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1. Can Agricultural Management Induced Changes in Soil Organic Carbon Be Detected Using Mid-Infrared Spectroscopy?

   https://doi.org/10.3390/rs13122265  

   Sanderman, Jonathan; Savage, Kathleen; Dangal, Shree R.; Duran, Gabriel; Rivard, Charlotte; Cavigelli, Michel A.; Gollany, Hero T.; Jin, Virginia L.; Liebig, Mark A.; Omondi, Emmanuel Chiwo; et al (June 2021, Remote Sensing)

   null (Ed.)

   A major limitation to building credible soil carbon sequestration programs is the cost of measuring soil carbon change. Diffuse reflectance spectroscopy (DRS) is considered a viable low-cost alternative to traditional laboratory analysis of soil organic carbon (SOC). While numerous studies have shown that DRS can produce accurate and precise estimates of SOC across landscapes, whether DRS can detect subtle management induced changes in SOC at a given site has not been resolved. Here, we leverage archived soil samples from seven long-term research trials in the U.S. to test this question using mid infrared (MIR) spectroscopy coupled with the USDA-NRCS Kellogg Soil Survey Laboratory MIR spectral library. Overall, MIR-based estimates of SOC%, with samples scanned on a secondary instrument, were excellent with the root mean square error ranging from 0.10 to 0.33% across the seven sites. In all but two instances, the same statistically significant (p < 0.10) management effect was found using both the lab-based SOC% and MIR estimated SOC% data. Despite some additional uncertainty, primarily in the form of bias, these results suggest that large existing MIR spectral libraries can be operationalized in other laboratories for successful carbon monitoring. 

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2. Patterns and predictors of soil organic carbon storage across a continental-scale network

   https://doi.org/10.1007/s10533-020-00745-9  

   Nave, L. E.; Bowman, M.; Gallo, A.; Hatten, J. A.; Heckman, K. A.; Matosziuk, L.; Possinger, A. R.; SanClements, M.; Sanderman, J.; Strahm, B. D.; et al (October 2021, Biogeochemistry)

   null (Ed.)

   Abstract The rarity of rapid campaigns to characterize soils across scales limits opportunities to investigate variation in soil carbon stocks (SOC) storage simultaneously at large and small scales, with and without site-level replication. We used data from two complementary campaigns at 40 sites in the United States across the National Ecological Observatory Network (NEON), in which one campaign sampled profiles from closely co-located intensive plots and physically composited similar horizons, and the other sampled dozens of pedons across the landscape at each site. We demonstrate some consistencies between these distinct designs, while also revealing that within-site replication reveals patterns and predictors of SOC stocks not detectable with non-replicated designs. Both designs demonstrate that SOC stocks of whole soil profiles vary across continental-scale climate gradients. However, broad climate patterns may mask the importance of localized variation in soil physicochemical properties, as captured by within-site sampling, especially for SOC stocks of discrete genetic horizons. Within-site replication also reveals examples in which expectations based on readily explained continental-scale patterns do not hold. For example, even wide-ranging drainage class sequences within landscapes do not duplicate the clear differences in profile SOC stocks across drainage classes at the continental scale, and physicochemical factors associated with increasing B horizon SOC stocks at continental scales frequently do not follow the same patterns within landscapes. Because inferences from SOC studies are a product of their context (where, when, how), this study provides context—in terms of SOC stocks and the factors that influence them—for others assessing soils and the C cycle at NEON sites. 

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3. Assessing the Potential for Mobilization of Old Soil Carbon After Permafrost Thaw: A Synthesis of ¹⁴ C Measurements From the Northern Permafrost Region

   https://doi.org/10.1029/2020GB006672  

   Estop‐Aragonés, Cristian; Olefeldt, David; Abbott, Benjamin W.; Chanton, Jeffrey P.; Czimczik, Claudia I.; Dean, Joshua F.; Egan, Jocelyn E.; Gandois, Laure; Garnett, Mark H.; Hartley, Iain P.; et al (September 2020, Global Biogeochemical Cycles)

   Abstract The magnitude of future emissions of greenhouse gases from the northern permafrost region depends crucially on the mineralization of soil organic carbon (SOC) that has accumulated over millennia in these perennially frozen soils. Many recent studies have used radiocarbon (¹⁴C) to quantify the release of this “old” SOC as CO₂or CH₄to the atmosphere or as dissolved and particulate organic carbon (DOC and POC) to surface waters. We compiled ~1,900¹⁴C measurements from 51 sites in the northern permafrost region to assess the vulnerability of thawing SOC in tundra, forest, peatland, lake, and river ecosystems. We found that growing season soil¹⁴C‐CO₂emissions generally had a modern (post‐1950s) signature, but that well‐drained, oxic soils had increased CO₂emissions derived from older sources following recent thaw. The age of CO₂and CH₄emitted from lakes depended primarily on the age and quantity of SOC in sediments and on the mode of emission, and indicated substantial losses of previously frozen SOC from actively expanding thermokarst lakes. Increased fluvial export of aged DOC and POC occurred from sites where permafrost thaw caused soil thermal erosion. There was limited evidence supporting release of previously frozen SOC as CO₂, CH₄, and DOC from thawing peatlands with anoxic soils. This synthesis thus suggests widespread but not universal release of permafrost SOC following thaw. We show that different definitions of “old” sources among studies hamper the comparison of vulnerability of permafrost SOC across ecosystems and disturbances. We also highlight opportunities for future¹⁴C studies in the permafrost region. 

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4. Climatic and Geochemical Controls on Soil Carbon at the Continental Scale: Interactions and Thresholds

   https://doi.org/10.1029/2020GB006781  

   Yu, Wenjuan; Weintraub, Samantha R.; Hall, Steven J. (March 2021, Global Biogeochemical Cycles)

   Abstract Previous studies found conflicting results on the importance of temperature and precipitation versus geochemical variables for predicting soil organic carbon (SOC) concentrations and trends with depth, and most utilized linear statistical models. To reconcile the controversy, we used data from 2574 mineral horizons from 675 pits from National Ecological Observatory Network sites across North America, typically collected to 1 m depth. Climate was a fundamental predictor of SOC and played similarly important roles as some geochemical predictors. Yet, this only emerged in the generalized additive mixed model and random forest model and was obscured in the linear mixed model. Relationships between water availability and SOC were strongest in very dry ecosystems and SOC increased most strongly at mean annual temperature < 0°C. In all models, depth, oxalate‐extractable Al (Al_ox), pH, and exchangeable calcium plus exchangeable magnesium were important while silt + clay, oxalate‐extractable Fe (Fe_ox), and vegetation type were weaker predictors. Climate and pH were independently related to SOC and also interacted with geochemical composition: Fe_oxand Al_oxrelated more strongly to SOC in wet or cold climates. Most predictors had nonlinear threshold relationships with SOC, and a saturating response to increasing reactive metals indicates soils where SOC might be limited by C inputs. We observed a mostly constant relative importance of geochemical and climate predictors of SOC with increasing depth, challenging previous statements. Overall, our findings challenge the notion that climate is redundant after accounting for geochemistry and demonstrate that considering their nonlinearities and interactions improves spatial predictions of SOC. 

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5. Upscaling soil organic carbon measurements at the continental scale using multivariate clustering analysis and machine learning

   https://doi.org/10.5281/zenodo.8057232  

   wang, zhuonan; Kumar, Jitendra; R., Samantha Weintraub-Leff; Todd-Brown, Katherine; Mishra, Umakant; Sihi, Debjani (January 2023, Zenodo)

   {"Abstract":["Data Description<\/strong>:<\/p>\n\nTo improve SOC estimation in the United States, we upscaled site-based SOC measurements to the continental scale using multivariate geographic clustering (MGC) approach coupled with machine learning models. First, we used the MGC approach to segment the United States at 30 arc second resolution based on principal component information from environmental covariates (gNATSGO soil properties, WorldClim bioclimatic variables, MODIS biological variables, and physiographic variables) to 20 SOC regions. We then trained separate random forest model ensembles for each of the SOC regions identified using environmental covariates and soil profile measurements from the International Soil Carbon Network (ISCN) and an Alaska soil profile data. We estimated United States SOC for 0-30 cm and 0-100 cm depths were 52.6 + 3.2 and 108.3 + 8.2 Pg C, respectively.<\/p>\n\nFiles in collection (32):<\/p>\n\nCollection contains 22 soil properties geospatial rasters, 4 soil SOC geospatial rasters, 2 ISCN site SOC observations csv files, and 4 R scripts<\/p>\n\ngNATSGO TIF files:<\/p>\n\n├── available_water_storage_30arc_30cm_us.tif                   [30 cm depth soil available water storage]\n├── available_water_storage_30arc_100cm_us.tif                 [100 cm depth soil available water storage]\n├── caco3_30arc_30cm_us.tif                                                 [30 cm depth soil CaCO3 content]\n├── caco3_30arc_100cm_us.tif                                               [100 cm depth soil CaCO3 content]\n├── cec_30arc_30cm_us.tif                                                     [30 cm depth soil cation exchange capacity]\n├── cec_30arc_100cm_us.tif                                                   [100 cm depth soil cation exchange capacity]\n├── clay_30arc_30cm_us.tif                                                     [30 cm depth soil clay content]\n├── clay_30arc_100cm_us.tif                                                   [100 cm depth soil clay content]\n├── depthWT_30arc_us.tif                                                        [depth to water table]\n├── kfactor_30arc_30cm_us.tif                                                 [30 cm depth soil erosion factor]\n├── kfactor_30arc_100cm_us.tif                                               [100 cm depth soil erosion factor]\n├── ph_30arc_100cm_us.tif                                                      [100 cm depth soil pH]\n├── ph_30arc_100cm_us.tif                                                      [30 cm depth soil pH]\n├── pondingFre_30arc_us.tif                                                     [ponding frequency]\n├── sand_30arc_30cm_us.tif                                                    [30 cm depth soil sand content]\n├── sand_30arc_100cm_us.tif                                                  [100 cm depth soil sand content]\n├── silt_30arc_30cm_us.tif                                                        [30 cm depth soil silt content]\n├── silt_30arc_100cm_us.tif                                                      [100 cm depth soil silt content]\n├── water_content_30arc_30cm_us.tif                                      [30 cm depth soil water content]\n└── water_content_30arc_100cm_us.tif                                   [100 cm depth soil water content]<\/p>\n\nSOC TIF files:<\/p>\n\n├──30cm SOC mean.tif                             [30 cm depth soil SOC]\n├──100cm SOC mean.tif                           [100 cm depth soil SOC]\n├──30cm SOC CV.tif                                 [30 cm depth soil SOC coefficient of variation]\n└──100cm SOC CV.tif                              [100 cm depth soil SOC coefficient of variation]<\/p>\n\nsite observations csv files:<\/p>\n\nISCN_rmNRCS_addNCSS_30cm.csv       30cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data<\/p>\n\nISCN_rmNRCS_addNCSS_100cm.csv       100cm ISCN sites SOC replaced NRCS sites with NCSS centroid removed data<\/p>\n\n\nData format<\/strong>:<\/p>\n\nGeospatial files are provided in Geotiff format in Lat/Lon WGS84 EPSG: 4326 projection at 30 arc second resolution.<\/p>\n\nGeospatial projection<\/strong>: <\/p>\n\nGEOGCS["GCS_WGS_1984",\n DATUM["D_WGS_1984",\n SPHEROID["WGS_1984",6378137,298.257223563]],\n PRIMEM["Greenwich",0],\n UNIT["Degree",0.017453292519943295]]\n(base) [jbk@theseus ltar_regionalization]$ g.proj -w\nGEOGCS["wgs84",\n DATUM["WGS_1984",\n SPHEROID["WGS_1984",6378137,298.257223563]],\n PRIMEM["Greenwich",0],\n UNIT["degree",0.0174532925199433]]\n<\/code>\n\n <\/p>"]} 

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