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 δ13C
values, particularly across smallholder farming systems
in sub-Saharan Africa. This study aimed to develop
predictive models for δ13C values in soil using near
infrared spectroscopy (NIRS) to increase overall sample
size. In addition, this study aimed to assess the δ13C
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
δ13C values, −26.1‰; followed by woodland, −21.9‰;
cropland, −19.0‰; shrubland, −16.5‰; and grassland,
−13.9‰. Prediction models were developed for δ13C
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 δ13C 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 δ13C in soil, which will
allow for landscape-scale assessments to better understand carbon dynamics