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Quantifying soil carbon and nitrogen under different types of vegetation cover using near infrared-spectroscopy: a case study from India J. Dinakaran*and
Quantifying soil carbon and nitrogen under different types of
vegetation cover using near infrared-spectroscopy: a case study
from India J. Dinakaran*and K.S. Rao Natural Resource Management
laboratory, Department of Botany, University of Delhi (north
campus,) Delhi 110007, India. *Contact author Email:
[email protected]@gmail.com Quantifying soil carbon and
nitrogen under different types of vegetation cover using near
infrared-spectroscopy: a case study from India J. Dinakaran*and
K.S. Rao Natural Resource Management laboratory, Department of
Botany, University of Delhi (north campus,) Delhi 110007, India.
*Contact author Email: [email protected]@gmail.com Methods
Total carbon and nitrogen in the collected soil samples were
determined by dry combustion using a vario- micro CHNS analyzer.
Totally 180 soil samples were used for developing equations
(calibration data n=136), validation of the equations (n=31) and
evaluate the effects of soil moisture on predicting equations
(n=13). All the soil samples were analysed in a FOSS NIRS system
5000 working in reflectance mode between 1100 and 2498 nm at 2 nm
intervals. Ring cup has been used for all the measurements. The
resulting spectrum of each sample is the average of 32 scans
recorded as absorbance (log 1/R). Calibrations were developed for
predicting the soil carbon and nitrogen by using the chemometric
software Win ISI III project manager ver. 1.61. Prediction
equations were obtained by using a modified partial least square
(MPLS) regression method. To evaluate the effect of moisture on the
accuracy of NIRS prediction formula, the soil samples (n=13) were
wetted evenly (up to sticky) and the samples were scanned and
weighed. Then the samples were kept for air drying at laboratory
condition (25 o C). Subsequently each day (for 3 days) the samples
were weighed and scanned. Methods Total carbon and nitrogen in the
collected soil samples were determined by dry combustion using a
vario- micro CHNS analyzer. Totally 180 soil samples were used for
developing equations (calibration data n=136), validation of the
equations (n=31) and evaluate the effects of soil moisture on
predicting equations (n=13). All the soil samples were analysed in
a FOSS NIRS system 5000 working in reflectance mode between 1100
and 2498 nm at 2 nm intervals. Ring cup has been used for all the
measurements. The resulting spectrum of each sample is the average
of 32 scans recorded as absorbance (log 1/R). Calibrations were
developed for predicting the soil carbon and nitrogen by using the
chemometric software Win ISI III project manager ver. 1.61.
Prediction equations were obtained by using a modified partial
least square (MPLS) regression method. To evaluate the effect of
moisture on the accuracy of NIRS prediction formula, the soil
samples (n=13) were wetted evenly (up to sticky) and the samples
were scanned and weighed. Then the samples were kept for air drying
at laboratory condition (25 o C). Subsequently each day (for 3
days) the samples were weighed and scanned. Soil samples were
collected up to a depth of 30 cm with 10 cm interval under
different vegetation cover in three climatic regions such as 1)
semiarid condition (annual rain fall 500-750mm) 2) Dry sub- humid
condition (annual rain fall 750-1000mm) and 3) moist sub-humid
condition (annual rain fall 1000-1250mm). The collected soil
samples were air dried and passed through 2 mm sieve before
analyses. Background o Understanding the rate and storage of carbon
and nitrogen in soils with respect to land use changes and land
management activities required an obvious measurement of soil
carbon and nitrogen in regional to global level at frequent
interval. o Measuring the soil carbon and nitrogen in soils of
various land use systems by conventional methods such as
tri-titrometric, dry combustion and other chemical based techniques
are expensive and time consuming. o Recently the diffuse
reflectance spectroscopy has been widely used for the rapid
quantification of carbon, nitrogen and other properties in soils
(Chang et al., 2001; Viscarra Rossel and Webster, 2011). The main
objectives of this study were 1. To assess the efficacy of NIRS to
predict the soil carbon and nitrogen content. 2. To evaluate the
effects of different soil moisture contents on predicting the soil
carbon and nitrogen content. Background o Understanding the rate
and storage of carbon and nitrogen in soils with respect to land
use changes and land management activities required an obvious
measurement of soil carbon and nitrogen in regional to global level
at frequent interval. o Measuring the soil carbon and nitrogen in
soils of various land use systems by conventional methods such as
tri-titrometric, dry combustion and other chemical based techniques
are expensive and time consuming. o Recently the diffuse
reflectance spectroscopy has been widely used for the rapid
quantification of carbon, nitrogen and other properties in soils
(Chang et al., 2001; Viscarra Rossel and Webster, 2011). The main
objectives of this study were 1. To assess the efficacy of NIRS to
predict the soil carbon and nitrogen content. 2. To evaluate the
effects of different soil moisture contents on predicting the soil
carbon and nitrogen content. References 1.Chang et al., (2001).
Near-Infrared Reflectance Spectroscopy Principal Components
Regression Analyses of Soil Properties. Soil Science Society of
America Journal, 65:480- 490. 2.Raju et al., (2013). Revisiting
climatic classification in India: a district-level analysis.
Current Science, 105 (4):492-495. 3.Viscarra Rossel., R.A., R.
Webster (2011). Discrimination of Australian soil horizons and
classes from their visible near infrared spectra. European Journal
of Soil science, 62: 637-647. References 1.Chang et al., (2001).
Near-Infrared Reflectance Spectroscopy Principal Components
Regression Analyses of Soil Properties. Soil Science Society of
America Journal, 65:480- 490. 2.Raju et al., (2013). Revisiting
climatic classification in India: a district-level analysis.
Current Science, 105 (4):492-495. 3.Viscarra Rossel., R.A., R.
Webster (2011). Discrimination of Australian soil horizons and
classes from their visible near infrared spectra. European Journal
of Soil science, 62: 637-647. Acknowledgements We thank the Head
Department of Botany, University of Delhi for access to NIR
instrument, to the DST (SERB) for financial assistance through
DST-FTY- Project (SR/FT/LS-59/2012) and the International travel
support Unit (ITS) in SERB. Also thankful to my colleagues in
Natural Resource Management Laboratory, Department of Botany,
University of Delhi for providing the soil samples.
Acknowledgements We thank the Head Department of Botany, University
of Delhi for access to NIR instrument, to the DST (SERB) for
financial assistance through DST-FTY- Project (SR/FT/LS-59/2012)
and the International travel support Unit (ITS) in SERB. Also
thankful to my colleagues in Natural Resource Management
Laboratory, Department of Botany, University of Delhi for providing
the soil samples. All soils tested in this study had similar near
infrared reflectance spectra irrespective of their sites under
different vegetation cover. We observed the prominent peaks, at
1400nm, 1900nm, 2200nm and 2300nm (less prominent), in all the
collected soil spectra (Fig. 1). Soil carbon and nitrogen was
successfully predicted (R 2 = 0.90 for carbon and R 2 = 0.85 for
nitrogen) by the equations developed by NIRS (Table 1 and Figs. 2
& 3). The standard error of prediction (SEP), standard error of
prediction corrected for bias SEP (C) and bias for predicting
equations of carbon and nitrogen was 0.73, 0.73, 0.04 and 0.07,
0.07, 0.005, respectively (Table 1). The root mean square error
(RMSE) and ratio performance deviation (RPD) for the validation of
predicted equation of carbon and nitrogen was 1.19, 1.43 and 0.02,
4.52, respectively(Table 2 and Fig. 4). Compared with the moisture
containing soils the dried soils have lower absorbance between
1400nm and 1900nm (Fig. 5). Also the NIRS predicted concentration
of soil carbon and nitrogen more accurately in dried samples (Fig.
6 and Table 3). All soils tested in this study had similar near
infrared reflectance spectra irrespective of their sites under
different vegetation cover. We observed the prominent peaks, at
1400nm, 1900nm, 2200nm and 2300nm (less prominent), in all the
collected soil spectra (Fig. 1). Soil carbon and nitrogen was
successfully predicted (R 2 = 0.90 for carbon and R 2 = 0.85 for
nitrogen) by the equations developed by NIRS (Table 1 and Figs. 2
& 3). The standard error of prediction (SEP), standard error of
prediction corrected for bias SEP (C) and bias for predicting
equations of carbon and nitrogen was 0.73, 0.73, 0.04 and 0.07,
0.07, 0.005, respectively (Table 1). The root mean square error
(RMSE) and ratio performance deviation (RPD) for the validation of
predicted equation of carbon and nitrogen was 1.19, 1.43 and 0.02,
4.52, respectively(Table 2 and Fig. 4). Compared with the moisture
containing soils the dried soils have lower absorbance between
1400nm and 1900nm (Fig. 5). Also the NIRS predicted concentration
of soil carbon and nitrogen more accurately in dried samples (Fig.
6 and Table 3). Results Source: Raju et al., 2013 Conclusion and
future perspectives The results of this study confirms that the
equations developed by NIRS predicted concentrations of carbon and
nitrogen more accurately in dried soil samples than soils with
moisture content. This study could be extended towards developing
equations for predicting other soil properties in the same and
other bio climatic zones of India. Conclusion and future
perspectives The results of this study confirms that the equations
developed by NIRS predicted concentrations of carbon and nitrogen
more accurately in dried soil samples than soils with moisture
content. This study could be extended towards developing equations
for predicting other soil properties in the same and other bio
climatic zones of India. Calibration data set (n=136)C (%)N (%)
Calibration statistics1-4-4-1 Mean2.420.23 SD2.290.19
Minimum0.320.01 Maximum12.241.03 R20.9030.853 Slope1.0831.001
SEP0.7330.076 Bias0.04-0.005 SEP (C)0.7340.076 Validation data set
(n=31) PropertyC (%)N (%) Mean2.710.26 SD1.710.1 Minimum0.810.12
Maximum8.100.65 Slope0.640.69 Intercept0.130.01 R20.780.59
RMSE1.190.02 RPD1.434.52 N= 13 C (%) Moisture level
(%)R2SlopeInterceptRMSERPD Day 1 (16-27)0.860.1751.5352.641.06 Day
2 (8-11)0.860.5621.1371.12 Day 3 (0.1- 0.2)0.840.6831.1221.182.38 N
(%) Day 1 (16-27)0.500.170.9150.690.36 Day 2
(8-11)0.540.7760.1730.2091.18 Day 3 (0.1-
0.2)0.940.7540.0910.073.31 R2R2 0.78 RMSE1.19 RPD1.43 R2R2 0.59
RMSE0.02 RPD4.52 R2R2 0.86 RMSE2.64 RPD1.06 R2R2 0.50 RMSE0.69
RPD0.36 R2R2 0.86 RMSE1.1 RPD2.0 R2R2 0.54 RMSE0.21 RPD1.18 R2R2
0.84 RMSE1.18 RPD2.38 R2R2 0.94 RMSE0.07 RPD3.31 3017'03.97"N to
3039'52.56"N 78 19'46.6"E to 7859'30.37"E 3017'03.97"N to
3039'52.56"N 78 19'46.6"E to 7859'30.37"E Prosopis sps Pinus sps
Rhododendron sps Quercus sps Mixed coverPasture land Agricultural
field Soil sample collection in the field Table 1: Calibration
statistic values obtained for predicting soil carbon and nitrogen
Table 2: Validation statistic values obtained for predicting soil
carbon and nitrogen Table 3: Accuracy of prediction for NIRS
calibrations for carbon and nitrogen in moist soils 332332.41"N to
333341.00"N 741838.42"E to 743452.00"E 332332.41"N to 333341.00"N
741838.42"E to 743452.00"E 27076"N to 283210.6"N 771033.7"E to
77339"E 27076"N to 283210.6"N 771033.7"E to 77339"E Moisture level
: Day 1 Fig. 1: Log 1/R spectra of soil samples (dried and
grounded) Fig. 2: Relationship between NIRS predicted and CHNS
analyzer measured values of total carbon concentration Fig. 3:
Relationship between NIRS predicted and CHNS analyzer measured
values of total nitrogen concentration Fig. 4 Relationship between
NIRS predicted and CHNS analyzer measured values of total carbon
and nitrogen concentration for validation Fig. 6: Linear
relationship between NIRS predicted and measured values of carbon
and nitrogen at three different days with different moisture levels
Moisture level : Day 2 Moisture level : Day 3 Fig. 5: Log 1/R
spectra of soil samples with different moisture level European
Geosciences Union General Assembly 2014, Vienna, Austria, 27 April
02 May 2014