Using near-infrared spectroscopy as a cost-effective method to characterise soil and leaf properties in native forest - Álvarez, V.E.; Arias-Rios, J.A.; Guidalevich, V.; Marchelli, P.; Tittonell, P.A.; El Mujtar, V.A. (Geoderma Regional, 2025)
Forests conservation and sustainable management of forests require an understanding of ecological traits that influence carbon and nutrient turnover in forest ecosystems. This study evaluates the potential of Near Infrared Spectroscopy (NIRS) as a rapid, non-destructive and cost-effective tool for characterising soil and trees in natural forests and forest-frontier ecosystems. Soil samples were collected at four depths from three land uses (native forest, grazed grassland, and horticultural land), while leaf samples were obtained from two provenances of Nothofagus alpina. Spectra were used to classify samples, predict biological and chemical properties, estimate relatedness matrices for both soils and leaves and compared them with those obtained from genetic data. Principal component analysis separated soil samples from different land uses and depths as well as leaf samples from the two provenances. NIRS-based models showed high predictive accuracy for soil microbial biomass, biological activity and total carbon (R2 = 0.80, 0.94, and 0.86, respectively), although leaf pigment estimation was less reliable (R2 = 0.60–0.40). Correlations between genetic and NIRS relatedness matrices were low, highlighting that both methodologies are relevant for sample characterisation. These findings demonstrate that NIRS is a useful method for assessing soil ecological traits associated with nutrient cycling offering a practical and cost-efficient alternative for ecological monitoring in forest ecosystems. However, further methodological improvements are needed to enhance its accuracy, particularly for leaf traits characterisation. This study highlights the broader potential of NIRS for large-scale forest management, conservation strategies, and ecological research. •NIRS has proven effective at both supervised and unsupervised evaluations.•NIRS is effective for classifying soil and tree samples.•NIRS is effective for modelling soil biological activity and microbial biomass.