1394 SSSAJ: Volume 72: Number 5 • September–October 2008

Soil Sci. Soc. Am. J. 72:1394-1400doi:10.2136/sssaj2007.0188Received 24 May 2007.*Corresponding author (g.tranter@usyd.edu.au).© Soil Science Society of America677 S. Segoe Rd. Madison WI 53711 USAAll rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.

Extensive research has shown MIR spectroscopy to be use-ful in providing cheap, rapid, and relatively accurate pre-

dictions for a number of soil properties (Viscarra Rossel et al., 2006). While MIR spectroscopy has been shown to be effec-tive in predicting fundamental properties, a growing body of work shows that it can provide accurate predictions for more complex properties such as soil moisture retention. Chang et al. (2001) suggested that the ability for spectroscopy to predict secondary properties may be due to correlations with primary properties with a primary spectral response. Similarly, Janik et al. (2007) suggested that, as MIR is sensitive to many organic

and inorganic components affecting soil structure, spectros-copy may provide accurate predictions of moisture retention.

As an alternative to spectroscopy, statistical functions (PTFs) have been developed where predictions are made by quantifying relationships between the soil moisture character-istic and existing or readily acquired data such as particle-size distribution and organic C (Rawls et al., 2004). Briggs and Shantz (1912) fi rst showed a relationship between soil com-position and moisture retention, specifi cally the permanent wilting point. Despite changes in equipment, analysis, and statistical procedures, a vast majority of work still recognizes particle-size distribution and bulk density as the most reliable moisture retention predictors, while contradictory results have been reported for organic C. For their respective data sets, soil moisture retention PTFs have proven to be relatively accu-rate but, as with any PTF application, poorer performance is observed when empirical models are applied to soils dissimilar to the training data (McBratney et al., 2002). Cresswell et al. (2006) found, however, that functions derived from Australian soils performed similarly well when applied to French soils and suggested that functions without an empirical basis have greater transferability.

G. Tranter*B. MinasnyA. B. McBratneyFaculty of Agriculture, Food and Natural Resour.Univ. of SydneyNSW, Australia

R. A. Viscarra RosselFaculty of Agriculture, Food and Natural Resour.Univ. of SydneyNSW, Australia

Currently atCSIRO Land and WaterBruce E. Butler LaboratoryCanberraACT, Australia

B. W. MurphyNew South Wales Dep. of Environ, and Climate ChangeCowraNSW, Australia

SOIL

PH

YSI

CS

Comparing Spectral Soil Inference Systems and Mid-Infrared Spectroscopic Predictions of Soil Moisture Retention

Mid-infrared spectroscopy has been proposed as a cheap yet accurate alternative to a number of laboratory methods for measuring soil properties. While accurate predictions of a number of basic soil constituents have been reported, properties associated with soil structure have received far less attention. In this study, we looked at the effi cacy of mid-infrared refl ectance spectroscopy in predicting moisture retention and whether better predictions can be achieved using pedotransfer functions using spectroscopic predictions of basic soil constituents as inputs. Three methods were used to predict volumetric moisture retention: (i) mid-infrared (MIR) spectra and partial least squares regression, (ii) a neural network pedotransfer function (PTF) using laboratory particle-size distribution and bulk density data, and (iii) a pedo-transfer function with MIR-predicted particle-size distribution and bulk density as inputs. We used Lin’s concordance correlation coeffi cient as a goodness-of-fi t measure. Predictions of volumetric moisture retention on intact structured soils were generally poor, particularly at the wet end. Improved predictions were observed at dry-end matric potentials, where moisture retention was more correlated with particle-size distribution than soil structure. The neural network PTF was found to have better goodness of fi t for all matric potentials; however, predictions at larger matric potentials (wet end) were still poor due to poor bulk density predictions. In light of this work, we propose that while MIR spectroscopy may be a valuable predictor of fundamental soil constituents such as particle-size fractions and organic C, predictions of soil properties dependant on soil structure, such as volumetric moisture retention, may prove diffi cult. Mid-infrared spectroscopy in combination with PTFs should provide improvements to moisture retention predictions through improved representation of the infl uential processes, namely soil structure and adsorptive forces.

Abbreviations: LAB-PTF, estimation of hydraulic properties using pedotransfer functions with input from laboratory-measured data; MIR, mid-infrared; NSW, New South Wales; PLSR, partial least squares regression; PTF, pedotransfer function; SALIS, Soil and Land Information System; SPEC-SINFERS, spectral soil inference systems.

SSSAJ: Volume 72: Number 5 • September –October 2008 1395

Despite their similar objectives, developments in MIR spectroscopy and PTFs have progressed independently. Founding work by McBratney et al. (2006) suggested the concept of spectral soil inference systems (SPEC-SINFERS) that link spectroscopy with PTFs to improve predictions of properties with poor spectral response. Such an approach exploits MIR as a cheap, rapid, and accurate provider of basic soil information, reducing the cost associated with PTF input data capture.

In this study, the SPEC-SINFERS approach was applied to the prediction of soil moisture retention and compared with direct MIR predictions. As such, the aims of this study were: (i) to assess the accuracy of MIR spectroscopy in predicting moisture retention of crushed (<2-mm) samples and intact soil cores; (ii) to assess the accuracy of the SPEC-SINFERS approach for the prediction of volumetric moisture content of intact soil cores; and (iii) to compare the advantages and limi-tations of both MIR and SPEC-SINFERS methods.

Although a number of studies have found near-infrared spectroscopy to be effi cient in predicting gravimetric moisture content (Chang et al., 2001; Hummel et al., 2001; Whiting et al., 2004) little attention has been paid to predicting moisture retention. Of note, Janik et al. (1998, 2007) reported accu-rate predictions for soil moisture retention using MIR spectra and partial least squares regression. Results from this study will complement the work by Janik et al. (1998, 2007) and assess the ability of MIR spectroscopy in predicting soil moisture retention at numerous matric potentials.

MATERIALS AND METHODSSample Preparation and Spectroscopy Analysis

Two Australian data sets were used in the study: (i) volumetric moisture retention of intact soil cores from the Geeves et al. (1995) study, hereby known as the Geeves data set; and (ii) gravimetric moisture retention of crushed (<2-mm) soil samples, obtained from the Soil and Land Information System (SALIS), supported by the Department of Natural Resources, New South Wales (NSW), hereby known as the SALIS data set.

As can be seen in Fig. 1, the Geeves data set represents soils from southern NSW and northern Victoria, with agriculture being the dominant land use. Table 1 shows the diversity of soil texture in the

data set and the generally low organic C. Volumetric moisture reten-tion (θ) was determined on core samples using ceramic tension plates and a pressure plate apparatus. Soil core samples 0.098 m in diameter and 0.075 m in depth were used to determine volumetric moisture retention at saturation (θsat) and −10 kPa (θ−10kPa) matric potential, while cores 0.048 m in diameter and 0.015 m in depth were used to determine volumetric moisture retention at matric potentials −66 kPa (θ−66kPa), −100 kPa (θ−100kPa), −500 kPa (θ−500kPa), and −1500 kPa (θ−1500kPa). Samples were analyzed using cores to preserve soil structure. To enable calculation of volumetric moisture content, bulk densities were calculated using the oven-dry weight of core sample material. The data were randomized and split to produce a training set of 85 samples for model construction and a validation set of 25 samples for model evaluation and validation.

The SALIS data set was provided courtesy of the NSW Department of Natural Resources. This data set contains particle-size distribution, organic C, and gravimetric moisture retention data. Gravimetric moisture retention for fi eld capacity (−10 kPa) and per-manent wilting point (−1500 kPa) was determined on crushed sam-ples (<2 mm) using pressure plates as described by New South Wales Department of Natural Resources (2005).

Fig. 1. Geeves (open circles) and SALIS data points (fi lled circles) lo-cated within the states of New South Wales (NSW) and Victoria (VIC), Australia.

Table 1. Statistical description of laboratory analysis data.

Data set Property†Training set Test set

n Mean Min. Max. SD n Mean Min. Max. SD

Geeves

θsat, m3 m−3 85 0.36 0.22 0.55 0.05 25 0.40 0.25 0.58 0.07

θ−10kPa, m3 m−3 85 0.27 0.16 0.45 0.05 25 0.32 0.20 0.46 0.08

θ−66kPa, m3 m−3 85 0.22 0.08 0.35 0.06 25 0.26 0.13 0.39 0.07

θ−100kPa, m3 m−3 85 0.22 0.09 0.35 0.06 25 0.24 0.14 0.38 0.07

θ−500kPa, m3 m−3 85 0.18 0.06 0.32 0.06 25 0.20 0.11 0.34 0.07

θ−1500kPa, m3 m−3 85 0.15 0.05 0.29 0.06 25 0.17 0.08 0.33 0.07

clay, % 85 23.0 8.0 70.0 13.7 25 31.7 12 66.0 15.6sand, % 85 60.8 13.0 83.0 13.9 25 51.9 23 76.0 15.1organic C, % 85 1.4 0.14 7.2 1.3 25 1.1 0.2 5.0 1.2

SALIS

moisture retention at −10 kPa, kg kg−1 500 0.33 0.08 0.84 0.13 279 0.34 0.01 0.92 0.14moisture retention at −1500 kPa, kg kg−1 500 0.15 0.05 0.41 0.08 279 0.15 0.07 0.44 0.08clay, % 436 35.9 0.0 88.9 17.6 247 34.4 2.0 86.0 17.7sand, % 436 45.5 2.0 98.0 19.9 247 47.5 5.0 93.9 21.0organic C, % 436 0.59 0.05 5.67 0.72 247 0.56 0.03 2.30 0.52

† θ is volumetric water content; subscripts denote tested matric potential.

1396 SSSAJ: Volume 72: Number 5 • September–October 2008

Mid-infrared spectra were collected using approximately 400 mg of ground (<200-μm) air-dried soil in a Bruker TENSOR 37 FT-IR spec-trometer with a diffuse refl ectance attachment (Bruker Corp., Billerica, MA). Scans were collected in the wave number range of 400 to 4000 cm−1 (or 2500–25,000 nm) at 8 cm−1 resolution. Samples were ground to <200 μm and oven dried at 40°C for 8 h before scanning.

Model Development and TestingSpectral models were developed with partial least squares regres-

sion (PLSR) on mean centered log(1/refl ectance) data, using ParLeS, a chemometrics software package for multivariate modeling and predic-tion (Viscarra Rossel, 2008). Partial least squares regression is a gener-alization of multiple linear regression and is used to analyze noisy or strongly collinear data with numerous predictor variables. The PLSR algorithm identifi es orthogonal factors that maximize the covariance between predictor and response variables (Viscarra Rossel et al., 2006).

ParLeS uses leave-one-out cross-validation and the Akaike Information Criterion to identify the most parsimonious number of factors or input parameters to use in the partial least squares models. Model performance was evaluated using the independent validation data set. Predictive models were developed for the following properties:

1. Partial least squares models for particle-size distribution and organic C content were trained and tested using the SALIS data set.

2. Gravimetric moisture retention at fi eld capacity (−10kPa) and the permanent wilting point (−1500kPa) partial least square models were trained and tested using the SALIS data set. Note that retention data were determined using crushed soil samples (<2 mm).

3. Partial least squares models for volumetric moisture retention (at matric potentials 0 [saturated], −10, −66, −100, −500, and −1500 kPa) were trained and tested using the Geeves data set. Note that retention data were determined using intact soil cores.

Moisture retention was also predicted using a neural network model using the Neuro-m method (Minasny and McBratney, 2002). The PTF predicts parameters of the van Genuchten equation from particle-size dis-tribution and bulk density and yields a continuous θ(h) relationship, where h is the pressure head. The model was applied only to the Geeves data set, as volumetric moisture content was unavailable for the SALIS data.

The PTF uses particle-size distribution and soil bulk density to predict parameters of the van Genuchten equation: residual (θr) and saturated water content (θs), a scaling parameter (α), a curve shape factor (n), and an empirical constant (m).

First, to assess the accuracy of the neural-network model, the moisture retention parameters were predicted using laboratory par-ticle-size distribution and bulk density data as described by Minasny and McBratney (2002). Second, the moisture-retention parameters were predicted using the particle-size distribution predicted from the MIR spectra. Soil bulk density was predicted using a published PTF (Tranter et al., 2007), with spectral predictions of particle-size distri-bution and organic C as inputs. The function has the form

( ) ( )( )

2b 1.2 0.14 ln OC 0.0021 0.00006 47.95

0.043ln

S S

d

ρ = + + − −−

where ρb is bulk density (g cm−3), OC is organic C (%), S is the sand size fraction (20–2000 μm, %), and d is sample depth (m). This func-tion was chosen as it was trained on soils similar to those in this study.

The accuracy of all predictive models was evaluated on an inde-pendent validation set using the concordance correlation coeffi cient (ρc) and RMSE performance criteria. The concordance correlation coeffi cient was fi rst introduced by Lin (1989) to evaluate the agree-ment between paired readings and is of the form

2 2 2

2( )xy

cx y

ss s x y

ρ =+ + −

where 2 2

1

1 ( )n

x ii

s x xN =

= −∑ ,2 2

1

1 ( )n

y ii

s y yN =

= −∑ , 1

1 ( )( )n

xy i ii

s x x y yN =

= − −∑ ,

x , and y are sample means for populations X and Y, and xi and yi are paired ith values from populations X and Y. Lin’s concordance corre-lation coeffi cient measures the agreement between measured and pre-dicted samples, or how close the model prediction falls along a 45° line from the origin with the measured data (i.e., has a slope of exactly 1).

The value of the index is scaled between −1 and 1, with a value of 1 representing complete agreement between all paired sites. The mea-sure can only be applied to paired data (e.g., observed vs. predicted) and as such Pearson’s correlation (r) is used as a measure of correlation between moisture retention and its explanatory variables: particle-size fraction and bulk density.

Where applicable, performance statistics are reported for both the cross-validation and independent validation data sets. In this study, model performance is based on the validation results, while cross-validation results are provided to allow comparison with previ-ous studies lacking independent evaluation. The following volumetric moisture-retention models were compared based on the aforemen-tioned criteria:

1. Volumetric moisture retention was directly predicted using MIR spectra and PLSR, hereby known as MIR-PLSR.

2. Predictions of volumetric moisture retention were made via the neural network PTF, using particle-size distribution, organic C, and bulk density laboratory data, hereby known as LAB-PTF.

3. Particle-size distribution and organic C were predicted using MIR-PLSR; bulk density was then estimated from predicted sand size fraction and organic C. The resulting predictions of particle-size distribution (from MIR) and bulk density were then fed to the neural network PTF to provide volumetric moisture retention predictions, hereby known as SPEC-SINFERS.

The three schemes are illustrated in Fig. 2.

RESULTS AND DISCUSSIONA scatter plot of Principal Components 1 and 2 for the

SALIS (n = 500) and Geeves (n = 85) training data is shown in Fig. 3. The fi rst two components account for 85% of the spec-tral variation in this study. The overlap of the two sets indicates similar spectral characteristics for the two data sets (Islam et al., 2005). The greater variation exhibited by the SALIS data set is not surprising considering the wide spatial distribution of the data, while the Geeves data represent a far smaller and less diverse sampling area.

Particle-Size Distribution and Organic CarbonAs shown in Table 2, PLSR models from the soil spec-

tra produced good predictions for soil organic C and sand

SSSAJ: Volume 72: Number 5 • September –October 2008 1397

(20–2000-μm) and clay (<2-μm) size fractions for both the Geeves and SALIS validation data sets. These fi ndings are in agreement with previous soil spectroscopic research (Janik et al., 1998; Viscarra Rossel et al., 2006).

The partial least square predictions of particle-size dis-tribution and organic C were subsequently used to predict bulk density via a PTF. The PTF prediction was found to perform comparably to previous studies (ρc = 0.50, RMSE = 0.13 g cm−3). Direct prediction of bulk density by MIR-PLSR was comparable in terms of RMSE (0.12 g cm−3) yet performed poorly in terms of goodness of fi t (ρc = 0.36).

If spectral predictions of sand, clay, organic C, and bulk density are to be used as inputs in PTFs, accurate predictions are crucial to ensure minimal input uncertainty.

Moisture RetentionInspection of the regression coeffi cients provides qualitative

information relating spectral features to the property of interest (Janik et al., 2007). Diagnostic spectral features are character-ized by well-defi ned positive peaks, ideally isolated from spectral negative troughs, which represent model interference.

The PLSR coeffi cients are shown in Fig. 4. Prominent peaks for the SALIS data occur at 3630, 3200, 1640, and 1480 cm−1. Peaks at 3630 and 3200 cm−1 are related to clay minerals, in particular smectite, gibbsite, and kaolinite. The peak at 1640 cm−1 is associated with organic C, while quartz and carbonates have shown response in the region around 1480 cm−1. Previous work in MIR (Janik et al., 2007) and PTFs (Minasny and McBratney, 2002; Williams et al., 1983)

Fig. 3. Scatter plot of Principal Component 1 and Principal Compo-nent 2 for soil spectra of the Geeves (n = 110; Geeves et al., 1995) and SALIS (n = 697) data sets. Overlap of 95% confi -dence lines indicates similar spectral responses

Table 2. Performance summary of partial least squares regres-sion models for constituent soil properties applied to the Geeves and SALIS validation data sets.

Propertyρc† RMSE

Geeves SALIS Geeves SALIS

Clay size particle 0.89 0.89 9.1 8.50Sand size particle 0.91 0.79 10.4 10.10Organic C percentage 0.88 0.89 0.4 0.36

† Lin’s concordance correlation coeffi cient between predicted and measured water retention.

Fig. 4. Partial least squares regression coeffi cients for water retention at −10 kPa (thin line) and −1500 kPa (thick line) for SALIS (left) and Geeves (right) training data sets.

Fig. 2. Diagram of the three schemes—mid-infrared spectroscopy and partial least squares regression (MIR-PLSR), pedotransfer func-tions with input from laboratory data (LAB-PTF), and spectral soil inference systems (SPEC-SINFERS)—used to predict soil moisture retention (BD, bulk density; h, pressure head; θ, volu-metric water content).

1398 SSSAJ: Volume 72: Number 5 • September–October 2008

also related clay content, mineralogy, and organic C content to soil moisture retention.

Extensive research shows that moisture retention is a com-plex function of soil composition and structure (Rawls et al., 1991, 2004). More specifi cally, Cresswell (2002) suggested that at near-zero matric potentials, moisture retention is largely a function of capillarity and pore-size distribution, while at more negative potentials, adsorptive forces related to particle-size dis-tribution and mineralogy are more infl uential. In light of this, the similarities in spectral response for moisture retention at

−10 kPa (w−10kPa) and −1500 kPa (w−1500kPa) are interesting given the differing mechanisms governing moisture retention at their respective matric potentials.

The use of crushed soil samples (<2 mm) to determine moisture retention means that capillary water associated with the “natural” soil macrostructure is unaccounted for. As a result, adsorptive forces associated with particle-size distribution and organic C are more infl uential, even at wet-end matric potentials. As MIR is sensitive to such elemental constituents, it is under-standable that spectroscopy can provide accurate predictions where soil structure is not involved, i.e., on ground soil samples or at very negative potentials.

On the Geeves samples, a broad peak at approximately 3700 cm−1 represents the only signifi cant spectral feature cor-

related to θ−1500kPa. The peak at 3700 cm−1 is associated with clay minerals and concurs with numerous pedotransfer stud-ies relating moisture reten-tion to soil texture (Rawls et al., 1991; Wösten et al., 2001). Coeffi cients for θ−10kPa showed very few diagnostic features, signifying poor spec-tral response. The regression coeffi cients for both θ−10kPa and θ−1500kPa were observed to be up to a magnitude of 10 less

than the observed coeffi cients of w−10kPa and w−1500kPa. This is a further indication that MIR is relatively insensitive to volu-metric moisture retention on intact, structured soils.

Table 3 shows the performance of MIR-PLSR in predict-ing moisture retention. On the crushed SALIS samples, good concordance correlation coeffi cients were recorded for both w−10kPa (ρc = 0.85) and w−1500kPa (ρc = 0.89). On the intact Geeves samples, however, direct predictions of volumetric mois-ture retention were generally very poor. As with the SALIS data set, performance of the Geeves data set model was observed to improve with decreasing matric potential. At θsat and θ−10kPa, no signifi cant correlations were observed and, while still considered poor, predictions for θ−1500kPa were signifi cantly more accurate. Figure 5 shows that MIR-PLSR predictions of θ−10kPa are poorly distributed and appear to be adhering to a mean around 0.25 m3 m−3. With decreasing matric potential, predictions adhered to the 1:1 line better, refl ected in the improved RMSE.

As shown in Fig. 5, SPEC-SINFERS predictions of θ−10kPa were very poor and were systematically overestimated at low mois-ture contents and underestimated at high moisture contents. As matric potential decreased, SPEC-SINFERS predictions were observed to be better distributed, with improved adherence to the 1:1 line. At −10 kPa, LAB-PTF (ρc = 0.48) performed con-siderably better than the SPEC-SINFERS (ρc = 0.15) model. As

matric potential decreased, however, the differ-ence between LAB-PTF and SPEC-SINFERS performance decreased.

Table 4 shows that predictions from both pedotransfer models, SPEC-SINFERS and LAB-PTF, returned improved predictions for all potentials. Very poor predictions were again recorded for wet-end matric potentials of the SPEC-SINFERS model.

To determine the effect of soil structure and adsorptive forces on moisture retention, simple univariate models were fi tted. Bulk density and clay percentage were used as surro-gate measures of soil structure and adsorptive forces, respectively. As shown in Table 4, the infl uence of bulk density on moisture reten-tion was shown to decrease with decreasing matric potential. Conversely, clay percentage was shown to be infl uential at very negative matric potentials while proving no signifi cant

Table 3. Cross-validation and test set statistics for partial least squares (PLS) regression models.

Data set

PropertyMatric

potentialCross-validation Test setPLS factors n n ρc† RMSE MEP‡

kPaGeeves volumetric water content, m3 m−3 saturation 1 85 25 0.05 0.08 0.04

−10 1 85 25 0.02 0.08 0.04−66 2 85 25 0.35 0.06 0.02

−100 2 85 25 0.43 0.06 0.01−500 2 85 25 0.53 0.06 0.01

−1500 2 85 25 0.50 0.06 −0.01SALIS moisture retention, kg kg−1 −10 9 500 279 0.85 0.08 0.00

−1500 9 500 279 0.89 0.04 0.00† Lin’s concordance correlation coeffi cient between predicted and measured water retention.

‡ Mean error of prediction.

Table 4. Performance summary of pedotransfer functions and relationship between volumetric moisture content measured at various matric potentials and measured bulk density and clay percentage for the Geeves data set.

Matric potential

Explaining variable Prediction methods†

Bulk density Clay MIR-PLSR SPEC-SINFERS LAB-PTF

r‡ RMSE r RMSE ρc§ RMSE ρc RMSE ρc RMSE

Saturation −0.75 0.05 0.09 0.06 0.05 0.08 0.12 0.08 0.56 0.09

−10 kPa −0.55 0.07 0.04 0.06 0.02 0.08 0.15 0.08 0.48 0.06

−66 kPa −0.1 0.07 0.61 0.05 0.35 0.06 0.55 0.06 0.65 0.05

−100 kPa 0.00 0.08 0.69 0.05 0.43 0.06 0.59 0.06 0.62 0.06

−500 kPa 0.00 0.09 0.71 0.05 0.53 0.06 0.64 0.06 0.70 0.05

−1500 kPa 0.00 0.09 0.73 0.05 0.50 0.06 0.67 0.06 0.71 0.05

† MIR-PLSR, direct prediction using mid-infrared spectra and partial least squares regression; LAB-PTF, pedotransfer functions with input from laboratory data; SPEC-SINFERS, neural network pedotransfer functions with inputs derived from mid-infrared spectra.

‡ r is Pearson’s correlation coeffi cient between the variable and water retention.

§ ρc is Lin’s concordance correlation coeffi cient between predicted and measured water retention.

SSSAJ: Volume 72: Number 5 • September –October 2008 1399

correlation at wet-end matric poten-tials. Considering this, it is under-standable that SPEC-SINFERS predictions improve with decreasing matric potential, as MIR predic-tions of clay percentage were rela-tively accurate while bulk density predictions were less accurate. Poor predictio sity refl ect the inability of PTFs to ns of bulk dendescribe soil structure using basic soil constitu-ents (Tomasella et al., 2003).

Janik et al. (2007) suggested that soil structure and soil moisture retention can, in part, be described by the underlying soil composition and chemistry such as organic C, particle-size distribution, and min-eralogy. Rawls et al. (1991) wrote, however, that the soil moisture characteristic is a function of com-plex relationships between intrin-sic soil components and extrinsic environmental factors. Williams et al. (1983), in agreement, suggested that it is unlikely that unique ana-lytical relationships exist. It is the inability to characterize such inter-actions that limits the accuracy of both pedotransfer and spectro-scopic predictions (Tomasella et al., 2003). By including morphologi-cal soil structure data into PTFs, improvements to moisture reten-tion predictions have been reported (McKenzie and MacLeod, 1989; O’Connell and Ryan, 2002).

CONCLUSIONSIn this study, direct prediction of soil water retention on

intact structured soil samples using MIR spectroscopy was very poor, particularly at wet-end matric potentials. Improved predictions of moisture retention were achieved using PTFs where the inputs were provided via spectroscopic predictions of particle-size fraction and organic C.

This study supports the SPEC-SINFERS concept whereby soil spectra are used to provide estimates of basic soil properties, which are used as inputs in PTFs to predict other important or functional soil properties (McBratney et al., 2006). The system exploits spectroscopy as a cheap, rapid, and accurate method of data capture as an alternative to laboratory analysis for PTF input data.

ACKNOWLEDGMENTSWe wish to acknowledge the following for their respective contributions: the Australian Research Council for funding this work under the Linkage project entitled “Soil Inference Systems”; Greg Chapman, Department of Environment and Climate Change, NSW, for the provision of soil samples and data; and Paul McDougall for analysis and spectra interpretation.

REFERENCESBriggs, L.G., and H.L. Shantz. 1912. The wilting coeffi cient and its indirect

measurement. Bot. Gaz. 53:20–37.Chang, C., D.A. Laird, M.J. Mausbach, and C.R. Hurburgh. 2001. Near-

infrared refl ectance spectroscopy: Principal components regression analyses of soil properties. Soil Sci. Soc. Am. J. 65:480–490.

Cresswell, H.P. 2002. The soil water characteristic. p. 59–84. In N.J. McKenzie et al. (ed.) Soil physical measurement and interpretation for land evaluation. CSIRO Publ., Collingwood, VIC, Australia.

Cresswell, H.P., Y. Coquet, A. Bruand, and N.J. McKenzie. 2006. The transferability of Australian pedotransfer functions for predicting water retention characteristics of French soils. Soil Use Manage. 22:62–70.

Geeves, G.W., H.P. Cresswell, B.W. Murphy, P.E. Gessler, C.J. Chartres, I.P. Little, and G.M. Bowman. 1995. The physical, chemical and morphological properties of soils in the wheat-belt of southern NSW and northern Victoria. NSW Dep. of Conservation and Land Manage., Sydney.

Hummel, J.W., K.A. Sudduth, and S.E. Hollinger. 2001. Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor. Comput. Electron. Agric. 32:149–165.

Islam, K., A.B. McBratney, and B. Singh. 2005. Rapid estimation of soil variability from the convex hull biplot area of topsoil ultra-violet, visible and near-infrared diffuse refl ectance spectroscopy. Geoderma 128:249–257.

Janik, L., R.H. Merry, S.T. Forrester, D.M. Lanyon, and A. Rawson. 2007. Rapid prediction of soil water retention using mid infrared spectroscopy. Soil Sci. Soc. Am. J. 71:507–514.

Janik, L., R.H. Merry, and J.O. Skjemstad. 1998. Can mid infra-red diffuse refl ectance analysis replace soil extractions? Aust. J. Soil Res. 38:681–696.

Lin, L.I. 1989. A concordance correlation coeffi cient to evaluate reproducability.

Fig. 5. Measured and predicted volumetric moisture content (θ) of Geeves training (open circles) and validation data sets (fi lled circles) using mid-infrared spectroscopy and partial least squares regression (MIR-PLSR), pedotransfer functions with input from laboratory data (LAB-PTF), and spectral soil inference systems (SPEC-SINFERS).

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Biometrics 45:255–268.McBratney, A.B., B. Minasny, S.R. Cattle, and R.W. Vervoort. 2002. From

pedotransfer functions to soil inference systems. Geoderma 109:41–73.McBratney, A.B., B. Minasny, and R.A. Viscarra Rossel. 2006. Spectral soil

analysis and inference systems: A powerful combination for solving the soil data crisis. Geoderma 136:272–278.

McKenzie, N.J., and D.A. MacLeod. 1989. Relationships between soil morphology and soil properties relevant to irrigated and dryland agriculture. Aust. J. Soil Res. 27:235–258.

Minasny, B., and A.B. McBratney. 2002. The Neuro-m method for fi tting neural network parametric pedotransfer functions. Soil Sci. Soc. Am. J. 66:352–361.

New South Wales Department of Natural Resources. 2005. Soil survey standard test methods. Available at www.dnr.nsw.gov.au/care/soil/soil_pubs/soil_tests/soil_test_methods.html (verifi ed 31 May 2008). Dep. of Natural Resources, Sydney.

O’Connell, D.A., and P.J. Ryan. 2002. Prediction of three key hydraulic properties in a soil survey of a small forested catchment. Aust. J. Soil Res. 40:191–206.

Rawls, W.J., T.J. Gish, and D.L. Brakensiek. 1991. Estimating soil water retention from soil physical properties and characteristics. Adv. Soil Sci. 16:213–234.

Rawls, W.J., A. Nemes, and Ya.A. Pachepsky. 2004. Effect of soil organic carbon on soil hydraulic properties. p. 95–115. In Ya.A. Pachepsky

and W.J. Rawls (ed.) Development of pedotransfer functions in soil hydrology. Elsevier, Amsterdam.

Tomasella, J., Ya.A. Pachepsky, S. Crestana, and W.J. Rawls. 2003. Comparison of two techniques to develop pedotransfer functions for water retention. Soil Sci. Soc. Am. J. 67:1085–1092.

Tranter, G., B. Minasny, A.B. McBratney, B.W. Murphy, N.J. McKenzie, M. Grundy, and D. Brough. 2007. Building and testing conceptual and empirical models for predicting soil bulk density. Soil Use Manage. 23:437–443.

Viscarra Rossel, R.A. 2008. ParLeS: Software for chemometric analysis of spectroscopic data. Chemom. Intell. Lab. Syst. 90:72–83.

Viscarra Rossel, R.A., D.J.J. Walvoort, A.B. McBratney, L. Janik, and J.O. Skjemstad. 2006. Visible, near infrared or combined diffuse refl ectance spectroscopy for simultaneous assessment of various soil properties. Geoderma 131:59–75.

Whiting, M.L., L. Li, and S.L. Ustin. 2004. Predicting water content using Gaussian model on soil spectra. Remote Sens. Environ. 89:535–552.

Williams, J., R.E. Prebble, W.T. Williams, and C.T. Hignett. 1983. The infl uence of texture, structure and clay mineralogy on the soil moisture characteristic. Aust. J. Soil Res. 21:15–32.

Wösten, J.H.M., Ya.A. Pachepsky, and W.J. Rawls. 2001. Pedotransfer functions: Bridging the gap between available basic soil data and missing soil hydraulic characteristics. J. Hydrol. 251:123–150.