Optimizing Sampling Strategies for Near-Surface Soil Carbon Inventory: One Size Doesn’t Fit All

dc.contributor.authorBettigole, Charles
dc.contributor.authorHanle, Juliana
dc.contributor.authorKane, Daniel A.
dc.contributor.authorPagliaro, Zoe
dc.contributor.authorKolodney, Shaylan
dc.contributor.authorSzuhay, Sylvana
dc.contributor.authorChandler, Miles
dc.contributor.authorHersh, Eli
dc.contributor.authorWood, Stephen A.
dc.contributor.authorBasso, Bruno
dc.contributor.authorGoodwin, Douglas Jeffrey
dc.contributor.authorHardy, Shane
dc.contributor.authorWolf, Zachary
dc.contributor.authorCovey, Kristofer R.
dc.description© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The Version of Scholarly Record of this Article is published in Soil Systems, 2023, available online at: https://www.mdpi.com/2571-8789/7/1/27 . Keywords: soil carbon; sampling; grazing; agriculture; stratification; inventory.
dc.description.abstractSoils comprise the largest pool of terrestrial carbon yet have lost significant stocks due to human activity. Changes to land management in cropland and grazing systems present opportunities to sequester carbon in soils at large scales. Uncertainty in the magnitude of this potential impact is largely driven by the difficulties and costs associated with measuring near-surface (0–30 cm) soil carbon concentrations; a key component of soil carbon stock assessments. Many techniques exist to optimize sampling, yet few studies have compared these techniques at varying sample intensities. In this study, we performed ex-ante, high-intensity sampling for soil carbon concentrations at four farms in the eastern United States. We used post hoc Monte-Carlo bootstrapping to investigate the most efficient sampling approaches for soil carbon inventory: K-means stratification, Conditioned Latin Hypercube Sampling (cLHS), simple random, and regular grid. No two study sites displayed similar patterns across all sampling techniques, although cLHS and grid emerged as the most efficient sampling schemes across all sites and strata sizes. The number of strata chosen when using K-means stratification can have a significant impact on sample efficiency, and we caution future inventories from using small strata n, while avoiding even allocation of sample between strata. Our findings reinforce the need for adaptive sampling methodologies where initial site inventory can inform primary, robust inventory with site-specific sampling techniques.
dc.description.sponsorshipThis research was funded by Caney Fork Farms, Oak Spring Garden Foundation, Noble Research Institute, the Skidmore College Faculty-Student Research Fund, The Walton Family Founda tion, TomKat Ranch, Globetrotter Foundation, and The Soil Inventory Project. In-kind support from the Stone Barns Center for Food and Agriculture.
dc.identifier.citation: Bettigole, C.; Hanle, J.; Kane, D.A.; Pagliaro, Z.; Kolodney, S.; Szuhay, S.; Chandler, M.; Hersh, E.; Wood, S.A.; Basso, B.; et al. Optimizing Sampling Strategies for Near-Surface Soil Carbon Inventory: One Size Doesn’t Fit All. Soil Syst. 2023, 7, 27. https://doi.org/10.3390/ soilsystems7010027
dc.identifier.otherhttps://doi.org/10.3390/ soilsystems7010027
dc.titleOptimizing Sampling Strategies for Near-Surface Soil Carbon Inventory: One Size Doesn’t Fit All
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