Phosphorus-availability tests typically provide an indication of quantity of P available (Colwell bicarbonateextractable P), or of the intensity of supply (0.01 M CaCl 2 -extractable P). The soil's capacity to buffer P is more difficult to assess, and is generally estimated using a P-adsorption curve. The diffusive gradient in thin films (DGT) approach may provide a simpler means of assessing a soil's ability to maintain soil solution P. Optimal extraction conditions were found to be 24 h exposure of DGT samplers to saturated soil. The DGT approach was evaluated on a range of 24 soils, some of which had high Colwell-(>100 µg g −1 ) and Bray 1-(>30 µg g −1 ) extractable P content, but showed a tomato (Lycopersicon esculentum Mill.) yield response to the addition of P fertilizer. The DGT approach provided an excellent separation of soils on which tomato showed a yield response, from those where fertilizer P did not increase dry-matter yield. Phosphorus accumulation was strongly correlated with soil solution P concentration and anion exchange resin-extractable P, but showed poor correlation with Colwellor Bray 1-extractable P. The DGT P accumulation rate of 3.62 × 10 −7 to 4.79 × 10 −5 mol s −1 m −3 for the soils tested was comparable to the uptake rate of roots of tomato plants that were adequately supplied with P (2.25 × 10 −5 mol s −1 m −3 ).Abbreviations: DGT -diffusive gradient in thin films; ICPAES -inductively coupled plasma atomic emission spectroscopy.

A field method has been developed for rapid in situ assessment of soil carbon (C) and nitrogen (N) content using a portable spectroradiometer (ASD FieldSpecPro). The technique was evaluated at 7 field sites in permanent pasture, and in 1-year, 3-year, and 5-year pine-to-pasture conversions on Pumice, Allophanic, and Tephric Recent Soils in the Taupo and Rotorua region of New Zealand. A total of 210 samples were collected from 2 depths: 37.5 and 112.5 mm. Field measurement of diffuse spectral reflectance was recorded from a flat sectioned horizontal soil surface of a soil core using a purpose-built contact probe attached by fibre optic cable to the spectroradiometer. A 15-mm soil slice was collected from each cut surface for analysis of total C and N using a LECO Analyser. Soils had a wide range of total C and N (0.26–11.21% C, 0.02–1.01% N). Partial least-squares regression analysis was used to develop calibration models between smoothed-first derivative 5-nm-spaced spectral data and LECO-measured total C and N. The models successfully predicted total C and N in the validation sets with the best prediction for C (RPD 2.01, r2 0.75, RMSEP 1.21%) and N (RPD 2.66, r2 0.86, RMSEP 0.07%). Prediction accuracy using different selection methods of calibration and validation set is reported. This study indicates that in situ assessment of soil C and N by field spectroscopy has considerable potential for spatially rapid measurement of soil C and N in the landscape.

This paper reports the development of a proximal sensing technique used to predict maize root density, soil carbon (C) and nitrogen (N) content from the visible and near-infrared (Vis-NIR) spectral reflectance of soil cores. Eighteen soil cores (0-60 cm depth with a 4.6 cm diameter) were collected from two sites within a field of 90-day-old maize silage; Kairanga silt loam and Kairanga fine sandy loam (Gley Soils). At each site, three replicate soil cores were taken at 0, 15 and 30 cm distance from the row of maize plants (rows were 60 cm apart). Each soil core was sectioned at 5 depths (7.5, 15, 30, 45, and 60 cm) and soil reflectance spectra were acquired from the freshly cut surface at each depth. A 1.5 cm soil slice was taken at each surface to obtain root mass and total soil C and N reference (measured) data. Root densities decreased with depth and distance from plant and were lower in the silt loam, which had the higher total C and N contents. Calibration models, developed using partial least squares regression (PLSR) between the first derivative of soil reflectance and the reference data, were able to predict with moderate accuracy the soil profile root density (r 2 =0.75; ratio of prediction to deviation [RPD]=2.03; root mean square error of crossvalidation [RMSECV] = 1.68 mg/cm 3 ), soil% C (r 2 =0.86; RPD=2.66; RMSECV=0.48%) and soil% N (r 2 =0.81; RPD=2.32; RMSECV=0.05%) distribution patterns. The important wavelengths chosen by the PLSR model to predict root density were different to those chosen to predict soil C or N. In addition, predicted root densities were not strongly autocorrelated to soil C (r=0.60) or N (r=0.53) values, indicating that root density can be predicted independently from soil C. This research has identified a potential method for assessing root densities in field soils enabling study of their role in soil organic matter synthesis.