5.2.1. Contact Probe Modification
A prototype of a soil probe was developed (Kusumo et al. 2008a), based on the plant contact probe supplied by Analytical Spectral Devices (ASD, Boulder, Colorado, USA) with an internal light source replaced by a stronger intensity of parabolic reflector halogen lamp (Welch Allyntm 4.5 watt). A round casing was developed to avoid ambient light and direct contact of the quartz probe window with the soil, and to provide a fixed distance (30.5 mm) between soil surface and the probe window. The soil core was rotated 360o, to give a field of view of 561 mm2 (Figure 5.1b).
(a)
(b)
(c) (d)
Figure 5.1 (a) Diffuse reflectance were collected from (a) soil core using (b, c) modified contact probe, then (d) the soil was sliced for laboratory analysis.
5.2.2. Site Locations, and Plant and Soil Property Measurement
On the day of harvest, soil cores (18 x 600 mm depth by 46 mm diameter) were collected from two sites within a field of 90-day-old maize (Zea mays, grown for silage) in Kairanga, Manawatu, New Zealand. At one site, the soil was dominantly silt loam and at the second site fine sandy loam (Gley Soils, Hewitt 1998). At each site, three replicate soil cores were taken at 0, 15 and 30 cm distance from the maize stem towards
the centre of the 60 cm row (see Figure 5.2a). The distance between each replicate was 40 cm. A soil core was sectioned at 5 depths (7.5, 15, 30, 45, and 60 cm) and at each depth the soil reflectance spectra was acquired in situ from the freshly cut surface using a purpose built soil probe attached by fibre optic cable to a field spectroradiometer (ASD FieldSpecPro, Boulder, Colorado, USA). A 1.5 cm soil slice (slice A) was taken above this surface to obtain root mass reference data (using wet sieve laboratory root measurement). A total of 90 samples were taken (Figure 5.2b). Root was separated from soil in slice A by washing it with tap water on the following sieve sizes: 710 μm, 500
μm, 355 μm, 250 μm, and 63 μm diameter. The root retained on the sieves of 710 μm, 500 μm and 355 μm was bulked and dried at 50oC oven for 3 days. Root density was presented in mg dry root / cm3.
Another 1.5 cm soil slice (slice B) was for total soil C and N determinations (LECO FP-2000 CSN Analyser, combustion method) (Blakemore et al. 1987), and water content measurement (Figure 5.2b). Total C and N concentrations (LECO-C and LECO- N in figure legends) were measured using 2-mm sieved air-dry soils from 40 soil slices selected from replicates 1, 2 and 3 at 30 cm distance from the stem, and soil cores at replicate 2 at 15 cm distance from the stem. Water content was determined gravimetrically by drying to constant weight at 105oC.
A soil slice C (adjacent to slice A; between each depth) (Figure 5.2b) was used to measure soil bulk density.
Chemical (pH H2O with ratio of soil and water 1 : 2.5, P-Olsen, P-retention, CEC,
SO4, K, Ca, Mg, Na) and physical properties (soil texture) of the two soils were each
determined using three soil samples, each a composite of three replicate soil cores taken from the 0-10 cm soil depth (Blackmore et al. 1987).
Biomass data of stem and cob were collected one day before harvesting and the fresh weight of each stem and cob was measured. Stem and cob dry matter values were obtained by drying them at 60oC for 12 days.
(a) (b)
Figure 5.2 (a) Position of soil core and (b) soil slice.
5.2.3. Developing Calibration Model
The spectroradiometer records spectra from 350-2500 nm with 1-nm spaced data. An average spectrum for each soil sample was acquired, which was the average of 10 replicate scans. Then, the spectral data were pre-processed (SpectraProc V 1.1, (Hueni and Tuohy 2006) by first eliminating the noisy data (350-470 nm and 2440-2500 nm), smoothing using Savitzky-Golay filter with window size 33 nm and polynomial order 4, reducing the data by taking every 5th waveband, transforming them into a first derivative and averaging the ten replicates recorded. The processed data were imported to Minitab 14 (MINITAB Inc. 2003) for partial least squares regression (PLSR) analysis. Calibration models were developed by PLSR-1 using processed spectral data and each of the reference data (root density, soil C and N). The PLSR calibration models were used to predict root density, soil C and N concentrations from unknown samples. The accuracy of the models was tested internally using leave-one-out cross- validation procedure. An external validation test was conducted only on root density prediction by allocating samples into separate calibration and validation sets with a 1:1 ratio. Separating these two sets was carried out by ranking the measured root density from the lowest to the highest root density, and odd and even ranked numbers were allocated to calibration and validation set, respectively. MINITAB automatically selected the optimum principal components (latent variables or factors) that produced
Plant row 60cm Plant row
0 cm 15 cm 30 cm 40 cm Depth Rep-1 Rep-2 Rep-3 7.5 cm 15 cm 30 cm 45 cm 60 cm Soil core position Plant samples 40 cm
the lowest PRESS (predicted residual error sum of square) using a cross-validation procedure (Miller and Miller 2005). During PLSR processing, samples which had a standardized residual > 2.0 were removed as outliers from the calibration and validation set (MINITAB Inc. 2003).
5.2.4. Principal Component Analysis
Prior to PLSR analysis, principal component analysis (PCA) was carried out to describe the pattern of variability in the first derivative of the spectral data. A score plot of the first two components which account for the highest variance of spectral data was used to illustrate the pattern of sample scattering.
5.2.5. Regression Model Accuracy
The ability of the PLSR model to predict soil properties was assessed using the following statistics. RMSE (root mean square error) is the standard deviation of the difference between the measured and the predicted soil property values. RMSE which is calculated from cross-validation and separate validation set is called RMSECV and RMSEP, respectively. RPD (ratio of prediction to deviation) is the ratio of the standard deviation of the measured value of soil properties to the RMSE. RER (ratio error range) is the ratio of the range of measured values of soil properties to the RMSE. The best prediction model is shown by the largest RPD, RER, r2 and the smallest error (RMSECV or RMSEP) (Kusumo et al. 2008a).