NRW.42 Dataset

Description

  • Target Soil Properties: SOC, pH, Clay

  • Groups of Features: MIR

  • Sample size: 42

  • Number of Features: 1,686

  • Coordinates: Without coordinates because of privacy concerns

  • Location: North Rhine-Westphalia, Germany

  • Sampling Design: Regular grid sampling

  • Study Area Size: 1.5 ha

  • Geological Setting: Pleistocene periglacial slope deposits consisting of weathered Devonian sand-, silt-, and claystones, partially covered by Weichselian loess

  • Previous Data Publication: None

  • 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 dataset contains the following target soil properties and features:

Target Soil Properties:

SOC - Soil Organic Carbon

  • Code: SOC_target

  • Unit: %

  • Protocol: Determined by the difference of total carbon and inorganic carbon, where total carbon was obtained through elemental analysis by measuring the CO₂ release during dry combustion (DIN ISO 10694) without acid pretreatment and inorganic carbon as 0.12 x the calcium carbonate content, determined by the gas-volumetric Scheibler Method (ISO 10693)

  • Sampling Date: April 2013

  • Sampling Depth: 0 - 30 cm

pH

  • Code: pH_target

  • Unit: Unitless

  • Protocol: Measured in CaCl₂ suspension with a glass electrode with a 5:1 liquid:soil volumetric ratio (DIN ISO 10390)

  • Sampling Date: April 2013

  • Sampling Depth: 0 - 30 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: April 2013

  • Sampling Depth: 0 - 30 cm

Groups of Features:

MIR – Mid Infrared Spectroscopy

  • Number of Features: 1,686

  • Code(s): wn_3799, wn_3797.1, wn_3795.1 … wn_549.6

  • Unit: % (Reflectance)

  • Sensing: MIR spectrometer (Bruker Tensor 27 HTS-XT, Bruker Optik, Ettlingen, Germany), on dried and sieved samples (<2 mm) in the laboratory, spectral range was 7,500 – 550 cm⁻¹ at 4 cm⁻¹ intervals

  • Processing: Discarding irrelevant spectral data of the spectrum (7,500 - 3,799 cm⁻¹), resampling to ~2 cm⁻¹ intervals

  • Sampling Date: April 2013

  • Spectral Information (After Data Processing):

    • Data Representation: Wavenumber (in cm⁻¹)

    • Spectral Resolution: ~2 cm⁻¹

    • Spectral Range: 3,799 - 549.6 cm⁻¹

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("NRW.42")
dataset = data["Dataset"]
folds = data["Folds"]
coords = data["Coordinates"]  # Will be NA for NRW.42

# 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

Gee, G.W. & Bauder, J.W. (1986) Particle-Size Analysis. In: Klute, A., Ed., Methods of Soil Analysis, Part 1. Physical and Mineralogical Methods, Agronomy Monograph No. 9, 2nd Edition, American Society of Agronomy/Soil Science Society of America, Madison, WI, 383-411.

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.