Estimation of Organic Carbon Stocks and Sequestration Rates from Soil Core Data • BlueCarbon

The goal of BlueCarbon is to facilitate the estimation of organic carbon stocks and sequestration rates from soil/sediment cores from blue carbon ecosystems.

It contains seven main functions to deal with core compaction (estimate and correct for the compaction of cores when sampling), transform laboratory data (estimate sample thickness and estimate organic carbon content from organic matter content) and estimate organic carbon stocks and sequestration rates (estimate organic carbon stocks and sequestration rates and visualize the error of stock extrapolation).

estimate_compaction - Estimate Core Compaction

Sampling soil cores by manual percussion usually leads to the compaction of the material retrieved. This function will estimate the percentage of compaction from measurements taken in the field after inserting the corer tube and before extracting it: length of the corer tube (sampler_length), distance between the surface of the soil and the top of the tube in the outside (external_distance) and distance between the surface of the soil and the top of the tube in the inside of the tube (internal_distance).

decompact - Calculate sediment properties after decompaction

Core compaction derived from field extraction can be mathematically corrected to estimate the original depth of the samples. This function will apply a linear correction (all the core material is assumed to have been compacted equally) to correct sample depth and, if provided, dry bulk density.

estimate_oc - Organic carbon content estimation from organic carbon data

Typically the relationship between organic carbon and organic matter content can be modelled with a linear regression model. This relationship can change between ecosystems and sampling sites due to changes in organic matter composition among other factors. This function (estimate_oc()) fits a linear model between organic matter and organic carbon content in your samples to estimate the content of organic carbon in those samples lacking those values. The estimation of organic carbon is done by means of linear regressions on log(organic carbon) ~ log(organic matter). If there is a value for organic carbon for that sample it returns the same value; otherwise, it estimates organic carbon from a model fitted to that site, or a model fitted to that species, or else a model fitted to that ecosystem. If there are too few samples to build a reliable model or the model fit is too poor,estimate_oc() uses the equations in Fourqurean et al. (2012) for seagrasses, Maxwell et al. (2023) for salt marshes and Piñeiro-Juncal (in prep.) for mangroves to estimate the organic carbon. It is unlikely, but possible, that a model predicts higher organic carbon than organic matter content. As this is not possible in nature, the function will give a warning and it is recommended to discard that model.

estimate_h - Sample thickness estimation

For those cores where only selected samples were measured it is necessary to assign a carbon density to the empty spaces to estimate the total stock. This function (estimate_h()) checks for gaps between samples and, if any, divides this space between the previous and next sample to return sample thickness without gaps in the core. The middle point between one sample and the other is estimated from the bottom of the previous sample to the top of the next sample, and not from the middle point of one sample to the middle part of the next, as this would unequally distribute the gaps between samples if the samples have different thickness. The stock and sequestration rate estimation functions (estimate_oc_stock() and estimate_seq_rate()) have this function incorporated and it is not necessary to run it beforehand.

Gap distribution between samples to estimate accumulated organic carbon mass.

estimate_oc_stock - Organic carbon stock estimation

Estimates carbon stocks from soil core data down to a specified depth, 100 cm by default. If the core does not reach the desired depth, it extrapolates the stock from a linear model between accumulated mass of organic carbon and depth.

test_extrapolation - Visualize the error of stock extrapolation

This function subset those cores that reach the desired depth, estimates the stock (observed stock), estimates the stock from the linear relation of organic carbon accumulated mass and depth using the 90, 75, 50 and 25% top length of the indicated desired depth, and compares the observed stock with the estimated stocks by extrapolation. This function requires that some of your cores do reach the desired depth.

estimate_seq_rate - Organic carbon sequestration rates estimation

Estimate the average organic carbon sequestration rate in the soil in a given time frame (by default last 100 years) from the organic carbon concentration and the age of the samples.

Installation

BlueCarbon can be installed directly from GitHub:

# install.packages("remotes")
remotes::install_github("EcologyR/BlueCarbon")

or from R-universe:

install.packages("BlueCarbon", repos = c("https://pakillo.r-universe.dev", "https://cloud.r-project.org"))

Citation

If using this package, please cite it:

citation("BlueCarbon")
To cite package 'BlueCarbon' in publications use:

  Piñeiro-Juncal N, Astigarraga J, Costa V, Martins M,
  Rodriguez-Sanchez F (2025). _BlueCarbon: Estimation of Organic Carbon
  Stocks and Sequestration Rates From Soil Core Data_. R package
  version 0.1.0, https://EcologyR.github.io/BlueCarbon/,
  <https://github.com/EcologyR/BlueCarbon>.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {BlueCarbon: Estimation of Organic Carbon Stocks and Sequestration Rates From Soil Core Data},
    author = {Nerea Piñeiro-Juncal and Julen Astigarraga and Valentina Costa and Marcio Martins and Francisco Rodriguez-Sanchez},
    year = {2025},
    url = {https://github.com/EcologyR/BlueCarbon},
    note = {R package version 0.1.0, https://EcologyR.github.io/BlueCarbon/},
  }

Code of Conduct

Please note that the BlueCarbon project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

Funding

The development of this software has been funded by Fondo Europeo de Desarrollo Regional (FEDER) and Consejería de Transformación Económica, Industria, Conocimiento y Universidades of Junta de Andalucía (proyecto US-1381388 led by Francisco Rodríguez Sánchez, Universidad de Sevilla). NPJ was supported by a Juan de la Cierva fellowship (JDC2022-048342-I). JA acknowledges funding from the CLIMB-FOREST Horizon Europe Project (No 101059888) that was funded by the European Union.