Topsoil Organic Carbon Content Research Articles

A European soil organic carbon monitoring system leveraging Sentinel 2 imagery and the LUCAS soil data base

The Worldsoils project has developed a pre-operational Soil Organic Carbon (SOC) monitoring system in a cloud environment. The system predicts topsoil organic carbon content at regional and continental scales from Earth Observation (EO) satellite data with a continuous cover over Europe. The system utilizes spectral models for croplands and a digital soil mapping approach for permanently vegetated areas such as grasslands and forests. Models strongly rely on soil reflectance composites from the Sentinel 2 multispectral instrument providing the median reflectance for all valid pixels over a period of three years. The bare soil frequency, a proxy for the degree of crop cover, is clearly lower in a Mediterranean pilot region compared to croplands in temperate regions. This is due to the extensive crop cover in the Mediterranean with winter cereals and fodder crops. The graphical user interface provides SOC content and the prediction interval ratio (i.e. 90 % uncertainty interval divided by the median) for 50 m pixels in three pilot regions and 100 m pixels for the rest of Europe. The SOC prediction algorithms are reasonable compared to others at the continental scale (R2: 0.41 for croplands and 0.28 for permanently vegetated areas). Apart from tree crops in Macedonia (Greece) the soil reflectance composite attributes the correct model to validation sets of cropland and grassland in the pilot regions. The SOC prediction is satisfactory in Wallonia (Belgium; R2 0.51) but is less accurate in Greece and the Czech Republic. In particular in Greece, the poor performance is linked to the low bare soil frequency due to the abundance of tree crops, cereals and fodder crops. The monitoring system can reproduce spatial patterns in SOC content similar to the ones obtained from a detailed regional algorithm using the new generation of hyperspectral satellites. However, the very high values in kettle holes in a morainic landscape of Northern Germany are underestimated.

Regional topsoil organic carbon content in the agricultural soils of Slovakia and its drivers, as revealed by the most recent national soil monitoring data

Soil organic carbon (SOC) is a primary constituent of soil organic matter and plays an important role in the regulation of many soil processes, including greenhouse gas emissions. Recently, SOC also became an indicator for monitoring climate change mitigation policies in the agricultural sector. The availability of up-to-date SOC inventories is thus crucial in terms of supporting SOC–related actions at country or sub-country scales. Currently, the National Monitoring System of the Agricultural Soils of Slovakia (CMS-P), whose network of 318 monitoring sites was last surveyed in 2018, is the only available source of up-to-date topsoil SOC data for agricultural land in Slovakia. Although very useful at the national scale, the number of CMS-P observations it contains is too limited for much needed sub-national SOC inventories. We hypothesized that with the aid of well-chosen macro-scale drivers of topsoil SOC accumulation in agricultural land in Slovakia, and by mapping those drivers geographically, we could upscale the CMS-P observations and produce a regional estimate of topsoil SOC. Altitude, land cover, topsoil texture, and soil type were assumed to be the key factors controlling topsoil SOC accumulation in Slovakia, and based on these, the country was classified into 14 macro-scale geographical regions. Typical ranges and mid-class values of 0–30cm topsoil SOC concentrations (%) and stocks (t ha−1) were calculated for each macro-scale region from CMS-P data. The average topsoil SOC content in agricultural land was estimated to be 2.13% (72.9 t ha−1). The highest topsoil SOC stock (> 90 t ha−1) was estimated for the lowlands of Slovakia, and the lowest (< 50 t ha−1) for the shallow and stony soils of mountain regions. When aggregated to 78 administrative regions at LAU1 level, the area-weighted averages ranged between 39.20 t ha−1 and 80.0 t ha−1, with the highest values (> 65 t ha−1) being in LAU1 regions in the south-west, south-east, and north of Slovakia where arable land is most prevalent. Total SOC storage in 0–30cm topsoil of agricultural land in Slovakia was estimated at 118.39 Mt, with two-thirds of this amount stored in arable soils in 33 south-west, south-east, and south LAU1 administrative regions. As there is no alternative and up-to-date dataset on topsoil SOC content in Slovakia, the upscaling algorithm presented in this study is an important step toward utilizing CMS-P data for sub-national SOC inventories. It may also offer a new way of providing inputs to help predict future or alternative regional topsoil SOC accumulation trajectories in Slovakian agricultural land using process-based or statistical models.

Major contributions of agricultural management practices to topsoil organic carbon distribution and accumulation in croplands of East China over three decades

Multiple natural and anthropogenic factors jointly drive the spatial distribution of soil organic carbon (SOC) and its dynamics in croplands. Among these factors, agricultural management practices have caused considerable impacts. Previous studies on the driving factors of SOC in croplands have provided significant understanding on this matter. However, whether and how the effects and interplay of these drivers change over time is often unknown, especially agricultural activities. To measure the effects of agricultural management practices on the spatial distribution and temporal change of SOC incorporating the network relationships with other natural drivers at the regional scale, we conducted partial least squares path analysis on the topsoil organic carbon content using two historical soil datasets from cropland samples in East China obtained during the 1980s and the 2010s. Eight indicators and their temporal changes reflecting climate, agricultural management, and edaphic conditions were used to quantify the driving mechanisms of the spatial distribution and temporal change of SOC. The drivers of SOC distribution showed that high SOC was mostly distributed in soils with a relatively low pH and high clay content in warm humid climates. High SOC was associated with the application of N fertilizer, crop residue input, and agricultural machinery in the 1980s and 2010s. The effect of N fertilization on SOC distribution increased from the 1980s to the 2010s, whereas the total edaphic effects significantly decreased from 0.62 to 0.25 (P < 0.01). Regarding the drivers of SOC change over the three decades, the edaphic effects presented the strongest effect (path coefficient of 0.74, P < 0.01), including the negative effects of topsoil acidity and baseline SOC level, as well as the positive effects of total nitrogen (TN) and clay content change. Croplands with lower intensity of management practices in the 1980s generally attained more development in agricultural modernization, which led to a considerable SOC increase. Our research revealed the importance of agricultural management practices on the spatial distribution and temporal change of cropland SOC at the regional scale. The results emphasize the need to measure the changing driving mechanisms of SOC dynamics. The findings indicate that indicators reflecting the effects of agricultural management practices should be included in digital mapping and process-based modeling of soil carbon at different spatiotemporal scales to improve prediction accuracy.

Revised emission factors for estimating direct nitrous oxide emissions from nitrogen inputs in Australia’s agricultural production systems: a meta-analysis

Context Agricultural soils are a major source of emissions of the greenhouse gas nitrous oxide (N2O). Aim Quantify direct N2O emissions from Australian agricultural production systems receiving nitrogen (N) inputs from synthetic and organic fertilisers, crop residues, urine and dung. Method A meta-analysis of N2O emissions from Australian agriculture (2003–2021) identified 394 valid emission factors (EFs), including 102 EFs with enhanced efficiency fertilisers (EEFs). Key results The average EF from all N sources (excluding EEFs) was 0.57%. Industry-based EFs for synthetic N fertiliser (excluding EEFs) ranged from 0.17% (non-irrigated pasture) to 1.77% (sugar cane), with an average Australia-wide EF of 0.70%. Emission factors were independent of topsoil organic carbon content, bulk density and pH. The revised EF for the non-irrigated cropping (grains) industry is now 0.41%; however, geographically-defined EFs are recommended. Urea was the most common N source with an average EF of 0.72% compared to urine (0.20%), dung (0.06%) and organo-mineral mixtures (0.26%). The EF for synthetic N fertilisers in rainfed environments increased by 0.16% for every 100 mm over 300 mm mean annual rainfall. For each additional 50 kg N ha−1 of synthetic fertiliser, EFs increased by 0.13%, 0.31% and 0.38% for the horticulture, irrigated and high rainfall non-irrigated cropping industries, respectively. The use of 3,4 dimethylpyrazole-phosphate (DMPP) produced significant reductions in EFs of 55%, 80% and 84% for the horticulture, non-irrigated and irrigated cropping industries, respectively. Conclusions and implications Incorporation of the revised EFs into the 2020 National Greenhouse Accounts (NGA) produced a 12% increase in direct N2O emissions from the application of synthetic N fertilisers. The lack of country-specific crop residue decomposition data is a major deficiency in the NGA.

An Extensive Field-Scale Dataset of Topsoil Organic Carbon Content Aimed to Assess Remote Sensed Datasets and Data-Derived Products from Modeling Approaches

The geosciences suffer from a lack of large georeferenced datasets that can be used to assess and monitor the role of soil organic carbon (SOC) in plant growth, soil fertility, and CO2 sequestration. Publicly available, large field-scale georeferenced datasets are often limited in number and design to serve these purposes. This study provides the first publicly accessible dataset of georeferenced topsoil SOC measurements (n = 840) over a 26-hectare (ha) agricultural field located in southern Ontario, Canada, with a sampling density of ~32 points per ha. As SOC is usually influenced by site topography (i.e., slope and landscape position), each point of the database is associated with a wide range of remote sensing topographic derivatives; as well as with normalized difference vegetation index (NDVI) based value. The NDVI data were extracted from remote sensing Sentinel-2 imagery from over a five-year period (2017–2021). In this paper, the methodology for topsoil sampling, SOC measurement in the lab, as well as producing the suite of topographic derivatives is described. We discuss the opportunities that the database offers in terms of spatially explicit and continuous soil information to support international efforts in digital soil mapping (i.e., SoilGrids250m) as well as other potential applications detailed in the discussion section. We believe that the database with very dense point location measurements can help in conducting carbon stocks and sequestration studies. Such information can be used to help bridge the gap between ground data and remotely sensed datasets or data-derived products from modeling approaches intended to evaluate field-scale rates of agricultural carbon accumulation. The generated topsoil database in this study is archived and publicly available on the Zenodo open-access repository.

Prediction of topsoil organic carbon content with Sentinel-2 imagery and spectroscopic measurements under different conditions using an ensemble model approach with multiple pre-treatment combinations

Estimating soil organic carbon (SOC) using visible near infrared (Vis-NIR) spectroscopy has proven to be a rapid and reliable approach. However, when working across large geographical scales, remote sensing may be more suitable. Acquiring these spectra data normally under different measurement conditions could introduce artefacts that reduce SOC prediction accuracy. A common procedure has been using calibration or multivariate techniques in conjunction with one or more pre-treatment algorithms. The results of several comparative studies based on these predictive calibration techniques used alone were inconsistent. Moreover, protocols to select the most appropriate pre-treatment algorithms rarely exist. This study combines predictions from different techniques into a single model based on an ensemble learning approach. The main objective is to improve the accuracy of SOC prediction by assessing the effectiveness of using different calibration techniques individually against an ensemble model consisting of one statistical method, which includes partial least squares regression (PLSR), and three machine learning (ML) algorithms, including random forest (RF), support vector machine regression (SVMR), and Cubist. Several pre-treatment algorithms were also employed to improve the spectral data before prediction. Spectra data were collected from three different agricultural fields (with different soil types), under different spectral measurement conditions (field, wet and dry). Additionally, Sentinel-2 (S2) data was collected from one of these fields. Furthermore, to ascertain the effectiveness of the developed model on regional scale dataset, two options were employed: (1) merged data from all fields, and (2) merged data from fields measured under the same spectral measurement conditions. The models were evaluated using root mean square error of prediction (RMSEPCV), the coefficient of determination (R²CV), the ratio of performance to interquartile range (RPIQ), the ratio of performance to deviation (RPD) and BIAS. The results show that, across the three agricultural fields, the ensemble model predicted SOC more accurately than each of the individual calibration techniques (R2CV = 0.92, RMSEPCV (g/kg) = 1.00, RPD = 3.06, RPIQ = 3.74, BIAS (g/kg) = 0.067). The models derived from merged data (regional dataset) show that the ensemble approach predicted SOC more accurately with option 2 than option 1. Finally, while the ensemble model improves SOC accuracy with S2 data, the final output was poor. Further research to determine the underlying problem is strongly recommended. Nonetheless, these results indicate that the ensemble model is advantageous because it improved the prediction accuracy of SOC and reduced the error margin.

On-Farm Relationships Between Agricultural Practices and Annual Changes in Organic Carbon Content at a Regional Scale

Both soil quality degradation and climate change mitigation issues emphasize the need to increase, or at least stabilize, the topsoil organic carbon content (wt%) in arable land. This on-farm study aimed at measuring the impact of agricultural practices on changes in soil organic carbon (SOC) content over 10 years. A total of 120 fields belonging to 120 farms representative of the cropping systems and soil properties in Western Switzerland (Lake Geneva region) was randomly selected. The field 0–20 cm topsoil was sampled at a 10-years interval, and the corresponding cropping practices were gathered using farmer’s interviews and the mandatory records of yearly practices at field level in Swiss-farms. Only 1) organic matter inputs and 2) cover-crop intensity were significantly correlated to SOC increase while 3) the soil tillage intensity and 4) the soil saturation in carbon expressed as a SOC to clay content ratio were correlated to SOC decrease. Among others, temporary meadows were not correlated to changes in SOC content mainly due to increased tillage and decreased cover-crops between meadows. Organic farming did not correlate either with SOC changes due to the large tillage intensity applied for weed control. The observed SOC content changes ranged from −56‰ to +74‰ and were well explained by a linear regression model with additive effect of the four identified SOC change factors. The additivity of these factors means that farmers can emphasize the methods of their choice when regenerating their soils. This study advocates that strict no-till is not required at low carbon saturation level (small SOC:Clay ratio). However, as carbon saturation increases, conservation tillage and then no-till practices become necessary to further increase SOC contents. These findings are in accordance with previous studies showing that since 2015 SOC is increasing at more than +4‰ on average in the region and provide practical insights to further manage the transition of farming systems towards soil regeneration.

Contrasting changes in soil carbon under first rotation, secondary and historic woodland in England and Wales

This study investigates changes in soil carbon under woodland combining data from the National Soil Inventory of England and Wales with data from the National Inventory of Woodlands and Trees to create a unique dataset with woodland management information at the sites where soil carbon was measured in 1980 and 2003. Three woodland management stages were compared: first rotation (i.e. recently planted on land not previously under woodland and not yet harvested), second rotation (i.e. harvested at least once), and historic woodlands. Woodlands in their first rotation demonstrated a reduction (p < 0.01) in topsoil organic carbon content typically losing over 2% per year, whereas no change (p ≥ 0.10) was observed for the other two woodland types. This large reduction in organic carbon could not be statistically explained by a higher inherent soil carbon, as the mean soil carbon content of the first rotation and second rotation woodlands were not (p ≥ 0.50) different. The average age of the woodlands under first rotation was 42 years, indicating that the period of significant soil carbon loss could go on for about 40 years after planting.

An integrated approach for the evaluation of quantitative soil maps through Taylor and solar diagrams

For many decades, soil scientists have produced spatial estimates of soil properties using statistical and non-statistical mapping models. Commonly in soil mapping studies the map quality is assessed through pairwise comparison of observed and predicted values of a soil property, from which statistical indices summarizing the quality of the entire map are computed. Often these indices are based on average error and correlation statistics. In this study, we recommend a more appropriate and effective method of map evaluation by means of Taylor and solar diagrams. Taylor and solar diagrams are summary diagrams exploiting the relationship between statistical indices to visualize differentiable aspects of map quality into a single plot. An important advantage over current map quality evaluation is that map quality can be assessed from the combined effect of a few statistical quantities, not just on the basis of a single index or list of indices. We illustrate the use of common statistical indices and their combination into summary diagrams with a simulation study and two applications on soil data. In the simulation study nine maps with known statistical properties are produced and evaluated with tables and summary diagrams. In the first case study with soil data, change in the quality of a large-scale topsoil organic carbon map is tracked for a number of permutations in the mapping model parameters, whereas in the second case study several maps of topsoil organic carbon content for the same area, made by various statistical and non-statistical models, are compared and evaluated. We consider that in all cases better insights in map quality are obtained with summary diagrams, instead of using a single index or an extensive list of indices. This underpins the importance of using integrated summary graphics to communicate on quantitative map quality so as to avoid excessive trust that a single map quality index may suggest.

Changes in topsoil organic carbon content in the Swiss leman region cropland from 1993 to present. Insights from large scale on-farm study

Increasing cropland topsoil organic carbon (SOC) content is a key goal for soil improving quality and adaptating soils to climate change. Moreover, the short term potential of climate mitigation by carbon sequestration is mostly attributed to increasing topsoil SOC content (Balesdent and Arrouays, 1999; Chambers et al., 2016; Minasny et al., 2017; Balesdent et al., 2018). However, the possibility to increase SOC content is highly disputed in current literature which is mostly based on field experiments.We quantified the on-farm SOC content deficit and SOC content change rate of cropland topsoil (0–20 cm) from western Switzerland using the data bases of Geneva and Vaud cantons containing more than 30,000 topsoil analyses, performed every ten years on every cultivated field of the region since 1993. SOC deficit was estimated as the amount of SOC necessary to reach the 0.1 SOC:clay ratio considered as the minimum required SOC amount for acceptable soil quality. Cropland topsoils of the Vaud and Geneva cantons displayed a 20% and 70% SOC content deficit, respectively.In both cantons, the range of observed rates of change in SOC content from 1993 to present was very large, from −30 to +30‰ per year, with a median value of 0. However, the time trends showed a highly significant linear increase of rates from −5‰ to +6‰ per year on average, in 1995 and 2015, respectively, with no change in SOC content reached by 2005–2007.These trends were attributed to the Swiss agri-environmental schemes applied at the end of 20th century, namely mandatory cover crops and minimum rotations of 4 crops. Further, SOC content increase was accordant with the continuing adoption of minimum tillage, conservation agriculture and multi-species intense cover crops.These findings oppose to those obtained in Swiss long-term experiments, which emphasizes the need to use on-farm information when adressing agriculture policy, climate mitigation or soil quality management issues.

Digital mapping of topsoil organic carbon content in an alluvial plain area of the Terai region of Nepal

Topsoil (0–20 cm) soil organic carbon content (SOC, g kg−1) was predicted and mapped at 30 m resolution on an intensively cultivated alluvial plain in the Sarlahi district of Nepal. It has been reported that SOC content in this region has decreased to alarmingly low levels; however, no documented work on the mapping of SOC at high spatial resolutions has been found. We compared the performance of the Stepwise-Multiple-Linear-Regression-Kriging (SMLRK) and Random Forest (RF) techniques for mapping SOC content. Environmental covariates of SOC were selected following the SCORPAN framework of digital soil mapping. Prediction uncertainty was quantified using Quantile Regression Forest technique. Predicted SOC was further calculated for different soil and land cover unit combinations. It was found that RF performed better than SMLRK as evidenced by more favourable error statistics. Silt content, distance to major river systems, precipitation during coldest quarter of the year, and NDVI for January-February were found to be the most important variables affecting SOC content in this region. Mean SOC of the study area was predicted as 10.98 ± 0.01 g kg−1. Calcaric Phaeozems under Shrubs (14.85 ± 0.09 g kg−1) and forests (14.54 ± 0.02 g kg−1) had the highest mean SOC contents. Cultivated lands within irrigation command areas had less SOC content (9.41 ± 0.01 g kg−1) than those outside the areas (11.84 ± 0.01 g kg−1). The SOC spatial distribution map could guide efforts to prioritize SOC content enhancement activities in this area. It was concluded that in such low-relief alluvial regions, the processes of silt deposition due to the proximity to river systems are key predictors than other variables, and that the RF technique could predict SOC content better than SMLRK. The success of activities to raise SOC contents might thus be subject to the control of silt deposition from the upstream hills.

Temporal mosaicking approaches of Sentinel-2 images for extending topsoil organic carbon content mapping in croplands

The spatial assessment of soil organic carbon (SOC) is a major environmental challenge, notably for evaluating soil carbon stocks. Recent works have shown the capability of Sentinel-2 to predict SOC content over temperate agroecosystems characterized with annual crops. However, because spectral models are only applicable on bare soils, the mapping of SOC is often obtained on limited areas. A possible improvement for increasing the number of pixels on which SOC can be retrieved by inverting bare soil reflectance spectra, consists of using optical images acquired at several dates. This study compares different approaches of Sentinel–2 images temporal mosaicking to produce a composite multi-date bare soil image for predicting SOC content over agricultural topsoils. A first approach for temporal mosaicking was based on a per-pixel selection and was driven by soil surface characteristics: bare soil or dry bare soil with/without removing dry vegetation. A second approach for creating composite images was based on a per-date selection and driven either by the models performance from single-date, or by average soil surface indicators of bare soil or dry bare soil. To characterize soil surface, Sentinel-1 (S1)-derived soil moisture and/or spectral indices such as normalized difference vegetation index (NDVI), Normalized Burn Ratio 2 (NBR2), bare soil index (BSI) and a soil surface moisture index (S2WI) were used either separately or in combination. This study highlighted the following results: i) none of the temporal mosaic images improved model performance for SOC prediction compared to the best single-date image; ii) of the per-pixel approaches, temporal mosaics driven by the S1-derived moisture content, and to a lesser extent, by NBR2 index, outperformed the mosaic driven by the BSI index but they did not increase the bare soil area predicted; iii) of the per-date approaches, the best trade-off between predicted area and model performance was achieved from the temporal mosaic driven by the S1-derived moisture content (R2 ~ 0.5, RPD ~ 1.4, RMSE ~ 3.7 g.kg-1) which enabled to more than double (*2.44) the predicted area. This study suggests that a number of bare soil mosaics based on several indicators (moisture, bare soil, roughness…), preferably in combination, might maintain acceptable accuracies for SOC prediction whilst extending over larger areas than single-date images.

Effects of nitrogen fertilization and bioenergy crop type on topsoil organic carbon and total Nitrogen contents in middle Tennessee USA.

Nitrogen (N) fertilization affects bioenergy crop growth and productivity and consequently carbon (C) and N contents in soil, it however remains unclear whether N fertilization and crop type individually or interactively influence soil organic carbon (SOC) and total N (TN). In a three-year long fertilization experiment in switchgrass (SG: Panicum virgatum L.) and gamagrass (GG: Tripsacum dactyloides L.) croplands in Middle Tennessee USA, soil samples (0–15cm) were collected in plots with no N input (NN), low N input (LN: 84 kg N ha-1 yr-1 in urea) and high N input (HN: 168 kg N ha-1 yr-1 in urea). Besides SOC and TN, the aboveground plant biomass was also quantified. In addition to a summary of published root morphology data based on a separated mesocosm experiment, the root leachable dissolved organic matter (DOM) of both crops was also measured using archived samples. Results showed no significant interaction of N fertilization and crop type on SOC, TN or plant aboveground biomass (ABG). Relative to NN, HN (not LN) significantly increased SOC and TN in both crops. Though SG showed a 15–68% significantly higher ABG than GG, GG showed a 9.3–12% significantly higher SOC and TN than SG. The positive linear relationships of SOC or TN with ABG were identified for SG. However, GG showed structurally more complex and less readily decomposed root DOM, a larger root volume, total root length and surface area than SG. Collectively, these suggested that intensive N fertilization could increase C and N stocks in bioenergy cropland soils but these effects may be more likely mediated by the aboveground biomass in SG and root chemistry and morphology in GG. Future studies are expected to examine the root characteristics in different bioenergy croplands under the field fertilization experiment.

Leveraging drought risk reduction for sustainable food, soil and climate via soil organic carbon sequestration

Drought is a major risk in global agriculture. Building-up soil organic carbon (SOC) enhances soil fertility and efficient use of rainwater, which can increase drought tolerance in food production. SOC management demonstrates its benefit at various locations and is a promising means to achieve food security and climate mitigation at once. However, no global assessment of its potential and co-benefits gained from SOC enhancement has been presented. Here we evaluated the extent to which SOC build-up could reduce agricultural drought risk. Using statistical analysis of spatially-explicit global crop and soil datasets, we find that relatively small enhancement in topsoil (0–30 cm) organic carbon content (OCtop) could increase drought tolerance of the food production systems operating over 70% of the global harvested area (particularly drylands). By closing the gap between current and upper limit of tolerance levels through SOC addition of 4.87 GtC at the global scale, farmer’s economic output in drought years would increase by ~16%. This level of SOC increase has co-benefit of reducing global decadal mean temperature warming by 0.011 °C. Our findings highlight that progress towards multiple development goals can be leveraged by SOC enhancement in carbon (C)-poor soils in drier regions around the world.

Improving prediction of soil organic carbon content in croplands using phenological parameters extracted from NDVI time series data

Mapping the spatial distribution of soil organic carbon (SOC) content or stock is important for climate change studies and land management decisions. When using environmental covariates to map SOC content or stock, variables indicating human activities have drawn growing attentions. Crop species/crop rotations and agricultural management significantly affect the spatial variation of SOC in croplands. For areas where climatic conditions and farming managements are generally consistent in cultivation territory of one crop species, crop phenology largely indicates the crop response to soil. Therefore, phenological parameters incorporating with crop rotation could be effective for mapping soil organic carbon in these areas. In this study, we extracted phenological parameters from Normalized Difference Vegetation Index (NDVI) time series data, and used these variables with crop rotation for predicting topsoil organic carbon content in a cropland area in Anhui province, China. Forty-nine sampling points were collected in field. For these points, there were three crop rotations each with two crop species per year. Twenty-two HJ-1 A/B images for 2010 year with a 30 m resolution were obtained. Eleven phenological parameters for each of the two growing seasons were obtained with a dynamic threshold method. Various combinations of predictive variables were developed based on variable importance and experimented for predicting topsoil organic carbon using random forest. The prediction results were validated using a cross validation approach. Results showed that base levels (given as the average of the left and right minimum values of a time series profile) for both seasons were the most important predictors in this area. Adding both crop rotation and the two phenological parameters to the natural environment variables increased the prediction accuracies by 50% in terms of R2 and 13.4% in terms of root mean square error (RMSE). This study demonstrates the effectiveness of crop phenology in mapping SOC in croplands.

A Comparative Assessment of Geostatistical, Machine Learning, and Hybrid Approaches for Mapping Topsoil Organic Carbon Content

Accurate digital soil mapping (DSM) of soil organic carbon (SOC) is still a challenging subject because of its spatial variability and dependency. This study is aimed at comparing six typical methods in three types of DSM techniques for SOC mapping in an area surrounding Changchun in Northeast China. The methods include ordinary kriging (OK) and geographically weighted regression (GWR) from geostatistics, support vector machines for regression (SVR) and artificial neural networks (ANN) from machine learning, and geographically weighted regression kriging (GWRK) and artificial neural networks kriging (ANNK) from hybrid approaches. The hybrid approaches, in particular, integrated the GWR from geostatistics and ANN from machine learning with the estimation of residuals by ordinary kriging, respectively. Environmental variables, including soil properties, climatic, topographic, and remote sensing data, were used for modeling. The mapping results of SOC content from different models were validated by independent testing data based on values of the mean error, root mean squared error and coefficient of determination. The prediction maps depicted spatial variation and patterns of SOC content of the study area. The results showed the accuracy ranking of the compared methods in decreasing order was ANNK, SVR, ANN, GWRK, OK, and GWR. Two-step hybrid approaches performed better than the corresponding individual models, and non-linear models performed better than the linear models. When considering the uncertainty and efficiency, ML and two-step approach are more suitable than geostatistics in regional landscapes with the high heterogeneity. The study concludes that ANNK is a promising approach for mapping SOC content at a local scale.

Predicting soil organic carbon content in croplands using crop rotation and Fourier transform decomposed variables

Previous studies on soil organic carbon content or stock mapping mostly use natural environmental covariates and do not consider the soil management practice factor. However, human activities have become an important influencing factor for soil organic carbon, especially for agricultural soils. Crop species/crop rotations and management practices significantly affect the amount and spatial variation of soil organic carbon in croplands, but have not been considered for mapping soil organic carbon. In this study, we used direct crop rotation information and variables generated using Fourier transform on HJ-1A/1B NDVI time series data to capture the periodic effect of crop rotation, and explored the effectiveness of incorporating such information in predicting topsoil organic carbon content in cropland. A case study applied such method in a largely agricultural area in Anhui province, China. Crop rotation information was obtained through field investigation. Various combinations of predictive environmental variables were experimented for mapping soil organic carbon. The results were validated using field samples. Results showed that the combination of natural environment variables with both crop rotation type and variables derived through Fourier transform yielded the highest accuracy. In addition, only using the Fourier decomposed variables and crop rotation information were able to achieve a similar accuracy with using only soil formative natural environmental variables. This indicates that crop rotation information has comparable predictive power of soil organic carbon as natural environment variables. This study demonstrates the effectiveness of including agricultural practice information in digital soil mapping in agricultural landscapes with differences in crop rotation.

Improvement of Saline-Sodic Grassland Soils Properties by Rotational Grazing in Argentina

We investigated the effectiveness of rotational and permanent grazing exclosure periods for improving topsoil quality in three commercial farms devoted to cattle breeding in sodic grassland (halophytic steppe) soils of the Flooding Pampa of Argentina. We compared two plots under continuous grazing (C1-C2) with two plots under more than 8 yr of rotational grazing management (R1-R2) and two adjacent plots under permanent grazing exclosure for more than 8 (E1) and 4 (E2) yr. Periodic and permanent grazing exclosure periods caused significant (P < 0.05) and progressive increases in topsoil organic carbon content and organic carbon stock (0−20 cm; from 24 to 61 Mg ha−1) as follows: (C1 = C2) < (R1 = R2 = E2) < E1 plots. Topsoil physical properties (bulk density, structural instability, and bearing capacity) and salinity were higher (P < 0.05) in C1 and C2 than in the other plots, while infiltration rate was higher in the oldest exclosure (E1) than in the other plots. Topsoil pH decreased from C1-C2 plots (9.5−9.9) to R1-R2 plots (7.3−8.2) to E1-E2 plots (6.5−7.5), while SAR was highest in C1-C2 and lowest in E1 plots. We propose a conceptual model leading to soil recovery in this halophytic steppe community, triggered by organic carbon accumulation induced by grazing management. Short-time grazing exclusion periods (i.e., rotational grazing) are a plausible and low-cost management option to be recommended to the farmers in this highly restrictive environment.

Conversion of natural forest results in a significant degradation of soil hydraulic properties in the highlands of Kenya

Land use change, especially conversion of native forests can have large impacts on water resources. Large scale conversion of native forests to agricultural land has occurred in the last few decades in the Mau Forest region. To quantify and understand landscape hydrologic responses, this study aimed at evaluating the effects of land use on soil infiltration, saturated hydraulic conductivity, bulk density, sorptivity, and soil moisture retention. A total of 136 plots representing five different land uses (native forest: n=39, forest plantations: n=14, tea plantations: n=24, croplands: n=23 and pasture: n=36) were sampled in three catchments with similar parental material in the Mau Forest region, Western Kenya. Native forest topsoils (0–5cm) had a bulk density of 1.0±0.2gcm−3 which was similar to values found for topsoils of forest plantations (1.1±0.2gcm−3), but significantly lower than topsoils from croplands (1.4±0.2gcm−3), tea plantation (1.3±0.3gcm−3) and pastures (1.4±0.2gcm−3). Similarly, soil infiltration rates were higher in native forest (76.1±50cmh−1) and in forest plantation (60.2±47.9cmh−1) than in croplands (40.5±21.5cmh‐1), tea plantations (43.3±29.2cmh−1) and pastures (13.8±14.6cmh−1). Native forest had the highest topsoil organic carbon contents (8.11±2.42%) and field capacity (0.62 ±0.12cm3cm−3), while the highest permanent wilting point was recorded for pasture soils (mean of 0.41±0.06cmcm−3). The highest plant available water capacity was recorded for soils in native forest (mean of 0.27±0.14cmcm−3). Our study indicates that land use changes result in a significant degradation of soil hydraulic properties, which has likely resulted in changes of the regional water balance. Given the magnitude in which managed land use types have changed infiltration rates in our study area, we conclude that changes in land use types occurring in our study region in the last decades have already affected the hydrological regime of the landscapes and the compositions of flow components. The reduction in infiltration and hydraulic conductivity could result in increased surface run-off, erosion and frequency of flooding events.

Understanding the spatial distribution of factors controlling topsoil organic carbon content in European soils

Soil Organic Carbon (SOC) constitutes the largest terrestrial carbon pool. The understanding of its dynamics and the environmental factors that influence its behaviour as sink or source of atmospheric CO2 is crucial to quantify the carbon budget at the global scale. At the European scale, most of the existing studies to account for SOC stocks are centred in the fitting of predictive model to ascertain the distribution of SOC. However, the development of methodologies for monitoring and identifying the environmental factors that control SOC storage in Europe remains a key research challenge. Here we present a modelling procedure for mapping and monitoring SOC contents that uses Visible-Near Infrared (VNIR) spectroscopic measurements and a series of environmental covariates to ascertain the key environmental processes that have a major contribution into SOC sequestration processes. Our results show that it follows a geographically non-stationary process in which the influencing environmental factors have different weights depending on the spatial location. This implies that SOC stock modelling should not rely on a single model but on a combination of different statistical models depending on the environmental characteristics of each area. A cluster classification of European soils in relation to those factors resulted in the determination of four groups for which specific models have been obtained. Differences in climate, soil pH, content of coarse fragments or land cover type are the main factors explaining the differences in SOC in topsoil from Europe. We found that climatic conditions are the main driver of SOC storage at the continental scale, but we also found that parameters like land cover type influence SOC content found at the local scales in certain areas. Our methodology developed at continental scale could be used in future research aimed to improve the predictive performance of SOC assessments at European scale.