DOI: 10.5061/dryad.z08kprrjp
title: Testing the feasibility of quantifying change in agricultural soil carbon stocks through empirical sampling

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.
description: Last modified: October 19th 2023 # Title of Dataset Testing the
 Feasibility of Quantifying Change in Agricultural Soil Carbon Stocks
 Through Empirical Sampling. By, Bradford MA, Eash L, Polussa A, Jevon FV,
 Kuebbing SE, Hammac WA, Rosenzweig S, Oldfield EE. Geoderma, for review.
 *** ## Description of the data and file structure TThe code file is named
 ‘Bradford02\_R\_analysis.txt’ and contains all R (the statistical freeware
 package) code used for data analysis, simulations and graphing in the
 manuscript. Analytical steps are explained ahead of each section of code
 within the code file. The data files are named ‘Proj\_area.csv’ and
 ‘Proj\_area.csv’ A full description of the original purpose of these data,
 as well as the methods used to collect them, are described in the
 manuscript listed above. In addition, header row names, units and
 description are fully described for both data sets in
 BradfordSoilCarbonMetadata.csv Data in the Proj\_area.csv file are
 organized across 7 columns, and 45 rows of data, with a single header row
 with the column title. Data in the Proj\_area.csv file are organized
 across 4 columns, and 1593 rows of data, with a single header row with the
 column title. ## Sharing/Access information Data and code, along with this
 ReadMe file (README\_BradfordSoilCarbon.txt), were submitted to the Dryad
 data repository July 2023 for private peer review, and then updated
 October 19th 2023 during manuscript revision, with the intent to make them
 fully accessible on Dryad following paper acceptance, for the above paper
 submitted to the journal Geoderma. If you have queries about the data or
 code please contact Mark A. Bradford at Yale University, USA
 (mark.bradford@yale.edu). The persons associated with the code and data
 have dedicated the work to the public domain by waiving all of their
 rights to the code and data worldwide under copyright law, including all
 related and neighboring rights, to the extent allowed by law. You can
 copy, distribute and use the code and data, even for commercial purposes,
 all without asking permission. ## Code/Software The code file is named
 ‘Bradford02\_R\_analysis.txt’ and contains all R (the statistical freeware
 package) code used for data analysis, simulations and graphing in the
 manuscript. Analytical steps are explained ahead of each section of code
 within the code file.