Calibration Spiking of MIR-DRIFTS Soil Spectra for Carbon Predictions Using PLSR Extensions and Log-Ratio Transformations

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027006%3A_____%2F22%3A10175134" target="_blank" >RIV/00027006:_____/22:10175134 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2077-0472/12/5/682/pdf?version=1652256636" target="_blank" >https://www.mdpi.com/2077-0472/12/5/682/pdf?version=1652256636</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/agriculture12050682" target="_blank" >10.3390/agriculture12050682</a>

Result language

angličtina

Original language name

Calibration Spiking of MIR-DRIFTS Soil Spectra for Carbon Predictions Using PLSR Extensions and Log-Ratio Transformations

Original language description

There is a need to minimize the usage of traditional laboratory reference methods in favor of spectroscopy for routine soil carbon monitoring, with potential cost savings existing especially for labile pools. Mid-infrared spectroscopy has been associated with accurate soil carbon predictions, but the method has not been researched extensively in connection to C lability. More studies are also needed on reducing the numbers of samples and on how to account for the compositional nature of C pools. This study compares performance of two classes of partial least squares regression models to predict soil carbon in a global (models trained to data from a spectral library), local (models trained to data from a target area), and calibration-spiking (spectral library augmented with target-area spectra) scheme. Topsoil samples were+ scanned with a Fourier-transform infrared spectrometer, total and hot-water extractable carbon determined, and isometric log-ratio coordinates derived from the latter measurements. The best RMSEP was estimated as 0.38 and 0.23 percentage points TC for the district and field scale, respectively-values sufficiently low to make only qualitative predictions according to the RPD and RPIQ criteria. Models estimating soil carbon lability performed unsatisfactorily, presumably due to low labile pool concentration. Traditional weighing of spiking samples by including multiple copies thereof in training data yielded better results than canonical partial least squares regression modeling with embedded weighing. Although local modeling was associated with the most accurate predictions, calibration spiking addressed better the trade-off between data acquisition costs and model quality. Calibration spiking with compositional data analysis is, therefore, recommended for routine monitoring.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

40106 - Agronomy, plant breeding and plant protection; (Agricultural biotechnology to be 4.4)

Project

<a href="/en/project/QK21010124" target="_blank" >QK21010124: Soil organic matter - evaluating of quality parameters</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Agriculture-Basel

ISSN

2077-0472

e-ISSN

—

Volume of the periodical

12

Issue of the periodical within the volume

5

Country of publishing house

CH - SWITZERLAND

Number of pages

26

Pages from-to

682

UT code for WoS article

000801860800001

EID of the result in the Scopus database

2-s2.0-85130374822