Monday, 10 July 2006 - 4:00 PM

Diffuse Reflectance Spectroscopy as a Major Input to the Soil Inference System.

Budiman Minasny, Alex McBratney, and Raphael Viscarra-Rossel. The Univ of Sydney, Faculty of Agriculture, Food and Natural Resorces, JRA McMillan Building A05, Sydney, NSW, 2006, Australia

Inference is defined as a process of deriving logical conclusions from the basis of empirical evidence and prior observations and conclusions rather than on the basis of direct observation. McBratney et al. (2002) proposed Soil Inference Systems (SINFERS) as a knowledge base to infer soil properties and populate the soil digital databases. SINFERS takes measurements with a given level of certainty and infers data that is not known with minimal uncertainties by means of logically linked predictive functions. These predictive functions in a non-spatial context are referred as pedotransfer functions. The basic assumption underlying SINFERS is if we know or are able to predict the basic fundamental properties of the soil, we should be able to infer all other physical and chemical properties using pedotransfer functions. PedoTransfer Functions (PTFs) relate basic soil properties to other more difficult or expensive to measure soil properties by means of regression and various data mining tools. The keys to soil inference systems are reliable inputs and the ability to link basic soil information. The most basic and useful sets of properties is particle-size distribution. Clay content has been demonstrated to influence many physical and chemical properties. The inputs to the inference system can be from various sources: - Soil survey, i.e. from a soil morphological description: field texture, pH, structure, colour. - Laboratory measurement of soil physical and chemical properties. - Spectroscopically, where several key physical and chemical properties can be predicted.

Diffuse reflectance spectroscopy (DRS) is attracting a lot of interest in the soil science community. It has a number of advantages over conventional methods of soil analyses: DRS is rapid, timely, cheaper and hence more efficient at obtaining the data when a large number of samples and analysis are required. Moreover, a single spectrum may be used to assess various physical, chemical and biological soil properties (Viscarra Rossel et al., 2003).. Until now, research in soil spectroscopy has focused on spectral calibration and prediction of soil properties using multivariate statistics. In this instance we show how these predictions may be used in an inference system to predict other important and functional soil properties using pedotransfer functions (PTFs). The aims of this paper are: to use of soil spectral calibration and predictions as input and complement to a soil inference system (SPEC-SINFERS), and to demonstrate the implementation of SPEC-SINFERS.First the basic soil properties are predicted from the spectra using partial least-squares (PLS) (Geladi and Kowalski, 1986). The predicted properties are: sand, silt and clay content, pH, organic C, and CEC. Both model and input uncertainties are quantified, the model uncertainty using bootstrap technique, while input uncertainty using Latin hypercube sampling with Monte Carlo simulation. From the predicted basic soil properties, other more difficult-to-measure properties can be derived. Examples are: - The water retention curve, - hydraulic conductivity characteristics, - soil pH buffering capacity. Model and input uncertainties are propagated through the calculations. Although the accuracy of the basic soil properties obtained from spectroscopy is lower than laboratory analysis, the efficiency of the measurements in terms of cost and time is much higher. SPEC-SINFERS uses soil spectra to estimate various basic soil properties which are then to infer other important and functional soil properties via pedotransfer functions. The important feature of SPEC-SINFERS is the propagation of both input and model uncertainties. References: (i) Geladi, P. and Kowalski, B.R. 1986. Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185: 1-17. (ii) McBratney, A.B., Minasny, B., Cattle, S.R., Vervoort, R.W., 2002. From pedotransfer functions to soil inference systems. Geoderma 109, 41-73. (iii) Viscarra Rossel, R.A., Walvoort, D.J.J., McBratney, A.B., Janik L.J. and Skjemstad, J.O. (2005). Visible, near-infrared, mid-infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties. Geoderma (in-press)

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