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Rapid and cost‐effective techniques for soil analysis are essential to guide sustainable land management and production agriculture. This study aimed at evaluating the performance and consistency of portable handheld Fourier‐transform near‐infrared spectrometers and the NeoSpectra scanners in estimating 12 common soil physical and chemical properties including pH; organic carbon (OC); inorganic carbon (IC); total nitrogen (TN); cation exchange capacity (CEC); clay, silt, and sand fractions; and exchangeable potassium (K), phosphorus (P), calcium (Ca), and magnesium (Mg). A diverse set of samples (n = 600) were retrieved from a national‐scale soil archive of the Kellogg Soil Survey Laboratory of USDA‐NRCS and scanned with five NeoSpectra scanners. Predictive models for the soil properties were developed using partial least squares regression (PLSR), Cubist, and memory‐based learning (MBL). Cubist outperformed PLSR and MBL, with the best prediction performance for clay, OC, and CEC (R2 > 0.7), followed by IC, sand, silt, and Mg (R2 > 0.6), and then pH, TN, and Ca (R2 > 0.5). K and P were predicted somewhat poorly with R2 of 0.48 and 0.22. All five NeoSpectra yielded comparable near‐infrared (NIR) spectral data and the PLSR models for the soil properties (in terms of model regression coefficients). However, the consistency assessment showed that the model performance was significantly decreased when the training and testing spectra were from different NeoSpectra scanners. It is concluded that NeoSpectra scanners could be rapid and cost effective for estimating certain soil properties, and calibration transfer should be considered for applications where multiple devices are involved and high estimation accuracy from NIR data is required. Core Ideas: Soil samples (n = 600) were retrieved from Kellogg Soil Survey Laboratory and analyzed with 5 NeoSpectra scanners.Partial least squares regression (PLSR), Cubist, and memory‐based learning (MBL) were employed to estimate 12 soil properties from NIR data.Cubist outperformed PLSR and MBL, with 10 of 12 properties having R2 > 0.5.The five NeoSpectra scanners yielded comparable NIR spectral data and PLSR models.Cross‐device assessment suggested that calibration transfer is needed to maintain higher accuracy of NIR models. [ABSTRACT FROM AUTHOR]

Quantifying carbon fluxes into and out of coastal soils is critical to meeting greenhouse gas reduction and coastal resiliency goals. Numerous 'blue carbon' studies have generated, or benefitted from, synthetic datasets. However, the community those efforts inspired does not have a centralized, standardized database of disaggregated data used to estimate carbon stocks and fluxes. In this paper, we describe a data structure designed to standardize data reporting, maximize reuse, and maintain a chain of credit from synthesis to original source. We introduce version 1.0.0. of the Coastal Carbon Library, a global database of 6723 soil profiles representing blue carbon‐storing systems including marshes, mangroves, tidal freshwater forests, and seagrasses. We also present the Coastal Carbon Atlas, an R‐shiny application that can be used to visualize, query, and download portions of the Coastal Carbon Library. The majority (4815) of entries in the database can be used for carbon stock assessments without the need for interpolating missing soil variables, 533 are available for estimating carbon burial rate, and 326 are useful for fitting dynamic soil formation models. Organic matter density significantly varied by habitat with tidal freshwater forests having the highest density, and seagrasses having the lowest. Future work could involve expansion of the synthesis to include more deep stock assessments, increasing the representation of data outside of the U.S., and increasing the amount of data available for mangroves and seagrasses, especially carbon burial rate data. We present proposed best practices for blue carbon data including an emphasis on disaggregation, data publication, dataset documentation, and use of standardized vocabulary and templates whenever appropriate. To conclude, the Coastal Carbon Library and Atlas serve as a general example of a grassroots F.A.I.R. (Findable, Accessible, Interoperable, and Reusable) data effort demonstrating how data producers can coordinate to develop tools relevant to policy and decision‐making. [ABSTRACT FROM AUTHOR]

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 <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. [ABSTRACT FROM AUTHOR]

Large and publicly available soil spectral libraries, such as the USDA National Soil Survey Center–Kellogg Soil Survey Laboratory (NSSC‐KSSL) mid‐infrared (MIR) spectral library, are enormously valuable resources enabling laboratories around the world to make rapid low‐cost estimates of a number of soil properties. A limitation to widespread sharing of soil spectral data is the need to ensure that spectra collected on a secondary spectrometer are compatible with the spectra in the primary or reference library. Various spectral preprocessing and calibration transfer techniques have been proposed to overcome this limitation. We tested the transferability of models developed using the USDA NSSC‐KSSL MIR library to a secondary instrument. For the soil properties, total carbon (TC), pH, and clay content, we found that good performance (ratio of performance to deviation [RPD] = 4.9, 2.0, and 3.6, respectively) could be achieved on an independent test set with Savitzky‐Golay smoothing and first derivative preprocessing of the secondary spectra using a memory‐based learning chemometric approach. We tested three calibration transfer techniques (direct standardization [DS], piecewise direct standardization [PDS], and spectral space transformation [SST]) using different size transfer sets selected to be representative of the entire NSSC‐KSSL library. Among the transfer methods, SST consistently outperformed DS and PDS with 50 transfer samples being an optimal number for transfer model development. For TC and pH, performance was improved using the SST transfer (RPD = 7.7 and 2.2, respectively) primarily through the elimination of bias. Calibration transfer could not improve predictions for clay. These findings suggest that calibration transfer may not always be necessary, but users should test to confirm this assumption using a small set of representative samples scanned at both laboratories. CORE IDEAS: Spectroscopy is becoming a common tool in soil science.Sharing spectral libraries is still a challenge due to compatibility issues.Spectral pretreatment helped standardize spectra and produced good predictions.Calibration transfer could improve performance for some properties by eliminating bias. [ABSTRACT FROM AUTHOR]

Salt marsh ponds expand and deepen over time, potentially reducing ecosystem carbon storage and resilience. The water filled volumes of ponds represent missing carbon due to prevented soil accumulation and removal by erosion and decomposition. Removal mechanisms have different implications as eroded carbon can be redistributed while decomposition results in loss. We constrained ponding effects on carbon dynamics in a New England marsh and determined whether expansion and deepening impact nearby soils by conducting geochemical characterizations of cores from three ponds and surrounding high marshes and models of wind‐driven erosion. Radioisotope profiles demonstrate that ponds are not depositional environments and that contemporaneous marsh accretion represents prevented accumulation accounting for 32%–42% of the missing carbon. Erosion accounted for 0%–38% and was bracketed using radioisotope inventories and wind‐driven resuspension models. Decomposition, calculated by difference, removes 22%–68%, and when normalized over pond lifespans, produces rates that agree with previous metabolism measurements. Pond surface soils contain new contributions from submerged primary producers and evidence of microbial alteration of underlying peat, as higher levels of detrital biomarkers and thermal stability indices, compared to the marsh. Below pond surface horizons, soil properties and organic matter composition were similar to the marsh, indicating that ponding effects are shallow. Soil bulk density, elemental content, and accretion rates were similar between marsh sites but different from ponds, suggesting that lateral effects are spatially confined. Consequently, ponds negatively impact ecosystem carbon storage but at current densities are not causing pervasive degradation of marshes in this system. Plain Language Summary: Ponds are natural features of salt marshes but their expansion may be an indicator of ecosystem deterioration because they impede the marsh's ability to keep pace with sea‐level rise and remove decades of buried soil carbon. The water filled holes created by ponds represent volumes of marsh soil carbon that are missing due to prevented accumulation or lost through erosion and decomposition. These loss pathways have different implications for coastal carbon cycling as eroded soils can be redeposited elsewhere while microbial decomposition represents permanent loss. We used geochemical and modeling approaches to assess how much of the carbon missing from ponds can be attributed to prevented soil accumulation, erosion, and decomposition as well as whether ponds reduce the integrity of the surrounding marsh. We estimate that these processes represent 32%–42%, 0%–38%, and 22%–68%, respectively, of soil carbon missing from three ponds in a New England salt marsh. The range of potential erosion losses reflect differences in fetch and wind‐driven waves used in the models. Decomposition was calculated by subtracting the contributions of prevented accretion and erosion from the volume of missing carbon and, while the range is large, losses normalized over time are comparable to previously measured respiration rates. Comparisons of soil properties and composition between the ponds and surrounding marsh demonstrate that the effects of expansion are confined to within a 10 m perimeter. Consequently, in this system, ponds represent net losses from the carbon budget and at current densities are not causing pervasive degradation of the marsh. Key Points: Salt marsh ponds deepen and expand over time but their effects are localized and do not result in deterioration of the surrounding marshThree processes account for soil carbon missing from deepening ponds: prevented deposition, erosion, and decompositionEroded soils may be redistributed and retained within the marsh, while decomposition represents carbon loss [ABSTRACT FROM AUTHOR]

Terrestrial soil organic carbon (SOC) dynamics play an important but uncertain role in the global carbon (C) cycle. Current modeling efforts to quantify SOC dynamics in response to global environmental changes do not accurately represent the size, distribution and flux of C from the soil. Here, we modified the daily Century (DAYCENT) biogeochemical model by tuning decomposition rates of conceptual SOC pools to match measurable C fraction data, followed by historical and future simulations of SOC dynamics. Results showed that simulations using fraction‐constrained DAYCENT (DCfrac) led to better initialization of SOC stocks and distribution compared to default/SOC‐only‐constrained DAYCENT (DCdef) at long‐term research sites. Regional simulation using DCfrac demonstrated higher SOC stocks for both croplands (34.86 vs. 26.17 MgC ha−1) and grasslands (54.05 vs. 40.82 MgC ha−1) compared to DCdef for the contemporary period (2001–2005 average), which better matched observationally constrained data‐driven maps of current SOC distributions. Projection of SOC dynamics in response to land cover change under a high warming climate showed average absolute SOC loss of 8.44 and 10.43 MgC ha−1 for grasslands and croplands, respectively, using DCfrac whereas, SOC losses were 6.55 and 7.85 MgC ha−1 for grasslands and croplands, respectively, using DCdef. The projected SOC loss using DCfrac was 33% and 29% higher for croplands and grasslands compared to DCdef. Our modeling study demonstrates that initializing SOC pools with measurable C fraction data led to more accurate representation of SOC stocks and distribution of SOC into individual carbon pools resulting in the prediction of greater sensitivity to agricultural intensification and warming. Plain Language Summary: We aim to improve the representation of soil organic carbon (SOC) dynamics in the earth system model by matching the conceptual soil pools with carbon fraction data. We found large divergence in SOC stocks with higher absolute and relative losses under historical and projected climate and land use using the fraction‐constrained compared to the default/SOC‐only‐constrained model. This implies that the conceptual soil pools parameterized to match with carbon fraction data can better simulate SOC dynamics now and into the future. Key Points: The fraction‐constrained (optimized) model led to better initialization and distribution of soil organic carbon (SOC) stocks compared to the default modelThe fraction‐constrained (optimized) model led to larger absolute and relative losses of SOC compared to the default model during 1895–2005Under high warming, projected SOC losses were 33% for croplands and 29% for grasslands using the optimized compared to the default model [ABSTRACT FROM AUTHOR]

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 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. Highlights: We tested the use of machine learning for space–time mapping of soil organic carbon (SOC) stock.Predictions for Argentina from 1982 to 2017 showed a 3% decrease of the topsoil SOC stock over time.The machine learning model predicted a greater temporal variation than the IPCC Tier 1 approach.Accurate machine learning SOC stock prediction requires dense soil sampling in space and time. [ABSTRACT FROM AUTHOR]

Mangroves have among the highest carbon densities of any tropical forest. These 'blue carbon' ecosystems can store large amounts of carbon for long periods, and their protection reduces greenhouse gas emissions and supports climate change mitigation. Incorporating mangroves into Nationally Determined Contributions to the Paris Agreement and their valuation on carbon markets requires predicting how the management of different land‐uses can prevent future greenhouse gas emissions and increase CO2 sequestration. We integrated comprehensive global datasets for carbon stocks, mangrove distribution, deforestation rates, and land‐use change drivers into a predictive model of mangrove carbon emissions. We project emissions and foregone soil carbon sequestration potential under 'business as usual' rates of mangrove loss. Emissions from mangrove loss could reach 2391 Tg CO2 eq by the end of the century, or 3392 Tg CO2 eq when considering foregone soil carbon sequestration. The highest emissions were predicted in southeast and south Asia (West Coral Triangle, Sunda Shelf, and the Bay of Bengal) due to conversion to aquaculture or agriculture, followed by the Caribbean (Tropical Northwest Atlantic) due to clearing and erosion, and the Andaman coast (West Myanmar) and north Brazil due to erosion. Together, these six regions accounted for 90% of the total potential CO2 eq future emissions. Mangrove loss has been slowing, and global emissions could be more than halved if reduced loss rates remain in the future. Notably, the location of global emission hotspots was consistent with every dataset used to calculate deforestation rates or with alternative assumptions about carbon storage and emissions. Our results indicate the regions in need of policy actions to address emissions arising from mangrove loss and the drivers that could be managed to prevent them. [ABSTRACT FROM AUTHOR]

Wildfire alters hydrologic and geomorphic systems, promoting increases in runoff and erosion relative to unburned areas. As a result, pyrogenic carbon (PyC) produced by wildfires can experience substantial lateral redistribution from overland flow. Since landscape position helps to determine the fate of PyC, it is critical to understand the geomorphic factors that govern its lateral redistribution as well as the sensitivity of those factors to soil burn severity, which controls the magnitude of many wildfire‐induced hydrologic and geomorphic changes. In this study, we quantified the spatial distribution of near‐surface (0–5 cm) PyC on three hillslopes roughly 1 year after the 2018 Buzzard Fire in the Gila National Forest, New Mexico, USA. We then use hydrologic monitoring data, terrain analysis, and rainfall‐runoff modeling to explain the observed spatial distribution of PyC. Near‐surface PyC concentrations decreased from averages of roughly 12–8 g/kg as slope and unit stream power increased by factors of ∼2 and 4, respectively, on a hillslope burned at low severity. This suggests that susceptibility to runoff‐driven erosion was a dominant control on the redistribution of PyC. In contrast, the spatial distribution of PyC in areas burned at moderate to high severity was independent of slope and unit stream power. We attribute this pattern to intense runoff and lack of canopy cover, which promoted erosion of PyC regardless of slope and landscape position. Results demonstrate that how wildfire‐induced hydrogeomorphic changes can modulate the importance of terrain attributes in controlling the spatial distribution of PyC in upland landscapes. Key Points: Soil burn severity impacted relationships between the spatial distribution of pyrogenic carbon (PyC) on hillslopes and hydrogeomorphic variablesNear‐surface PyC on a low burn severity hillslope is best correlated with topographic slope following 1 year of erosionNear‐surface PyC on moderate/high burn severity hillslopes is not correlated with slope or unit stream power after 1 year of erosion [ABSTRACT FROM AUTHOR]

Mid‐infrared (MIR) spectroscopy models have been developed for rapid assessment of soils but are often soil and instrument specific because of differences in laboratory conditions and sensor setup. Calibration transfer is required to apply a spectral model such as partial least squares (PLS) regression developed from a primary instrument to a spectral dataset measured by a secondary instrument with statistically retained accuracy and precision. The study aimed to compare the performance of three transfer methods (i.e., direct standardization [DS], piecewise direct standardization [PDS], and spectral space transfer [SST]) and investigate the effects of transfer sample size and sample selection methods. The transfer methods were developed for predicting total C, clay, silt, and sand contents, cation exchange capacity (CEC), pH in water (pHW) and CaCl2, CaCO3 equivalent, and −1,500‐kPa water retention using spectral measurements of a secondary instrument. Calibration transfer methods of three PLS models for estimating soil properties with a high (total C), intermediate (clay content), and low (pHW) predictability were discussed. The effect of sample size required for the development of the calibration transfer and the selection method of the transferred samples were investigated. It was found that SST was most favorable for a relatively small sample size used in calibration transfer (≤12 samples). The performance of transfer methods was optimal when the transfer samples accounted for the variability of MIR spectra from the secondary instrument. We conclude that SST and PDS have the potential to be applied in spectroscopy for predicting soil properties using secondary instruments. Core Ideas: Piecewise direct standardization (PDS) and spectral space transformation (SST) perform best.The SST was most favorable for a relatively small number of transfer samples.The performance of the SST was not influenced by the variability of the soil property. [ABSTRACT FROM AUTHOR]

In mid‐infrared diffuse reflectance (MIR) soil spectroscopy, grinding is one major step that can have pronounced effects on spectra and model calibrations. The reported literature on the effects of fine grinding on spectroscopic model performance have been inconsistent, likely in part because of limitations in sample set and model calibrations in previous studies. This study was focused on answering the question whether fine grinding is necessary for MIR spectroscopy in order to minimize model uncertainty. The main goal of this study was to compare model performance with and without fine grinding for eight soil properties using two different modeling techniques: partial least squares regression (PLS) and artificial neural networks (ANN). Approximately 500 soil samples were extracted from a large MIR spectral library in the United States to obtain spectra at non‐fine ground (NG, <2 mm,) and fine‐ground (FG, <0.18 mm,) states. Performance of calibration models built using subsets of the 500 FG and 500 NG spectra were compared with models built using the entire FG spectral library (n > 40,000). All the model calibrations and validations were repeated 100 times to evaluate the uncertainty of the model performances. The results showed that PLS performed similar to ANN for the smaller dataset, but the best model performance was obtained with the FG full spectral library with ANN models. Predictions on the FG spectra always outperformed predictions on the NG spectra in terms of goodness‐of‐fit and variance of statistics. Overall, this study confirmed the importance of fine grinding to ensure the best MIR spectroscopic model performance. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Salt marsh survival with rising sea levels increasingly depends on the accumulation and preservation of buried organic carbon. Marsh soil organic carbon development reflects at least three processes: burial of differently reactive compounds that derive from local grasses, redeposition of old carbon (9%–17%), and microbial reworking. Decomposition results in a progressive decrease in the thermal reactivity of soil organic carbon, and disturbances such as ponding can accelerate this shift. Modeled rates of geochemically defined soil organic carbon pools turnover on the order of centuries‐to‐millennia and can refine predictions of salt marsh sustainability. Key Points: Salt marsh soils preserved compounds with a range of reactivities that were derived from local grasses9%–17% of surface soil carbon was old, likely reflecting redeposition of eroded creekbank materialDecomposition decreases soil organic carbon thermal reactivity, and disturbances accelerate this shift, particularly in surface horizons [ABSTRACT FROM AUTHOR]

Quantifying the holistic notion of soil health requires a large suite of measurements spanning the physical, chemical and biological properties of soil. The cost of measuring the full suite of soil health indicators via traditional methods is cost prohibitive for the spatial and temporal monitoring many land managers would like. Here we investigated the ability to make predictions of numerous soil health indicators from the large mid‐infrared (MIR) spectral library developed by the National Soil Survey Center. We developed nationally applicable models with test‐set validation coefficient of determination (R2) > 0.80 for water retention (WT), bulk density (BD), texture (as percentages of sand, silt and clay), cation exchange capacity (CEC), exchangeable bases, base saturation (BS), electrical conductivity (EC), pH, soil organic carbon (SOC) and total nitrogen (TN). Using the memory‐based learning (MBL) model, a locally weighted approach, we found that this database could also predict aggregate stability, plant available P and K with an R2 > 0.70 on independent validation samples. Models for nitrate and micronutrients were less successful but these properties were only sparsely represented in the database. Two test cases were used to demonstrate the utility of MIR spectroscopy in providing a wealth of data for a fraction of the time and cost involved with traditional analyses. While MIR spectroscopy‐based predictions may not always be appropriate for applications requiring very high levels of accuracy, results from this work and elsewhere suggest that this technology can replace, or at least supplement, traditional analyses for many purposes. [ABSTRACT FROM AUTHOR]

Salt marshes sequester carbon at rates more than an order of magnitude greater than their terrestrial counterparts, helping to mitigate climate change. As nitrogen loading to coastal waters continues, primarily in the form of nitrate, it is unclear what effect it will have on carbon storage capacity of these highly productive systems. This uncertainty is largely driven by the dual role nitrate can play in biological processes, where it can serve as a nutrient‐stimulating primary production or a thermodynamically favorable electron acceptor fueling heterotrophic metabolism. Here, we used a controlled flow‐through reactor experiment to test the role of nitrate as an electron acceptor, and its effect on organic matter decomposition and the associated microbial community in salt marsh sediments. Organic matter decomposition significantly increased in response to nitrate, even at sediment depths typically considered resistant to decomposition. The use of isotope tracers suggests that this pattern was largely driven by stimulated denitrification. Nitrate addition also significantly altered the microbial community and decreased alpha diversity, selecting for taxa belonging to groups known to reduce nitrate and oxidize more complex forms of organic matter. Fourier Transform‐Infrared Spectroscopy further supported these results, suggesting that nitrate facilitated decomposition of complex organic matter compounds into more bioavailable forms. Taken together, these results suggest the existence of organic matter pools that only become accessible with nitrate and would otherwise remain stabilized in the sediment. The existence of such pools could have important implications for carbon storage, since greater decomposition rates as N loading increases may result in less overall burial of organic‐rich sediment. Given the extent of nitrogen loading along our coastlines, it is imperative that we better understand the resilience of salt marsh systems to nutrient enrichment, especially if we hope to rely on salt marshes, and other blue carbon systems, for long‐term carbon storage. [ABSTRACT FROM AUTHOR]

To predict the behavior of the terrestrial carbon cycle, it is critical to understand the source, formation pathway, and chemical composition of soil organic matter (SOM). There is emerging consensus that slow‐cycling SOM generally consists of relatively low molecular weight organic carbon substrates that enter the mineral soil as dissolved organic matter and associate with mineral surfaces (referred to as "mineral‐associated OM," or MAOM). However, much debate and contradictory evidence persist around: (a) whether the organic C substrates within the MAOM pool primarily originate from aboveground vs. belowground plant sources and (b) whether C substrates directly sorb to mineral surfaces or undergo microbial transformation prior to their incorporation into MAOM. Here, we attempt to reconcile disparate views on the formation of MAOM by proposing a spatially explicit set of processes that link plant C source with MAOM formation pathway. Specifically, because belowground vs. aboveground sources of plant C enter spatially distinct regions of the mineral soil, we propose that fine‐scale differences in microbial abundance should determine the probability of substrate–microbe vs. substrate–mineral interaction. Thus, formation of MAOM in areas of high microbial density (e.g., the rhizosphere and other microbial hotspots) should primarily occur through an in vivo microbial turnover pathway and favor C substrates that are first biosynthesized with high microbial carbon‐use efficiency prior to incorporation in the MAOM pool. In contrast, in areas of low microbial density (e.g., certain regions of the bulk soil), MAOM formation should primarily occur through the direct sorption of intact or partially oxidized plant compounds to uncolonized mineral surfaces, minimizing the importance of carbon‐use efficiency, and favoring C substrates with strong "sorptive affinity." Through this framework, we thus describe how the primacy of biotic vs. abiotic controls on MAOM dynamics is not mutually exclusive, but rather spatially dictated. Such an understanding may be integral to more accurately modeling soil organic matter dynamics across different spatial scales. We posit that how a plant carbon compound forms slow‐cycling mineral‐associated soil organic matter is tied to its point of entry to the mineral soil. In our spatially explicit conceptual model of soil organic matter formation, the microbial formation pathway is more dominant for belowground root inputs entering into the rhizosphere, whereas the direct sorption pathway is more dominant for aboveground dissolved organic matter inputs entering into the bulk soil. Due to these differences, the primacy of biotic vs. abiotic controls on soil organic matter formation will vary at fine spatial scales in soil space. [ABSTRACT FROM AUTHOR]

Glob Change Biol. 2021;27:215-217. wileyonlinelibrary.com/journal/gcb | 215 © 2020 John Wiley & Sons Ltd Received: 28 September 2020 | Revised: 11 October 2020 | Accepted: 14 October 2020 DOI: 10.1111/gcb.15413 L E T T E R T O T H E E D I T O R Delayed impact of natural climate solutions To limit global temperature rise, scientists have proposed significant potentials for climate change mitigation from protecting and managing natural systems (Griscom et al., 2017; Paustian et al., 2016; Roe et al., 2019; Smith et al., 2019). Moreover, our estimates excluded impacts from delayed action (i.e., type 1) that applies to all NCS pathways, including those unaffected by T e or T i. The timeline to reduce global GHG emissions would be pushed back if NCS remains an "armchair strategy." Delayed or lack of action on implementation would push back the timeline to reduce greenhouse gas (GHG) emissions, largely undermining the Paris goals. [Extracted from the article]

Abstract: Decomposition of plant litter is a key control over carbon (C) storage in the soil. The biochemistry of the litter being produced, the environment in which the decomposition is taking place, and the community composition and metabolism of the decomposer organisms exert a combined influence over decomposition rates. As deciduous shrubs and trees are expanding into tundra ecosystems as a result of regional climate warming, this change in vegetation represents a change in litter input to tundra soils and a change in the environment in which litter decomposes. To test the importance of litter biochemistry and environment in determining litter mass loss, we reciprocally transplanted litter between heath (Empetrum nigrum), shrub (Betula nana), and forest (Betula pubescens) at a sub‐Arctic treeline in Sweden. As expansion of shrubs and trees promotes deeper snow, we also used a snow fence experiment in a tundra heath environment to understand the importance of snow depth, relative to other factors, in the decomposition of litter. Our results show that B. pubescens and B. nana leaf litter decomposed at faster rates than E. nigrum litter across all environments, while all litter species decomposed at faster rates in the forest and shrub environments than in the tundra heath. The effect of increased snow on decomposition was minimal, leading us to conclude that microbial activity over summer in the productive forest and shrub vegetation is driving increased mass loss compared to the heath. Using B. pubescens and E. nigrum litter, we demonstrate that degradation of carbohydrate‐C is a significant driver of mass loss in the forest. This pathway was less prominent in the heath, which is consistent with observations that tundra soils typically have high concentrations of “labile” C. This experiment suggests that further expansion of shrubs and trees may stimulate the loss of undecomposed carbohydrate C in the tundra. [ABSTRACT FROM AUTHOR]

In a sediment core collected from an estuary in a rapidly urbanizing region in South East Queensland, Australia, we used multiple geochemical tracers to understand the provenance, form, and rate of accumulating organic matter (OM) focusing on the application of two tools often used in studies of soil OM: solid-state 13C nuclear magnetic resonance (NMR) spectroscopy and interpretation of radiocarbon (14C) data by modeling the uptake of the bomb-spike in atmospheric 14C. While elemental (C, N) and stable isotope (13C, 15N) data generally could not distinguish OM sources, these data did indicate that there has been a steady increase in nutrient loading to the estuary and that there were periodic deposition events where the OM was dominated by C3 terrestrial vegetation. The NMR results showed that the OM was primarily of terrestrial origin being dominated by lignin and stable char-like carbon compounds in all samples. The 14C data and modeling indicated that the OM was composed of a mixture of material with widely varying ages. In general, the OM was hundreds of years older than the age of the sediments as determined by fallout radionuclide dating (1950-2009 chronology), indicating that a large fraction of the OM has resided on the terrestrial landscape for a substantial period before being transported and buried in the estuary sediments. By applying this combination of techniques, we have been able to develop a coherent picture of a very complex deposition and accumulation history in these estuary sediments. [ABSTRACT FROM AUTHOR]

Topography plays a critical role in controlling rates of nitrogen (N) transformation and loss to streams through its effects on reaction and transport, yet few studies have coupled measurements of soil N cycling within a catchment to hydrologic N losses and sources of those losses. We examined the processes controlling temporal patterns of stream N export using hydrometric methods and dual isotopes of nitrate (NO3−) in a small headwater catchment on the coast of Northern California. Soil nitrate pools accumulated in the hollow during the dry summer due to sustained rates of net nitrification and elevated soil moisture, and then contributed to the first flush of NO3− in macropore soil-water and stream water in the winter. Macropore soil-waters had higher concentrations of all forms of N than matrix soil-waters, especially in the hollow. A plot of stream water δ15N versus δ18O values in NO3− indicated that NO3− was primarily derived from nitrification or microbial NO3−. Further analysis revealed a mixing of two microbial NO3− sources combined with seasonal progressive denitrification. Mass balance estimates suggested microbial NO3− was consumed by denitrification when conditions of high NO3−, dissolved organic matter, and soil-water contents converged. Our study is the first to show a mixing of two sources of microbial NO3− and seasonal progressive denitrification using dual isotopes. Our observations suggest that the physical conditions in the convergent hollow are important constraints on stream N chemistry, and that shifts in runoff mechanisms and flow paths control the source and mixing of NO3− from various watershed sources. [ABSTRACT FROM AUTHOR]

The movement of soil organic carbon ( SOC) during erosion and deposition events represents a major perturbation to the terrestrial carbon cycle. Despite the recognized impact soil redistribution can have on the carbon cycle, few major carbon accounting models currently allow for soil mass flux. Here, we modified a commonly used SOC model to include a soil redistribution term and then applied it to scenarios which explore the implications of unrecognized erosion and deposition for SOC accounting. We show that models that assume a static landscape may be calibrated incorrectly as erosion of SOC is hidden within the decay constants. This implicit inclusion of erosion then limits the predictive capacity of these models when applied to sites with different soil redistribution histories. Decay constants were found to be 15-50% slower when an erosion rate of 15 t soil ha−1 yr−1 was explicitly included in the SOC model calibration. Static models cannot account for SOC change resulting from agricultural management practices focused on reducing erosion rates. Without accounting for soil redistribution, a soil sampling scheme which uses a fixed depth to support model development can create large errors in actual and relative changes in SOC stocks. When modest levels of erosion were ignored, the combined uncertainty in carbon sequestration rates was 0.3-1.0 t CO2 ha−1 yr−1. This range is similar to expected sequestration rates for many management options aimed at increasing SOC levels. It is evident from these analyses that explicit recognition of soil redistribution is critical to the success of a carbon monitoring or trading scheme which seeks to credit agricultural activities. [ABSTRACT FROM AUTHOR]

Soils retain large quantities of carbon, thereby slowing its return to the atmosphere. The mechanisms governing organic carbon sequestration in soil remain poorly understood, yet are integral to understanding soil-climate feedbacks. We evaluated the biochemistry of dissolved and solid organic carbon in potential source and sink horizons across a chronosequence of volcanic soils in Hawai'i. The soils are derived from similar basaltic parent material on gently sloping volcanic shield surfaces, support the same vegetation assemblage, and yet exhibit strong shifts in soil mineralogy and soil carbon content as a function of volcanic substrate age. Solid-state13carbon nuclear magnetic resonance spectra indicate that the most persistent mineral-bound carbon is comprised of partially oxidized aromatic compounds with strong chemical resemblance to dissolved organic matter derived from plant litter. A molecular mixing model indicates that protein, lipid, carbohydrate, and char content decreased whereas oxidized lignin and carboxyl/carbonyl content increased with increasing short-range order mineral content. When solutions rich in dissolved organic matter were passed through Bw-horizon mineral cores, aromatic compounds were preferentially sorbed with the greatest retention occurring in horizons containing the greatest amount of short-range ordered minerals. These minerals are reactive metastable nanocrystals that are most common in volcanic soils, but exist in smaller amounts in nearly all major soil classes. Our results indicate that long-term carbon storage in short-range ordered minerals occurs via chemical retention with dissolved aromatic acids derived from plant litter and carried along preferential flow-paths to deeper B horizons. [ABSTRACT FROM AUTHOR]

Anthropogenically induced change in soil redistribution plays an important role in the soil organic carbon ( SOC) budget. Uncertainty of its impact is large because of the dearth of recent soil redistribution estimates concomitant with changing land use and management practices. An Australian national survey used the artificial radionuclide caesium-137 (137Cs) to estimate net (1950s-1990) soil redistribution. South-eastern Australia showed a median net soil loss of 9.7 t ha−1 yr−1. We resurveyed the region using the same 137Cs technique and found a median net (1990-2010) soil gain of 3.9 t ha−1 yr−1 with an interquartile range from −1.6 t ha−1 yr−1 to +10.7 t ha−1 yr−1. Despite this variation, soil erosion across the region has declined as a likely consequence of the widespread adoption of soil conservation measures over the last ca 30 years. The implication of omitted soil redistribution dynamics in SOC accounting is to increase uncertainty and diminish its accuracy. [ABSTRACT FROM AUTHOR]

Despite the wealth of information available on respiratory fluxes of CO2 from the soil surface, relatively few studies have examined the depth-related flux and production of CO2 within the soil. We developed a depth-integrated CO2 flux model based on soil profile CO2 measurements to explore both the temporal and spatial patterns of, and controls on, CO2 production in two contrasting California coastal ecosystems: a redwood [Sequoia sempervirens (D. Don) EndI.] forest and a seasonal grassland ecosystem. In the forest, CO2 production increased exponentially with temperature but the dominant control on CO2 efflux was due to changes in soil diffusivity with water content. Here, ∼67% of the total efflux was produced in the top 20 cm, with little seasonal variation. In the grassland, soil temperature and water content both exerted strong positive controls on CO2 production, with CO2 efflux highest in early spring (8 g Cm-2 d-1) and lowest in late summer (0.4 g Cm-2 d-1). Here, the relative proportion of total respiration in the top 20 cm varied from 5% at the end of the dry season to 70% during the wet winter months. Soil respiration in the redwood soil appeared to be well buffered from climate extremes due to the stable microclimate created by the forest canopy. The grassland soil, however, lacking such buffering, was subject to large and rapid changes in CO2 production throughout the year. These results indicate that despite some uncertainty in the calculation of diffusion rates, significantly more information on soil biological processes is gained from the CO2 production-diffusion approach than from surface flux measurements alone. [ABSTRACT FROM AUTHOR]

To understand the hydrologic and biogeochemical controls on the age and recalcitrance of dissolved organic matter (DOM) found in stream waters, we combined hydrometric monitoring along a topographic gradient from ridge to channel with isotopic (13C and 14C) and spectroscopic (UV and 13C nuclear magnetic resonance) analyses of soil and stream water samples in a small coastal watershed in California. With increasing discharge, dissolved organic carbon concentrations increased from 2.2 to 10.9 mg C L−1, Δ14C values increased from −125 to +120‰, δ13C values decreased from −24 to −29‰, C:N ratios increased from 6.5 to 15.4, and specific UV adsorption increased from 1.4 to 3.8 L mg C−1 m−1. These changes in DOM composition are consistent with a shift in source from old and recalcitrant soil organic matter (OM) sources found in deep soil horizons to young and relatively fresh OM sources found in the surface horizons. Results from this study suggest upland soils of the watershed become DOM production limited as indicated by a seasonal depletion and chemical shift in soil DOM, whereas highly productive soils in the hollow act as a near-infinite DOM source. Hydrologic connectivity of this DOM-rich riparian source region to the stream ultimately constrains DOM export, and the stream DOM composition reflects the combined influence of soil biogeochemical cycling of OM and hydrologic routing of water through the landscape. [ABSTRACT FROM AUTHOR]