Large errors in soil carbon measurements attributed to inconsistent sample processing
<p>To build confidence in the efficacy of soil carbon (C) crediting programs, precise quantification of soil organic carbon (SOC) is critical. Detecting a true change in SOC after a management shift has occurred, specifically in agricultural lands, is difficult as it requires robust soil sampl...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-01-01
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| Series: | SOIL |
| Online Access: | https://soil.copernicus.org/articles/11/17/2025/soil-11-17-2025.pdf |
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| Summary: | <p>To build confidence in the efficacy of soil carbon (C) crediting programs, precise quantification of soil organic carbon (SOC) is critical. Detecting a true change in SOC after a management shift has occurred, specifically in agricultural lands, is difficult as it requires robust soil sampling and soil processing procedures. Informative and meaningful comparisons across spatial and temporal timescales can only be made with reliable soil C measurements and estimates, which begin on the ground and in soil testing facilities. To gauge soil C measurement inter-variability, we conducted a blind external service laboratory comparison across eight laboratories selected based on status and involvement in SOC data curation used to inform C market exchanges, which could include demonstration projects, model validation, and project verification activities. Further, to better understand how soil processing procedures and quantification methods commonly used in soil testing laboratories affect soil C concentration measurements, we designed an internal experiment assessing the individual effect of several alternative procedures (i.e., sieving, fine grinding, and drying) and quantification methods on total (TC), inorganic (SIC), and organic (SOC) soil C concentration estimates. We analyzed 12 different agricultural soils using 11 procedures that varied in either the sieving, fine-grinding, drying, or quantification step. We found that a mechanical grinder, the most commonly used method for sieving in service laboratories, did not effectively remove coarse materials (i.e., roots and rocks) and thus resulted in higher variability and significantly different C concentration measurements from the other sieving procedures (i.e., 8 <span class="inline-formula">+</span> 2, 4, and 2 mm with a rolling pin). A finer grind generally resulted in a lower coefficient of variance, where the finest grind to <span class="inline-formula"><</span> 125 <span class="inline-formula">µ</span>m had the lowest coefficient of variance, followed by the <span class="inline-formula"><</span> 250 <span class="inline-formula">µ</span>m grind and, lastly, the <span class="inline-formula"><</span> 2000 <span class="inline-formula">µ</span>m grind. Not drying soils in an oven prior to elemental analysis on average resulted in a 3.5 % lower TC and 5 % lower SOC relative to samples dried at 105 °C due to inadequate removal of moisture. Compared to the reference method used in our study where % TC was quantified by dry combustion on an elemental analyzer, % SIC was measured using a pressure transducer, and % SOC was calculated by the difference in % TC and % SIC, predictions of all three soil properties (% TC, % SIC, and % SOC) using Fourier-transformed infrared spectroscopy (FTIR) were in high agreement (<span class="inline-formula"><i>R</i><sup>2</sup></span> <span class="inline-formula">=</span> 0.97, 0.99, and 0.90, respectively). For % SOC, quantification by loss on ignition had a relatively low coefficient of variance (5.42 <span class="inline-formula">±</span> 3.06 %) but the least agreement (<span class="inline-formula"><i>R</i><sup>2</sup></span> <span class="inline-formula">=</span> 0.83) with the reference method. We conclude that sieving to <span class="inline-formula"><</span> 2 mm with a mortar and pestle or rolling pin to remove coarse materials, drying soils at 105 °C, and fine-grinding soils prior to elemental analysis are required to improve accuracy and precision of soil C measurements. Moreover, we show promising results using FTIR spectroscopy coupled with predictive modeling for estimating % TC, % SIC, and % SOC in regions where spectral libraries exist.</p> |
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| ISSN: | 2199-3971 2199-398X |