The DOI matches its Crossref record.
The prediction of soil carbon fractions using mid-infrared-partial least square analysis
Abstract
This paper describes the application of mid-infrared (MIR) spectroscopy and partial least-squares (PLS) analysis to predict the concentration of organic carbon fractions present in soil. The PLS calibrations were derived from a standard set of soils that had been analysed for total organic carbon (TOC), particulate organic carbon (POC), and charcoal carbon (char-C) using physical and chemical means. PLS calibration models from this standard set of soils allowed the prediction of TOC, POC, and char-C fractions with a coefficient of determination (R2) of measured v. predicted data ranging between 0.97 and 0.73. For the POC fraction, the coefficient of determination could be improved (R2 = 0.94) through the use of local calibration sets. The capacity to estimate soil fractions such as char-C rapidly and inexpensively makes this approach highly attractive for studies where large numbers of analyses are required. Inclusion of a set of soils from Kenya demonstrated the robustness of the method for total organic carbon and charcoal carbon prediction.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Minasny, B., Malone, B. P., McBratney, A. B., Angers, D. A., Arrouays, D., Chambers, A., Chaplot, V., Chen, Z.-S., Cheng, K., Das, B. S., et al. (2017) · Geoderma · doi:10.1016/j.geoderma.2017.01.002 · Cited by 2,239
A review of biochar and its use and function in soil
Sohi, S. P., Krull, E., Lopez-Capel, E., & Bol, R. (2010) · Advances in Agronomy · doi:10.1016/s0065-2113(10)05002-9 · Cited by 2,240
Macdonald, L. M., Herrmann, T., & Baldock, J. A. (2013) · Soil Research · doi:10.1071/sr13156 · Cited by 9
Metadata
- Authors
- Janik, L. J., Skjemstad, J. O., Shepherd, K. D., & Spouncer, L. R.
- Venue
- Soil Research
- Publisher
- CSIRO Publishing
- Published
- 2007-03-28
- Volume
- 45
- Issue
- 2
- Pages
- 73-81
- ISSN
- 0004-9573 (ISSN-L)
- 1838-675X
- 1838-6768
- OpenAlex
- W2001511595
- Cited by
- 298
- References
- 0
- FWCI
- 12.62
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The prediction of soil carbon fractions using mid-infrared-partial least square analysis. https://app.cochat.ai/papers/10.1071/sr06083
- The paper
- Janik, L. J., Skjemstad, J. O., Shepherd, K. D., & Spouncer, L. R. (2007). The prediction of soil carbon fractions using mid-infrared-partial least square analysis. Soil Research. https://doi.org/10.1071/sr06083
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
