Computing C Stock for the BE-Lcr site in 2017


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Rock fragments

Warning: The name of the attribute in the database has changed in 2020. The \(`sosmW30s`\) variable is now \(`sosmW105s`\). Therefore, we have 2 ways for computing the RF.

Before 2020, the RF was computed in the following way:

\[RF_{30} = \frac{sosmW30s_{sp2}} {sosmW30s_{sp2}+sosmW30e_{sp2} }\]

After 2020, RF is computed using the weight of the coarse elements dried at 105°C:

\[RF_{105} = \frac{sosmW105s_{sp2}} {sosmW105s_{sp2}+sosmW30e_{sp2} }\]

Load and curate the data

This file is gathering the results for the site \(BE-Lcr\)

Extract the soil data

As a first step, we load and prepare the data. They are extracted from the ICOS soil database located in the French soil information system (DONESOL), which contains all the data from all sites. These files came from a SQL query.

Then, I extract the data and the sampling year for the selected site.

Extract coordinates and site stratification details

To compute design-based estimates of soil properties, we rely on spatial stratification parameters derived from the ETC’s pre-sampling analysis.

Using the spcosa package and KMZ files (which contain spatial layers), we extract:

  • \(idstratum\): Stratum identifier (ranging from 1 to 10).

  • \(H\): Total number of strata.

  • \(n_h\): Sample size (number of SP-I plots) per stratum.

  • \(nSites\): Total sample size (sum of all SP-I plots across strata).

  • \(stratumArea\): Area of each stratum (in pixels).

  • \(a_h\): Relative area per stratum.

Note: For KML files, extraction from the KMZ archive is required—this can be done using Windows.

Extract the soil depth limits

Accurate soil depth limits are essential for BADM quality control by ETC-Soil. While some errors may evade automated checks during C portal submission, uncorrected depth values directly impact derived calculations (e.g., soil stock estimates).

Impact on Downstream Calculations

  • Soil Stock Errors: Uncorrected thickness values bias carbon stock estimates.

  • ETC Feedback: Proactive corrections reduce revision requests post-submission.

Protocol Definitions

Standard Layer Depths

Layer type Description Order
O (undifferentiated) WRB 2022 classification -4
Oi (initial decomposition) WRB 2022 classification -3
Oe (intermediate) WRB 2022 classification -2
Oa (advanced) WRB 2022 classification -1
0 - 5 cm ICOS standard depth 1
5 - 15 cm ICOS standard depth 2
15 - 30 cm ICOS standard depth 3
30 - 60 cm ICOS standard depth 4
60 - 100 cm ICOS standard depth 5

Exceptions

  1. Peatlands and wetlands: May have not standard depths due to hydromorphic conditions.

  2. Rocky Soils: Often exhibit shallow profiles with non-standard final layers.

Key Rules

To compute the mean and uncertainties accurately, we need to harmonize the soil layers as follow:

  • If the depth of the last layer is incomplete and varies across observations (e.g., extends only to 80 cm instead of the full 100 cm), we apply a frequency-weighted mean to harmonize this layer.

  • For non-standard layer intervals, we apply a spline model to interpolate the variables at the standard ICOS depth.

  • The correct organic layer ordering is (Oa → Oe → Oi). If stratification is unclear, we aggregate the O layers into a single one (O layer).

  • Within a single SP-I plot, all associated SP-II units should share the same classification for the O layer.

  • The thickness (cm) of the O layer within an SP-I plot is assumed to be the average of the corresponding SP-II measurements.

Note: If you notice any inconsistencies in your depth limits, please contact ETC-Soil to update your BADM accordingly.

In this site, the PI implemented the following depths:

Layer group Soil Layers Layer order Number of samples
Mineral 0-5 cm 1 100
Mineral 5-15 cm 2 100
Mineral 15-30 cm 3 100
Mineral 30-60 cm 4 100
Mineral 60-100 cm 5 100

The soil depth limits were:

Layer group Layer order SOSM_UD SOSM_LD
Mineral 1 0 5
Mineral 2 5 15
Mineral 3 15 30
Mineral 4 30 60
Mineral 5 60 100


Soil properties and Quality Control

In this section, we outline the procedures used to compute the key soil properties necessary for calculating carbon and nitrogen stocks. Variables such as bulk density and fine soil stock are derived from standardized field measurements and imputed where missing values are detected.

Data quality control focuses on identifying and addressing missing and negative values. With the exception of SIC content, negative values are not expected in the dataset. Negative values are replaced with \(NA\), which are followed by imputation using the layer-specific average.

Checking the soil organic carbon (SOC) content

Checking for negative and missing values in SOC content. If the table is not empty, it means that SOC content is negative or missing.

MinOrg depth_horizon sp1 sp2 no_profil_sp2 carbone

Negative and missing values of SOC content are imputed using the layer-specific averages.

The following graph shows the organic carbon content frequency \(SOC_{layer}\) distribution across soil layers, following imputation of missing values using the layer-specific averages.

Checking the total nitrogen (NT) content

Checking for negative and missing values in NT content.

MinOrg depth_horizon sp1 sp2 no_profil_sp2 Nitrogen

Negative values are replaced with \(NA\) and the missing values of NT are imputed using the layer-specific averages.

The following graph shows the NT content frequency \(NT_{layer}\) distribution across soil layers, following imputation of missing values using the layer-specific averages.

Compute the soil inorganic carbon (SIC) content

Soil inorganic carbon (SIC; g.kg-1) was calculated as the product of the result of the calcimetry analysis (CaCO3; g.kg-1 equivalent) multiplied by 0.12.

Checking for missing values in inorganic carbon content.

MinOrg depth_horizon sp1 sp2 no_profil_sp2 Carbonates

Note: Negative values of carbonates are not replaced with the mean because they represent the detection threshold of the laboratory analysis.

The following graph shows the \(SIC_{layer}\) content frequency distribution across soil layers

Compute residual water content using the wet-basis approach

We compute the residual water content (\(RW^l_{ik}\)) using the wet based approach as follow :

For mineral layers at SP-I level:

\[ RW^l_{ik} = \frac{SOSM\_WX30\_E^l_{ik} - SOSM\_WX105\_E^l_{ik}}{SOSM\_WX30\_E^l_{ik}} \]

For organic layers at SP-I level:

SOSM_W30_E is considered as a dry mass because residual water content is assumed to be negligible.


In order to check the data quality, missing and negative values of soil water mass at 30 and 105°C are listed in the two tables below:

If the table below is not empty, it means that either or both SOSM_WX30_E or SOSM_WX105_E are missing:

sp1 sp2 no_profil_sp2 sosm_wx30e_sp1 sosm_wx105e_sp1


If the table below is not empty, it means that the residual water is negative, that is, SOSM_WX30_E < SOSM_WX105_E.

MinOrg depth_horizon sp1 sp2 no_profil_sp2 Residual water (g/g)
Mineral 15-30 cm SP-I_03 SP-II_03-01 BE-Lcr/SP-II_03-01 -0.0215870
Mineral 15-30 cm SP-I_03 SP-II_03-02 BE-Lcr/SP-II_03-02 -0.0215870
Mineral 15-30 cm SP-I_03 SP-II_03-03 BE-Lcr/SP-II_03-03 -0.0215870
Mineral 15-30 cm SP-I_03 SP-II_03-04 BE-Lcr/SP-II_03-04 -0.0215870
Mineral 15-30 cm SP-I_03 SP-II_03-05 BE-Lcr/SP-II_03-05 -0.0215870
Mineral 5-15 cm SP-I_13 SP-II_13-01 BE-Lcr/SP-II_13-01 -0.0229796
Mineral 5-15 cm SP-I_13 SP-II_13-02 BE-Lcr/SP-II_13-02 -0.0229796
Mineral 5-15 cm SP-I_13 SP-II_13-03 BE-Lcr/SP-II_13-03 -0.0229796
Mineral 5-15 cm SP-I_13 SP-II_13-04 BE-Lcr/SP-II_13-04 -0.0229796
Mineral 5-15 cm SP-I_13 SP-II_13-05 BE-Lcr/SP-II_13-05 -0.0229796

The negative values are replaced with \(NA\).

Negative values are replaced with \(NA\) and the missing values of \(RW^l_{ik}\) are imputed using the layer-specific averages.

The following graph shows the \(RW^l_{ik}\) frequency distribution across soil layers, following imputation of missing values using the layer-specific averages.

Compute the density and mass of soil fractions for SP-II plots

Bulk density

\[ BD^l_{ik} = \frac{ \left( SOSM\_W30\_E^{l}_{ik}\times (1 - RW^{l}_{ik}) \right) + SOSM\_W105\_S^{l}_{ik} + SOSM\_W70\_R^{l}_{ik} }{SOSM\_VOLUME^{l}_{ik}} \]

In order to check the data quality of bulk density, missing values are tracked in the following table.

If the following table is not empty, it means that one of the following variables contain missing values: SOSM_W30_E, SOSM_W105_S (or SOSM_W30_S in previous versions), SOSM_W70_R, or SOSM_VOLUME.

no_profil_sp1 depth_horizon residWater sosm_w30e_sp2 sosm_w30s_sp2 sosm_w70r_sp2 sosm_volume_sp2
BE-Lcr/SP-I_01 5-15 cm 0.0110039 NA 0 0 458.9366
BE-Lcr/SP-I_18 15-30 cm 0.0104464 NA 0 0 688.4049

The missing values of \(BD^l_{ik}\) are imputed using the layer-specific averages.

The following graph shows the \(BD^l_{ik}\) frequency distribution across soil layers, following imputation of missing values using the layer-specific averages.

Coarse organic fragments

As coarse organic fragments is mostly composed by root fragments, this variable is named \(RD^l_{ik}\), that denotes root density.

\[ RD^l_{ik} = \frac{SOSM\_W70\_R^{l}_{ik}}{SOSM\_VOLUME^{l}_{ik}} \]

In order to check the data quality of \(RD^l_{ik}\), missing values are tracked in the following table.

If the table is not empty, it means that one of the following properties contains missing values: SOSM_W30_E, SOSM_W105S (or SOSM_W30_S in previous versions), SOSM_W70_R, or SOSM_VOLUME.

no_profil_sp1 depth_horizon residWater sosm_w30e_sp2 sosm_w30s_sp2 sosm_w70r_sp2 sosm_volume_sp2

The missing values of \(RD^l_{ik}\) are imputed using the layer-specific averages.

The following graph shows the \(RD^l_{ik}\) frequency distribution across soil layers, following imputation of missing values using the layer-specific averages.

Fine soil stock for mineral layers

Fine soil stock (FSS) is equivalent to mass of fine earth (FE), as commonly defined in scientific literature.

\[ FSS^l_{ik} = \frac{ (SOSM\_LD^{l}_{ik} -SOSM\_UD^{l}_{ik}) \times (SOSM\_W30\_E^l_{ik}) \times (1 - RW ^l_ik)}{SOSM\_VOLUME^{l}_{ik}} \]

In order to check the \(FSS^l_{ik}\) quality, missing data are tracked in the following table.

If the table is not empty, it means that either or both SOSM_W30_E or SOSM_VOLUME are missing:

no_profil_sp1 depth_horizon sosm_w30e_sp2 residWater sosm_volume_sp2
BE-Lcr/SP-I_01 5-15 cm NA 0.0110039 458.9366
BE-Lcr/SP-I_18 15-30 cm NA 0.0104464 688.4049

Missing values of \(FSS^l_{ik}\) are imputed using the layer-specific averages.

The following graph shows the fine soil stock frequency \(FSS^l_{ik}\) distribution across soil layers, following imputation of missing values using the layer-specific averages.

Organic material (OM) mass in O layers

This fraction represents the organic material in the O horizon after removing stones and living material.

\[ OM^l_{ik} = \frac{ SOSM\_W30^l_{ik} \times (1 - residWater^l_{ik})}{SOSM\_AREA^l_{ik}} \]

Coarse mineral elements (rock fragments)

\[ RF^l_{ik} = \frac { SOSM\_W105\_S^l_{ik}}{ SOSM\_W105\_S^l_{ik} + SOSM\_W30\_E^l_{ik}} \]

Warning: In the version 20200319 of the “Instruction for SOIL SAMPLING AND PREPARATION”, the drying temperature of coarse elements was set to 105°C instead of 30 °C. Therefore, the variable name of rock fragments changed from SOSM_W30_S to SOSM_W105_Sin the BADM.

In order to check the \(RF^l_{ik}\) quality, missing data are tracked in the following table.

MinOrg depth_horizon no_profil_sp1 no_profil_sp2 sosm_w30e_sp2 sosm_w30s_sp2 RF
Mineral 5-15 cm BE-Lcr/SP-I_01 BE-Lcr/SP-II_01-04 NA 0 NA
Mineral 15-30 cm BE-Lcr/SP-I_18 BE-Lcr/SP-II_18-01 NA 0 NA

The following graph shows the \(RF^l_{ik}\) distribution across soil layers, following imputation of missing values using the layer-specific averages.

Compute the SP-I data

Here we compute the stock \(z_{i}^l\) for the monitored properties using the formula described in the protocol. The data are aggregated at SP-I level.

We now check if all the observations and layers are present.

Specifically, we compute:

  • The number of SP-I plots in \(BE-Lcr\) is \(20\).
  • The number of available SP-I plots should total 20.
Stratum ID nb. of SP-I
1 2
2 2
3 2
4 2
5 2
6 2
7 2
8 2
9 2
10 2
  • The number of SP-I plots per stratum should total 2.
sp1 nb. of soil layer
SP-I_01 5
SP-I_02 5
SP-I_03 5
SP-I_04 5
SP-I_05 5
SP-I_06 5
SP-I_07 5
SP-I_08 5
SP-I_09 5
SP-I_10 5
SP-I_11 5
SP-I_12 5
SP-I_13 5
SP-I_14 5
SP-I_15 5
SP-I_16 5
SP-I_17 5
SP-I_18 5
SP-I_19 5
SP-I_20 5
  • The number of layers per SP-I plot, which should total between 5 (only mineral) or 7 (mineral + organic)

  • The number of observations is \(100\).

Profile plot of the data

Using the package aqp, it is possible to plot the collection of profiles.

Horizon-level attributes can be symbolized with color.

Note to PI: If the profile plot present some anomaly (e.g; incomplete depth limits), please contact ETC-Soil for verification.

We plot here the bulk density

We plot here the fine soil stock

We plot here the organic carbon content

We plot here the soil inorganic carbon content

We plot here the total nitrogen content

We plot here the organic carbon stocks

We plot here the soil inorganic carbon stocks

And finally we plot here the total nitrogen stocks

Plot of the relation C/N

Some Maps to visualize the raw data at SP-I site

In this section, we provide a set of maps per soil layer for the different monitored parameters.

The dots represent the value measured on the SP-I plots.

Bulk density

The map shows the bulk density per layer.

Rock fragments

The map shows the rock fragments per layer.