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1. 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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2. Long‐term nitrogen deposition inhibits soil priming effects by enhancing phosphorus limitation in a subtropical forest

   https://doi.org/10.1111/gcb.16718  

   Wang, Xiaohong; Li, Shiyining; Zhu, Biao; Homyak, Peter M.; Chen, Guangshui; Yao, Xiaodong; Wu, Dongmei; Yang, Zhijie; Lyu, Maokui; Yang, Yusheng (April 2023, Global Change Biology)

   Abstract It is widely accepted that phosphorus (P) limits microbial metabolic processes and thus soil organic carbon (SOC) decomposition in tropical forests. Global change factors like elevated atmospheric nitrogen (N) deposition can enhance P limitation, raising concerns about the fate of SOC. However, how elevated N deposition affects the soil priming effect (PE) (i.e., fresh C inputs induced changes in SOC decomposition) in tropical forests remains unclear. We incubated soils exposed to 9 years of experimental N deposition in a subtropical evergreen broadleaved forest with two types of¹³C‐labeled substrates of contrasting bioavailability (glucose and cellulose) with and without P amendments. We found that N deposition decreased soil total P and microbial biomass P, suggesting enhanced P limitation. In P unamended soils, N deposition significantly inhibited the PE. In contrast, adding P significantly increased the PE under N deposition and by a larger extent for the PE of cellulose (PE_cellu) than the PE of glucose (PE_glu). Relative to adding glucose or cellulose solely, adding P with glucose alleviated the suppression of soil microbial biomass and C‐acquiring enzymes induced by N deposition, whereas adding P with cellulose attenuated the stimulation of acid phosphatase (AP) induced by N deposition. Across treatments, the PE_gluincreased as C‐acquiring enzyme activity increased, whereas the PE_celluincreased as AP activity decreased. This suggests that P limitation, enhanced by N deposition, inhibits the soil PE through varying mechanisms depending on substrate bioavailability; that is, P limitation regulates the PE_gluby affecting soil microbial growth and investment in C acquisition, whereas regulates the PE_celluby affecting microbial investment in P acquisition. These findings provide new insights for tropical forests impacted by N loading, suggesting that expected changes in C quality and P limitation can affect the long‐term regulation of the soil PE. 

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3. Upscaling Soil Organic Carbon Measurements at the Continental Scale Using Multivariate Clustering Analysis and Machine Learning

   https://doi.org/10.1029/2023JG007702  

   Wang, Zhuonan; Kumar, Jitendra; Weintraub‐Leff, Samantha R.; Todd‐Brown, Katherine; Mishra, Umakant; Sihi, Debjani (February 2024, Journal of Geophysical Research: Biogeosciences)

   Abstract Estimates of soil organic carbon (SOC) stocks are essential for many environmental applications. However, significant inconsistencies exist in SOC stock estimates for the U.S. across current SOC maps. We propose a framework that combines unsupervised multivariate geographic clustering (MGC) and supervised Random Forests regression, improving SOC maps by capturing heterogeneous relationships with SOC drivers. We first used MGC to divide the U.S. into 20 SOC regions based on the similarity of covariates (soil biogeochemical, bioclimatic, biological, and physiographic variables). Subsequently, separate Random Forests models were trained for each SOC region, utilizing environmental covariates and SOC observations. Our estimated SOC stocks for the U.S. (52.6 ± 3.2 Pg for 0–30 cm and 108.3 ± 8.2 Pg for 0–100 cm depth) were within the range estimated by existing products like Harmonized World Soil Database, HWSD (46.7 Pg for 0–30 cm and 90.7 Pg for 0–100 cm depth) and SoilGrids 2.0 (45.7 Pg for 0–30 cm and 133.0 Pg for 0–100 cm depth). However, independent validation with soil profile data from the National Ecological Observatory Network showed that our approach (R² = 0.51) outperformed the estimates obtained from Harmonized World Soil Database (R² = 0.23) and SoilGrids 2.0 (R² = 0.39) for the topsoil (0–30 cm). Uncertainty analysis (e.g., low representativeness and high coefficients of variation) identified regions requiring more measurements, such as Alaska and the deserts of the U.S. Southwest. Our approach effectively captures the heterogeneous relationships between widely available predictors and the current SOC baseline across regions, offering reliable SOC estimates at 1 km resolution for benchmarking Earth system models. 

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4. Agricultural Management Legacy Effects on Switchgrass Growth and Soil Carbon Gains

   https://doi.org/10.1111/gcbb.70051  

   Chakraborty, Poulamee; Falvo, Grant; Robertson, G Philip; Kravchenko, Alexandra (July 2025, GCB Bioenergy)

   ABSTRACT Switchgrass (Panicum virgatumL.) is a native North American grass currently considered a high‐potential bioenergy feedstock crop. However, previous reports questioned its effectiveness in generating soil organic carbon (SOC) gains, with resultant uncertainty regarding the monoculture switchgrass's impact on the environmental sustainability of bioenergy agriculture. We hypothesize that the inconsistencies in past SOC accrual results might be due, in part, to differences in prior land management among the systems subsequently planted to switchgrass. To test this hypothesis, we measured SOC and other soil properties, root biomass, and switchgrass growth in an experimental site with a 30‐year history of contrasting tillage and N‐fertilization treatments, 7 years after switchgrass establishment. We determined switchgrass' monthly gross primary production (GPP) for six consecutive years and conducted deep soil sampling. Nitrogen fertilization expectedly stimulated switchgrass growth; however, a tendency for better plant growth was also observed under unfertilized settings in the former no‐till soil. In topsoil, SOC significantly increased from 2007 to 2023 in fertilized treatments of both tillage histories, with the greatest increase observed in fertilized no‐till. Fertilized no‐till also had the highest particulate organic matter content in the topsoil, with no differences among the treatments observed in deeper soil layers. However, regardless of fertilization, the tillage history had a strong effect on stratification with depth of SOC, total N, and microbial biomass C. Results suggested that historic and ongoing N fertilization had a substantial impact on switchgrass growth and soil characteristics, while tillage legacy had a much weaker, but still discernible, effect. 

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5. Wetting‐induced soil CO₂ emission pulses are driven by interactions among soil temperature, carbon, and nitrogen limitation in the Colorado Desert

   https://doi.org/10.1111/gcb.16669  

   Andrews, Holly M.; Krichels, Alexander H.; Homyak, Peter M.; Piper, Stephanie; Aronson, Emma L.; Botthoff, Jon; Greene, Aral C.; Jenerette, G. Darrel (March 2023, Global Change Biology)

   Abstract Warming‐induced changes in precipitation regimes, coupled with anthropogenically enhanced nitrogen (N) deposition, are likely to increase the prevalence, duration, and magnitude of soil respiration pulses following wetting via interactions among temperature and carbon (C) and N availability. Quantifying the importance of these interactive controls on soil respiration is a key challenge as pulses can be large terrestrial sources of atmospheric carbon dioxide (CO₂) over comparatively short timescales. Using an automated sensor system, we measured soil CO₂flux dynamics in the Colorado Desert—a system characterized by pronounced transitions from dry‐to‐wet soil conditions—through a multi‐year series of experimental wetting campaigns. Experimental manipulations included combinations of C and N additions across a range of ambient temperatures and across five sites varying in atmospheric N deposition. We found soil CO₂pulses following wetting were highly predictable from peak instantaneous CO₂flux measurements. CO₂pulses consistently increased with temperature, and temperature at time of wetting positively correlated to CO₂pulse magnitude. Experimentally adding N along the N deposition gradient generated contrasting pulse responses: adding N increased CO₂pulses in low N deposition sites, whereas adding N decreased CO₂pulses in high N deposition sites. At a low N deposition site, simultaneous additions of C and N during wetting led to the highest observed soil CO₂fluxes reported globally at 299.5 μmol CO₂ m⁻² s⁻¹. Our results suggest that soils have the capacity to emit high amounts of CO₂within small timeframes following infrequent wetting, and pulse sizes reflect a non‐linear combination of soil resource and temperature interactions. Importantly, the largest soil CO₂emissions occurred when multiple resources were amended simultaneously in historically resource‐limited desert soils, pointing to regions experiencing simultaneous effects of desertification and urbanization as key locations in future global C balance. 

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