| Full text | |
| Author(s): |
Briedis, Clever
[1, 2]
;
Baldock, Jeff
[3]
;
de Moraes Sa, Joao Carlos
[1]
;
dos Santos, Josiane Burkner
[4]
;
Bastos Pereira Milori, Debora Marcondes
[2]
Total Authors: 5
|
| Affiliation: | [1] Univ Estadual Ponta Grossa, Dept Soil Sci & Agr Engn, Av Carlos Cavalcanti 4748, BR-84030900 Ponta Grossa, PR - Brazil
[2] Brazilian Agr Res Corp, EMBRAPA Agr Instrumentat Ctr, CNPDIA, Rua 15 Novembro 1452, BR-13560970 Sao Carlos, SP - Brazil
[3] CSIRO Agr & Food, Locked Bag 2, Glen Osmond, SA 5064 - Australia
[4] Agron Inst Parana IAPAR, Av Presidente Kennedy, Polo Reg Ponta Grossa, Av Presidente Kennedy S-N, Ponta Grossa, PR - Brazil
Total Affiliations: 4
|
| Document type: | Journal article |
| Source: | Geoderma; v. 373, AUG 15 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
At present, organic carbon (OC) levels are measured using techniques that can be very expensive and time-consuming or can generate toxic residue. Mid-infrared (MIR) spectroscopy, which is an inexpensive and safe technique, can be used for OC prediction; however, appropriate modeling is required. This study aimed to evaluate different strategies for MIR modeling calibration in order to improve the accuracy and cost-effective-ness of OC prediction in bulk soils and fractions (e.g., particulate OC, POC; humic OC, HOC; resistant OC, ROC). We used regional topsoil samples (i.e. 0-20 cm) from Brazil (BRreL) and an existing Australian national library (AUnaL) to calibrate different models, which were used to predict OC content in target samples (i.e. subset of the Brazilian sample set). In total, eight strategies were tested, including the use of different soil libraries, different chemometric approaches (i.e. common multivariate regression and local-type modeling), and spiking. The highest accuracy for OC prediction in Brazilian regional target samples for both bulk soils (RPIQ = 5.86; RMSE = 3.17 g kg(-1)) and fractions (RPIQ = 1.58, 5.72, and 5.21 for POC, HOC, and ROC, respectively) was obtained when the BRreL was used to calibrate a partial least square regression. When AUnaL was used alone as the calibration set, the OC prediction accuracy sharply decreased and even the local-type models Cubist and spectrum-based learning, in general, were not able to achieve high OC predictions accuracy. With this strategy, the averaged RPIQ values (among the three models) were 2.92, 0.55, 2.17, and 0.65 for bulk OC, POC, HOC, and ROC, respectively. However, when 20 Brazilian regional samples (representing 8 and 42% of the original calibration samples used for total and fraction OC, respectively) were spiked to the AUnaL, the OC predictions accuracy markedly improved for all OC pools, regardless of the modeling approach used. With this strategy, the averaged RPIQ values were 4.74, 1.35, 4.49, and 2.29 for bulk OC, POC, HOC, and ROC, respectively. In addition, high OC prediction accuracy (RPIQ = 4.49 for bulk OC; RPIQ = 1.58, 6.87, and 4.26 for POC, HOC, and ROC, respectively) was obtained when models were calibrated with the 20-spiking subset only. Our results suggest that a proper selection of a small calibration set, that could be used alone or to augment an existing library, is critical and could make feasible the OC prediction in bulk soils and fractions by MIR spectroscopy. (AU) | |
| FAPESP's process: | 16/02387-8 - Qualitative changes of organic matter affected by soil use and management systems as revealed by spectroscopic techniques |
| Grantee: | Clever Briedis |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| FAPESP's process: | 17/23159-6 - Changes in quantity and quality of SOM in whole soils and fractions affected by times of adoption of no-till system: evidence from natural 13C abundance and solid-state 13C NMR spectroscopy |
| Grantee: | Clever Briedis |
| Support Opportunities: | Scholarships abroad - Research Internship - Post-doctor |