SC.50 Dataset

Description

  • Target Soil Properties: SOC, pH, Clay

  • Groups of Features: DEM, ERa

  • Sample size: 50

  • Number of Features: 3

  • Coordinates: With coordinates (EPSG: 32722)

  • Location: Santa Catarina, Brazil

  • Sampling Design: Regular grid sampling

  • Study Area Size: 13 ha

  • Geological Setting: Heavily weathered soils originating from Mesozoic basalt rocks

  • Previous Data Publication: Full dataset published in Bottega & Safanelli (2024)

  • Contact Information:

  • License: CC BY-SA 4.0

  • Publication/Modification Date (d/m/y): 28.02.25, version 1.0

  • Changelog:

    • Version 1.0 (28.02.25): Initial release

Details

Dataset

The SC.50 dataset contains soil measurements and features organized in a dataframe with the following properties:

Target Soil Properties:

SOC - Soil Organic Carbon

  • Code: SOC_target

  • Unit: %

  • Protocol: Measured through titration after oxidization of the organic carbon (Walkley & Black 1934)

  • Sampling Date: November 2013

  • Sampling Depth: 0 – 20 cm

pH

  • Code: pH_target

  • Unit: Unitless

  • Protocol: Measured in water suspension with a glass electrode with a 5:1 liquid:soil volumetric ratio

  • Sampling Date: November 2013

  • Sampling Depth: 0 – 20 cm

Clay

  • Code: Clay_target

  • Unit: %

  • Protocol: Sieve-Pipette method, measured through fractioning the soil into the sand fractions by sieving, and the silt and clay fractions by sedimentation in water, German adaptation (DIN ISO 11277)

  • Sampling Date: November 2013

  • Sampling Depth: 0 – 20 cm

Groups of Features:

DEM – Digital Elevation Model and Terrain Parameters

  • Number of Features: 2

  • Code(s): Altitude, Slope

  • Unit: Altitude in m, Slope in °

  • Sensing: Digital elevation model raster (30 m) based on synthetic aperture radar from “Copernicus Open Access Hub”

  • Processing: Calculating Slope with terrain function of the raster R-package, extracting DEM values from raster at soil sampling locations

  • Sampling Date: October 2011

ERa – Apparent Electrical Resistivity

  • Number of Features: 1

  • Code(s): ERa

  • Unit: Ω m

  • Sensing: LandMapper ERM-02 conductivity meter (Landviser, League City, USA) with exploration depth of 0 - 20 cm, in-situ

  • Processing: None

  • Sampling Date: November 2014

Examples

from LimeSoDa import load_dataset, split_dataset
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score, mean_squared_error
import numpy as np

# Load and explore the dataset
data = load_dataset("SC.50")
dataset = data["Dataset"]
folds = data["Folds"]
coords = data["Coordinates"]

# Split into train/test using fold 1
X_train, X_test, y_train, y_test = split_dataset(
    data=data,
    fold=1,
    targets=["pH_target", "SOC_target", "Clay_target"]
)

# Fit model and get predictions
model = LinearRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)

# Calculate performance metrics
r2 = r2_score(y_test, predictions)
rmse = np.sqrt(mean_squared_error(y_test, predictions))
print(f"R-squared: {r2:.7f}")
print(f"RMSE: {rmse:.7f}")

References

Bottega, E. L. & Safanelli J. L. (2024). Data for “Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil”. Zenodo repository. https://doi.org/10.5281/zenodo.13770031

Walkley, A. & Black, I. A. (1934). An examination of the Degtjareff method for determining soil organic matter, and a proposed modification of the chromic acid titration method. Soil science, 37(1), 29-38.