Strategies to improve the prediction of bulk soil and fraction organic carbon in Brazilian samples by using an Australian national mid-infrared spectral library

Geoderma ◽  
2020 ◽  
Vol 373 ◽  
pp. 114401
Author(s):  
Clever Briedis ◽  
Jeff Baldock ◽  
João Carlos de Moraes Sá ◽  
Josiane Burkner dos Santos ◽  
Débora Marcondes Bastos Pereira Milori
Geoderma ◽  
2012 ◽  
Vol 183-184 ◽  
pp. 41-48 ◽  
Author(s):  
A.H. Cambule ◽  
D.G. Rossiter ◽  
J.J. Stoorvogel ◽  
E.M.A. Smaling

2010 ◽  
Vol 74 (5) ◽  
pp. 1792-1799 ◽  
Author(s):  
Thomas Terhoeven-Urselmans ◽  
Tor-Gunnar Vagen ◽  
Otto Spaargaren ◽  
Keith D. Shepherd

Soil Systems ◽  
2019 ◽  
Vol 3 (1) ◽  
pp. 11 ◽  
Author(s):  
Shree Dangal ◽  
Jonathan Sanderman ◽  
Skye Wills ◽  
Leonardo Ramirez-Lopez

Diffuse reflectance spectroscopy (DRS) is emerging as a rapid and cost-effective alternative to routine laboratory analysis for many soil properties. However, it has primarily been applied in project-specific contexts. Here, we provide an assessment of DRS spectroscopy at the scale of the continental United States by utilizing the large (n > 50,000) USDA National Soil Survey Center mid-infrared spectral library and associated soil characterization database. We tested and optimized several advanced statistical approaches for providing routine predictions of numerous soil properties relevant to studying carbon cycling. On independent validation sets, the machine learning algorithms Cubist and memory-based learner (MBL) both outperformed random forest (RF) and partial least squares regressions (PLSR) and produced excellent overall models with a mean R2 of 0.92 (mean ratio of performance to deviation = 6.5) across all 10 soil properties. We found that the use of root-mean-square error (RMSE) was misleading for understanding the actual uncertainty about any particular prediction; therefore, we developed routines to assess the prediction uncertainty for all models except Cubist. The MBL models produced much more precise predictions compared with global PLSR and RF. Finally, we present several techniques that can be used to flag predictions of new samples that may not be reliable because their spectra fall outside of the calibration set.


2008 ◽  
Vol 62 (6) ◽  
pp. 661-670 ◽  
Author(s):  
J. Brian Loudermilk ◽  
David S. Himmelsbach ◽  
Franklin E. Barton ◽  
James A. de Haseth

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Xiaodan Sun ◽  
Gang Wang ◽  
Qingxu Ma ◽  
Jiahui Liao ◽  
Dong Wang ◽  
...  

Abstract Background Soil organic carbon (SOC) is important for soil quality and fertility in forest ecosystems. Labile SOC fractions are sensitive to environmental changes, which reflect the impact of short-term internal and external management measures on the soil carbon pool. Organic mulching (OM) alters the soil environment and promotes plant growth. However, little is known about the responses of SOC fractions in rhizosphere or bulk soil to OM in urban forests and its correlation with carbon composition in plants. Methods A one-year field experiment with four treatments (OM at 0, 5, 10, and 20 cm thicknesses) was conducted in a 15-year-old Ligustrum lucidum plantation. Changes in the SOC fractions in the rhizosphere and bulk soil; the carbon content in the plant fine roots, leaves, and organic mulch; and several soil physicochemical properties were measured. The relationships between SOC fractions and the measured variables were analysed. Results The OM treatments had no significant effect on the SOC fractions, except for the dissolved organic carbon (DOC). OM promoted the movement of SOC to deeper soil because of the increased carbon content in fine roots of subsoil. There were significant correlations between DOC and microbial biomass carbon and SOC and easily oxidised organic carbon. The OM had a greater effect on organic carbon fractions in the bulk soil than in the rhizosphere. The thinnest (5 cm) mulching layers showed the most rapid carbon decomposition over time. The time after OM had the greatest effect on the SOC fractions, followed by soil layer. Conclusions The frequent addition of small amounts of organic mulch increased SOC accumulation in the present study. OM is a potential management model to enhance soil organic matter storage for maintaining urban forest productivity.


2021 ◽  
Vol 13 (12) ◽  
pp. 2265
Author(s):  
Jonathan Sanderman ◽  
Kathleen Savage ◽  
Shree Dangal ◽  
Gabriel Duran ◽  
Charlotte Rivard ◽  
...  

A major limitation to building credible soil carbon sequestration programs is the cost of measuring soil carbon change. Diffuse reflectance spectroscopy (DRS) is considered a viable low-cost alternative to traditional laboratory analysis of soil organic carbon (SOC). While numerous studies have shown that DRS can produce accurate and precise estimates of SOC across landscapes, whether DRS can detect subtle management induced changes in SOC at a given site has not been resolved. Here, we leverage archived soil samples from seven long-term research trials in the U.S. to test this question using mid infrared (MIR) spectroscopy coupled with the USDA-NRCS Kellogg Soil Survey Laboratory MIR spectral library. Overall, MIR-based estimates of SOC%, with samples scanned on a secondary instrument, were excellent with the root mean square error ranging from 0.10 to 0.33% across the seven sites. In all but two instances, the same statistically significant (p < 0.10) management effect was found using both the lab-based SOC% and MIR estimated SOC% data. Despite some additional uncertainty, primarily in the form of bias, these results suggest that large existing MIR spectral libraries can be operationalized in other laboratories for successful carbon monitoring.


2016 ◽  
Vol 24 (15) ◽  
pp. 16705 ◽  
Author(s):  
Florian Habel ◽  
Michael Trubetskov ◽  
Vladimir Pervak

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