Monitoring Land Management Practices Using Vis–NIR Spectroscopy Provides Insights into Predicting Soil Organic Carbon and Limestone Levels in Agricultural Plots

• Published in: Agronomy (Basel) Vol. 14; no. 6; p. 1150
• Main Authors: Herranz-Luque, Juan E., Gonzalez-Canales, Javier, Martín-Sanz, Juan P., Antón, Omar, Moreno-Delafuente, Ana, Navas-Vázquez, Mariela J., Ramos-Nieto, Rubén, Bienes, Ramón, García-Díaz, Andrés, Marques, Maria Jose, Sastre, Blanca
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2024
• Subjects: Agricultural industry; Agricultural land; Agriculture; Analysis; Calcium carbonate; Carbon; Carbon content; Chemical properties; Crop management; Farm management; Infrared spectroscopy; Land degradation; Land management; Land use planning; Limestone; Methods; Organic carbon; Organic soils; PARLES; Particle size; Plagioclase; Prediction models; reflectance spectra; Remote sensing; Soil chemistry; Soil erosion; soil organic carbon; Soil properties; Soil sampling; Soil sciences; Soils; Spectrum analysis; Sustainable agriculture; Vis–NIR; Spain
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy14061150

This study aimed to establish sound relationships between soil properties of agricultural land in central Spain and their spectral attributes to contribute to deriving an indicator for sustainable farm management. Sixteen plots, managed under various conditions, eight with traditional tillage and eight with other alternative managements, were selected to gather soil samples representing three predominant soil orders (Leptosols, Cambisols, and Luvisols). Soil sampling was conducted from depths ranging from 0 to 30 cm (0–50; 5–10, 10–20, and 20–30 cm), ensuring a broad spectrum of sample variability across different times and locations. The reflectance of soil 64 soil samples was measured within the range of 400 to 900 nm, and the corresponding concentrations of soil organic carbon and majoritarian minerals, calcium carbonate, quartz, phyllosilicates, K-Feldspar, and plagioclase were determined for each sample. Partial least squares analysis was employed to construct prediction models using a calibration dataset comprising 66% of randomly selected samples. The remaining 33% of samples were utilized for model validation. The prediction models for the measured soil chemical properties yielded R2 values ranging from 0.14 to 0.79. Only SOC and limestone provided accurate prediction models. These findings hold promise for developing a soil health indicator tailored for site-specific crop management. However, the complex composition of soil organic carbon and calcium carbonate in certain soils underscores the importance of careful interpretation and validation of remote sensing data, as well as the need for advanced modeling approaches that can account for the interactions between multiple soil constituents.