Soil Testing
Soil Carbon Content Analysis
Field Research Part Two
Introduction
This project aims to acquire expertise in collecting and performing calculations necessary for creating a soil profile and estimating the carbon content sequestered using a standardized method that accounts for the range of soil conditions. This report will utilize data obtained from a soil core sampler, which offers a standardized volume of soil that can be analyzed for carbon content percentage to determine bulk density. The soil samples in this report as collected from the top 20cm of the soil; assuming this sample was collected from a relatively standard soil sample that has not experienced significant erosion, it likely contains samples from the O, A, and B horizons. These three horizons offer valuable insights into soil health, as they contain the humus and living biomass necessary for decomposing and recycling organic residues, thereby maintaining a thriving ecosystem.
Soil Profiles
Typical soil profiles consist of several layers with varying thicknesses, which can be observed using a profile sampler. The first layer encountered is the Organic horizon, usually relatively thin and composed of recently deposited organic residues in the initial stages of decomposition. Plant debris, animal droppings, leaf litter, and living microorganisms vital for breaking down organic matter create a nutrient-rich and dense layer of humus. This layer is crucial for soil fertility, water retention, and erosion prevention.
Beneath the O horizon lies the topsoil or A Horizon, which comprises a blend of organic components and inorganic minerals weathered from below. These elements are essential for supporting sustainable root systems. Although the A horizon is lighter than the O horizon, it exhibits a darker coloration than the lower soil layers due to the presence of organic components filtered through the O horizon.
Subsequently, the B horizon is characterized by the accumulation of finer particles such as clay and sand and minerals, and humus transported downward by subsurface drainage. This layer is influenced by the composition of the Parent material underlying the soil profile, which can vary based on geological features.
Finally, the C horizon, or soil base, represents the last layer before bedrock and is the least weathered. However, it determines many of the chemical and physical properties of the overlying soil layers.
Soil Organic Cabon
Soil carbon stock is a important derived variable for understanding the effective capacity for growth that the soil can support. Soil potentially serves as a considerable sink for carbon, with potentially as much as 2/3 of a forests carbon being stored in the soil organic matter (SOM) Mondragón, Hurtado, and Jaramillo Jaramillo (2022) . Several factors, physical, chemical and biological, play a role in the concentration of carbon present in soil; porosity, water holding capacity, toxins, microorganism activity, root permeation are directly impacted by variation in soil organic carbon.
Some of the factors that are responsible for variation in soil organic carbon include vegetation, land use, topography, elevation, and climate.
Materials and Methods
For the field study portion of this report, the SNHU arboretum was utilized to observe a soil profile in person. A soil profile sampler was used to sample two plots selected on parameters such as distance from a water source, elevation, and proximity to a previously measured tree stand. The area of the Arboretum used for this field study is located approximately 100 feet from the parking lot, nearby to a significant amount of foot traffic. The depth of the O Horizon for the first plot taken within 10 feet from the walking path was measured to be 3 inches. The second plot measured was situated roughly 75 feet from the initial point, further from pedestrian areas but within 25 feet of a research monitoring station, which suggests come additional foot traffic. The depth of the O horizon measured at this location with the soil profile sampler with found to be 2.5 inches.
Results
Inputting the data
Code
carbon_concentration <- c(5.4,6.1,4.9)
soil_dry_mass <- c(310,250,290)
sample_depth <- 20
radius <- 2.5
# Create a data frame with the data
measurements <- data.frame(
"Carbon Concentration (%)" = carbon_concentration,
"Soil Dry Mass (g) "= soil_dry_mass,
"Sample Depth (cm)" = rep(sample_depth, length(carbon_concentration)),
"Radius (cm)" = rep(radius, length(carbon_concentration))
)
# Transpose the data frame
transposed_data_frame <- as.data.frame(t(measurements))
# Reset row names and add a new 'Variable' column
transposed_data_frame <- transposed_data_frame %>%
rownames_to_column("Measurements") %>%
rename_all(list(~gsub("V", "Location ", .)))
# Print the transposed data frame
transposed_data_frame %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = c("striped", "hover"))| Measurements | Location 1 | Location 2 | Location 3 |
|---|---|---|---|
| Carbon.Concentration.... | 5.4 | 6.1 | 4.9 |
| Soil.Dry.Mass..g.. | 310.0 | 250.0 | 290.0 |
| Sample.Depth..cm. | 20.0 | 20.0 | 20.0 |
| Radius..cm. | 2.5 | 2.5 | 2.5 |
Calculating Bulk Density
Code
# Providing the variables
carbon_concentration <- c(5.4,6.1,4.9)
soil_dry_mass <- c(310,250,290)
sample_depth <- 20
radius <- 2.5
#Calculating Volume of ring/sample
sample_volume <- pi * (radius^2) * sample_depth
# Calculating the bulk density for each sample
bulk_density <- soil_dry_mass / sample_volume
bulk_density_df <- data.frame(bulk_density)
# Rename the column to the desired name, for example: "New_Column_Name"
bulk_density_df <- bulk_density_df %>%
mutate("Bulk Density" = bulk_density) %>%
select(-bulk_density)
bulk_density_df$Location <- c(" 1", " 2", "3")
# Reorder the columns so that "Location" is the first column
bulk_density_df <- bulk_density_df[, c("Location", "Bulk Density")]
# Display the table with the new column name
bulk_density_df %>%
kable(digits = 2) %>%
kable_styling(bootstrap_options = c("striped", "hover"))| Location | Bulk Density |
|---|---|
| 1 | 0.79 |
| 2 | 0.64 |
| 3 | 0.74 |
Calculating Soil Carbon Content
Code
# Create the new variable using the provided equation
soc <- carbon_concentration * bulk_density * sample_depth
soc_df <- data.frame(soc)
# Rename the column to the desired name
soc_df <- soc_df %>%
mutate("Soil Carbon Content" = soc) %>%
select(-soc)
# Create a data frame with SOC
soc_df <- data.frame(SOC = soc)
# Add the Location column
soc_df$Location <- c(" 1", " 2", " 3")
# Reorder the columns so that "Location" is the first column
soc_df <- soc_df[, c("Location", "SOC")]
# Rename the column to the desired name
## tC/h = tons of carbon per hectare
soc_df <- soc_df %>%
mutate(`Soil Carbon Content (tC/h)` = SOC) %>%
select(-SOC)
# Display the table using kable
soc_df %>%
kable(digits = 1) %>%
kable_styling(bootstrap_options = c("striped", "hover"))| Location | Soil Carbon Content (tC/h) |
|---|---|
| 1 | 85.3 |
| 2 | 77.7 |
| 3 | 72.4 |
Summary
This report presents the results of an investigation comparing soil carbon content in three different locations. Carbon concentration and dry soil mass were measured at each location to determine the bulk density and soil carbon content (tC/h). For Location 1, the carbon concentration was 5.4%, and the dry soil mass was 310.0 g. The sample depth and radius were consistent across all locations, with a depth of 20.0 cm and a radius of 2.5 cm. The calculated bulk density for Location 1 was 0.79 g/cm³, and carbon content was 85.3 tC/h.
Location 2 exhibited a slightly higher carbon concentration of 6.1%, but a lower soil dry mass of 250.0 g. The bulk density at this location two was 0.64 g/cm³, and soil carbon content was 77.7 tC/h. Finally, at Location 3, the carbon concentration was 4.9%, and the dry soil mass was 290.0 g. The bulk density was 0.74 g/cm³, and the soil carbon content was found to be 72.4 tC/h.
In summary, the results indicate that Location 1 had the highest soil carbon content (85.3 tC/h), followed by Location 2 (77.7 tC/h) and Location 3 (72.4 tC/h). Location 2 had the highest carbon concentration (6.1%), but its lower soil dry mass and bulk density contributed to a lower overall soil carbon content than Location 1.