Resumen:
The role of soil in the global carbon cycle and carbon-climate feedback mechanisms have become important topics of research in recent decades. Consequently, the development of simple, rapid, and inexpensive methods to support the study of carbon dynamics in the soil is of great interest. Near-infrared (NIR) spectroscopy has emerged as a rapid and cost-effective method for measuring soil properties. The aim of this study is to develop and validate a predictive model for 13C value using NIR spectroscopy (NIRS) in a wide variety of soil profiles. Eleven sites were selected within a transect in Chile between 30° and 50° S. These sites represent different soil moisture and soil temperature regimes, clay mineralogy, parent materials, and climate; have prairie vegetation conditions; and contain C3-type vegetation. Airdried soil samples were scanned in the NIR at a resolution of 4 cm-1, and the carbon isotopic composition, expressed as 13C relative to the Vienna Pee Dee Belemnite (VPDB) standard, was analyzed via an elemental analyzer-isotope ratio mass spectrometer (EA-IRMS) system. A prediction model for δ13C values based on NIRS data was established trough a partial least-squares regression model, using nine latent variables. The R2 value for the validation set was 0.79, and the root mean square error prediction was 1.16‰. These parameters of the model performance indicate that NIRS can be used to predict 13C for the selected dataset. The established prediction model was also applied to estimate δ13C values in soil profiles from ten additional sites distributed along the transect that were not used in the calibration and validation of the model. The results of this study support the use of NIRS as a predictive tool in soil analysis and as a nondestructive and waste-free methodology for the study of carbon dynamics in soil...
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