4.2.1. Site Locations and Soil Property Measurement
The root density assessment was evaluated at two permanent pasture sites: the first on Ramiha silt loam (Allophanic soil, Andic Dystrochrept derived from loess and andesitic ash) and the second on Manawatu fine sandy loam (Fluvial Recent soil, Dystric Fluventic Eutrochrept derived from greywacke alluvium) in the Manawatu region, New Zealand (Hewitt 1998). The permanent ryegrass (Lolium perenne) white clover (Trifolium repens)dominant pastures at both sites had been present for more than 20 years.A total of 18 soil cores, 10 m apart from each other, were collected from each site at 3 depths (15, 30 and 60 mm), providing 54 samples at each site and a total of 108 soil samples. Soil reflectance spectra were acquired at three horizontal surfaces (15, 30 and 60 mm) cut through an 80 mm diameter soil core (Figure 4.1c - 1e). A 3 mm soil slice (slice A), was collected, at each surface, and stored moist at 4oC for no more than 3 days, before root densities were measured (Figure 4.1f) using the wet sieve method (Kusumo et al. 2008a).Root density was expressed as mg dry root g-1 dry soil.
A further 3 mm soil slice (slice B) was collected and used for determining dry soil weight and soil moisture content (see Figure 4.1c). Water content of subsample slice B was determined using the gravimetric method of drying at 105oC in an oven until reaching a constant weight. For total C and N analysis, 42 and 36 samples were analysed from Ramiha and Manawatu soil respectively. Total C and N were measured from an air-dry subsample of slice B using a Leco FP-2000 CNS analyser (LECO Corp., St Joseph, MI, USA). Chemical (pH H2O with ratio of soil and water 1 : 2.5, P-
retention, Olsen-P, cation exchange capacity – CEC, sulphate, exchangeable-K -Ca -Mg and -Na, total C, total N) and physical properties (soil texture) of the two soils were also determined using three soil samples, each a composite of six replicate soil cores taken from the 0 – 100 mm soil depth (Blakemore et al. 1987). Bulk density was measured using separate soil cores.
(a) (b)
(c)
(d)
(e) (f)
Figure 4.1 (a) Modified soil probe was used (e) to collect reflectance spectra from (b and c) a soil core, and then (d) the soil slice (f) was washed to separate root from soil using wet sieve method.
4.2.2. Developing Calibration Model
Diffuse spectral reflectance of each freshly cut soil surface was recorded using a purpose-built soil probe (Figure 4.1a-e). The spectral data were pre-processed (for details see Kusumo et al., 2009a) and first derivatives of 5-nm spaced data calculated using SpectraProc V 1.1 software (Hueni and Tuohy 2006). The first derivative data were imported to Minitab 14 (MINITAB Inc. 2003) for partial least squares regression (PLSR) analysis. Calibration models were developed by using PLSR to fit the reference data (root density, soil C and N) to pre-processed spectral data. The resulting regression models were then used to predict root density, soil C and N in unknown samples. The accuracy of the models was tested internally using a leave-one-out cross-validation procedure and externally from separate validation sample set. Separate calibration and validation sets were determined by ranking the soil samples from the lowest to the highest root density, and odd and even ranked numbers were allocated to calibration and validation set, respectively. This resulted in 1:1 ratio of calibration and validation set. The number of principal components (latent variables or factors) used to develop calibration models were those which produce the lowest PRESS (predicted residual error sum of squares) in the leave-one-out cross-validation procedure (Miller and Miller 2005). During PLSR processing, samples which had a standardized residual > 2.0 were removed as outliers (MINITAB Inc. 2003) from the calibration and validation sets.
4.2.3. Principal Component Analysis
Prior to PLSR analysis, a principal component analysis (PCA) was conducted on the first derivative of the spectral data. A score plot of the first two components (PC1 and PC2) was used to explore the pattern of spectral differences between the Ramiha and Manawatu samples.
4.2.4. Predicting Field Root Density Using the Calibration Model
Constructed from Glasshouse Data
Different root densities were created (Kusumo et al. 2009a) by differential nitrogen and phosphorus fertilization of ryegrass (Lolium multiflorum Lam.) grown in pots of Ramiha and Manawatu soil in a glasshouse. A PLSR calibration model was developed from the first derivative spectra and root density data from the glasshouse grown ryegrass plants. This calibration model was used to predict root density in this study.
The root density unit used in the glasshouse study was mg dry root cm-3 and using pot bulk densities was converted to mg dry root g-1 dry soil to make it compatible with the measurements made in the field study.
4.2.5. Regression Model Accuracy
The ability of PLSR models 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 is called RMSECV, and from validation data is RMSEP. RPD (ratio of prediction to deviation) is the ratio of the standard deviation of 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 highest RPD, RER, r2 and the lowest RMSECV or RMSEP (for more detailed explanation see Kusumo et al., 2009a).