MacBratney, A.B.

Viscarra Rossel, R.A.

Walvoort, D.J.J.

English

NARCIS 

Proximal sensing of soil pH and lime requirement by mid infrared diffuse reflectance spectroscopy

Wageningen Yield 

PROXIMAL SENSING OF SOIL pH AND LIME REQUIREMENT BY MID 
INFRARED DIFFUSE REFLECTANCE SPECTROSCOPY 
R.A. VISCARRA RÖSSEL1'2*, D.J.J. WALVOORT1'3, A.B. McBRATNEY1, L.J. JANIK4, 
J.O. SKJEMSTAD4 
'Australian Centre for Precision Agriculture, The University of Sydney, NSW 2006, Australia 
2INRA Laboratoire de Science du Sol, 35042 Rennes, France 
3
 Wageningen University & Research Centre, Wageningen, The Netherlands 
4CSIRO Land and Water, Glen Osmond SA 5064, Australia 
'Corresponding author -mail: r.rossel@agec.usyd.edu.au 
ABSTRACT 
We describe the use of mid-infrared (MIR) diffuse reflectance spectroscopy in combination 
with partial least squares regression (PLS) for the prediction of soil pH and lime requirement. 
Delete-one-jackknife cross-validation was used to determine the optimal number of factors for 
the most accurate prediction of each soil property. Predictions of soil pH in 0.01M CaCb 
(pHcaci2), H2O (pHH2o) and lime-requirement buffer (pHbuffer) were compared to those derived 
from laboratory extractions. Comparisons of lime requirement were made using pHbuffer 
measurements and a response surface regression. The mid-infrared with partial least squares 
predictions were less accurate. The nugget variance of lime requirements derived using both 
the conventional and the MIR showed that the latter may be a more precise analytical 
technique. Kriging of the MIR data produced a map suitable for variable-rate liming. 
INTRODUCTION 
Increasingly, spectroscopic methods are being considered as possible alternatives to enhance 
or replace conventional laboratory methods of soil analysis (e.g. Janik et al.,1995; Viscarra 
Rössel & McBratney, 1998a). The reasons for this may be that the techniques are rapid, 
timely, less expensive than conventional soil analysis (after initial set-up costs), and are 
occasionally more accurate than conventional laboratory methods (e.g. McCauley et al., 
1993). The development of a proximal spectrometric soil sensing system would be an useful 
step towards real-time, continuous site-specific management, which is the ultimate aim of 
precision agriculture (PA). The aims of this work are: (i.) to determine the utility of MIR soil 
analysis with partial least-squares regression (MIR-PLS) for the prediction of soil pH and 
lime requirement, and (ii.) to assess its adaptability for use in precision agriculture.The MIR 
portion of the electromagnetic spectrum occurs in the 2500 to 25000 nm wavelength range, 
corresponding to a wave number range from 4000 to 400 cm'1. Mid-infrared spectroscopy has 
a particular advantage over other spectroscopic techniques for soil analysis (e.g. near-infrared) 
as it is sensitive to the intense fundamental vibrations of both the organic and inorganic 
phases of the soil. A review of the technique may be found in Janik et al. (1998). 
METHODS 
Field Sampling and Analyses 
Surface soil samples were collected from a 17.5 ha field at Kelso NSW, Australia on a 40 m 
grid at each of 122 grid nodes. Viscarra Rössel & McBratney (2001) described the sampling 
strategy and laboratory procedures for measurements of soil pHcaci2 and pHH2o. The Mehlich 
lime-requirement buffer was used for soil:lime requirement measurements (pHbuffer)- The lime 
497 
required to attain a target pH of 6.5 was predicted using a response surface model derived by 
Viscarra Rössel & McBratney (2001). The model has the following form: 
LR (Mg/ha) = ßo + ßix + ßy + ßix2 + ftxy + fcy2 + £ 
where ßn are parameters of the model; x is the equilibrated pHbuffer, y is the selected target pH 
post-liming, and £~N(0, ö2) is a normally distributed error term with zero mean and variance 
à. 
Mid Infrared Analyses 
Portions of the soil samples were ground and sieved to a size fraction smaller than 80 [im for 
analyses as neat powders using the diffuse reflectance infrared method described by Janik et 
al. (1995). Spectra were recorded from 4000 to 500 cm"1 at 3.9 cm'1 intervals. The equipment 
consisted of a rapid scanning Fourier Transform spectrometer, with an extended range KBr 
beam splitter and Peltier-cooled DTGS detector with a spectral range of 8300 - 470 cm"1 and a 
resolution of 8 cm"1. 
Partial Least Squares and Cross Validation 
Given a soil sample, we want to use its MIR spectrum to predict soil property y, in our case 
soil pH or lime requirement. Therefore we need a calibration model. Suppose we have taken n 
soil samples i-\...n. Each soil sample / is analysed for property y by means of a conventional 
laboratory method. In addition, MIR spectra x, = [x\, xi, ..., **]' consisting of absorbances x 
are collected at k wavenumbers. Let y = \y\, yi,..., y„]' be a vector of n measurements of soil 
property y, and let X = [xi, \j, ..., x„j' be a matrix of corresponding MIR spectra. A 
calibration model can now be constructed by means of partial least squares regression (PLS). 
PLS decomposes X and y into factor scores (T) and factor loadings (P and q) according to: 
X = TP'+E 
y = Tq + f 
X and y are usually zero-centred prior to decomposition. The decomposition is performed in 
such a way that the first/? (< min(w,£)) factors explain most of the variation in X and y. The 
remaining factors resemble noise and can thus be ignored, hence the addition of residuals E 
and f. The resulting matrices and vectors generally have a much lower dimension than X and 
y. Given a new spectrum x, soil property y can be estimated as a linear combination of the 
factors scores and factor loadings of x. For more details on PLS see e.g. Martens and Naes 
(1989). Delete-one-jackknifing (Efron and Tibshirani, 1994) was used to estimate the number 
of factors to retain in our model. The Root Mean Squared prediction Error (RMSE) was used 
to summarise the results. Generally, the model with the lowest RMSE is selected. 
Spatial Analysis 
Semi-variograms fitted with exponential models were used to quantify the spatial dependence 
of lime requirements derived from the response surface model and those predicted by the 
MIR-PLS technique. The semi-variograms were used to block krige the data using 10 m 
blocks and 2.5 m spacing. A minimum of 50 points were used for each estimation. Kriged 
estimates were compared, and a map of MIR-PLS predicted lime requirements contoured. 
RESULTS 
Soil Data Descriptions 
Soil pHcaci2, pHH2o and lime requirements were negatively skewed. The soil pHbutrer data were 
normally distributed but for a few low outliers. Summary statistics of the data from 
conventional laboratory analyses are given on the left side of Table 1. 
498 
TABLE 1. Statistical description of'known' soil data analysed using conventional laboratory 
methods of analyses, and that analysed using the mid infrared technique, n = 118 
Conventional laboratory analyses MIR-PLS predictions 
Property Mean St. Dev. Mean St. Dev. 
pHCaCI2 
pHH2o 
pHbufler 
LR(6.5) Mg/ha 
4.37 
5.3 
5.85 
5.82 
0.26 
0.27 
0.1 
1.21 
4.37 
5.29 
5.85 
5.83 
0.25 
0.25 
0.1 
1.13 
Qualitative Mid Infrared Analyses 
Qualitative analysis of soil composition may be made by observation of peaks in the MIR 
spectra of soils and their PLS loadings. A representative spectra of the soil from the 
experimental field showing some dominant soil components is given in Figure 1. 
Wavelength (nm) 
3333 4000 5000 6667 20000 
4000 3500 3000 2500 2000 1500 1000 500 
Wavenumbers (cm' ) 
FIGURE 1 Representative mid infrared spectrum of soil from the experimental field. 
Dominant soil components: quartz (Q) as sand, organic matter (OM), kaolinite (K), and 
smectite (S) 
The major spectral features associated with the first three factor loadings weights (see below) 
for the predictions of soil pHcaCi2, pHbuffer and lime requirement are shown in Figure 2. 
a. b. c. 
Wavelength (nm) 
25<» 2857 3333 4XÏ) SX« 6667 H M « a n « 25m 2857 3333 « I « 5nm 6667 UHU) » I K « 25(K 2857 3333 « X « SXX) 6667 l u x » 2HX« 
i \ 
• • / \ 
ü. r > ^ - ^ 
iii. 
Y ^ ~ \ 
f>t „ 
V 
'i 
-vV 
r 
\ 
! \ 
il \ 
.s 
ü. 1 \ ^ 
in. ^y^~ 
ƒ £•• 
^
J V ' 
v A y « k 
•^-^_ A / 
~^J\$ 
4X1) 351« 3(XU 2500 » X » 151» ]<XX) S ü «X» 3500 3000 2500 2000 1500 looo 500 4X1) 351» 3000 251» 211» I5U0 ](X» 51» 
W'avenumber (cm'1) 
FIGURE 2. The first three factor loading weights (i., ii., iii., respectively) used for partial 
least squares predictions of (a.) soil pHcaCi2, (b.) soil Mehlich pHbuffer, and (c.) lime 
requirement to a target pH of 6.5. 
499 
The first few factor loadings contain most of the information, which indicate the correlations 
between the soil property in question and the MIR frequencies (Janik et al., 1995). Generally, 
for pHcaci2 and lime requirement the first three factor loadings show positive contributions 
from organic matter and smectite and negative contributions due to quartz as sand, and kaolin 
(Figure 2a and 2c, cf. Figure 1). All of these properties affect the buffering capacity of soil. 
The inverse relationship between pHbuffer and lime requirement is shown by their loadings 
spectra (Figure 2b and 2c). 
Quantitative Mid Infrared Analyses with Partial Least Squares and Cross Validation 
Quantitative analysis to determine the suitability of the PLS factor loadings and scores to 
model the spectra involved testing up to 25 factors for each soil property. Results for eighteen 
of these factors are shown in Figure 3 but fifteen factors were sufficient for adequate 
predictions of the soil properties tested. 
b. 
0.24 
0.2 
0.16 
0.12 1 
0.08 
0.28 
0.24 
0.2 
0.161 
°ooo0ooo 
0.12 
01 
008-
0.06 
^ 0.04 
°°onooO 
1.1 
09 
° ° 0.7 1 
6 12 18 0 6 12 18 0 
Number of factors 
i 0.5 
6 12 18 
°°o0ooo 
12 18 
FIGURE 3. Root mean-square error (RMSE) of prediction for (a.) soil pHcaci2, (b.) pHn2o, (c.) 
pHbuffer and (d.) lime requirement to a target pH of 6.5 
For all soil properties, rapid reductions in the RMSE occurred for ten factors, after which only 
small decreases were evident (Figure 3). The optimal number of factor loadings (i.e., 
minimum RMSE values resulting from the cross-validation procedure) was twelve for all soil 
properties except pHcaci2 whose optimal number of factors for prediction and spectral 
reconstruction was fifteen. Without significant loss in prediction accuracy, it was possible to 
consider only eleven factor loadings for both pHcaCi2 and pHH2o (equivalent to a RMSE of 
0.1), and nine factors for both pHbuffer and lime requirement (equivalent to a RMSE of 0.05 
and 0.6 respectively). The large number of factors detected and used in the analysis indicates 
the complexity of the soil matrix. These factor loadings were then used to construct the 
calibration matrix for the (independent) prediction of soil properties using the delete-one-
jackknife procedure. 
Linear regression plots between 'known' soil properties determined using conventional 
laboratory techniques, and those predicted from MIR-PLS cross-validation are given in Figure 
4. Soil pHcaCi2, pHH2o, pHbuffer and lime requirements had relatively low, acceptable RMSEs of 
prediction (Figure 4). Best predictions were obtained for soil pHcaCi2 with a R2 value of 0.85 
and a slope of 0.9 (Figure 4a). Lime requirement tended to be over-estimated at low values 
and under-estimated at high lime requirements (Figure 4d). This is reflected by the poorer 
prediction R2 value of 0.75 and the lower slope 0.82 (Figure 4a). 
500 
a. 
• 
'* 
•' >!% 
*S* * 
^ — , — , 
1:1 .' 
\"V 
• " ^JPL " • 
*&%: 
yt.r 
FL-9 
RMSE - 0.05 
Slope - 0.82 
R--0 74 
5 56 57 58 59 
1 0 -I 
9-
_> 7 -
K 
Is-
4 
1^ 
* 
v*^ 
'•i?w 
• ^ 
'V* . 
,'. 
' 
1 :1 , « 
/' , 
'''.S?' 
/ 
FL-9 
RMSE - 0.61 
Slope - 0 82 
R--0 75 
- ,—i—i—i—.—i 
LR(6.5) pHccli pHiijO pHbuifc 
FIGURE 4. Regression plots for MIR-PLS predicted vs. 'known' soil data analysed using 
conventional laboratory methods of analyses for (a.) pHcaci2, (b.) pHH2o, (c.) pHbUfrer and (d.) 
lime requirement to a target pH of 6.5. Given for each property are the number of factor 
loadings (FL) used in the predictions as well as the corresponding RMSE. n = 118 
Spatial Analysis 
An advantage of the MIR technique is that many properties may be determined from a single 
spectrum. For example in this instance, maps of soil pHcaci2, pHH2o, pHbuirer and lime 
requirement may be easily drawn from the MIR-PLS predictions (statistics of predictions 
given in lower half of Table 1). For brevity of presentation, only the spatial analysis of lime 
requirements will be presented. The exponential semi-variogram parameters for lime 
requirements derived using the response surface and those predicted using the MIR-PLS 
technique are shown in Table 2. 
TABLE 2. Exponential semi-variogram parameters for lime requirement to a target pH of 6.5 
(LR(6.5)) (Mg/ha) predicted using the response surface (RS) and mid infrared analysis (MIR) 
Co C CQ/CQ+C a'(m) 
RS LR(6.5) 
MIR LR(6.5) 
0.26 
0.15 
1.22 
1.15 
0.18 
0.12 
59 
56 
Note: a' is the the effective range, i.e. the lag where %a ') is approximately equal to (Co + 0.95C), 
The most notable difference in the semi-variogram parameters shown in Table 2 are the 
nugget variances (Co), Co being higher for the lime requirements derived using conventional 
laboratory methods and the response surface model. The proportion of nugget to sill variance 
for the latter is 18 %, while for the lime requirements predicted using the MIR-PLS technique 
is 12 %. This may suggest that the analytical precision of the MIR technique is higher than 
conventional analysis. Viscarra Rössel & McBratney (1998b) discussed the implications that 
the nugget variance has on soil sampling and analysis for precision agriculture. 
The kriged predictions of lime requirements are compared in Figure 5a and the kriged map of 
MIR-PLS predicted lime requirements shown in Figure 5b. 
Kriged lime requirements were comparable (Figure 5a). The larger nugget to sill ratio of the 
lime requirements derived by conventional analysis and using the response surface model 
resulted in a more even distribution of estimates having higher values and a slightly larger 
kriging variance (data not shown). Thus the lime requirement map derived from interpolation 
of the MIR-PLS predictions (Figure 5b) is slightly more variable and may be deemed suitable 
for variable-rate lime applications. 
CONCLUSIONS 
Mid-infrared analysis may be used to enhance existing information, with the possibility for its 
use in the development of a field-based proximal soil MIR sensing system. An advantage of 
501 
the technique is that from a single spectrum many properties may be determined, thus offering 
the possibility for considerable cost savings and increased efficiency over conventional 
laboratory soil extractions. Furthermore, the MIR technique is rapid, making it possible to 
analyse a large number of samples in a practical and timely manner. These properties make 
MIR-PLS analyses very attractive for spatial soil studies and precision agriculture. 
a. b. 
V ) 
5£. 
ei 
S 
7.5 • 
7 • 
6.5 -
6 -
5.5 -
5 • 
1 . 3 
4.5 
•* 
X 
l'' 
1 
5 
/ ' / _. "" 
/' / .*'" 
- • - ,j* 
r' 
y = 1.055X-0.331 
R2 = 0.91 
1 1 1 1 1 
5.5 6 6.5 7 7.5 
RS LR(6.5) 5 55 6 6.5 7 
FIGURE 5. (a.) regression plot between kriged mid infrared (MIR) and response surface (RS) 
lime requirements to a target pH of 6.5 LR(6.5), and (b.) MIR map of LR(6.5). All lime 
requirements in Mg/ha. 
REFERENCES 
Efron, B., Tibshirani, R.J. (1994). An Introduction to the Bootstrap. Monographs on Statistics 
and Applied Probability, No 57. CRC Press, pp. 436 
Janik, L.J., Skjemstad, J.O. and Raven, M.D. 1995. Characterisation and analysis of soils 
using mid-infrared partial least squares. I. Correlations with XRF-determined major 
element composition. Australian Journal of Soil research 33: 621-636. 
Janik, L.J., Merry, R.H. and Skjemstad, J.O. 1998. Can mid infra-red diffuse reflectance 
analysis replace soil extractions? Australian Journal of Experimental Agriculture, 
38(7): 681-696. 
Martens H., Nass, T. (1989). Multivariate Calibration. John Wiley & Sons, Chichester, p. 419. 
McCauley, J.D., Engel, B.A., Scudder, CE., Morgan, M.T. and Elliot, P.W. 1993. Assessing 
the spatial variability of organic matter. American Society of Agricultural Engineers. 
ASAE Paper No. 93-1555, St. Joseph, MI, USA. 
Viscarra Rössel R.A. and McBratney, A.B. 2001. A response-surface calibration model for 
rapid and versatile site-specific lime-requirement predictions in south-eastern Australia. 
Australian Journal of Soil Research 39: 185-201. 
Viscarra Rössel, R.A. and McBratney, A.B. 1998a. Laboratory evaluation of a proximal 
sensing technique for simultaneous measurement of clay and water content. Geoderma, 
85(1): 19-39. 
Viscarra Rössel, R.A. and McBratney, A.B. 1998b. Soil chemical analytical accuracy and 
costs: implications from Precision Agriculture. Australian Journal of Experimental 
Agriculture 38: 765-775. 
502 


A response-surface calibration model for rapid and versatile site-specific lime-requirement predictions in south-eastern Australia.

An Introduction to the Bootstrap. Monographs on Statistics and Applied Probability, No 57.

Assessing the spatial variability of organic matter.

Can mid infra-red diffuse reflectance analysis replace soil extractions?

Laboratory evaluation of a proximal sensing technique for simultaneous measurement of clay and water content.

Multivariate Calibration.

Soil chemical analytical accuracy and costs: implications from Precision Agriculture.

Proximal sensing of soil pH and lime requirement by mid infrared diffuse reflectance spectroscopy

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