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

• Published in: Plant and soil Vol. 421; no. 1/2; pp. 259 - 272
• Main Authors: Winowiecki, Leigh Ann, Vågen, Tor-Gunnar, Boeckx, Pascal, Dungait, Jennifer A. J.
• Format: Journal Article
• Language: English
• Published: Cham Springer 01.12.2017; Springer International Publishing; Springer Nature B.V; Springer Nature
• Subjects: Agricultural land; Assessments; Biomedical and Life Sciences; Canopy gaps; Carbon; Carbon isotopes; cropland; Eastern Africa; Ecology; Farming systems; Food chains; Food processing; Food webs; Forest management; Grasslands; I.R. radiation; Infrared spectra; Infrared spectroscopy; Isotopes; Land degradation; Landscape; Life Sciences; Mathematical models; Measurement; model validation; monitoring; Near infrared radiation; Near-infrared spectroscopy; Organic carbon; Plant Physiology; Plant Sciences; prediction; Prediction models; Regression analysis; Regular; Regular Article; shrublands; Small farms; small-scale farming; Soil carbon; Soil classification; Soil dynamics; soil sampling; Soil Science & Conservation; Soils; Spectrum analysis; stable isotopes; Subsoils; Topsoil; tracer techniques; Tracers; Vegetation; Woodlands; Africa; East Africa; Eastern Africa; Landscape scale assessments; Random forest modeling; Carbon cycling; Random forest modelling
• ISSN: 0032-079X; 1573-5036
• DOI: 10.1007/s11104-017-3418-3

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.