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

A - Papers appearing in refereed journals

Winowiecki, L. A., Vagen, T. G., Boeckx, P. and Dungait, J. A. J. 2017. Landscape-scale assessments of stable carbon isotopes in soil under diverse vegetation classes in East Africa: application of near-infrared spectroscopy. Plant and Soil. 421 (1-2), pp. 259-272. https://doi.org/10.1007/s11104-017-3418-3

AuthorsWinowiecki, L. A., Vagen, T. G., Boeckx, P. and Dungait, J. A. J.
Abstract

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

KeywordsCarbon cycling; Landscape scale assessments; Random forest modelling
Year of Publication2017
JournalPlant and Soil
Journal citation421 (1-2), pp. 259-272
Digital Object Identifier (DOI)https://doi.org/10.1007/s11104-017-3418-3
Open accessPublished as green open access
FunderBiotechnology and Biological Sciences Research Council
Bill and Melinda Gates Foundation
Publisher's version
Output statusPublished
Publication dates
Online16 Oct 2017
Publication process dates
Accepted08 Sep 2017
PublisherSpringer Nature
Springer
Copyright licenseCC BY
ISSN0032-079X

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