Optimizing Sampling Strategies for Near-Surface Soil Carbon Inventory: One Size Doesn’t Fit All
Soils 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 largel...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Soil Systems |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2571-8789/7/1/27 |
| _version_ | 1797608935038910464 |
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| author | Charles Bettigole Juliana Hanle Daniel A. Kane Zoe Pagliaro Shaylan Kolodney Sylvana Szuhay Miles Chandler Eli Hersh Stephen A. Wood Bruno Basso Douglas Jeffrey Goodwin Shane Hardy Zachary Wolf Kristofer R. Covey |
| author_facet | Charles Bettigole Juliana Hanle Daniel A. Kane Zoe Pagliaro Shaylan Kolodney Sylvana Szuhay Miles Chandler Eli Hersh Stephen A. Wood Bruno Basso Douglas Jeffrey Goodwin Shane Hardy Zachary Wolf Kristofer R. Covey |
| author_sort | Charles Bettigole |
| collection | DOAJ |
| description | Soils 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. |
| first_indexed | 2024-03-11T05:54:39Z |
| format | Article |
| id | doaj.art-c0c6e3b44a774877bdef01ebda2a27e8 |
| institution | Directory Open Access Journal |
| issn | 2571-8789 |
| language | English |
| last_indexed | 2024-03-11T05:54:39Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Soil Systems |
| spelling | doaj.art-c0c6e3b44a774877bdef01ebda2a27e82023-11-17T13:53:05ZengMDPI AGSoil Systems2571-87892023-03-01712710.3390/soilsystems7010027Optimizing Sampling Strategies for Near-Surface Soil Carbon Inventory: One Size Doesn’t Fit AllCharles Bettigole0Juliana Hanle1Daniel A. Kane2Zoe Pagliaro3Shaylan Kolodney4Sylvana Szuhay5Miles Chandler6Eli Hersh7Stephen A. Wood8Bruno Basso9Douglas Jeffrey Goodwin10Shane Hardy11Zachary Wolf12Kristofer R. Covey13Skidmore College, Saratoga Springs, NY 12866, USADepartment of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48823, USAYale School of the Environment, Yale University, New Haven, CT 06511, USASkidmore College, Saratoga Springs, NY 12866, USASkidmore College, Saratoga Springs, NY 12866, USASkidmore College, Saratoga Springs, NY 12866, USASkidmore College, Saratoga Springs, NY 12866, USASkidmore College, Saratoga Springs, NY 12866, USAYale School of the Environment, Yale University, New Haven, CT 06511, USADepartment of Earth and Environmental Sciences, Michigan State University, East Lansing, MI 48823, USATexas A&M AgriLife Research, College Station, TX 77845, USAStone Barns Center for Food and Agriculture, Tarrytown, NY 10591, USACaney Fork Farms, Carthage, TN 37030, USASkidmore College, Saratoga Springs, NY 12866, USASoils 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.https://www.mdpi.com/2571-8789/7/1/27soil carbonsamplinggrazingagriculturestratificationinventory |
| spellingShingle | Charles Bettigole Juliana Hanle Daniel A. Kane Zoe Pagliaro Shaylan Kolodney Sylvana Szuhay Miles Chandler Eli Hersh Stephen A. Wood Bruno Basso Douglas Jeffrey Goodwin Shane Hardy Zachary Wolf Kristofer R. Covey Optimizing Sampling Strategies for Near-Surface Soil Carbon Inventory: One Size Doesn’t Fit All Soil Systems soil carbon sampling grazing agriculture stratification inventory |
| title | Optimizing Sampling Strategies for Near-Surface Soil Carbon Inventory: One Size Doesn’t Fit All |
| title_full | Optimizing Sampling Strategies for Near-Surface Soil Carbon Inventory: One Size Doesn’t Fit All |
| title_fullStr | Optimizing Sampling Strategies for Near-Surface Soil Carbon Inventory: One Size Doesn’t Fit All |
| title_full_unstemmed | Optimizing Sampling Strategies for Near-Surface Soil Carbon Inventory: One Size Doesn’t Fit All |
| title_short | Optimizing Sampling Strategies for Near-Surface Soil Carbon Inventory: One Size Doesn’t Fit All |
| title_sort | optimizing sampling strategies for near surface soil carbon inventory one size doesn t fit all |
| topic | soil carbon sampling grazing agriculture stratification inventory |
| url | https://www.mdpi.com/2571-8789/7/1/27 |
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