Testing the feasibility of quantifying change in agricultural soil carbon stocks through empirical sampling
Data files
Nov 17, 2023 version files 32.90 KB
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Proj_area.csv
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Proj_df.csv
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README.md
Abstract
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.
README: Title of Dataset
Last modified: October 19th 2023
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.
Usage notes
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.