Landscape-scale assessments of stable carbon isotopes in soil under diverse vegetation classes in East Africa: application of near-infrared spectroscopy.

AIMS

Stable carbon isotopes are important tracers used to understand ecological food web processes and vegetation shifts over time. However, gaps exist in understanding soil and plant processes that influence δC values, particularly across smallholder farming systems in sub-Saharan Africa. This study aimed to develop predictive models for δC values in soil using near infrared spectroscopy (NIRS) to increase overall sample size. In addition, this study aimed to assess the δC values between five vegetation classes.

METHODS

The Land Degradation Surveillance Framework (LDSF) was used to collect a stratified random set of soil samples and to classify vegetation. A total of 154 topsoil and 186 subsoil samples were collected and analyzed using NIRS, organic carbon (OC) and stable carbon isotopes.

RESULTS

Forested plots had the most negative average δC values, -26.1‰; followed by woodland, -21.9‰; cropland, -19.0‰; shrubland, -16.5‰; and grassland, -13.9‰. Prediction models were developed for δC using partial least squares (PLS) regression and random forest (RF) models. Model performance was acceptable and similar with both models. The root mean square error of prediction (RMSEP) values for the three independent validation runs for δC using PLS ranged from 1.91 to 2.03 compared to 1.52 to 1.98 using RF.

CONCLUSIONS

This model performance indicates that NIR can be used to predict δC in soil, which will allow for landscape-scale assessments to better understand carbon dynamics.