Academic Journal
Quantifying soil properties relevant to soil organic carbon biogeochemical cycles by infrared spectroscopy: The importance of compositional data analysis
| العنوان: | Quantifying soil properties relevant to soil organic carbon biogeochemical cycles by infrared spectroscopy: The importance of compositional data analysis |
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| المؤلفون: | Zhao, P., Fallu, D.J., Pears, B.R., Allonsius, C., Lembrechts, J.J., Van de Vondel, S., Meysman, F.J.R., Cucchiaro, S., Tarolli, P., Shi, Pu, Six, J., Brown, A.G., van Wesemael, Bas, Van Oost, Kristof |
| المساهمون: | UCL - SST/ELI/ELIC - Earth & Climate |
| المصدر: | Soil & Tillage Research, Vol. 231, p. 14 (2023) |
| بيانات النشر: | Elsevier BV |
| سنة النشر: | 2023 |
| المجموعة: | DIAL@USL-B (Université Saint-Louis, Bruxelles) |
| الوصف: | Oxyhydroxides, soil texture and soil organic carbon (SOC) fractions are key parameters determining organic carbon cycling in soils. Standard laboratory methods to determine these soil properties are, however, time–consuming and expensive. Visible near infrared (Vis–NIR) and mid infrared (MIR) spectroscopy have been recognized as a promising alternative, but previous studies have not explicitly considered the above–mentioned soil properties as compositional data. The fractional components in compositional data are interrelated but their sum should be unity – these features should be represented in the spectral modeling process to minimize the prediction bias. In this study, two unique datasets were used to test these premises. The first one consisted of 655 samples collected from agricultural terraces and lynchets across Europe, which were scanned to acquire MIR spectra, while in the second one 4516 samples from private gardens across Flanders, Belgium were used to acquire Vis–NIR spectra. Memory–based learning models were optimized using both raw data (conventional method) and transformed data of soil properties by additive log–ratio (alr), centered log–ratio (clr), and isometric log–ratio (ilr) transformation methods. Results showed that the log–ratio transformation methods produced predictions as accurate as the conventional method, whilst also added two significant benefits: (1) they ensured the predicted fractions added up to 100% and (2) they reduced the number of samples with extreme prediction errors. We found that for 11 out of 18 investigated soil properties, the three log–ratio transformation methods provided similar model performance, whilst ilr outperformed clr for the prediction of silt and sand content of garden soils, for coarse particulate SOC (>250 µm) and microaggregate–associated SOC (250–53 µm) of terrace soils. For the remaining three properties (Al oxyhydroxides) alr outperformed ilr. Fair to excellent predictive models (RPD from 1.4 to 4.3; R2 from 0.50 to ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 0167-1987 |
| Relation: | boreal:283062; http://hdl.handle.net/2078.1/283062; urn:ISSN:0167-1987 |
| DOI: | 10.1016/j.still.2023.105718 |
| الاتاحة: | http://hdl.handle.net/2078.1/283062 https://doi.org/10.1016/j.still.2023.105718 |
| Rights: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.D5AD489B |
| قاعدة البيانات: | BASE |
| تدمد: | 01671987 |
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| DOI: | 10.1016/j.still.2023.105718 |