Ramped thermal analysis for isolating biologically meaningful soil organic matter fractions with distinct residence times

In this work, we assess whether or not ramped thermal oxidation coupled with determination of the radiocarbon content of the evolved CO2 can be used to isolate distinct thermal fractions of soil organic matter (SOM) along with direct information on the turnover rate of each thermal fraction. Using a 30-year time series of soil samples from a well-characterized agronomic trial, we found that the incorporation of the bomb spike in atmospheric 14CO2 into thermal fractions of increasing resistance to thermal decomposition could be successfully modeled. With increasing temperature, which is proportional to activation energy, the mean residence time of the thermal fractions increased from 10 to 400 years. Importantly, the first four of five thermal fractions appeared to be a mixture of fast- and increasingly slower-cycling SOM. To further understand the composition of different thermal fractions, stepped pyrolysis–gas chromatography–mass spectrometry (Py-GC/MS) experiments were performed at five temperatures ranging from 330 to 735 ∘C. The Py-GC/MS data showed a reproducible shift in the chemistry of pyrolysis products across the temperature gradient trending from polysaccharides and lipids at low temperature to lignin- and microbe-derived compounds at middle temperatures to aromatic and unknown compounds at the highest temperatures. Integrating the 14C and Py-GC/MS data suggests the organic compounds, with the exception of aromatic moieties likely derived from wildfire, with centennial residence times are not more complex but may be protected from pyrolysis, and likely also from biological mineralization, by interactions with mineral surfaces

Decreased soil organic matter in a long-term soil warming experiment lowers soil water holding capacity and affects soil thermal and hydrological buffering

Author Posting. © American Geophysical Union, 2020. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research- Biogeosciences 125(4), (2020): e2019JG005158, doi:10.1029/2019JG005158.Long‐term soil warming can decrease soil organic matter (SOM), resulting in self‐reinforcing feedback to the global climate system. We investigated additional consequences of SOM reduction for soil water holding capacity (WHC) and soil thermal and hydrological buffering. At a long‐term soil warming experiment in a temperate forest in the northeastern United States, we suspended the warming treatment for 104 days during the summer of 2017. The formerly heated plot remained warmer (+0.39 °C) and drier (−0.024 cm3 H2O cm−3 soil) than the control plot throughout the suspension. We measured decreased SOM content (−0.184 g SOM g−1 for O horizon soil, −0.010 g SOM g−1 for A horizon soil) and WHC (−0.82 g H2O g−1 for O horizon soil, −0.18 g H2O g−1 for A horizon soil) in the formerly heated plot relative to the control plot. Reduced SOM content accounted for 62% of the WHC reduction in the O horizon and 22% in the A horizon. We investigated differences in SOM composition as a possible explanation for the remaining reductions with Fourier transform infrared (FTIR) spectra. We found FTIR spectra that correlated more strongly with WHC than SOM, but those particular spectra did not differ between the heated and control plots, suggesting that SOM composition affects WHC but does not explain treatment differences in this study. We conclude that SOM reductions due to soil warming can reduce WHC and hydrological and thermal buffering, further warming soil and decreasing SOM. This feedback may operate in parallel, and perhaps synergistically, with carbon cycle feedbacks to climate change.We would like to acknowledge Jeffery Blanchard, Priya Chowdhury, Kristen DeAngelis, Luiz Dominguez‐Horta, Kevin Geyer, Rachelle Lacroix, Xaiojun Liu, William Rodriguez, and Alexander Truchonand and for assistance with field sampling. We would like to acknowledge Michael Bernard for assistance with field sampling and lab work. We would like to acknowledge Aaron Ellison for statistical consultation. This research was financially supported by the U.S. National Science Foundation's Long Term Ecological Research Program (NSF‐DEB‐0620443 and NSF‐DEB‐1237491), the Long Term Research in Environmental Biology Program (NSF DEB‐1456528) , and the U.S. Department of Energy (DOE‐DE‐SC0005421 and DOE‐DE‐SC0010740). Data used in this study are available from the Harvard Forest Data Archive (Datasets HF018‐03, HF018‐04, and HF018‐13), accessible at https://harvardforest.fas.harvard.edu/harvard‐forest‐data‐archive.2020-10-0

Losses of mineral soil carbon largely offset biomass accumulation fifteen years after whole-tree harvest in a northern hardwood forest

Changes in soil carbon stocks following forest harvest can be an important component of ecosystem and landscape-scale C budgets in systems managed for bioenergy or carbon-trading markets. However, these changes are characterized less often and with less certainty than easier-to-measure aboveground stocks. We sampled soils prior to the whole-tree harvest of Watershed 5 at the Hubbard Brook Experimental Forest in 1983, and again in years 3, 8, and 15 following harvest. The repeated measures of total soil C in this stand show no net change in the O horizon over 15 years, though mixing with the mineral soil reduced observed O horizon C in disturbed areas in post-harvest years 3 and 8. Mineral soil C decreased by 15% (20 Mg ha-1) relative to pre-harvest levels by year 8, with no recovery in soil C stocks by year 15. Proportional changes in N stocks were similar. The loss of mineral soil C offset two-thirds of the C accumulation in aboveground biomass over the same 15 years, leading to near-zero net C accumulation post-harvest, after also accounting for the decomposition of slash and roots. If this result is broadly representative, and the extent of forest harvesting is expanded to meet demand for bioenergy or to manage ecosystem carbon sequestration, then it will take substantially longer than previously assumed to offset harvest- or bioenergy-related carbon dioxide emissions with carbon uptake during forest regrowth

Flaws in the methodologies for organic carbon analysis in seagrass blue carbon soils

The ability to accurately measure organic carbon (OC) in marine sediments or soils is overall taken for granted in scientific communities, yet this seemingly mundane task remains a methodological challenge when the soil matrix contains calcium carbonate (CaCO3), creating inaccuracies in Blue Carbon estimates. Here, we compared five common methods combining acidification, combustion, and wet oxidation pre-treatments for determination of OC in sediments and soils containing CaCO3 based on the analyses of artificial soil mixtures made of different OC and CaCO3 contents, and multiple soils from Australian seagrass cores. The results obtained showed that methods involving acidification pre-treatment entailed −17 ± 0.2% (mean ± SE) underestimation of OC content (ranging from −8% to −26%), whereas the combustion-based method was accurate for samples with high CaCO3 content but entailed 32–47% overestimation in samples with low CaCO3 content. The Heanes method (wet oxidation method) showed \u3c 5% deviation from the known OC content, but this method is not suitable for soil samples containing reduced iron, sulfur and potentially manganese compounds. The differences observed among methods have significant impacts on local, regional, and global Blue Carbon storage calculations. We provide key methodological guidelines for the analysis of OC in soils with high and low CaCO3 contents, aiming at improving accuracy in current Blue Carbon science

Characterization of pysio-chemical properties of novel one stop chemical method in preparations of copper nanofluids and possible explanations

Nanofluid is a dilute suspension containing particles in nanometer sized which are dispersed in the base fluid like ethylene glycol or water. Nanofluid is one of the crucial discovery in modern science which found to be having better thermal properties compared with conventional fluids like water or ethylene glycol thus makes it ideal to be applied and utilized in many areas in heat transfer area such as cooling, utilized as fluid for heat echangers and etc. Besides, the nanofluid with the improved thermal properties could solve the problem faced by various industries in the area of heat transfer. For example, in the semiconductor industry, the needs of superior cooling coolant are very crucialJn this paper, presents about preparation of copper nanofluid using novel one stop chemical method by reducing copper sulphate pentahydrate using reduction agent which is sodium hypophosphite in ethylene glycol as base fluids. The obtained nanofluid by using this novel one stop method is more stable besides cheaper and faster compared with two stop method whereby in the two step method, the production of the nanoparticles and the nanofluids are isolated. The process of drying, storage and transportation of the nanoparticles that takes place in two step method have cause the agglomeration and sedimentation of the nanofluids. As the result, the agglomeration could cause the settlement and clogging in the microchannel besides reduce the thermal conductivity. Therefore in the novel one stop method the production of the nanoparticles and the nanofluids are combined and not separated to avoid the process of drying, storage and transportation of nanoparticles. Meanwhile the nanofluid that obtained were analyzed using Transmission Electron Microscopy (TEM), UV-Vis Spectrophotometer, Viscometer and Fourier Transform Infared Spectroscopy (FTIR). The effect and influences of pH and dilution to the reaction rate and properties of nanofluid were also investigated

A global soil spectral calibration library and estimation service

There is growing global interest in the potential for soil reflectance spectroscopy to fill an urgent need for more data on soil properties for improved decision-making on soil security at local to global scales. This is driven by the capability of soil spectroscopy to estimate a wide range of soil properties from a rapid, inexpensive, and highly reproducible measurement using only light. However, several obstacles are preventing wider adoption of soil spectroscopy. The biggest obstacles are the large variation in the soil analytical methods and operating procedures used in different laboratories, poor reproducibility of analyses within and amongst laboratories and a lack of soil physical archives. In addition, adoption is hindered by the expense and complexity of building soil spectral libraries and calibration models. The Global Soil Spectral Calibration Library and Estimation Service is proposed to overcome these obstacles by providing a freely available estimation service based on an open, high quality and diverse spectral calibration library and the extensive soil archives of the Kellogg Soil Survey Laboratory (KSSL) of the Natural Resources Conservation Service of the United States Department of Agriculture (USDA). The initiative is supported by the Global Soil Laboratory Network (GLOSOLAN) of the Global Soil Partnership and the Soil Spectroscopy for Global Good network, which provide additional support through dissemination of standards, capacity development and research. This service is a global public good which stands to benefit soil assessments globally, but especially developing countries where soil data and resources for conventional soil analyses are most limited

Soil organic carbon development and turnover in natural and disturbed salt marsh environments

Author Posting. © American Geophysical Union, 2021. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 48(2), (2021): e2020GL090287, https://doi.org/10.1029/2020GL090287.Salt marsh survival with sea‐level rise (SLR) increasingly relies on soil organic carbon (SOC) accumulation and preservation. Using a novel combination of geochemical approaches, we characterized fine SOC (≤1 mm) supporting marsh elevation maintenance. Overlaying thermal reactivity, source (δ13C), and age (F14C) information demonstrates several processes contributing to soil development: marsh grass production, redeposition of eroded material, and microbial reworking. Redeposition of old carbon, likely from creekbanks, represented ∼9%–17% of shallow SOC (≤26 cm). Soils stored marsh grass‐derived compounds with a range of reactivities that were reworked over centuries‐to‐millennia. Decomposition decreases SOC thermal reactivity throughout the soil column while the decades‐long disturbance of ponding accelerated this shift in surface horizons. Empirically derived estimates of SOC turnover based on geochemical composition spanned a wide range (640–9,951 years) and have the potential to inform predictions of marsh ecosystem evolution.This work was supported by NSF (OCE1233678) and NOAA (NA14OAR4170104 and NA14NOS4190145) grants to ACS, USGS Coastal & Marine Geology Program, and PIE‐LTER (NSF OCE1238212 and OCE1637630).2021-06-1

Delayed impact of natural climate solutions

Acknowledgement: This work was supported by the National Basic Research Program of China (2016YFA0602701), the National Natural Science Foundation of China (41975113; 91937302), and the Guangdong Provincial Department of Science and Technology (2019ZT08G090). We appreciate the support from the China Association for Science and Technology Working Group for UN Environment Consultation. The authors declare no conflict of interests.Peer reviewedPostprin

Machine learning in space and time for modelling soil organic carbon change

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 change mitigation. In this work we report on the development, implementation and application of a data-driven, statistical method for mapping SOC stocks in space and time, using Argentina as a pilot. We used quantile regression forest machine learning to predict annual SOC stock at 0–30 cm depth at 250 m resolution for Argentina between 1982 and 2017. The model was calibrated using over 5,000 SOC stock values from the 36-year time period and 35 environmental covariates. We preprocessed normalized difference vegetation index (NDVI) dynamic covariates 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. Predictions had modest temporal variation, with an average decrease for the entire country from 2.55 to 2.48 kg C m−2 over the 36-year period (equivalent to a decline of 211 Gg C, 3.0% of the total 0–30 cm SOC stock in Argentina). The Pampa region had a larger estimated SOC stock decrease from 4.62 to 4.34 kg C m−2 (5.9%) during the same period. For the 2001–2015 period, predicted temporal variation was seven-fold larger than that obtained using the Tier 1 approach of the Intergovernmental Panel on Climate Change and United Nations Convention to Combat Desertification. Prediction uncertainties turned out to be substantial, mainly due to the limited number and poor spatial and static, whereas SOC is dynamic and SOC dynamics are of particular interest to carbon sequestration and land degradation studies. Thus, there is a clear need to extend spatial SOC mapping to space–time SOC mapping. 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 kg C m−2 and a root mean squared error of 2.04 kg C m−2. 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 policymakers, provided that SOC observation density in space and time is sufficiently large.Fil: Heuvelink, Gerard B.M. ISRIC - World soil information; Holanda. Wageningen University. Soil Geography and Landscape Group; HolandaFil: Angelici, Marcos E. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Suelos; ArgentinaFil: Poggio, Laura ISRIC - World soil information, Wageningen; HolandaFil: Bai, Zhanguo ISRIC - World soil information, Wageningen, The NetherlandsFil: Batjes, Niels H. ISRIC - World soil information, Wageningen, The NetherlandsFil: an den Bosch, Rik ISRIC - World soil information, Wageningen, The NetherlandsFil: Bossio, Deborah The Nature Conservancy; Estados UnidosFil: Estella, Sergio Vizzuality; EspañaFil: Lehmann, Jhoannes. Cornell University. Soil and Crop Sciences; Estados UnidosFil: Olmedo, Guillermo F. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Mendoza; ArgentinaFil: Sandermann, Jonathan. Woods Hole Research Center; Estados Unido

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

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 \u3c 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