Diffuse reflectance spectroscopy for estimating soil organic carbon and make nitrogen recommendations

Diffuse reflectance spectroscopy (DRS) has the potential to predict soil organic carbon (SOC). However, it is still little used as a matter of routine in soil laboratories in Brazil. The objective of this study was to make evaluations as to whether SOC predicted by spectral techniques can replace measurement by routine chemical methods with no loss in quality and be applied in the recommendation of nitrogen fertilizer as well as identifying the best prediction strategies to use. A data set containing 2,471 samples from six soil spectral libraries (SSL) was used to develop spectroscopic models for SOC content prediction, including consideration of sample stratification and preprocessing techniques. The SOC was quantified through the analytical-chemical methods of wet combustion with determination by titration, designated as the reference method (REM), and colorimeter, designated as the routine method (ROM in an independent data set). SOC contents predicted by the spectral analysis method (SAM) were compared to the REM and ROM results, converted to soil organic matter (SOM) and used for N recommendations. The best estimate for SOM content using the SAM was achieved through stratification of the SSL and application of the standard normal variate (SNV) preprocessing. The SOC predicted by spectral techniques proved capable of replacing the SOC measured by routine chemical methods with no loss of quality and supported by an appropriate nitrogen fertilizer recommendation, provided the models met the conditions and possessed the characteristics of the samples to be analyzed.

Automated summary

This study aimed to evaluate the potential of using spectral techniques to predict soil organic carbon (SOC) and replacing routine chemical methods for SOC measurement without loss of quality, as well as applying it in nitrogen fertilizer recommendation. By developing spectroscopic models for SOC content prediction using a large dataset from soil spectral libraries, it was found that SOC predicted by spectral techniques could effectively replace SOC measured by routine chemical methods and support nitrogen fertilizer recommendation when the conditions and sample characteristics are met.