Global soil carbon: understanding and managing the largest terrestrial carbon pool

Today's agri-food systems face the triple challenge of addressing food security, adapting to climate change, and reducing the climate footprint by reducing the emission of greenhouse gases (GHG). In agri-food systems, changes in land use and land cover (LULC) could affect soil physicochemical properties, particularly soil organic carbon (SOC) stock. However, the impact varies depending on the physical, social, and economic conditions of a given region or watershed. Given this, a study was conducted to quantify the impact of LULC and slope gradient on SOC stock and C sequestration rate in the Anjeni watershed, which is a highly populated and intensively cultivated area in Northwest Ethiopia. Seventy-two soil samples were collected from 0-15 and 15-30cm soil depths representing four land use types and three slope gradients. Soil samples were selected systematically to match the historical records (30years) for SOC stock comparison. Four land use types were quantified using Landsat imagery analysis. As expected, plantation forest had a significantly (p < 0.05) higher SOC (1.94Mgha-1) than cultivated land (1.38Mgha-1), and gentle slopes (1-15%) had the highest SOC (1.77Mgha-1) than steeper slopes (> 30%). However, higher SOC stock (72.03Mgha-1) and SOC sequestration rate (3.00Mgha-1year-1) were recorded when cultivated land was converted to grassland, while lower SOC stock (8.87Mgha-1) and sequestration rate (0.77Mgha-1year-1) were recorded when land use changed from cultivation to a plantation forest. The results indicated that LULC changes and slope gradient had a major impact on SOC stock and C sequestration rate over 30years in a highly populated watershed. It is concluded that in intensively used watersheds, a carefully planned land use that involves the conversion of cultivated land to grassland could lead to an increase in soil C sequestration and contributes to reducing the carbon footprint of agri-food systems.

&amp;lt;p&amp;gt;The knowledge of spatial heterogeneity and environmental controllers of soil organic carbon (SOC) stocks is essential for upscaling and predicting SOC dynamics under changing land use and climatic conditions. &amp;amp;#160;This study investigated the spatial variability and intrinsic and extrinsic controllers of SOC stocks in a boreal forest catchment (320 ha) at the International Institute for Sustainable Development Experimental Lakes Area in Ontario, Canada. Forty-seven surface soil (0-30 cm) samples, representative of the spatial variability of topography, surface water flow patterns and vegetation distribution, were obtained within the catchment. Air dried soil samples were sieved to separate gravel (&amp;gt;2 mm) and fine-earth (&amp;lt;2 mm) fractions and were analyzed for SOC concentration using the loss-on-ignition method. Core sample method was used to determine the soil bulk density. SOC concentrations in surface soils showed a large spatial variability (1.2% to 50.4%, CV= 111.3%). Thick organic soil layers in the wetlands of the sub-catchment showed the highest SOC concentrations. The surface soil SOC stocks ranged between 14.5 to 240.5 Mg ha-1 with an average stock of 101.5 Mg ha-1. Spatial autocorrelations of SOC stocks were modelled by calculating relevant variograms. The variability of SOC stocks (sill = 834) was dominated by the random variability (nugget=275) whereas the variability of SOC concentration (sill = 2.5) was dominated by the spatially structured variability (nugget = 0). We found a strong spatial autocorrelation of the SOC concentrations within the catchment, but the SOC stocks were less spatially correlated. This was largely due to the heterogeneity in the thickness of the surface soil layer (10 cm - 30 cm) and in the gravel content (0-28.9%). We found that a large over-estimation of SOC stocks (52.5%) could result if these intrinsic factors are not considered. Extrinsic controllers were generally not significantly related to the SOC stock; Spearman&amp;amp;#8217;s rank correlation analysis on the entire dataset showed non-significant relationships between the SOC stock and extrinsic controllers, namely NDVI (r = 0.04) elevation (r = 0.2), slope (r = -0.1) and topographic indices, stream power index (r = -0.1), relative position index (r=-0.2) and plan curvature (r = -0.1). However, regression tree analysis revealed local-scale effects of aspect, NDVI, elevation, and distance to ridge on the SOC stocks. Many forest soil databases lack information of gravel content and soil depth. Thus, upscaling boreal forest SOC stocks without these two key intrinsic controllers can lead to higher uncertainties in &amp;amp;#160;SOC stock estimates. Further, the impacts of extrinsic controllers may vary across heterogenous landscapes. Machine learning-based digital soil mapping techniques such as Random Forest models are more appropriate for incorporating local-scale impacts of extrinsic controllers when upscaling SOC stocks of boreal forest soils.&amp;amp;#160;&amp;lt;/p&amp;gt;

Soil organic carbon (SOC) stocks represent the second-largest natural carbon reservoir globally, surpassed only by the oceans. SOC plays a vital role in maintaining ecosystem health, offering numerous benefits such as enhancing soil structure, increasing nutrient availability, and boosting water retention capacity. Beyond its ecological significance, SOC is integral to climate change mitigation, given its ability to sequester atmospheric carbon dioxide effectively. Additionally, SOC contributes to improving the physical, chemical, and biological properties of soil, making it indispensable for sustainable land management.&amp;#160;Taiwan, an island in the western Pacific Ocean, spans an area of approximately 35,800 square kilometers. Shaped like a tobacco leaf, the island extends 400 kilometers in length and 150 kilometers at its widest point. Taiwan&amp;#8217;s landscape is characterized by a Central Mountain Range running north to south, steep slopes, and geologically fragile formations. In recent decades, Taiwan has experienced significant changes in land use and land cover, particularly in urban areas where cropland and forest land on city outskirts have been replaced by infrastructure development. These transformations have directly impacted SOC levels across the island, underscoring the need for accurate mapping to estimate SOC stocks and assess soil functionality, particularly in agricultural regions.&amp;#160;Traditional ground sampling methods for estimating SOC, though precise, are often costly and labor-intensive. To address these limitations, alternative approaches, such as remote sensing, offer cost-effective solutions. Among various predictive modeling techniques, machine learning algorithms like Random Forest (RF) have emerged as highly effective tools for SOC estimation. RF models excel due to their ability to minimize correlation among individual decision trees and provide reliable error estimates, ensuring robust predictions.In this study, we combined field sampling data (2010&amp;#8211;2021) with remote sensing, topographic, and climatic datasets to estimate SOC stocks in the topsoil layer (0&amp;#8211;30 cm) of Taiwan&amp;#8217;s agricultural areas. Using the RF algorithm, we initially employed 23 explanatory variables and subsequently refined the model by eliminating less significant predictors, reducing the final set to 12 variables. The refined model demonstrated strong predictive accuracy, with R&amp;#178; values exceeding 0.70 for agriculture land in Taiwan.&amp;#160;Our findings revealed spatial variations in SOC levels, with mountainous regions exhibiting higher SOC stocks compared to suburban and low-lying agricultural areas, where values were notably lower. SOC levels for agricultural lands ranged from a maximum of 7.14 kg/m&amp;#178; to a minimum of 2.55 kg/m&amp;#178;, with an average value of 3.43 kg/m&amp;#178;. Agricultural practices incorporating agroforestry techniques showed relatively higher SOC stocks, emphasizing the role of sustainable practices in enhancing soil carbon storage.&amp;#160;The results of this study hold significant implications for long-term monitoring of SOC in Taiwan and provide a crucial reference for policymakers aiming to develop effective carbon sequestration strategies. By integrating field data with advanced modeling and remote sensing technologies, this research contributes to a deeper understanding of SOC dynamics and supports efforts to promote sustainable land management and climate resilience.

Stability of soil organic carbon pools affected by land use and land cover changes in forests of eastern Himalayan region, India

National soil organic carbon estimates can improve global estimates

Socioeconomic activities and natural environmental changes are the main drivers of land use and land cover (LULC) changes worldwide and this directly affects the amount of soil organic carbon (SOC) stock. Though LULC affects the amount of SOC stocks and fluxes, the mid-lands of Tigray are less studied and represented. In this study, an attempt has been made to determine the effects of LULC change on SOC at watershed scale in the Central Tigray, Northern mid-lands of Ethiopia. Geographically the study area is found in Kola Tembien district, which is about 125 km away from Mekelle, the capital city of Tigray to the west. Five LULC types (cropland, grazing land, forest land, shrub/bush land and settlement) were identified. Nine composite and undisturbed soil samples at a depth of 20 cm and 10 cm respectively were collected randomly from each LULC type. Analysis of LULC change has been undertaken using satellite images of Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 (OLI) with 30 m spatial resolution in ArcGIS10.3 and ERDAS Imagine14. One-way ANOVA was used for SOC analysis among LULC types. The analysis has showed that LULC change was undertaken in Jira watershed during the last 30 years (1987-2017). The highest SOC concentration was observed in forest land and the lowest was observed in cropland in all studied years. The amount of SOC concentration between each LULC types during all study years was significantly different at p &lt; 0.05 except, cropland and grazing land in 1987 and grazing land and shrub/bush land in all study years. Change in LULC has affected the amount of SOC stock in the LULC types. For example, 18.7 t/ha of SOC was gained during the last 30 years due to the conversion of grazing land into forest land. During the thirty years interval (1987-2017) a total of 714.7 ton of SOC stock was gained due to the conversion of the land uses and management interventions. The highest SOC was gained due to the conversion of grazing land in to forest land (2007-2017) which has offered a total of 635.53 ton of SOC. A model was created to predict SOC of similar environments by using the observed SOC and NDVI values of the 2017 LULC types. Finally, the study concluded that the LULC change has affected the amount of SOC stock in the watershed and it is better to increase coverage of forest lands to store more organic carbon in the soil.

Refining benchmarks for soil organic carbon in Australia’s temperate forests

This study analyses the decrease in soil organic carbon (SOC) stocks due to changes in land use following the earthquake in Düzce, Turkey, 1999. The primary objective of the study is to determine the changes in land use within Düzce and to provide a multi-dimensional approach to the spatial and quantitative distributions of SOC losses. Corine Land Use- Land Cover (LULC) within the study is used to determine the change in land use. The loss of LULC and carbon stocks were identified by means of LULC with transfer matrix method and GIS-based analysis. The study of land-use change caused by urbanisation and agricultural activity shows that the limited green spaces around the urban core created by degrading natural areas do not compensate for the loss of SOC. SOC stocks decline after the land use changes from agricultural regions to artificial areas (− 5%), Natural- Semi-natural (N-SN) regions to artificial areas (− 15%), N-SN areas to agricultural areas (− 20.9%) and agricultural areas to water bodies (− 9%), and SOC stocks increase after land use changes from artificial areas to N-SN areas (+ 29.6%), artificial areas to agricultural areas (+ 8%), agricultural areas to N-SN areas (+ 25%). However, in some agricultural areas, SOC stocks are similar to semi-natural and natural areas. For instance, in sparsely vegetated areas, SOC stocks from fruit and berry plantations may be poor. Although it is generally assumed that SOC loss can occur on land transformed from natural areas, this rule of thumb may be revised in some particular circumstances. Therefore, local ecological restoration decisions should not be based on land cover generalisations.

Soil organic carbon (SOC) constitutes a large pool within the global carbon cycle. Land use change significantly drives SOC stock variation. In tropical central and eastern Africa, how changes in land use and land cover impact on soil C stocks remains unclear. Variability in the existing data is typically explained by soil and climate factors with little consideration given to land use and management history. To address this knowledge gap, we classified the current and historical land cover and measured SOC stocks under different land cover, soil group and slope type in the humid zone of south-west Rwanda. It was observed that SOC levels were best explained by contemporary land cover types, and not by soil group, conversion history or slope position, although the latter factors explained partly the variation within annual crop land cover type. Lack of the influence of land use history on SOC stocks suggests that after conversion to a new land use/land cover, SOC stocks reached a new equilibrium within the timestep that was observed (25 years). For conversion to annual crops, SOC stocks reach a new equilibrium at about 2.5 % SOC concentration which is below the proposed soil fertility threshold of 3 % SOC content in the Eastern and central African region. SOC stock declined under transitions from banana-coffee to annual crop by 5 % or under transitions from natural forest to degraded forest by 21 % and increased for transitions from annual crops to plantation forest by 193 %. Forest clearing for agricultural use resulted in a loss of 72 %. Assuming steady states, the data can also be used to make inferences about SOC changes as a result of land cover changes. We recommend that SOC stocks should be reported by land cover type rather than by soil groups which masks local land cover and landscape differences. This study addresses a critical issue on sustainable management of SOC in the tropics and global carbon cycle given that it is performed in a part of the world that has high land cover dynamics while at the same time lacks data on land cover changes and SOC dynamics.

In their paper "Unrecognized threat to global soil carbon by a widespread invasive species" O'Bryan et al. (2021) suggested that wild boar (also named feral pigs or wild pigs) and their grubbing reduce global soil organic carbon (SOC) stocks. In this study models were used to estimate global wild boar abundance and postulated additional CO2 emissions due to wild boar bioturbation. However, the authors ignored experimental evidence about the effects of wild boar on SOC that points in a completely different direction altogether.

Land Use Change (LUC), especially deforestation in tropical regions, significantly contributes to global anthropogenic greenhouse gas (GHG) emissions. Here, we address potential combined impacts of LUC and Climate Change (CC) on Soil Organic Carbon (SOC) stocks in the Ethiopian highlands. The soil model Q was employed to predict SOC stocks for various combinations of LUC and CC scenarios until the year 2100. Four reference scenarios (cropland, bushland, natural forest, and Eucalyptus plantations under contemporary climatic conditions) were evaluated against reported measurements of SOC stocks. We studied impacts of six common LUC scenarios, including deforestation and planting Eucalyptus, on SOC stocks under contemporary and future climates. To assess the impact of CC, effects of elevated temperature (mean annual temperature + 2.6 °C) together with three litterfall scenarios (no change in litterfall, a 5% reduction and 22% increase, designated CC0, CCd, and CCi, respectively) were considered to test potential vegetation responses to increases in temperature and atmospheric CO2 concentrations. Most of the tested combinations of LUC and CC led to losses of SOC stocks. Losses were most severe, both relatively and absolutely, in the deforestation scenarios: up to 30% was lost if natural forest was converted to cropland and temperature increased (under the CC0 scenario). Gains in SOC stocks of 4–19% were modelled when sparse vegetation was converted to more dense vegetation like Eucalyptus plantation with substantially increased litterfall (the CCi scenario). Elevated temperature accelerated decomposition rates, leading to circa 8% losses of SOC stocks.We conclude that effects of LUC and CC on SOC stocks are additive and changes in litterfall caused by LUC determine which has the largest impact. Hence, deforestation is the biggest threat to SOC stocks in the Ethiopian highlands, and stocks in sparse vegetation systems like cropland and bushland are more sensitive to CC0 than LUC. We recommend conservation of natural forests and longer rotation periods for Eucalyptus plantations to preserve SOC stocks.Finally, we suggest that use of the Q model is a viable option for national reporting changes in SOC stocks at Tier 3 within the LULUCF sector to the United Nations Framework Convention on Climate Change (UNFCCC) as it is widely applicable and robust, although it only requires input data on a few generally available variables.

&amp;lt;p&amp;gt;&amp;amp;#8216;Strategic objective 1&amp;amp;#8217; of the United Nations Convention to Combat Desertification (UNCCD) aims to improve conditions of affected ecosystems, combat desertification/land degradation, promote sustainable land management, and contribute to land degradation neutrality. The indicator &amp;amp;#8216;Proportion of land that is degraded over total land area&amp;amp;#8217; (SO1) is compiled from three sub-indicators: &amp;amp;#8216;Trends in land cover&amp;amp;#8217; (SO1-1), &amp;amp;#8216;Trends in land productivity or functioning of the land&amp;amp;#8217; (SO1-2), &amp;amp;#8216;Trends in carbon stocks above and below ground&amp;amp;#8217; (SO1-3).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Soil organic carbon (SOC) stock can be adopted as the metric of SO1-3, until globally accepted methods for estimating the total terrestrial system carbon stocks will be elaborated. SOC can be considered as one of the most important properties of soil, which shows not just spatial but temporal variability. According to our previous results in the topic, UNCCD default data of SOC stock for Hungary is strongly recommended to be replaced with country specific estimation of SOC stock.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;SOC stock maps were compiled in the framework of DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) initiative, predicted by proper digital soil mapping (DSM) method. Reference soil data were derived from a countrywide monitoring system. The selection of environmental covariates was based on the SCORPAN model. The elaborated SOC stock mapping methodology have two components: (1) point support modelling, where SOC stock is computed at the level of soil profile, and (2) spatial modelling (quantile regression forest), where spatial prediction and uncertainty quantification are carried out using the computed SOC stock values.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We analyzed how SOC stock changed between 1998 and 2016. &amp;amp;#160;Nationwide SOC stock predictions were compiled for the years 1998, 2010, 2013, and 2016. For the intermediate years, we do not recommend to calculate SOC stock values, because we have no information on the dynamics of change in the intervening years. Based on the 1998 SOC stock prediction, we compiled a SOC stock map for 2018, using only land use conversion factors, according to the default data conversion values.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;According to the elaborated scheme during the respective period, significant changes cannot be detected, only tendentious SOC stock changes appear. Based on our results, we recommend to use spatially predicted layers for all years when data are available, rather than calculating SOC stock change based on land use conversion factors.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Acknowledgment:&amp;lt;/strong&amp;gt; Our research was supported by the Hungarian National Research, Development and Innovation Office (NKFIH; K-131820) and by the Premium Postdoctoral Scholarship of the Hungarian Academy of Sciences (PREMIUM-2019-390) (G&amp;amp;#225;bor Szatm&amp;amp;#225;ri).&amp;lt;/p&amp;gt;

National and sub-national assessments of soil organic carbon stocks and changes: The GEFSOC modelling system

Land-use changes affect soil organic carbon (SOC) stocks over decades. However, IPCC default for greenhouse gas emissions reporting suggests a simple linear SOC stock change over 20 years only. Using process-based modelling approaches such as RothC to describe SOC dynamics after land-use change requires model validation. However, there are only few long-term field experiments where SOC stocks have been observed long enough to get sufficient data for such a model validation. This lack of data makes validating models for large-scale use challenging.Based on empirical data from over 3000 sites from the German Agricultural Soil Inventory we selected 204 sites with land-use change history within the last 60 years and created an artificial data-set using a reciprocal modeling approach. This approach utilizes machine learning models trained on sites under permanent land use to predict SOC stocks for similar sites where the land use had been changed. In addition, we extracted further empirical data from over 30 sites with land-use change in the temperate zone from a comprehensive meta-analysis.These two datasets were used to test the ability of the well-known SOC model RothC to simulate land-use change effects on SOC stocks. In these tests, we use the observed or predicted SOC stocks assumed at equilibrium to model the carbon input under permanent land use and corresponding SOC dynamics after land-use change. These modelled SOC dynamics are then compared with observed SOC stocks after land-use change.We will discuss opportunities and challenges of using process-based models to describe SOC dynamics after land-use change on regional to national scale.&amp;#160;

&amp;lt;p&amp;gt;The minimum set of indicators recommended for tracking progress towards LDN against a baseline are: land cover, land productivity and carbon stocks above and below ground. While land cover and its change can be and actually is operatively monitored by Earth Observation in a relatively straightforward manner, spatio-temporal assessment of the two other, soil related indicators poses challenges.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Soil organic carbon (SOC) stock in Hungary was first mapped in the frame of Global Soil Organic Carbon Map initiative. The Hungarian Soil Information and Monitoring System was used to create the GSOC product with quantile regression forest, which made the assessment of local uncertainty possible. &amp;amp;#160;The map was produced with 500 meter spatial resolution and aggregated for the predefined 1 km grid. Since it used data collected in the first field campaign, in 1994, consequently its estimates represent that year&amp;amp;#8217;s state.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In 2018 a national report was expected by UNCCD on LDN firstly quantifying trends in carbon stocks above and below the ground. Based on global databases (ESA Climate Change Initiative Land Cover Dataset, SoilGrids250) default values were assigned to countries, which were asked about its acceptance or providing more accurate estimations based on national datasets. Similarly to the global initiative, SOC change estimation was not based on soil reference data dating from two distinct dates, but on the only available spatial prediction and changes of SOC were exclusively attributed to changes in land cover. Corine Land Cover Change maps were used to derive the GSOC estimations for the base year (2000) as well as for the target year (2012) from the original SOC map (representing 1994) according to Trends.Earth tool guidelines. SOC change between 2000 and 2012 was estimated by the difference of the two predictions.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In the next step, the SOM measurements on the samples collected in 2010 in the frame of Hungarian Soil Information and Monitoring System became available to map soil organic carbon stock in the topsoils (0-30 cm) of Hungary for the year 2010. New modelling was carried out based on the experiences of GSOC estimations, the map was produced with 100 m resolution using quantile regression forest for both years. 10-fold cross-validation was used for checking the accuracy of the spatial predictions and uncertainty quantifications. The performance of the spatial predictions and uncertainty quantifications was appropriate, which was verified by the computed biases, the root mean square errors, accuracy plots and the G statistics. Based on the compiled SOC stock maps, we assessed the spatial and temporal changes of SOC stocks on the whole area of Hungary except artificial surfaces and water bodies. The total SOC stock in the topsoil increased by 27.18 Tg over the respective period. We compared our estimate with others provided by global and continental SOC stock inventories. The comparison pointed out that a SOC stock map compiled by a given country can provide more accurate estimates at national level. We recommend applying the SOC stock map of 1992 as baseline to track and assess SOC stock change in Hungary.&amp;lt;/p&amp;gt;