Abstract Enhanced weathering in agriculture is a potential gigatonne-scale carbon dioxide removal (CDR) pathway, but its potential remains difficult to constrain. We used a formal expert elicitation process to estimate CDR potential and efficiency, uncertainties, and key data needs for six feedstocks. Expert opinion of global potential varied by feedstock, with estimates averaging 0.2-0.7 Gt CO2e/yr, but with a wide range (from a source to greater than 5 Gt CO2e/yr removal). When focusing on the American Midwest (pH 5.5-6), carbon dioxide removal efficiency, meaning the fraction of potential ultimately realized, ranged from 27-39%. Key uncertainties included feedstock availability, calcite saturation, and deep soil/freshwater emission pathways. There is a need for empirical data in key stages, with potential to leverage liming data where appropriate. Overall, there appears to be strong potential CDR at broad scales. However, continued research is necessary to build confidence when quantifying that potential and actual removals.

Agricultural policies for climate mitigation and adaptation, and farmer decisions about soil health, both rely on sustainable management of soil carbon. Controlled field experiments demonstrate that regenerative practices increase soil carbon, yet expected changes are often small relative to spatial and sampling variability. These sources of variation are assumed to hinder the quantification of management effects at the scale of working landscapes. However, spatial variability in soil carbon change has rarely been evaluated in the context of estimating management effects. Here, we evaluate these sources of variability in the context of a counterfactual design in an agricultural growing region of the Hudson Valley, NY, USA. We assembled groups of comparable fields from a population of eligible fields by stratifying by initial soil carbon and texture. Within each of these strata, multiple fields under conventional and regenerative practices were selected (80 fields in total) and sampled at two time points in the non-growing seasons of 2023–2024 and 2024–2025 for surface soil carbon concentrations (0–15 cm). We sampled each selected field at a density of 0.6 ha per soil core (∼10 locations per field; 796 sampling points; 1592 samples). Spatial variability in soil carbon was large both within and among fields (between field SD 0.59 %C). Using the difference in soil carbon between the time points removed persistent spatial signal and reduced between-field variability in change more than seven times (SD 0.08 %); within-field variability remained high. Minimum detectable differences from repeated measurement were roughly six-times smaller than those inferred from spatial data alone (0.33 vs. 0.056 %C), providing empirical evidence that spatial variance is a poor proxy for temporal variance. Estimated management effects depended on which fields were selected, whereby selecting fields with higher or lower soil texture altered the estimated treatment effect. Our results suggest that counterfactual designs based on remeasurement and comparable groups can quantify management effects under real-world agricultural conditions. Such studies at the scale of commercial agriculture are needed to build confidence about when, where, and which regenerative practices deliver benefits such as improved soil health, informing policies for more sustainable and climate-smart agriculture.

Voluntary markets for agricultural carbon credits are expanding, promoting climate-smart practices purported to increase soil carbon and reduce greenhouse gas emissions. To contribute effectively to climate mitigation, markets must deliver credits that meet international standards guaranteeing credits are additional, conservative, and equivalent to at least 1 ton of CO _2 . Yet protocols for quantifying credits make different assumptions, raising questions about whether protocols meet the ‘equivalency standard’. We test for equivalency using a common dataset of 4988 US Midwestern corn-soybean fields, representing a carbon market project, to estimate credit issuances for adoption of no-till plus cover cropping practices. We find issuances, across the three major protocols being used for US croplands, differing for this common project by up to ∼130 000 credits per year. Our ‘Protocol Intercomparison Project’ reveals how quantitative evaluations can identify assumptions generating marked differences in crediting, thereby guiding research that informs protocol revisions to build confidence in mitigation and demonstrating paths forward for protocol harmonization efforts.

Causal approaches employed at the scale of commercial agriculture are required to build high-quality evidence that climate-smart agricultural interventions result in real emissions reductions and removals. Such project-scale empirical data are additionally required to demonstrate and advance the viability of process-based models and digital measurement, reporting and verification as tools to scale soil carbon accounting.

Agricultural carbon crediting predominantly relies on process-based biogeochemical models to estimate accrual of soil organic carbon stock (SOC). We investigate the conditions under which it may be economical to estimate SOC accrual by measuring and remeasuring SOC, which relies on fewer assumptions than modeling. We analyze multi-field measure-and-remeasure SOC projects with two key features: first, practice assignment is randomized to compare the effect of a treatment (e.g. no tillage) to a control (e.g. conventional tillage); second, a random subset of fields is sampled (two stage cluster sampling) to cost-effectively measure SOC changes. We use statistical modeling to characterize the estimated treatment effect, accounting for within-field and between-field variability in SOC change, as well as measurement error. We then use these statistics to evaluate how prices for measurement, treatment, and carbon credits influence the economics of measure-and-remeasure projects. We specifically investigate the potential advantages of larger spatial scale (number of fields) and temporal scale (years before remeasurement). We find economies of both spatial and temporal scale so that projects with thousands of fields, with only about 10% of fields measured for SOC change, are likely to yield a competitive return on investment in five years if the treatment effects found in the research literature can be achieved commercially. Our analysis suggests that measure-and-remeasure can be cost effective in both market and non-market SOC projects at scale. Moreover, measure-and-remeasure projects provide valuable data for independent validation on commercial farms of the accrual rates estimated by biogeochemical models using field trials. We provide next steps and software for researchers, credit registries, and project developers to move forward with measure-and-remeasure SOC projects.

Quantitative data at real-world scales are needed to assess the effects of cover cropping and other practices on soil carbon storage. Large-scale medical studies provide a proven methodology.

As interest in the impact of climate-smart agricultural practices grows, it is increasingly important to understand how much practice adoption to expect, how long it will take for certain practices to be adopted, and where adoption of these practices may be most likely. Many current mitigation potential estimates for climate-smart agricultural practices assume instantaneous, 100% adoption of practices across all croplands, failing to reflect realistic conditions. However, tools like the Adoption Diffusion Outcome Prediction Tool (ADOPT) can help explain and clarify practice adoption dynamics and therefore right-size mitigation potential estimates. Here, we reviewed and compiled papers that reported ADOPT-derived predicted peak adoption rates (PPAR) and predicted time to peak (PTTP) adoption for various agricultural innovation types. We summarize PPAR and PTTP across innovations and farmer motivations, providing insights on practice adoption and highlighting the utility of understanding predicted adoption rates and timing of agricultural innovations.

Structures from the Stone Age can provide unique insights into Late Glacial and Mesolithic cultures around the Baltic Sea. Such structures, however, usually did not survive within the densely populated Central European subcontinent. Here, we ...The Baltic Sea basins, some of which only submerged in the mid-Holocene, preserve Stone Age structures that did not survive on land. Yet, the discovery of these features is challenging and requires cross-disciplinary approaches between archeology and ...

Background As interest in the voluntary soil carbon market surges, carbon registries have been developing new soil carbon measurement, reporting, and verification (MRV) protocols. These protocols are inconsistent in their approaches to measuring soil organic carbon (SOC). Two areas of concern include the type of SOC stock accounting method (fixed-depth (FD) vs. equivalent soil mass (ESM)) and sampling depth requirement. Despite evidence that fixed-depth measurements can result in error because of changes in soil bulk density and that sampling to 30 cm neglects a significant portion of the soil profile’s SOC stock, most MRV protocols do not specify which sampling method to use and only require sampling to 30 cm. Using data from UC Davis’s Century Experiment (“Century”) and UW Madison’s Wisconsin Integrated Cropping Systems Trial (WICST), we quantify differences in SOC stock changes estimated by FD and ESM over 20 years, investigate how sampling at-depth (> 30 cm) affects SOC stock change estimates, and estimate how crediting outcomes taking an empirical sampling-only crediting approach differ when stocks are calculated using ESM or FD at different depths. Results We find that FD and ESM estimates of stock change can differ by over 100 percent and that, as expected, much of this difference is associated with changes in bulk density in surface soils (e.g., r = 0.90 for Century maize treatments). This led to substantial differences in crediting outcomes between ESM and FD-based stocks, although many treatments did not receive credits due to declines in SOC stocks over time. While increased variability of soils at depth makes it challenging to accurately quantify stocks across the profile, sampling to 60 cm can capture changes in bulk density, potential SOC redistribution, and a larger proportion of the overall SOC stock. Conclusions ESM accounting and sampling to 60 cm (using multiple depth increments) should be considered best practice when quantifying change in SOC stocks in annual, row crop agroecosystems. For carbon markets, the cost of achieving an accurate estimate of SOC stocks that reflect management impacts on soils at-depth should be reflected in the price of carbon credits.

The opportunity of agricultural management practices to sequester soil organic carbon (SOC) is recognized as an important strategy for mitigating climate change. However, there is low confidence when it comes to understanding the magnitude of the climate benefit we can expect from SOC sequestration or how best to achieve it. Several issues are often confounded when it comes to the mitigation potential of SOC sequestration and greenhouse gas (GHG) reductions from agriculture, creating confusion and making it difficult to clearly identify the knowns, unknowns and risks to implementing policy and practice recommendations. Here, we identify and explain four major areas of uncertainty: (1) the expected changes in soil carbon or GHG emissions resulting from agricultural management practice changes; (2) the extent to which social, environmental and economic factors constrain mitigation potential; (3) the ability to execute reliable measurement, monitoring, reporting and verification (MMRV) frameworks; and (4) the perception of risk associated with different ways of promoting practice adoption (e.g., voluntary carbon markets fueled by the private sector, pay-for-practice programs funded by public investment). We aim to pinpoint knowledge gaps and areas of disagreement to help right-size expectations and guide effective investment in GHG removals and reductions from agriculture.

Soil amendments are a broad class of materials that enhance physical, chemical or biological characteristics in croplands, pastures, or rangelands. While organic soil amendments such as manure, mulch and seaweed have well established agronomic benefits, there has been renewed private and governmental interest in quantifying and incentivizing their role in mitigating climate change. Likewise, biostimulants and biopesticides, which are intended to target specific plant or microbial processes, are emerging with claims of improved soil health, crop yields, soil organic carbon sequestration, and greenhouse gas emission reductions. We conducted a literature review to address the climate mitigation potential of organic soil amendments, including biostimulants and biopesticides. In doing so, we identify three elements of climate mitigation through the use of soil amendments: soil organic carbon sequestration, soil greenhouse gas emission reductions, and life cycle emission reductions. We review common soil amendment classes in detail, addressing the empirical evidence (or lack thereof) in which they meet these three elements of climate mitigation. We conclude by suggesting priorities for government and private investment.

There is disagreement about the potential for regenerative management practices to sequester sufficient soil organic carbon (SOC) to help mitigate climate change. Measuring change in SOC stocks following practice adoption at the grain of farm fields, within the extent of regional agriculture, could help resolve this disagreement. Yet sampling demands to quantify change are considered infeasible primarily because within-field variation in stock sizes is thought to obscure accurate quantification of management effects on incremental SOC accrual. We evaluate this ‘infeasibility assumption’ using high-density (e.g., 0.1 ha sample–1), within-field, sampling data from 45 cropland fields inventoried for SOC. We explore how more typical within-field sampling densities, as well as field numbers and magnitude of simulated change in SOC stocks, impacts the ability to accurately quantify management effects on SOC change. We find that (1) stock change estimates for individual fields are inaccurate and variable, where marked losses and gains in SOC stocks are frequently estimated even when no change has occurred. Higher sampling densities (e.g., 1.2 versus 4.0 ha sample–1) narrow the range of estimated stock changes but inaccuracies remain large. (2) The accuracy of stock change estimates at the project level (i.e., multiple fields) were similarly sensitive to sampling density. In contrast to individual fields, however, higher sampling densities, as well as a greater number of fields (e.g., 30), generated robust and accurate, mean project-level estimates of carbon accrual, with ∼ 80 % of the estimates falling within 20 % of the simulated stock change. Yet such monitoring designs do not account for dynamic baselines, which necessitates measurement of stock changes in control, non-regenerative fields. We find (3) that higher sampling densities (e.g., 1.2 versus 4.0 ha sample–1), field numbers (e.g., 30 versus 10 pairs of fields), and magnitudes of simulated SOC stock change (+3 and +5 versus +1 Mg C ha−1 10 y–1) are then collectively required to make accurate estimates of management effects on stock change at the project level. The simulated effect sizes that could be consistently detected under these conditions included rates of SOC accrual considered achievable and meaningful for climate mitigation (e.g., 3 Mg C ha−1 10 y–1), with field numbers and sampling densities that are reasonable given current sampling methods. Our findings reveal the potential to use empirical approaches to accurately quantify, at project scales, SOC stock responses to practice change. We provide recommendations for data that government, farmer and corporate entities should measure and share to build confidence in the effects of regenerative practices, freeing the SOC debate from overreliance on theory and data collected at scales mismatched with agricultural management.

There is strong disagreement about the potential for regenerative management practices to sequester sufficient soil carbon to help mitigate climate change. Measuring change in carbon stocks following practice adoption at the grain of farm fields, within the extent of regional agriculture, could help resolve this disagreement. Yet sampling demands to quantify change are considered infeasible primarily because within-field variation in stock sizes is thought to obscure accurate quantification of management effects on incremental carbon accrual. We evaluate this ‘infeasibility assumption’ using high-density, within-field, sampling data from 45 cropland fields inventoried for soil carbon. We explore how within-field sampling density, field numbers, and magnitude of simulated change in soil carbon stocks impacts the ability to accurately quantify management effects on soil carbon change. We find that (1) stock change estimates for individual fields are inaccurate and inconsistent, where marked losses and gains in carbon stocks are frequently estimated even when no change has occurred. Higher sampling densities narrow the range of estimated effect sizes but inaccuracies remain large. Similarly, (2) the accuracy of estimates of mean effects on stock change at the project level (i.e., multiple fields) were sensitive to sampling density and not magnitude of simulated stock change. In contrast to individual fields, however, higher sampling densities (e.g., 1.2 ha sample-1), as well as a greater number of fields (e.g., 30), generated consistent and accurate, mean project-level estimates of carbon accrual, with ~80% of the estimates falling within 20% of the simulated stock change. Yet such monitoring designs do not account for dynamic baselines, which necessitates measurement of stock changes in control, non-regenerative fields. We find (3) that higher sampling densities, field numbers, and magnitudes of simulated soil carbon stock change are then collectively required to make accurate estimates of management effects on stock change at the project level. The simulated effect sizes that could be consistently detected included rates of carbon accrual considered achievable and meaningful for climate mitigation (e.g., 3 t C ha-1 10 y-1), using field numbers and sampling densities that are reasonable given current sampling methods. However, the sampling densities and field numbers we report are not recommendations; they should be tailored to the fields in each project. Nevertheless, our findings reveal the potential to use empirical approaches to accurately quantify soil carbon stock responses to practice change. We provide recommendations for data that government, farmer and corporate entities should measure and share to build confidence in the effects of regenerative practices, freeing the soil carbon debate from overreliance on theory and data collected at scales mismatched with agricultural management.

Wood decomposition is regulated by multiple controls, including climate and wood traits, that vary at local to regional scales. Yet decomposition rates differ dramatically when these controls do not. Fungal community dynamics are often invoked to explain these differences, suggesting that knowledge of ecosystem properties that influence fungal communities will improve understanding and projection of wood decomposition. We hypothesize that deadwood inputs decompose faster in forests with higher stocks of downed coarse woody material (CWM) because CWM is a resource from which lignocellulolytic fungi rapidly colonize new inputs. To test this hypothesis, we measure decomposition of 1,116 pieces of fine woody material (FWM) of five species, incubated for 13 to 49 months at five locations spanning 10°-latitude in eastern U.S. forest. We place FWM pieces near and far from CWM across observational transects and experimental common gardens. Soil temperature positively affects location-level mean decomposition rates, but these among-location differences are smaller than within-location variation in decomposition. Some of this variability is caused by CWM, where FWM pieces next to CWM decompose more rapidly. These effects are greater with time of incubation and lower initial wood density of FWM. The effect size of CWM is of the same relative magnitude as for the known controls of temperature, deadwood density and diameter. Abundance data for CWM is available for many forests and hence may be an ecosystem variable amenable for inclusion in decomposition models. Our findings suggest that conservation efforts to rebuild depleted CWM stocks in temperate forests may accelerate decomposition of fresh deadwood inputs.

The voluntary carbon market for agricultural soil carbon sequestration is accelerating at a rapid pace with over a dozen companies and marketplaces having recently announced carbon crediting programs. These programs aim to bring verified carbon credits into the market using published measurement, reporting, and verification protocols. Given the varied approaches to measuring and accounting for changes in soil carbon represented among these different protocols, there is significant uncertainty whether a credit generated in one market has any equivalency to a credit generated in another program. We see a critical need for scientists to play an active role in helping guide protocol development and to conduct relevant research. To that end, we identify important areas where confusion about protocols and their implementation hamper progress on this front, and highlight key areas for improved communication and transparency between market stakeholders and the research community.

Regional consistency is necessary for carbon credit integrity.

The amount of soil organic matter (SOM) is considered a key indicator of soil properties associated with higher fertility. Despite the ubiquity of assumptions surrounding SOM's contributions to soil functioning, we lack quantitative relationships between SOM and yield outcomes on working farms. We quantified the relationship between SOM and yields of corn (Zea mays L.) and silage for a dataset of 170 fields arrayed across 49 farms in a network of growers based in Wisconsin and Minnesota, USA. As SOM concentrations increase so do yields, though gains start to level off around 4% SOM. When examining the relationship between yield and soil health indicators representative of biologically active C pools, we found that mineralizable C has a stronger relationship with yield than permanganate oxidizable C. Mineral fertilizer, manure, and SOM had relationships of similar magnitude with yield, highlighting that SOM in combination with exogenous inputs likely plays an important role in driving agricultural productivity in this region. An interaction between SOM and crop rotation indicated that the impact of SOM on crop yields varied depending on rotation (continuous corn vs. corn in rotation). That is, continuous corn had lower yields than corn in rotation despite higher SOM concentrations. Our findings provide insight into the relationship between indicators of soil health, farm management, and crop yields for a set of working farms and lend support to the goals of soil health initiatives that rest on building SOM in agricultural soils to improve agricultural outcomes.