Rock fragments

Warning The name of the attribute in the database has changed in 2020. \(`sosmW30s`\) to \(`sosmW105s`\)

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, it is computed using the weight of the coarse elements dried at 105°C:

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

For the available sites, only \(`sosmW30s`\) is populated

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. This files came from a sql query.

Then, I extract the data for the selected site

## [1] 382  48

In the protocol, the layer depth are defined as follow for mineral layer :

• Layer 1 : 0-5 cm ,
• Layer 2 : 5-15 cm ,
• Layer 3 : 15-30 cm ,
• Layer 4 : 30-60 cm ,
• Layer 5 : 60-100 cm.

For the organic layer, it is named O#.

In this file, the PI implemented the following depth:

Extract the Coordinates

We use the kmz file that is produced by ETC and contains 7 layers to extract the coordinates of the SPI sites that are taken from layer 5 and reproduced below. For this, it is needed to extract the kml file from the kmz using windows.

Compute residual water content

The first step is to compute the residual water content \(residWater\) for as follow :

for mineral layers:

\(residWater = \frac{sosmWx30e_{sp1}-sosmWx105e_{sp1}}{sosmWx30e_{sp1}}\)

for organic layers:

\(sosmW30\) is considered as a dry mass because residual water content is assumed to be negligible.

Here is the histogram of the residual water content data for all samples :

Histogram of Residual Water

In order to check the data quality, missing and negative residual water content are listed in the two tables below.

If the table below is not empty, it means that sosm_wx30E < sosm_wx105E :

If the table below is not empty, it means that either or both sosm_wx30e or sosm_wx105e are missing:

To fix this issue, we replace \(`NA`\) and negative values by the mean of residual humidity per layer (the effect on the soil stock calculation is supposed to be small)

Compute the Bulk density and Fine soil stock for SP2 plots

In a second step, we compute the Bulk density and fine soil stock for the sp2 plots the BD.

Compute rock fragments fraction for SP2 plots

Third step consists in checking the quality of the rock fragments fraction data.

Warning The name of the attribute in the database has changed in 2020. \(`sosmW30s`\) to \(`sosmW105s`\)

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, it is computed using the weight of the coarse elements dried at 105°C:

\[RF_{105} = \frac{sosmW105s_{sp2}} {sosmW105s_{sp2}+sosmW30e_{sp2} }\] for organic layer, it can be computed as follow:

\[RF_{105} = \frac{sosmW105s_{sp2}} {sosmW105s_{sp2}+sosmW30s_{sp2} }\]

The following graph shows the histogram of the rock weights and rock fraction among all SP2.

Raw Rock fragments and Weight of the coarse elements dried for SP2 plots

Now, we can compute the \(RF_{30}\) for the sp2 sites.

We check if some data are missing.

We estimate the missing \(RF\) using \(\overline{RF}\) per layer listed in the following table if missing values are present.

The following graph shows the histogram of the rock frament for each soil layer after replacing missing values by averages.

Compute the stock of soil inorganic carbon (SIC)

Soil inorganic carbon (SIC; g.kg^(-1)) was calculated as the product of the result of the calcimetry analysis (CaCO_3 ; g.kg-1 equivalent) multiplied by 0.12.

Compute the id of the strata

To compute the design-based estimates of the soil properties, I have to first guess the stratum id. I assume here 2 sites per stratum and that site are ordered in the file by stratum.

We may now check if all the observations are there. First, we can compute the number of available SPI sites.

Compute the SPI data

Here we compute the stock \(z_{i}^l\) for the monitored properties using the formula described in the protocol.

This number of SPI sites is \(153\). The maximim should be 100.

Here I compute the number of data per stratum x layer. We should have 10 times the 10 observations as we have 5 layers and 2 sI1 per stratum and, 10 Strata

## # A tibble: 10 × 3
##    idstratum     n nprof
##        <int> <int> <int>
##  1         1    18    10
##  2         2    20    10
##  3         3    17    10
##  4         4    17    10
##  5         5    17    10
##  6         6    14     8
##  7         7    18    10
##  8         8    10    10
##  9         9    10    10
## 10        10    12     9

The number of SP I is 18`

Bulk density

The map shows the bulk density per layer.

Fine soil stock

The map shows the Fine soil stock per layer.

Total N content

The map shows the content of nitrogen per layer.

Total organic carbon

The map shows the content of carbon per layer.

C to N ratio

The map shows the C to N ratio per layer.

Rock fragments

The map shows the rock fragments per layer.

Organic carbon Stock

The map shows the carbon stock per layer.

Inorganic Carbon Stock

The map shows the carbon stock per layer.

Nitrogen Stock

The map shows the nitrogen stock per layer.

Mean content per layer

We may now compute the estimate of the means of the diffrent parameters per layer (BD, FSS, RF, C , N, Stock C and Stock N)

\[\hat{ \overline{z^l} }\]

and its sampling variance \[\hat V (\hat{ \overline{z^l} } )\]

Organic carbon content

The estimates for the mean of the carbon content with its 95 % confidence interval are given below.

DB estimates of the mean and its CI for the C content per layer

Inorganic carbon content

The estimates for the mean of the carbon content with its 95 % confidence interval are given below.

DB estimates of the mean and its CI for the C content per layer

Nitrogen content

The estimates for the nitrogen content with its 95 % confidence interval are given below.

DB estimates of the mean and its CI for the N content per layer

Mean of the stock per layer

We may now compute the estimates of the mean C and N stock per layer

Mean of the total Stock for Carbon and Nitrogen

To compute the sum of the stock \(\hat{ \overline{t}}\), we first sum the sum of the stock over the different layers for each spI and then estimate the total stock.

Total Stock

To compute the total stock, we need the total area of the plot extrated from the kml files

For this site, the area is \(1.5642\times 10^{5}\) m^2

SP I raw and elaborated data

It is possible to export here the raw C and N data and the averages of FSS and rock fragments data. The computed stock are also provided

You may use the available formats.

The variable names and units are as follows:

DB estimate of the sampling means per layer and standard deviations

You may use the available formats.

DB estimate of the sampling means for the whole soil and standard deviations

That’s all !