Is land condition a useful indicator of soil organic carbon stock in Australia’s northern grazing land?

2016 ◽  
Vol 38 (3) ◽  
pp. 229 ◽  
Author(s):  
S. G. Bray ◽  
D. E. Allen ◽  
B. P. Harms ◽  
D. J. Reid ◽  
G. W. Fraser ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Type ◽  
Northern Australia ◽  
Grazing Land ◽  
Condition Indicators ◽  
Land Condition ◽  
Soc Stock ◽  
Grazing Lands ◽  
Soc Stocks

The grazing lands of northern Australia contain a substantial soil organic carbon (SOC) stock due to the large land area. Manipulating SOC stocks through grazing management has been presented as an option to offset national greenhouse gas emissions from agriculture and other industries. However, research into the response of SOC stocks to a range of management activities has variously shown positive, negative or negligible change. This uncertainty in predicting change in SOC stocks represents high project risk for government and industry in relation to SOC sequestration programs. In this paper, we seek to address the uncertainty in SOC stock prediction by assessing relationships between SOC stocks and grazing land condition indicators. We reviewed the literature to identify land condition indicators for analysis and tested relationships between identified land condition indicators and SOC stock using data from a paired-site sampling experiment (10 sites). We subsequently collated SOC stock datasets at two scales (quadrat and paddock) from across northern Australia (329 sites) to compare with the findings of the paired-site sampling experiment with the aim of identifying the land condition indicators that had the strongest relationship with SOC stock. The land condition indicators most closely correlated with SOC stocks across datasets and analysis scales were tree basal area, tree canopy cover, ground cover, pasture biomass and the density of perennial grass tussocks. In combination with soil type, these indicators accounted for up to 42% of the variation in the residuals after climate effects were removed. However, we found that responses often interacted with soil type, adding complexity and increasing the uncertainty associated with predicting SOC stock change at any particular location. We recommend that caution be exercised when considering SOC offset projects in northern Australian grazing lands due to the risk of incorrectly predicting changes in SOC stocks with change in land condition indicators and management activities for a particular paddock or property. Despite the uncertainty for generating SOC sequestration income, undertaking management activities to improve land condition is likely to have desirable complementary benefits such as improving productivity and profitability as well as reducing adverse environmental impact.

scholarly journals Estimating soil organic carbon stocks of Swiss forest soils by robust external-drift kriging

2014 ◽  
Vol 7 (3) ◽  
pp. 1197-1210 ◽  
Author(s):  
M. Nussbaum ◽  
A. Papritz ◽  
A. Baltensweiler ◽  
L. Walthert
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Near Infrared ◽  
Likelihood Method ◽  
Standard Errors ◽  
Soil Profiles ◽  
Near Infrared Reflectance ◽  
Soc Stock ◽  
Soc Stocks ◽  
External Drift

Abstract. Accurate estimates of soil organic carbon (SOC) stocks are required to quantify carbon sources and sinks caused by land use change at national scale. This study presents a novel robust kriging method to precisely estimate regional and national mean SOC stocks, along with truthful standard errors. We used this new approach to estimate mean forest SOC stock for Switzerland and for its five main ecoregions. Using data of 1033 forest soil profiles, we modelled stocks of two compartments (0–30, 0–100 cm depth) of mineral soils. Log-normal regression models that accounted for correlation between SOC stocks and environmental covariates and residual (spatial) auto-correlation were fitted by a newly developed robust restricted maximum likelihood method, which is insensitive to outliers in the data. Precipitation, near-infrared reflectance, topographic and aggregated information of a soil and a geotechnical map were retained in the models. Both models showed weak but significant residual autocorrelation. The predictive power of the fitted models, evaluated by comparing predictions with independent data of 175 soil profiles, was moderate (robust R2 = 0.34 for SOC stock in 0–30 cm and R2 = 0.40 in 0–100 cm). Prediction standard errors (SE), validated by comparing point prediction intervals with data, proved to be conservative. Using the fitted models, we mapped forest SOC stock by robust external-drift point kriging at high resolution across Switzerland. Predicted mean stocks in 0–30 and 0–100 cm depth were equal to 7.99 kg m−2 (SE 0.15 kg m−2) and 12.58 kg m−2 (SE 0.24 kg m−2), respectively. Hence, topsoils store about 64% of SOC stocks down to 100 cm depth. Previous studies underestimated SOC stocks of topsoil slightly and those of subsoils strongly. The comparison further revealed that our estimates have substantially smaller SE than previous estimates.

scholarly journals Soil Organic Carbon Stocks in Afforested Agricultural Land in Lithuanian Hemiboreal Forest Zone

Forests ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1562
Author(s):  
Iveta Varnagirytė-Kabašinskienė ◽  
Povilas Žemaitis ◽  
Kęstutis Armolaitis ◽  
Vidas Stakėnas ◽  
Gintautas Urbaitis
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Forest Floor ◽  
Agricultural Land ◽  
Forest Stand ◽  
Stand Age ◽  
Soc Stock ◽  
The Mean ◽  
Soc Stocks ◽  
Topsoil Layer

In the context of the specificity of soil organic carbon (SOC) storage in afforested land, nutrient-poor Arenosols and nutrient-rich Luvisols after afforestation with coniferous and deciduous tree species were studied in comparison to the same soils of croplands and grasslands. This study analysed the changes in SOC stock up to 30 years after afforestation of agricultural land in Lithuania, representing the cool temperate moist climate region of Europe. The SOC stocks were evaluated by applying the paired-site design. The mean mass and SOC stocks of the forest floor in afforested Arenosols increased more than in Luvisols. Almost twice as much forest floor mass was observed in coniferous than in deciduous stands 2–3 decades after afforestation. The mean bulk density of fine (<2 mm) soil in the 0–30 cm mineral topsoil layer of croplands was higher than in afforested sites and grasslands. The clear decreasing trend in mean bulk density due to forest stand age with the lowest values in the 21–30-year-old stands was found in afforested Luvisols. In contrast, the SOC concentrations in the 0–30 cm mineral topsoil layer, especially in Luvisols afforested with coniferous species, showed an increasing trend due to the influence of stand age. The mean SOC values in the 0–30 cm mineral topsoil layer of Arenosols and Luvisols during the 30 years after afforestation did not significantly differ from the adjacent croplands or grasslands. The mean SOC stock slightly increased with the forest stand age in Luvisols; however, the highest mean SOC stock was detected in the grasslands. In the Arenosols, there was higher SOC accumulation in the forest floor with increasing stand age than in the Luvisols, while the proportion of SOC stocks in mineral topsoil layers was similar and more comparable to grasslands. These findings suggest encouragement of afforestation of former agricultural land under the current climate and soil characteristics in the region, but the conversion of perennial grasslands to forest land should be done with caution.

scholarly journals On the rebound: soil organic carbon stocks can bounce back to near forest levels when agroforests replace agriculture in southern India

2015 ◽  
Vol 2 (2) ◽  
pp. 871-902 ◽  
Author(s):  
H. C. Hombegowda ◽  
O. van Straaten ◽  
M. Köhler ◽  
D. Hölscher
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Tree Species ◽  
Regional Scale ◽  
Clay Fraction ◽  
Carbon Stocks ◽  
Southern India ◽  
Reference Plot ◽  
Soc Stock ◽  
Soc Stocks

Abstract. Tropical agroforestry has an enormous potential to sequester carbon while simultaneously producing agricultural yields and tree products. The amount of soil organic carbon (SOC) sequestered is however influenced by the type of the agroforestry system established, the soil and climatic conditions and management. In this regional scale study, we utilized a chronosequence approach to investigate how SOC stocks changed when the original forests are converted to agriculture, and then subsequently to four different agroforestry systems (AFSs): homegarden, coffee, coconut and mango. In total we established 224 plots in 56 plot clusters across four climate zones in southern India. Each plot cluster consisted of four plots: a natural forest reference plot, an agriculture reference and two of the same AFS types of two ages (30–60 years and > 60 years). The conversion of forest to agriculture resulted in a large loss the original SOC stock (50–61 %) in the top meter of soil depending on the climate zone. The establishment of homegarden and coffee AFSs on agriculture land caused SOC stocks to rebound to near forest levels, while in mango and coconut AFSs the SOC stock increased only slightly above the agriculture stock. The most important variable regulating SOC stocks and its changes was tree basal area, possibly indicative of organic matter inputs. Furthermore, climatic variables such as temperature and precipitation, and soil variables such as clay fraction and soil pH were likewise all important regulators of SOC and SOC stock changes. Lastly, we found a strong correlation between tree species diversity in homegarden and coffee AFSs and SOC stocks, highlighting possibilities to increase carbon stocks by proper tree species assemblies.

scholarly journals Soil organic carbon stocks are systematically overestimated by misuse of the parameters bulk density and stone content

2016 ◽  
Author(s):  
Christopher Poeplau ◽  
Cora Vos ◽  
Axel Don
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Bulk Density ◽  
Agricultural Soils ◽  
Soil Layer ◽  
Future Studies ◽  
Soil Organic Carbon Stocks ◽  
Soc Stock ◽  
The Mean ◽  
Soc Stocks

Abstract. Estimation of soil organic carbon (SOC) stocks requires estimates of the carbon content, bulk density, stone content and depth of a respective soil layer. However, different application of these parameters could introduce a considerable bias. Here, we explain why three out of four frequently applied methods overestimate SOC stocks. In stone rich soils (> 30 Vol. %), SOC stocks could be overestimated by more than 100 %, as revealed by using German Agricultural Soil Inventory data. Due to relatively low stone content, the mean systematic overestimation for German agricultural soils was 2.1–10.1 % for three different commonly used equations. The equation ensemble as re-formulated here might help to unify SOC stock determination and avoid overestimation in future studies.

Potential soil organic carbon stock and its uncertainty under various cropping systems in Australian cropland

Soil Research ◽  
2014 ◽  
Vol 52 (5) ◽  
pp. 463 ◽  
Author(s):  
Zhongkui Luo ◽  
Enli Wang ◽  
Jeff Baldock ◽  
Hongtao Xing
Keyword(s):  
Monte Carlo ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Cropping Systems ◽  
Regional Scale ◽  
Cropping System ◽  
Soil Conditions ◽  
Sequestration Potential ◽  
Soc Stock ◽  
Soc Stocks

The diversity of cropping systems and its variation could lead to great uncertainty in the estimation of soil organic carbon (SOC) stock across time and space. Using the pre-validated Agricultural Production Systems Simulator, we simulated the long-term (1022 years) SOC dynamics in the top 0.3 m of soil at 613 reference sites under 59 representative cropping systems across Australia’s cereal-growing regions. The point simulation results were upscaled to the entire cereal-growing region using a Monte Carlo approach to quantify the spatial pattern of SOC stock and its uncertainty caused by cropping system and environment. The predicted potential SOC stocks at equilibrium state ranged from 10 to 140 t ha–1, with the majority in a range 30–70 t ha–1, averaged across all the representative cropping systems. Cropping system accounted for ~10% of the total variance in predicted SOC stocks. The type of cropping system that determined the carbon input into soil had significant effects on SOC sequestration potential. On average, the potential SOC stock in the top 0.3 m of soil was 30, 50 and 60 t ha–1 under low-, medium- and high-input cropping systems in terms of carbon input, corresponding to –2, 18 and 26 t ha–1 of SOC change. Across the entire region, the Monte Carlo simulations showed that the potential SOC stock was 51 t ha–1, with a 95% confidence interval ranging from 38 to 64 t ha–1 under the identified representative cropping systems. Overall, predicted SOC stock could increase by 0.99 Pg in Australian cropland under the identified representative cropping systems with optimal management. Uncertainty varied depending on cropping system, climate and soil conditions. Detailed information on cropping system and soil and climate characteristics is needed to obtain reliable estimates of potential SOC stock at regional scale, particularly in cooler and/or wetter regions.

A review of sampling designs for the measurement of soil organic carbon in Australian grazing lands

2010 ◽  
Vol 32 (2) ◽  
pp. 227 ◽  
Author(s):  
D. E. Allen ◽  
M. J. Pringle ◽  
K. L. Page ◽  
R. C. Dalal
Keyword(s):  
Microbial Biomass ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Plant Growth ◽  
Temporal Change ◽  
Organic C ◽  
Soil C ◽  
Efficient Sampling ◽  
Soc Stock ◽  
Grazing Lands

The accurate measurement of the soil organic carbon (SOC) stock in Australian grazing lands is important due to the major role that SOC plays in soil productivity and the potential influence of soil C cycling on Australia’s greenhouse gas emissions. However, the current sampling methodologies for SOC stock are varied and potentially conflicting. It was the objective of this paper to review the nature of, and reasons for, SOC variability; the sampling methodologies commonly used; and to identify knowledge gaps for SOC measurement in grazing lands. Soil C consists of a range of biological materials, in various SOC pools such as dissolved organic C, micro- and meso-fauna (microbial biomass), fungal hyphae and fresh plant residues in or on the soil (particulate organic C, light-fraction C), the products of decomposition (humus, slow pool C) and complexed organic C, and char and phytoliths (inert, passive or resistant C); and soil inorganic C (carbonates and bicarbonates). Microbial biomass and particulate or light-fraction organic C are most sensitive to management or land-use change; resistant organic C and soil carbonates are least sensitive. The SOC present at any location is influenced by a series of complex interactions between plant growth, climate, soil type or parent material, topography and site management. Because of this, SOC stock and SOC pools are highly variable on both spatial and temporal scales. This creates a challenge for efficient sampling. Sampling methods are predominantly based on design-based (classical) statistical techniques, crucial to which is a randomised sampling pattern that negates bias. Alternatively a model-based (geostatistical) analysis can be used, which does not require randomisation. Each approach is equally valid to characterise SOC in the rangelands. However, given that SOC reporting in the rangelands will almost certainly rely on average values for some aggregated scale (such as a paddock or property), we contend that the design-based approach might be preferred. We also challenge soil surveyors and their sponsors to realise that: (i) paired sites are the most efficient way of detecting a temporal change in SOC stock, but destructive sampling and cumulative measurement errors decrease our ability to detect change; (ii) due to (i), an efficient sampling scheme to estimate baseline status is not likely to be an efficient sampling scheme to estimate temporal change; (iii) samples should be collected as widely as possible within the area of interest; (iv) replicate of laboratory analyses is a critical step in being able to characterise temporal change. Sampling requirements for SOC stock in Australian grazing lands are yet to be explicitly quantified and an examination of a range of these ecosystems is required in order to assess the sampling densities and techniques necessary to detect specified changes in SOC stock and SOC pools. An examination of techniques that can help reduce sampling requirements (such as measurement of the SOC fractions that are most sensitive to management changes and/or measurement at specific times of the year – preferably before rapid plant growth – to decrease temporal variability), and new technologies for in situ SOC measurement is also required.

scholarly journals Capacity for increasing soil organic carbon stocks in dryland agricultural systems

Soil Research ◽  
2013 ◽  
Vol 51 (8) ◽  
pp. 657 ◽  
Author(s):  
F. C. Hoyle ◽  
M. D'Antuono ◽  
T. Overheu ◽  
D. V. Murphy
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Western Australia ◽  
Soil Type ◽  
Storage Capacity ◽  
Soil Depth ◽  
Continuous Cropping ◽  
Agricultural Systems ◽  
Soc Stock

Assessment of the potential for soil carbon sequestration based on soil type, land use, and climate scenarios is crucial for determining which agricultural regions can be used to help mitigate increasing atmospheric CO2 concentrations. In semi-arid and Mediterranean-type environments, soil organic carbon (SOC) storage capacity is rarely achieved under dryland agricultural systems. We aimed to assess both actual (measured) and attainable (modelled) SOC stock values for the dryland agricultural production zone of Western Australia. We measured actual SOC storage (0–0.3 m) and known constraints to plant growth for a range of soils types (3–27% clay) and land uses (continuous cropping, mixed cropping, annual and perennial pastures) on the Albany sand plain in Western Australia (n = 261 sites), spanning a rainfall gradient of 421–747 mm. Average actual SOC stocks for land use–soil type combinations ranged from 33 to 128 t C/ha (0–0.3 m). Up to 89% of the variability in actual SOC stock was explained by soil depth, rainfall, land use, and soil type. The scenarios modelled with Roth-C predicted that attainable SOC values of 59–140 t C/ha (0–0.3 m) could be achieved within 100 years. This indicated an additional storage capacity of 5–45% (7–27 t C/ha) depending on the specific land use–soil type combination. However, actual SOC in the surface 0–0.1 m was 95 to >100% of modelled attainable SOC values, suggesting this soil depth was ‘saturated’. Our findings highlight that additional SOC storage capacity in this region is limited to the subsoil below 0.1 m. This has implications for management strategies to increase SOC sequestration in dryland agricultural systems, as current practices tend to concentrate organic matter near the soil surface.

scholarly journals Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks

2014 ◽  
Vol 1 (1) ◽  
pp. 757-802 ◽  
Author(s):  
B. A. Miller ◽  
S. Koszinski ◽  
M. Wehrhan ◽  
M. Sommer
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variation ◽  
Spatial Association ◽  
Direct Approach ◽  
Indirect Approach ◽  
Global Issues ◽  
Soc Stock ◽  
Estimated Error ◽  
Soc Stocks

Abstract. The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which are to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m−2), covering an area of 122 km2, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.

Space-time machine learning for modelling soil organic carbon change

2020 ◽  
Author(s):  
Gerard Heuvelink ◽  
Marcos Angelini ◽  
Laura Poggio ◽  
Zhanguo Bai ◽  
Niels Batjes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Temporal Variation ◽  
Space Time ◽  
Calibration Data ◽  
Soc Stock ◽  
Soc Stocks ◽  
Over Time

&lt;p&gt;Spatially resolved estimates of change in soil organic carbon (SOC) stocks are necessary for supporting national and international policies aimed at achieving land degradation neutrality and climate mitigation through better land management. In this work we report on the development, implementation and application of a data-driven, statistical space-time method for mapping SOC stocks, using Argentina as a pilot area. We used the Quantile Regression Forest machine-learning algorithm to predict SOC stock at 0-30 cm depth at 250 m resolution for Argentina between 1982 and 2017, on an annual basis. The model was calibrated using over 5,000 SOC stock values from the 36-year time period and 35 environmental covariates. Most covariates were static and could only explain the spatial SOC distribution. SOC change over time was modelled using time series maps of the AVHRR NDVI vegetation index. These NDVI time series maps were pre-processed using a temporal low-pass filter to allow the SOC stock for a given year to depend on the NDVI of the current as well as preceding years. Spatial patterns of SOC stock predictions were persistent over time and comparable to baseline SOC stock maps of Argentina. Predictions had modest temporal variation with an average decrease for the entire country from 2.55 kg C m&lt;sup&gt;&amp;#8209;2&lt;/sup&gt; to 2.48&amp;#160;kg&amp;#160;C&amp;#160;m&lt;sup&gt;&amp;#8209;2&lt;/sup&gt; over the 36-year period (equivalent to a decline of 210.7&amp;#160;Gg&amp;#160;C, 3.0% of the total 0&amp;#8209;30&amp;#160;cm SOC stock in Argentina). The Pampa region had a larger estimated SOC stock decrease from 4.62&amp;#160;kg&amp;#160;C&amp;#160;m&lt;sup&gt;&amp;#8209;2&lt;/sup&gt; to 4.34&amp;#160;kg&amp;#160;C&amp;#160;m&lt;sup&gt;&amp;#8209;2&lt;/sup&gt; (5.9%) during the same period. For the 2001-2015 period, predicted temporal variation was 7-fold larger than that obtained using the Tier&amp;#160;1 approach of the Intergovernmental Panel on Climate Change and the United Nations Convention to Combat Desertification. Prediction uncertainties turned out to be substantial, mainly due to the limited number and poor spatial and temporal distribution of the calibration data, and the limited explanatory power of the covariates. Cross-validation confirmed that SOC stock prediction accuracy was limited, with a Mean Error of 0.03&amp;#160;kg C m&lt;sup&gt;-2&lt;/sup&gt; and a Root Mean Squared Error of 2.04&amp;#160;kg&amp;#160;C&amp;#160;m&lt;sup&gt;-2&lt;/sup&gt;. The model explained 45% of the SOC stock variation. In spite of the large uncertainties, this work showed that machine learning methods can be used for space-time SOC mapping and may yield valuable information to land managers and policy makers, provided that SOC observation density in space and time is sufficiently large.&lt;/p&gt;

Spatial heterogeneity and environmental controllers of soil organic carbon stocks in a boreal forest

2020 ◽  
Author(s):  
Udaya Vitharana ◽  
Nora Casson ◽  
Darshani Kumaragamage ◽  
Geoff Gunn ◽  
Scott Higgins ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spatial Variability ◽  
Boreal Forest ◽  
Spatial Heterogeneity ◽  
Surface Soil ◽  
Local Scale ◽  
Gravel Content ◽  
Soc Stock ◽  
Soc Stocks

&lt;p&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;#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 (&gt;2 mm) and fine-earth (&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;#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;#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;#160;&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document