This presentation was presented during the 3 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Martin Soriano-Disla, CSIRO Land and Water - Australia, in FAO Hq, Rome

1. The performance of portable mid-infrared
spectroscopy for the prediction of soil carbon
Martín Soriano Disla1,2
, Les Janik1
and Mike
McLaughlin1
1
CSIRO Environmental Contaminant Mitigation and Technologies
Program, CSIRO Land and Water, South Australia, Australia
2
Sustainable use, management and reclamation of soil and water
research group (GARSA). Universidad Politécnica de Cartagena, Spain
Global symposium on soil organic carbon, Rome (Italy), 21-23 March 2017

2. Outline
1.Background
2.Justification and
objectives
3. Materials and methods
4. Results and discussion
5. Conclusions
6. General discussion

3. 3
1. Background
SOC highly variable: management specific to each situation
This requires high spatial density of soil analytical data
Traditional laboratory analyses unable to satisfy
Infrared spectroscopy as an alternative (sensitive to SOC
molecules) to predict SOC and for general soil assessment
SOC = f (Spectra)
Calibration needed

4. 4
1. Background: MIR technology
Rapid (one spectrum = 15 seconds)
Cheap
Accurate
Minimal/no sample pretreatment
No chemical reagents
Portable capability (including MEMS):
in situ decisions
Multiple analytes determined
simultaneously (e.g. C pools)
Quantative and qualitative
applications: comprehensive
technique

5. 5
2. Objectives
Test the performance of a portable MIR instrument for the
prediction of soil C
Evaluate the influence of the reference analytical method on
the accuracy of predicted SOC
Test the influence of different multivariate algorithms on the
accuracy of the predictions of related key soil attributes

6. 6
3. Materials and methods: soil samples
Samples from CSIRO soil archive
458 cropping soils from soil profiles in NSW and SA
(Australia) corresponding with 9 soils orders mostly
Calcarosols, Chromosols, Dermosols, Sodosols and Vertosols
Samples dried at 40ºC and sieved < 2 mm
Analytes
• CEC, clay, pH and SOC (n = 300; W&B) provided by
archive
• SOC calculated from MIR predicted IC (accounting for the
presence of carbonates) and analysed TC
• We analysed TC (elemental analyzer)

7. 7
3. Materials and methods: spectra and modelling
Fourier-Transform infrared (FTIR) portable
spectrometer (ExoScan 4100, Agilent, USA)
Scanning configuration: diffuse reflectance
(DRIFT) accessory, four replicates, 8 cm-1
resolution, 15 s scanning time, SiC background
Spectra pre-processing: de-trend, average
Modelling (75% calibration, 25% independent
validation)
• PLSR (Unscrambler, CAMO)
• PLSR, MPLSR, LOCAL (WINSI, Foss) for
prediction of clay, pH and CEC
Prediction performance: R2
, RMSEP, RPD
1667–15385 nm
6000–650 cm-1
Exoscan

8. 8
4. Results and discussion: TC and SOC
High predictive
performance
Lower

9. 9
4. Results and discussion: SOC issues
MIR method?
Instrument?
Similar results found with benchtop instrument (not shown)
Analytical method and limited concentration range?
W&B analytical error, analysed at different laboratories at
different times. Prediction of calculated SOC performing
better than W&B. Limited concentration range
Median R2
, in Soriano-Disla et al., 2014
But MIR method sensitive to C-C, C-O, C-
H, N-H bonds and previous reported data

10. 10
4. Results and discussion: modelling
Software: Unscrambler slightly better
Optimum results with LOCAL
Method Analyte Conc. Range R2
RMSEP RPD
Unsc PLSR pH (1:5 w) 4.5-10.0 0.82 0.6 2.2
WinISI PLSR pH (1:5 w) 4.5-10.0 0.80 0.6 2.2
WinISI MPLSR pH (1:5 w) 4.5-10.0 0.82 0.6 2.2
WinISI LOCAL pH (1:5 w) 4.5-10.0 0.84 0.5 2.6
Unsc PLSR CEC (cmol+
/kg) 1-46 0.84 4.4 2.3
WinISI PLSR CEC (cmol+
/kg) 1-46 0.83 4.6 2.2
WinISI MPLSR CEC (cmol+
/kg) 1-46 0.83 4.5 2.3
WinISI LOCAL CEC (cmol+
/kg) 1-46 0.83 4.4 2.3
Unsc PLSR Clay (%) 5.0-69.3 0.77 7.5 2.1
WinISI PLSR Clay (%) 5.0-69.3 0.75 7.9 2.0
WinISI MPLSR Clay (%) 5.0-69.3 0.75 8.0 1.9
WinISI LOCAL Clay (%) 5.0-69.3 0.79 7.2 2.2

11. 11
5. Conclusions
Portable MIR ready for TC, SOC, clay, CEC and pH
Technique is able to detect issues in the analytical
method: quality control
LOCAL probably the best for large spectral libraries
It is not about replacing traditional methods but having
more information about soil and optimise such methods
Applications expanded with MEMS development and
other analytes
Issues/barriers
Reference data: frequent to use data at hand
Common protocols
Field conditions: work currently ongoing
Difficult to get support for further scientific development:
development of application by private sector/environmental
agencies

12. 12
6. General discussion: cost of IR predictions
Methodology
• Cost per sample of IR predictions (lab based)
o
Analysis of samples for calibration+update: influenced by number
of analytes and cost of analysis
o
Personnel: spectroscopist and technician
o
Instrument
o
Consumables
• Cost per sample of traditional analysis
Conclusions
• Around 10% the cost of traditional methods = 10 times more data →
better management → better decisions → increased benefits
• In agreement with previous references: McKenzie et al. (2003),
O’Rourke and Holden (2011)
• Differences between IR and reference larger if
• Number of samples to predict increase
• Number of analytes increase (especially if costly)
• Samples predicted in the field

13. 13
6. General discussion: current study area
Now working in Spain: UPCT (Cartagena, Spain)
% SOC topsoils. European Environmental Agency, European Union
Low SOC, high soil degradation
Climate: Low rainfall vs. high evaporation
(339 vs. 900 mm/y), high temperature
(average of 17.1ºC)
Severely affected by climate change
A range of activities and intensification
Economic, social and environmental
impacts (e.g. Mar Menor)
Tourism
Fragile
ecosistem (Mar
Menor)Agriculture
Mining
Industry
Urban
Protected
Area of aprox. 30 × 30 km

14. 14
6. General discussion: our response
Adaptation: experiences in North Africa, intelligent
agriculture to be more efficient (e.g. 1st Workshop on
Intelligent Systems for Agriculture Production and
Environment Protection (ISAPEP’17))
Managerial practices to increase SOC:
• Amendments, reduced tillage, green cover on
agriculture
• Diversification, sustainability and ecological
approaches: Diverfarming project, Chair on
sustainable agriculture
Measuring: use of infrared including C pools
Knowledge gaps: role of IC
SOC in local/regional policy: Ley de protección Mar
Menor, Pacto de Alcaldes

15. 15
6. General discussion: my view
Tendency: more sustainable, diversified and ecological
agriculture to increase SOC, protect soils and adapt to
climate change
Unique opportunity
Challenging
• Diversity of land uses and interests
• Change of mentality: short-term productivism and specific
approaches to medium/long-term and global
What is needed?
• Education: If the environment is affected we are affected,
Ecosystem services/functions, all connected in environment
(e.g. algal bloom in Mar Menor), short- vs medium/long-term
• Community engagement and support
• Bring local knowledge back

16. Thank you
Time for questions
martin.soriano@upct.es