Testing the feasibility of quantifying change in agricultural soil carbon stocks through empirical sampling
Creators: name: Bradford, Mark
name: Lisa, Eash
name: Alexander, Polussa
name: Jevon, Fiona
name: Kuebbing, Sara
name: Hammac, Ashley
name: Rosenzweig, Steven
name: Oldfield, Emily
Publisher: name: Zenodo
Year: 2023
Subjects: carbon credits
carbon markets
causal inference
Conservation agriculture
population sampling
regression to the mean
soil carbon stocks
soil monitoring
Sustainable agriculture
natural experiments

description: 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, within-field, sampling data from 45 cropland fields inventoried for SOC. We explore how within-field sampling density, 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 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 (e.g., 1.2 ha sample-1), 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, field numbers, and magnitudes of simulated SOC 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 SOC accrual considered achievable and meaningful for climate mitigation (e.g., 3 Mg C ha-1 10 y-1), using 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.
description: R code is open-source statistical software and can be converted as a text file. Other file types are text for metadata and csv for data.
Funding provided by: General Mills (United States)Crossref Funder Registry ID: https://ror.org/03kgyg741Award Number: 
Funding provided by: The Earth Fund*Crossref Funder Registry ID: Award Number: 
Funding provided by: King Philanthropies*Crossref Funder Registry ID: Award Number: 
Funding provided by: Arcadia FundCrossref Funder Registry ID: https://ror.org/051z6e826Award Number: 
Funding provided by: Yale Center for Natural Carbon Capture*Crossref Funder Registry ID: Award Number: