Soil Drainage Class Influences on Soil Carbon in a New England Forested Watershed

Raymond, Jay E.; Fernandez, Ivan J.; Ohno, Tsutomu; Simon, Kevin S.

doi:10.2136/sssaj2012.0129

Cited by 14 publications

(8 citation statements)

References 53 publications

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“…However, Figure 2a and 2b shows that in the SSURGO database there were no significant differences in SOC density among PD, SPD, WMD, and WD soils at 0-20 cm and 0-100 cm depth increments. Raymond et al (2012) reported similar results from their studies in a forested watershed in Maine. They concluded that illuvial accumulations of SOC combined with greater belowground SOC inputs from roots enhanced total SOC accumulations in MWD soils.…”

Section: Soil Drainage Class and Socsupporting

confidence: 70%

“…In contrast, approximately 54% of VPD soils in the SSURGO database were Histosols, and the rest were Inceptisols (38%), Entisols (7%), and Spodosols (1%) (Supplemental Table S1). Maine Inceptisols are often characterized as forested mineral wetland soils and have much lower SOC than Histosols, at least partly due to the extended aerobic periods at the soil surface that facilitate SOM decomposition (Raymond, Fernandez, & Simon, 2012). Results from the PCA analysis in Supplemental Table S2 shows that all SSURGO attributes studied (i.e., soil order, drainage class, parent material, and texture) influence SOC density, and illustrates the relative strengths of each factor in the determination of SOC density among the databases.…”

Section: Resultsmentioning

confidence: 99%

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Comparing publicly available databases to evaluate soil organic carbon in Maine, USA

Bai

Fernandez

2020

Soil Science Soc of Amer J

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Elevated greenhouse gas (GHG) emissions have resulted in growing societal interest in natural climate solutions that include enhancing soil organic carbon (SOC). This study evaluated SOC from three USDA soil databases for Maine, a state that is nearly 90% forested. These databases were: (1) Forest Inventory and Analysis (FIA), (2) NRCS Soil Survey Geographic (SSURGO), and (3) Rapid Carbon Assessment (RaCA). We compared estimates of SOC density and concentration among databases and analyzed relationships between key soil properties and SOC densities and concentrations. Estimates of SOC density to a 20 cm depth which allows comparison among all three databases were 77 ± 1.2 Mg ha −1 , 95 ± 0.3 Mg ha −1 , and 39 ± 2.2 Mg ha −1 for SSURGO, RaCA, and FIA, respectively. Significant differences existed among databases in SOC density at shallow depth increments, and marked differences existed among databases in the soil depths measured. Analysis of the influence of soil texture and drainage class on SOC highlighted the importance of silt and clay in SOC retention. A hypothetical increase in forest and agricultural SOC was compared to reported Maine GHG emissions to illustrate potential policy implications in the use of soil databases. We conclude that all three databases are valuable but not interchangeable. The database should be carefully chosen to match the objectives of the user. How to cite this article: Bai X, Fernandez IJ. Comparing publicly available databases to evaluate soil organic carbon in Maine, USA. Soil Sci. Soc.

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Section: Soil Drainage Class and Socsupporting

confidence: 70%

Section: Resultsmentioning

confidence: 99%

Comparing publicly available databases to evaluate soil organic carbon in Maine, USA

Bai

Fernandez

2020

Soil Science Soc of Amer J

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“…For example, for the 40 to 100 cm depth increment, C concentration at the Howland Forest was approximately 1.37% compared with 0.30%, 0.27%, and 0.24% for the selection, shelterwood, and commercial clearcut treatments on the PEF. Other studies have reported higher average mineral soil C stocks for sites in Maine (Birdsey and Lewis 2003;McLaughlin and Phillips 2006;Fernandez 2008;Raymond et al 2013).…”

Section: Discussionmentioning

confidence: 90%

“…For instance, Raymond et al (2013) found that mineral soil C stocks were greater in moderately well-drained soils than more poorly drained soils in a coniferous forest. However, Richardson and Stolt (2013) did not find significant differences in C stocks between well-drained and moderately well-drained soils in southern New England.…”

Section: Introductionmentioning

confidence: 99%

“…Timber harvesting also contributes large pulses of organic C to the mineral soil in the form of dead coarse roots following tree removal (Noormets et al 2012). Raymond et al (2013) hypothesized that the deeper rooting of trees on moderately well-drained soils compared with somewhat poorly drained and poorly drained soils could contribute to the higher mineral soil C stocks on soils with better drainage conditions.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of forest management effects on the mineral soil carbon pool of a lowland, mixed-species forest in Maine, USA

2016

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Concerns about climate change have increased interest in ways to maximize carbon (C) storage in forests through the use of alternative forest management strategies. However, the influence of these strategies on soil C pools is unclear. The primary objective of this study was to test for differences in mineral soil C stocks among various silvicultural and harvesting treatments that were initiated in the 1950s and have been maintained since on the Penobscot Experimental Forest in central Maine, USA. Five mineral soil cores below the surface organic horizon to a depth of 1 m were collected from each replicate (n = 2) of selection, shelterwood, and commercial clearcut treatments. For these treatments, the mean mineral soil C stock was 47.7 ± 16.4 Mg ha −1 (mean ± SD). We found no significant differences in average mineral soil C stocks among treatments. However, a post hoc power analysis indicated that the probability of detecting a significant treatment effect was only 6%. We determined that 98 stands per treatment would be required to be 80% certain that the F test would detect a difference in average mineral soil C stocks whenever any pair of treatments had C stocks differing by more than 5 Mg ha −1 .Key words: soil carbon variability, silviculture, coarse fragments.Résumé : Les préoccupations associées au changement climatique ont accru l'intérêt pour les façons dont on pourrait stocker un maximum de carbone (C) dans les forêts en recourant à de nouvelles stratégies de gestion. Malheureusement, on connaît mal l'influence de telles stratégies sur les réserves de C du sol. La présente étude devait essentiellement vérifier la fluctuation de ces réserves en fonction des méthodes de sylviculture et de récolte lancées dans les années 1950 et maintenues depuis, dans la forêt expérimentale de Penobscot, dans le centre du Maine, aux États-Unis. Les chercheurs ont prélevé cinq carottes de sol minéral sous l'horizon organique de surface à un mètre de profondeur sur chaque parcelle forestière (n = 2) de coupe jardinatoire, de coupe progressive et de coupe à blanc commerciale. Les réserves de C minéral dans le sol des parcelles correspondant à ces différents traitements s'élevaient en moyenne à 47,7 ± 16,4 Mg ha −1 (moyenne É.-T.). Les réserves moyennes de C minéral dans le sol ne varient pas de manière significative d'un traitement à l'autre. Cependant, une analyse de puissance ultérieure a révélé que la probabilité de déceler un effet significatif attribuable au traitement n'était que de 6 %. Les auteurs estiment qu'il faudrait examiner 98 peuplements par traitement pour être sûr à 80 % que le test F détecterait un écart dans les réserves moyennes de C minéral, chaque fois que ces dernières varient de plus de 5 Mg ha −1 entre deux traitements. [Traduit par la Rédaction]Mots-clés : variabilité du carbone dans le sol, sylviculture, fragments grossiers.

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Empirical relationships between environmental factors and soil organic carbon produce comparable prediction accuracy to machine learning

Mishra

Yeo

Adhikari

et al. 2022

Soil Science Soc of Amer J

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Accurate representation of environmental controllers of soil organic carbon (SOC) stocks in Earth System Model (ESM) land models could reduce uncertainties in future carbon–climate feedback projections. Using empirical relationships between environmental factors and SOC stocks to evaluate land models can help modelers understand prediction biases beyond what can be achieved with the observed SOC stocks alone. In this study, we used 31 observed environmental factors, field SOC observations (n = 6,213) from the continental United States, and two machine learning approaches (random forest [RF] and generalized additive modeling [GAM]) to (a) select important environmental predictors of SOC stocks, (b) derive empirical relationships between environmental factors and SOC stocks, and (c) use the derived relationships to predict SOC stocks and compare the prediction accuracy of simpler model developed with the machine learning predictions. Out of the 31 environmental factors we investigated, 12 were identified as important predictors of SOC stocks by the RF approach. In contrast, the GAM approach identified six (of those 12) environmental factors as important controllers of SOC stocks: potential evapotranspiration, normalized difference vegetation index, soil drainage condition, precipitation, elevation, and net primary productivity. The GAM approach showed minimal SOC predictive importance of the remaining six environmental factors identified by the RF approach. Our derived empirical relations produced comparable prediction accuracy to the GAM and RF approach using only a subset of environmental factors. The empirical relationships we derived using the GAM approach can serve as important benchmarks to evaluate environmental control representations of SOC stocks in ESMs, which could reduce uncertainty in predicting future carbon–climate feedbacks.

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Soil Drainage Class Influences on Soil Carbon in a New England Forested Watershed

Cited by 14 publications

References 53 publications

Comparing publicly available databases to evaluate soil organic carbon in Maine, USA

Comparing publicly available databases to evaluate soil organic carbon in Maine, USA

Evaluation of forest management effects on the mineral soil carbon pool of a lowland, mixed-species forest in Maine, USA

Empirical relationships between environmental factors and soil organic carbon produce comparable prediction accuracy to machine learning

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