accuracy among soil harvesting methods is needed. Here, we compare two different methods of soil sampling soil, a standard push probe and a novel drill‐auger system, which were used to collect soils for analysis of total C by dry combustion. We designed sampling plots to compare soil C concentrations at increasing spatial scales to understand the difference in soil C concentrations between methods relative to spatial heterogeneity. We sampled two farms in Upstate New York, one conventional and one organically managed, to assess the impact of these methods across relevant farm practices. We found that probe and auger methods provide indistinguishable soil carbon estimates at both farms. The difference between paired probe and auger samples taken less than 0.15 m apart was 0.22%, an amount equivalent to the difference in C concentrations at points 2 m apart and less than the difference at points 10 m apart. Using the soil auger was up to three times faster than using the push probe—a saving of 7 h of sampling time to inventory the two farms studied. Lowering inventory cost is a critical step in demonstrating the feasibility of soil carbon sequestration as a component of climate smart agriculture. The results presented indicate that the drill‐auger system is preferable to the push probe method for farm‐scale soil carbon inventory.

Soil Carbon Stock and Soil Properties under Different Land Use Types of Agriculture

Agriculture soils play a crucial role in carbon storage and food security. However, uncertainty remains about soil carbon stocks due to spatial variability. This study estimated soil carbon stocks in agricultural land and examined the impact of land use and soil properties on soil organic carbon in Ratchaburi Province, Thailand. Soil samples were collected at three depths (0-10, 10-20, and 20-30 cm) within five different land use types: cassava, coconut, paddy fields, pineapple, and sugarcane. The results revealed that soil organic carbon decreased with increasing depth. Significant differences in soil carbon and soil properties were observed among land uses. The carbon stocks at 0-30 cm depth were as follows: coconut (35.87 mg C/ha), paddy fields (31.17 mg C/ha), sugarcane (28.02 mg C/ha), pineapple (21.79 mg C/ha), and cassava (16.12 mg C/ha). The carbon stocks were significantly correlated with sand, density, clay, silt, and pH. This study highlights the impact of land use types on carbon stocks in agricultural soils and emphasizes the role of soil properties, particularly soil texture, in influencing carbon storage variability. Furthermore, the study highlights the carbon storage potential in agricultural areas, which could guide the formulation of policies to utilize agricultural land to offset CO2 emissions from other sectors.

Effects of gravel on the evaluation of soil organic carbon density in Pinus massoniana plantations.

Gravel (>2 mm) is one of the main parameters for estimating soil carbon pool. To assess the effects of gravel on soil bulk density (BD) and organic carbon density (SOCD) in Pinus massoniana plantations, we estimated the BD and SOCD at the 0-10, 10-20 and 20-40 cm soil depths of 131 plots under two different conditions, with and without removing gravel. The BD of each soil layer after removing gravel was 0.58-1.57, 0.60-1.67, and 0.59-1.75 g·cm-3, respectively, which was significantly lower than that before removing gravel. Gravel increased the BD by 6.5%-6.8%. The SOCD of each soil layer before removing gravel was 8.93-65.97, 7.63-59.08, and 8.79-94.53 t·hm-2, respectively, which was higher than that after removing gravel. Overall, by neglecting the effect of gravel, SOCD was overestimated by 4.9%-11.8%. As gravel content increased, the relative deviation in the estimated BD and SOCD among different methods increased. When the gravel content was higher than 20%, the estimated SOCD at soil layer of 0-40 cm showed a significant difference between neglecting gravel and removing gravel, with the former being 29.7%-47.4% higher than the latter. In conclusion, gravel markedly affected the estimations of BD and SOCD. It was recommended that SOCD should be estimated by the method that not only uses the BD after removing gravel but also considers gravel as a correction factor (especially when gravel content is above 20.0%) to avoid overestimation of soil carbon pool.

Seagrass soil carbon at two different designation zones in Karimunjawa National Park

This study aims to reveal the potential of sediment carbon in seagrass ecosystems in Karimunjawa National Park (KNP). Two seagrass sites located at two different zones in KNP were chosen as study sites i.e., Menjangan Besar (utilization zone) and Sintok (protection zone). There were nine soil cores for each 100×100 m2 site. There were three parameters used for estimating soil carbon stock i.e., compaction factor, dry bulk density, and Corg content (% soil dry weight). To collect the seagrass sediment, a PVC corer (length of 80 cm and diameter of 5.5 cm) was applied. Laboratory analysis showed that soil carbon stock at Sintok is 63.54±16.96 MgCorg/ha (mean±SE) in the top 58 cm of soil, whereas at Menjangan Besar is 65.32±11.71 MgCorg/ha (mean±SE) in the top 65 cm of soil. The result of T-test analysis showed that there was no significant difference in soil carbon stock between two sites. These seagrass soil carbon values highlight the need for implementing better management strategies in conserving seagrass ecosystems, particularly in managing zoning areas in KNP.

Optical assessment of the spatial variation in total soil carbon using laser-induced breakdown spectroscopy

Soil carbon storage is a substantial factor in the global carbon cycle. Carbon sequestration in agricultural soils, and the assessment and validation of soil carbon storage, are crucial for the mitigation of agricultural greenhouse gas emissions and for steering towards sustainable farming practices. Enforcement and verification of carbon sequestration policies, methods, and models require extensive soil carbon monitoring capability. However, current conventional laboratory-based methods for soil carbon estimation are laborious, expensive, and time-consuming. In this work, we have developed a compact, robust, and field-capable experimental device based on laser-induced breakdown spectroscopy (LIBS) for the rapid assessment of total soil carbon content and its spatial distribution in mineral soils. The carbon content quantification was performed using a spectral line of carbon at a wavelength of 193.1 nm emitted from the laser-induced plasma plume. The LIBS measurements were performed on soil samples collected from 28 different locations and various depths (up to 1 m) of a test field cultivated with a forage legume (red clover - Trifolium pratense, L.) and grass (Timothy - Phleum pratense, L.) mixture in eastern Finland. A calibration model was established based on a limited and randomly chosen sample set and validated by comparing soil carbon estimates obtained from various locations in the test field using the dry combustion (LECO) method. Further, we demonstrate here the usefulness of LIBS methodology for mapping three-dimensional carbon distribution at the test field. We emphasize here that the calibration model can be generalized to other sample areas under similar soil type with a relative error of less than 10 % and possesses potential for fast on-site determination of spatial variation in total soil carbon, reducing substantially the need of time-consuming sample processing in laboratory. Therefore, LIBS enables frequent and extensive spatial and temporal soil carbon mapping and has the potential to become part of the future carbon monitoring network.

Spatial estimation of soil carbon and nitrogen in a grassland shrubland transition

Shrub encroachment via anthropogenic introduction has been used for ecological restoration in degraded desert grassland. However, there have been debates concerning whether this activity can be effective, as contrasting results were frequently reported. The objectives of this study were to address if various Caragana korshinskii planting conditions (planting year and density) had varying influences on soil organic carbon (SOC), soil total nitrogen (STN), and their spatial heterogeneities along the transition from desert grassland to shrubland. We investigated SOC and STN at the community and landscape scales, with the former containing eight land types of enclosed grassland, grazed grassland, and shrubland with varying levels of planting years (3, 12, or 22 years) and planting intervals (2, 6, or 40 m), and the latter consisting of 43 grassland-shrubland mosaic sites (desert grassland, grassland-shrubland edge, and shrubland) covering approximately 2,976 km2 area. At the community scale, the SOC and STN contents from 0 to 100 cm soil depth in shrubland, regardless of varying planting years and densities, were higher than those observed in grazed grassland. Though it appeared that short-term of shrub establishment (3 and 12 years) in shrubland outscored enclosed grassland in SOC accumulation, it was still questionable if this can be consistent for a longer time period as no significant difference was seen for SOC contents between enclosed grassland and shrubland after 22 years of shrub establishment. At the landscape scale, the SOC contents were higher in shrubland than those in desert grassland. However, no significant difference was observed for STN among shrubland and desert grassland. Regarding their spatial heterogeneities, SOC exhibited stronger spatial autocorrelation in grassland-shrubland edge than in desert grassland and shrubland, while STN did not evidently differ in spatial autocorrelation among these transition states. In brief, we have observed differences in soil carbon and nitrogen at community scales, but when crossing over to broad scales do not show the kind of significant spatial heterogeneity that we thought would be present in shrub introductions over the past 30 years. There may be a certain lag in the evolution of soil carbon and nitrogen responses from the introduction of shrubs from the community scale to the landscape scale.