Topsoil Organic Carbon Stocks Research Articles

Mulch application as the overarching factor explaining increase in soil organic carbon stocks under conservation agriculture in two 8-year-old experiments in Zimbabwe

Abstract. Conservation agriculture (CA), combining reduced or no tillage, permanent soil cover, and improved rotations, is often promoted as a climate-smart practice. However, our understanding of the impact of CA and its respective three principles on top- and subsoil organic carbon stocks in the low-input cropping systems of sub-Saharan Africa is rather limited. This study was conducted at two long-term experimental sites established in Zimbabwe in 2013. The soil types were abruptic Lixisols at Domboshava Training Centre (DTC) and xanthic Ferralsol at the University of Zimbabwe farm (UZF). The following six treatments, which were replicated four times, were investigated: conventional tillage (CT), conventional tillage with rotation (CTR), no tillage (NT), no tillage with mulch (NTM), no tillage with rotation (NTR), and no tillage with mulch and rotation (NTMR). Maize (Zea mays L.) was the main crop, and treatments with rotation included cowpea (Vigna unguiculata L. Walp.). The soil organic carbon (SOC) concentration and soil bulk density were determined for samples taken from depths of 0–5, 5–10, 10–15, 15–20, 20–30, 30–40, 40–50, 50–75 and 75–100 cm. Cumulative organic inputs to the soil were also estimated for all treatments. SOC stocks at equivalent soil mass were significantly (p<0.05) higher in the NTM, NTR and NTMR treatments compared with the NT and CT treatments in the top 5 cm and top 10 cm layers at UZF, while SOC stocks were only significantly higher in the NTM and NTMR treatments compared with the NT and CT treatments in the top 5 cm at DTC. NT alone had a slightly negative impact on the top SOC stocks. Cumulative SOC stocks were not significantly different between treatments when considering the whole 100 cm soil profile. Our results show the overarching role of crop residue mulching in CA cropping systems with respect to enhancing SOC stocks but also that this effect is limited to the topsoil. The highest cumulative organic carbon inputs to the soil were observed in NTM treatments at the two sites, and this could probably explain the positive effect on SOC stocks. Moreover, our results show that the combination of at least two CA principles including mulch is required to increase SOC stocks in these low-nitrogen-input cropping systems.

Evaluation of digital soil mapping projection in soil organic carbon change modeling

There is increasing interest in the application of digital soil mapping (DSM) projections to infer changes in soil carbon across both space and time. This approach relies on the assumption that the spatially modeled soil carbon-environment relationship can be transferred over time. However, this assumption is rarely tested due to a lack of temporally independent validation data. This paper assesses this assumption by developing models of topsoil organic carbon stocks (SOCS) with a deep learning algorithm and data covering mainland China pertaining to the 1980s and 2010s. The temporal prediction performance of models capturing a specific period was assessed by evaluating their performance in the prediction of data during another period. The results revealed that the prediction accuracy of temporal modeling decreased, as indicated by the coefficient of determination, and was lower than that of spatial modeling. The lower prediction accuracy obtained with the DSM-projection approach may result from differences in the magnitudes of influential variables across periods. We found that different levels of environmental similarity and model projection sensitivity to dynamic variables may cause discrepancies in forecast and hindcast accuracy. Our results demonstrate that the prediction error in temporal modeling is related to the degree of environmental similarity between periods. Our findings generally support the implementation of the DSM-projection approach in soil carbon change modeling. However, caution should be exercised, as there exists much uncertainty regarding the projection of spatial models over time.

Global predictions of topsoil organic carbon stocks under changing climate in the 21st century

The organic carbon (OC) stored in global topsoil (0–30 cm) will be the most active participant in the carbon cycle under future climate change. Due to differences in focus regions or research methods, the spatio-temporal changes of future global topsoil OC stocks and how they will be affected by climate change are not systematically understood, which needs to be further explored. In this study, we developed data-driven models to predict the spatio-temporal dynamics of global topsoil OC stocks by combining 32,579 soil profiles with environmental variables and comprehensively explored the impact of future climate change on topsoil OC. It was found that the topsoil OC stocks were 1249.29 Pg in the baseline period (1971–1990). By 2100, under the normal and high representative concentration paths, it is predicted that the global topsoil OC stocks will decrease by 113.67 ± 25.93 Pg and 193.71 ± 39.76 Pg, respectively. In the future, the largest global topsoil carbon loss will occur in boreal forest areas, which are expected to lose 17.03–27.90 % (66.01–108.13Pg) of their carbon stocks. The influence of climate on topsoil OC stocks is mainly manifested in temperature, which has a negative influence on the global topsoil OC stock, and the contribution rate of temperature to the effect on the global topsoil OC stock is about 26.96 %. Overall, our results provide a high spatio-temporal resolution assessment of global topsoil OC stocks and their relationship to environmental factors, and highlight the spatial heterogeneity, which has been generally ignored in many experimental frameworks and prediction models. These results will help governments to make appropriate management decisions to mitigate climate change.

Rates of soil organic carbon change in cultivated and afforested sandy soils

Considerable advances have been made in the assessment and mapping of soil organic carbon stocks, but rates of change in carbon stocks remain to be quantified for many soils and ecosystems. We sampled 145 sandy soils (mostly Psamments) under permanent cultivation and forest. We used aerial imagery to determine the period of cultivation and to calculate changes in soil organic carbon stocks. Topsoil organic carbon stocks, including the A and O horizons, were highest in soils under forest which were never cultivated (36 Mg C ha−1) and lowest in soils under red pine with prior cultivation (31 Mg C ha−1). Average soil organic carbon stocks of the A horizons of cultivated soils were 33 Mg C ha−1. To meet the 4 per 1000 international initiative, these soils need to achieve a soil organic carbon sequestration rate of 0.1 Mg C ha−1 yr−1. A mean rate of change of −0.16 Mg C ha−1 year−1 was found. The A horizon thickness increased under cultivation, but soil organic carbon concentrations decreased leading to reduced soil organic carbon stocks. The decline in soil organic carbon stock could be explained by an increased rate of organic matter decomposition due to tillage, irrigation, nitrogen applications, and lower clay and silt contents. After about 70 years of afforestation with red pine, soil organic carbon stocks increased. The O horizon accrued organic carbon at a rate of +0.19 Mg ha−1 yr−1, but soil organic carbon stocks were lower in the A horizon compared to cultivated soils. Afforestation of abandoned cultivated fields maintained soil organic carbon in the A horizon and gained organic carbon in the O horizon, but the soil organic carbon stocks were below soils under forest which were never cultivated.

Root biomass and altitude jointly regulate the response of topsoil organic carbon density to severe degradation of high‐altitude alpine meadows

AbstractGrassland degradation can substantially reduce soil carbon sequestration capacity. However, the effects of grassland degradation on soil organic carbon (SOC) density remain unquantified in high‐altitude alpine meadows. In this study, the response and controlling mechanisms of topsoil (0–20 cm) organic carbon were explored in a field survey involving 11 pairs of healthy versus severely degraded plots of high‐altitude (above 4000 m) alpine meadows, as well as three short‐term (3–5 years) fencing restoration projects, across the source of the Yellow River in August of 2020 and 2021. The results showed that 0–20 cm root biomass, SOC content (SCC), and density (SCD) of healthy meadows averaged 533.7 ± 291.9 g/m2 (mean ± S.D), 21.17 ± 9.36 g/kg, and 4.54 ± 1.64 kg C/m2, respectively. Root biomass, SCC, and SCD were markedly reduced by 63.0%, 33.2%, and 17.6% in severely degraded grassland compared with healthy meadows. The SCC and SCD averaged 7.92 ± 2.21 g/kg and 2.2 ± 0.9 kg C/m2 in fencing plots, respectively, and were not significantly different from severely degraded meadows, suggesting a limited improvement in SOC from short‐term fencing restoration. Analysis of a piecewise structural equation model revealed that the effect of degradation on SCD (indicated by the difference in SCD between paired healthy and degraded meadows) was jointly regulated by the differential surface root biomass and site altitude, with a total positive effect of 0.39 and 0.26, respectively. Our findings indicate the losses of topsoil organic carbon stock caused by grassland degradation are root biomass‐ and altitude‐dependent, supporting the notion that antecedent prevention of degradation, more than subsequent restoration, should be the highest priority in the adaptive management of alpine meadows in harsh high‐altitude regions.

Experimental drought increased the belowground sink strength towards higher topsoil organic carbon stocks in a temperate mature forest

Reduced carbon assimilation by trees is often considered to lower the overall carbon sink function of drought-stressed forests. However, soil organic carbon (SOC) stocks may respond differently to drought than ecosystem carbon flux dynamics, leading to imprecise predictions of soil carbon sequestration when one value is inferred from the other. As a major component of soil organic matter, SOC is the largest actively cycling terrestrial carbon reservoir, and thus fulfills various important ecosystem services. Yet, there is uncertainty about how SOC quantity and quality respond to drought in temperate forests. This study addressed the depth distribution of SOC stocks and soil organic matter stability in a forest exposed to artificial drought for five consecutive growing seasons below clusters of temperate mature deciduous beech (Fagus sylvatica L.) and coniferous spruce (Picea abies (L.) Karst.). In addition to SOC stock determination, we measured concentrations of water-extractable organic carbon (WEOC), performed density fractionation, and determined beta values of SOC (slopes of linear regressions between δ13C of soil and log-transformed SOC content throughout soil depth profiles). Following drought, SOC stocks down to 30 cm depth increased by a factor of 1.5 under P. abies while they did not change with drought under F. sylvatica. Under both species, SOC stocks in the mineral topsoil (0–5 cm soil depth) increased by >80 % with drought, increasing the relative contribution of this thin depth section to total SOC from 5 % to >30 %. At 5–15 cm soil depth, SOC stocks decreased with drought under F. sylvatica but not under P. abies. With drought, carbon in the free light fraction (fLF) increased under F. sylvatica but declined marginally under P. abies. Results from density fractionation and beta values suggest decreased soil organic matter stability under F. sylvatica and increased stability under P. abies. Greater SOC accumulation suggests that the belowground carbon sink strength of drought-stressed forests increases, which contrasts with reduced ecosystem carbon uptake under drought.

Microbial properties determine dynamics of topsoil organic carbon stocks and fractions along an age-sequence of Mongolian pine plantations

Microbial properties determine dynamics of topsoil organic carbon stocks and fractions along an age-sequence of Mongolian pine plantations

The quadratic relationship between tree species richness and topsoil organic carbon stock in a subtropical mixed-species planted forest

The quadratic relationship between tree species richness and topsoil organic carbon stock in a subtropical mixed-species planted forest

Decoupled abiotic and biotic drivers of aboveground and topsoil organic carbon stocks in temperate forests

AbstractForests are major components of global carbon (C) cycling, and hence, it is crucial to explore the drivers of forest functions related to C sequestration. Here, using the multiple linear regression models (MLMs) and structural equation models (SEMs), we evaluated how abiotic (i.e., soil nutrients and topography) and biotic [i.e., functional trait diversity (FTD) and functional trait identity (FTI)] factors regulate aboveground C (AGC) and topsoil (0–30 cm) organic C (SOC) stocks across 104 plots in temperate forests of Northern Iran. The optimal MLMs showed that the community‐weighted mean (CWM) of wood density and functional divergence increased, but functional evenness decreased AGC stock, where FTI values contributed much (i.e., 74.40%) to the explained variance in AGC stock as compared to FTD indices (12.86%) and abiotic factors (12.74%). On contrary, SOC stock was mainly promoted by soil‐available phosphorus, where abiotic factors contributed much (92.62%) to the explained variance as compared to FTD indices (6.73%) and FTI values (0.65%). The final best‐fitted SEMs showed that AGC stock was strongly controlled (β = 0.64) by FTI values (i.e., a latent variable of CWM of wood density and plant maximum height), whereas SOC stock was strongly controlled (β = 0.74) by abiotic factors (i.e., a latent variable of soil‐available phosphorus and total nitrogen). We argue that suitable functional strategies in combination with soil nutrients should be taken into priority during the forestland management and policy plans for the improvement of C stocks in above‐ and belowground compartments of forest ecosystems.

Do tree species affect decadal changes in soil organic carbon and total nitrogen stocks in Danish common garden experiments?

AbstractTemperate forest soils are often considered as an important sink for atmospheric carbon (C), thereby buffering anthropogenic CO2 emissions. However, the effect of tree species composition on the magnitude of this sink is unclear. We resampled a tree species common garden experiment (six sites) a decade after initial sampling to evaluate whether forest floor (FF) and topsoil organic carbon (Corg) and total nitrogen (Nt) stocks changed in dependence of tree species (Norway spruce—Picea abies L., European beech—Fagus sylvatica L., pedunculate oak—Quercus robur L., sycamore maple—Acer pseudoplatanus L., European ash—Fraxinus excelsior L. and small‐leaved lime—Tilia cordata L.). Two groups of species were identified in terms of Corg and Nt distribution: (1) Spruce with high Corg and Nt stocks in the FF developed as a mor humus layer which tended to have smaller Corg and Nt stocks and a wider Corg:Nt ratio in the mineral topsoil, and (2) the broadleaved species, of which ash and maple distinguished most clearly from spruce by very low Corg and Nt stocks in the FF developed as mull humus layer, had greater Corg and Nt stocks, and narrow Corg:Nt ratios in the mineral topsoil. Over 11 years, FF Corg and Nt stocks increased most under spruce, while small decreases in bulk mineral soil (esp. in 0–15 cm and 0–30 cm depth) Corg and Nt stocks dominated irrespective of species. Observed decadal changes were associated with site‐related and tree species‐mediated soil properties in a way that hinted towards short‐term accumulation and mineralisation dynamics of easily available organic substances. We found no indication for Corg stabilisation. However, results indicated increasing Nt stabilisation with increasing biomass of burrowing earthworms, which were highest under ash, lime and maple and lowest under spruce.Highlights We studied if tree species differences in topsoil Corg and Nt stocks substantiate after a decade. The study is unique in its repeated soil sampling in a multisite common garden experiment. Forest floors increased under spruce, but topsoil stocks decreased irrespective of species. Changes were of short‐term nature. Nitrogen was most stable under arbuscular mycorrhizal species.

Computing stochastic Pareto frontiers between economic and environmental goals for a semi-arid agricultural production region in Austria

Multi-objective programming is frequently used in agro-environmental management studies to find compromises between competing policy objectives. We develop an integrated modelling framework to compute stochastic Pareto frontiers using realizations of three climate scenarios (SIMILAR, DRY, WET) for the semi-arid Seewinkel region in Austria. The stochastic Pareto frontiers relate net benefits of agricultural production (NB) to three environmental goal indicators including groundwater extraction (GWEX), nitrate leaching (NO3), and topsoil organic carbon stocks (SOC). The simulation results depict trade-offs and environmental co-benefits depending on the climate scenario and induced land use changes. A 1% (or 20%) decline of NB reduces GWEX by 11–83% (or 61–100%) and NO3 by 18–19% (or 49–53%), and increases SOC by 1% (or 5%), depending on the climate scenario. The results also reveal some environmental co-benefits for all Pareto frontier combinations. Climate change intensifies or alleviates trade-offs between NB and environmental goal indicators: in a dry climate scenario, trade-offs between NB and GWEX as well as SOC increase, while in a wet climate scenario, trade-offs between NB and NO3 increase. Overall, we find that efficient climate change adaptation of land use and management practices significantly improves the environmental outcomes for fairly small reductions in agricultural net benefits.

Impact of land use type and altitudinal gradient on topsoil organic carbon and nitrogen stocks in the semi-arid watershed of northern Ethiopia

Understanding the role of soils in the soil organic carbon (SOC) and total nitrogen (TN) cycle is essential, assumed that these parameters are among the key soil quality indicators in a given landscape. Nothing but their status is in a state of continual flux due to land-use, soil management practices, and nature of topographic features. Thus, this study has evaluated the effect of land-use types and altitudinal gradient on SOC and TN concentrations and stocks at a watershed scale in northern Ethiopia. A total of 450 topsoil samples (0–30 cm depth) were collected from four different land-use types (Fig.3) across three elevational categories (Fig.1(b)), and their SOC and TN distributions were studied using descriptive statistics and geostatistical methods. Results revealed significant (p < 0.05) differences in SOC and TN concentrations and stocks by land-use type, elevation, and their interactions. The highest SOC stock was recorded at the lower elevation in GL (7.24 Mg C ha−1), followed by PF (4.65 Mg C ha−1) in the middle and GL (4.61 Mg C ha−1) in the upper elevations, respectively. On the other hand, the lowest SOC stock was observed in the BL areas of the upper (2.34 Mg C ha−1) and middle (2.75 Mg C ha−1) elevations. Spatially, the mean SOC stocks of the different land-uses were in the following order: GL > PF > CL > BL in upper elevation, PF > GL > CL > BL in middle elevation, and GL˃CL in lower elevation, respectively. The estimated total SOC and TN stocks of the study watershed were about 46,868.66 ± 7747.38 Mg C and 7,008.02 ± 441.25 Mg N, respectively. The notable difference is attributable to lack of vegetation cover, unsustainable land-use system, and land degradation via water erosion. Hence, these physical landscape disturbances result in disruption of SOC and TN's storage and stability. The SOC and TN stocks have shown a significant (p < 0.05) negative correlation with soil bulk density in the study watershed. The study concludes that variations in the land-use along topographic gradients drive the soils' SOC and TN storage. Therefore, land suitability planning, soil and water conservation measures, and reforestation practices are needed and practical worth increasing SOC and TN storage in the watershed.

Comparison of Topsoil Organic Carbon Stocks on Slopes under Soil-Protecting Forests in Relation to the Adjacent Agricultural Slopes

Soil erosion is one of the major processes degrading the natural environment but also agricultural production areas. Soil erosion may lead to soil organic carbon (SOC) loss, especially from sloping agricultural terrain units. The use of phytomelioration in environmental management, particularly long-term, permanent forest vegetation, is widely recognized as a possible measure for soil erosion protection and mitigation of climate change through carbon sequestration. The aim of this study was to compare of the topsoil organic carbon stocks on the slopes under soil-protecting forests in relation to the adjacent agricultural slopes. The research was conducted in the young glacial landscape of North-Central Poland. The study indicated the significant role of forest management on the increase of soil organic matter content and SOC stock. The results show that land use and slope gradients are important factors controlling soil organic carbon pools in topsoil in young glacial areas. This topic is extremely important particularly as the effects of climate change become more and more visible, and society faces new challenges in preventing these changes.

Spatial changes and driving variables of topsoil organic carbon stocks in Chinese croplands under different fertilization strategies

The effect of different fertilization strategies on changes in soil organic carbon (SOC) largely depends on the current status of a given agricultural region. We analysed the results of 90 long-term field trials (20–37 years) in Chinese croplands to determine the effects of fertilization strategies [i.e., no fertilizer (CK), chemical fertilizer (NPK), manure only (M) and manure plus chemical fertilizers (NPKM)] on soil organic carbon stock (SOCs) at 0–20 cm depth in the North (NC), Northeast (NEC), Northwest (NWC) and South (SC) China. Compared with initial values, SOCs increased by 24–68% and 24–74% under NPKM and M applications, respectively, over the experimental periods. Furthermore, final SOCs under NPKM in NEC and NWC were significantly higher than those under other treatments, but there was no significant difference between NPKM and M in SC and no significant differences among fertilizer treatments in NC. Average SOC stock change rates (SOCr) were positive under all treatments for all regions except for CK and NPK in NEC, which were negative. There were regional differences in treatment effects: all treatments showed significantly different rates in NC and NWC, whereas there were no significant differences between the M and NPKM in NEC and SC. Random forest (RF) modeling showed that among the selected variables initial SOCs was the most important in accounting for differences in SOCr, followed by soil bulk density, mean annual temperature and precipitation for all treatments. Soil total nitrogen content was also an important explanatory variable for SOCr for CK and NPK, and soil pH for M. This study has highlighted the main driving variables of SOC change which can be of use in optimizing fertilization strategies, by taking account of the baseline SOCs status and environmental factors for different regions, to minimize soil carbon emissions while maximizing carbon sequestration in soils.

Geospatial modeling approaches for mapping topsoil organic carbon stock in northern part of Mongolia

Soil organic carbon (SOC) is one of the most important indicators of soil quality and agricultural productivity. This paper presents the application of Regression Kriging (RK), geographically weighted regression (GWR) and Geographically Weighted Regression Kriging (GWRK) for prediction of topsoil organic carbon stock in Tarialan. A total of 25 topsoil (0-30 cm) samples were collected from Tarialan soum of Khuvsgul aimag in Mongolia. In this study, seven independent variables were used including normalised difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), normalised difference moisture index (NDMI), land surface temperature (LST) and terrain factors (DEM, Slope, Aspect). We used root-mean-square error (RMSE), mean error (ME) and determination coefficient (R2) to evaluate the performance of these methods. Validation results showed that performance of the GWRK, GWR, and RK approaches were good with not only low values of root-mean-square error (1.38 kg/m2, 1.48 kg/m2, 0.69 kg/m2), mean error (0.28 kg/m2, -0.22 kg/m2, 0.17 kg/m2) but also high values of R2 (0.76, 0.72, 0.94). The estimated SOC stock values ranged from 0.28-16.26 kg/m2, 0.72–15.24 kg/m2, 0.16–15.83 kg/m2 using GWRK, GWR, RK approaches in the study area. The highest average SOC stock value was in the wetland (6.47 kg/m2, 6.08 kg/m2, 6.44 kg/m2) and the lowest was in cropland (1.63 kg/m2, 1.48 kg/m2, 1.80 kg/m2) using these approaches. According to the validation, GWRK, GWR, and RK approaches produced satisfactory results for estimating and mapping SOC stock. However, Regression Kriging was the best model, followed by GWRK and GWR to predict topsoil organic carbon stock in Tarialan.

Spatio-temporal assessment of topsoil organic carbon stock change in Hungary

We compiled maps for the topsoil (0–30 cm) organic carbon (SOC) stock and its prediction uncertainty in Hungary at 100 m resolution for the years 1992 and 2010 using a machine learning algorithm, namely, quantile regression forest. 10-fold cross-validation was used for checking the accuracy of the spatial predictions and uncertainty quantifications for both years. The performance of the spatial predictions and uncertainty quantifications was appropriate, which was verified by the computed biases (0.15 and 0.30 for 1992 and 2010), root mean square errors (21.99 and 21.39 for 1992 and 2010), accuracy plots and the G statistics (0.96 for both years) as well. Based on the compiled SOC stock maps, we assessed the spatio-temporal change of SOC stocks on the territory of Hungary. A scheme was elaborated based on the quantified prediction uncertainties for identifying and delimiting significant and tendentious changes of SOC stock during the respective period. The total SOC stock in the topsoil was found to be 424.41 Tg (1 teragram = 1012 grams) in 1992 and 451.59 Tg in 2010. Thus SOC stock increased by 27.18 Tg over the respective period. On those areas where the land use types did not change, we observed that the SOC stock increased under forests (by 16.29 Tg) and pastures (by 2.48 Tg), decreased under wetlands (by 0.49 Tg) and did not change under agricultural areas. On those areas where the land use has been changed during the 18-year period, we found that afforestation has increased the SOC stock, whereas cultivation of pastures has decreased it. Due to soil sealing 34,000 ha of soil have been lost resulting in approximately 1.7 Tg carbon loss. We compared our own total SOC stock estimate and map referring to 1992 with other estimates and maps provided by global and continental initiatives. The comparisons have pointed out that the SOC stock map of 1992 outperformed these maps. We recommend applying the SOC stock map of 1992 as a baseline for Hungary.

Determination of spatial distribution of topsoil organic carbon stock using geostatistical technique in a karst ecosystem

This study aimed to determine the spatial distribution of total soil organic carbon (TSOC) content in surface soils in the karst ecosystem located in the Mediterranean region of Turkey. Land use types and depression areas were determined using Landsat8 and Shuttle Radar Topography Mission (SRTM) data, respectively. Topsoil samples (0-10 cm) were collected in 108 sites and analysed for soil pH, SOC, bulk density (BD), total nitrogen content (N) and particle size distribution. The exponential model was the best model to describe SOC contents, stable model for BD and circular model for TSOC. The SOC content in different land uses ranked as forest (4.10%) > rangeland (2.60%) > cropland (1.41%) areas. Mean TSOC content was identified as 27.22 MgCha−1 in non-depressed areas and 48.71 MgCha−1 in depressed areas. TSOC dramatically changed from none-depressed areas to depressed areas were identified in cropland (ΔTSOCcropland = 68.05%). Forest areas were more stable in view of the change of carbon sequestration compared to the other areas (ΔTSOCforest3 = 28.24%). Depression areas play very important role in terms of carbon storage capacity in the Karst ecosystem.

Mapping topsoil organic carbon concentrations and stocks for Tanzania

Tanzania is one of the countries that has embarked on a national programme under the United Nations collaborative initiative on Reducing Emissions from Deforestation and forest Degradation (REDD). Tanzania is currently developing the capacity to enter into a carbon monitoring REDD+ regime. In this context spatially representative soil carbon datasets and accurate predictive maps are important for determining the soil organic carbon pool. The main objective of this study was to model and map the SOC stock for the 0–30-cm soil layer to provide baseline information for REDD+ purposes. Topsoil data of over 1400 locations spread throughout Tanzania from the National Forest Monitoring and Assessment (NAFORMA), were used, supplemented by two legacy datasets, to calibrate simple kriging with varying local means models. Maps of SOC concentrations (g kg−1) were generated for the 0–10-cm, 10–20-cm, 20–30-cm, 0–30-cm layers, and maps of bulk density and SOC stock (kg m−2) for the 0–30-cm layer. Two approaches for modelling SOC stocks were considered here: the calculate-then-model (CTM) approach and the model-then-calculate approach (MTC). The spatial predictions were validated by means of 10-fold cross-validation. Uncertainty associated to the estimated SOC stocks was quantified through conditional Gaussian simulation. Estimates of SOC stocks for the main land cover classes are provided. Environmental covariates related to soil and terrain proved to be the strongest predictors for all properties modelled. The mean predicted SOC stock for the 0–30-cm layer was 4.1 kg m−2 (CTM approach) translating to a total national stock of 3.6 Pg. The MTC approach gave similar results. The largest stocks are found in forest and grassland ecosystems, while woodlands and bushlands contain two thirds of the total SOC stock. The root mean squared error for the 0–30-cm layer was 1.8 kg m−2, and the R2-value was 0.51. The R2-value of SOC concentration for the 0–30-cm layer was 0.60 and that of bulk density 0.56. The R2-values of the predicted SOC concentrations for the 10-cm layers vary between 0.46 and 0.54. The 95% confidence interval of the predicted average SOC stock is 4.01–4.15 kg m−2, and that of the national total SOC stock 3.54–3.65 Pg. Uncertainty associated with SOC concentration had the largest contribution to SOC stock uncertainty. These findings have relevance for the ongoing REDD+ readiness process in Tanzania by supplementing the previous knowledge of significant carbon pools. The soil organic carbon pool makes up a relatively large proportion of carbon in Tanzania and is therefore an important carbon pool to consider alongside the ones related to the woody biomass. Going forward, the soil organic carbon data can potentially be used in the determination of reference emission levels and the future monitoring, reporting and verification of organic carbon pools.

Crop traits drive soil carbon sequestration under organic farming

Abstract Organic farming (OF) enhances top soil organic carbon (SOC) stocks in croplands compared with conventional farming (CF), which can contribute to sequester C. As farming system differences in the amount of C inputs to soil (e.g. fertilization and crop residues) are not enough to explain such increase, shifts in crop residue traits important for soil C losses such as litter decomposition may also play a role. To assess whether crop residue (leaf and root) traits determined SOC sequestration responses to OF, we coupled a global meta‐analysis with field measurements across a European‐wide network of sites. In the meta‐analysis, we related crop species averages of leaf N, leaf‐dry matter content, fine‐root C and N, with SOC stocks and sequestration responses in OF vs. CF. Across six European sites, we measured the management‐induced changes in SOC stocks and leaf litter traits after long‐term ecological intensive (e.g. OF) vs. CF comparisons. Our global meta‐analysis showed that the positive OF‐effects on soil respiration, SOC stocks, and SOC sequestration rates were significant even in organic farms with low manure application rates. Although fertilization intensity was the main driver of OF‐effects on SOC, leaf and root N concentrations also played a significant role. Across the six European sites, changes towards higher leaf litter N in CF also promoted lower SOC stocks. Our results highlight that crop species displaying traits indicative of resource‐acquisitive strategies (e.g. high leaf and root N) increase the difference in SOC between OF and CF. Indeed, changes towards higher crop residue decomposability was related with decreased SOC stocks under CF across European sites. Synthesis and applications. Our study emphasizes that, with management, changes in crop residue traits contribute to the positive effects of organic farming (OF) on soil carbon sequestration. These results provide a clear message to land managers: the choice of crop species, and more importantly their functional traits (e.g. leave and root nitrogen), should be considered in addition to management practices and climate, when evaluating the potential of OF for climate change mitigation.

Baseline map of organic carbon stock in farmland topsoil in East China

Soil organic carbon (SOC) is important to soil fertility and the global carbon cycle. Accurate estimates of SOC stock and its dynamics are critical for managing agricultural ecosystems and carbon accounting under climate change, especially for highly cultivated regions. We extensively surveyed the SOC levels in 29,927 sites in Zhejiang province, an intensively cultivated region of East China, from year 2007 to 2008. We then estimated the spatial distribution of topsoil (0–30cm) organic carbon stock using a random forest (RF) model, which is a powerful machine learning algorithm with superior predictive performance over parametric statistical models. The final RF model contained 23 predictor variables, covering soil properties, vegetation, climate, topography, land cover, farming practices, and locations. The RF model showed high performance in predicting the SOC stock, with a coefficient of determination (R2) of 0.76 and a root mean square error (RMSE) of 10.63tCha−1. This performance was superior to the General Linear Model (GLM) (R2=0.35, RMSE=19.93tCha−1) and the ordinary kriging (OK) method (R2=0.57, RMSE=14.44tCha−1), and was equivalent to Boosted Regressing Trees (BRT) (R2=0.73, RMSE=11.26tCha−1). According to the variable importance evaluation, soil properties were the most important predictor variables, followed by climate and location, with relative importance values of 61%, 17%, and 14%, respectively. The predicted SOC stock ranged from 14.8 to 125.5tCha−1, with an average±standard deviation of 50.1±12.3tCha−1. The mean SOC level obtained from this survey was considerably lower than the value of 60.5tCha−1 reported for the same region in the Harmonized World Soil Database (HWSD), which is the most commonly used soil database worldwide. A large spatial discrepancy of SOC stock was observed between this survey and HWSD in regional and sub-regional levels. This study provided an updated regional baseline map of SOC levels for improving farmland management and refining carbon accounting under climate change.