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.

This paper reports the use of visible/near-infrared reflectance spectroscopy (Vis-NIRS) to predict pasture root density. A population of varying grass root densities was created by growing Moata ryegrass (Lolium multiflorum Lam.) for 72 days in pots of Ramiha silt loam (Allophanic) and Manawatu fine sandy loam (Recent Fluvial) (60 pots for each soil) differentially fertilized with nitrogen (N) and phosphorus (P) in a glass house experiment. At harvest, the reflectance spectra (350-2500 nm) from flat sectioned horizontal soil slices (1.3 cm depth), taken from 57 selected pots, were recorded using a portable spectroradiometer (ASD FieldSpec Pro, Boulder, CO). Root densities within each of the soil slices were measured using a wet sieving technique. A large variation in root densities (0.46-5.02 mg dry root cm À3 ) was obtained from the glass house experiment as plant growth responded to the different soils and rates of N and P fertilizer treatment. Pots of the Manawatu soil contained greater ryegrass root densities (1.76-5.02 mg dry root cm À3 ) than pots of the Ramiha soil (0.46-3.84 mg dry root cm À3 ). Each soil had visually distinct reflectance spectra in the range 470-2440 nm, but different root masses produced relatively small differences in reflectance spectra. The first two principal components (PC1 and PC2) of a principal component analysis of the first derivative of the spectral reflectance accounted for 71.3% of the spectral variance and clearly separated the Ramiha and Manawatu soils. PC1, which accounted for 58.4% of the spectral variance, was also well correlated to root density. Partial least squares regression (PLSR) of the first derivative of the 10 nm spaced spectral data against measured root densities produced calibration models that allowed quantitative estimates of root densities (without removing outlier, r 2 cross-validation ¼ 0.78, ratio of prediction to deviation (RPD) ¼ 2.14, root mean squares error of cross-validation (RMSECV) ¼ 0.60 mg cm À3 ; with removing outliers, r 2 cross-validation ¼ 0.85, RPD ¼ 2.63, RMSECV ¼ 0.47 mg cm À3 ). The study indicated that spectral reflectance measurement has the potential to quantify root density in soils.

Soil organic matter accumulation and concomitant fertility changes in soils recently converted from plantation forest to pastoral agriculture in the Taupo-Rotorua Volcanic Zone have been observed, with a probable soil C sequestration rate of 6.1 t ha-1 year-1 , and a soil N sequestration rate of 0.451 ha-1 year-1 , to 150 mm soil depth, for the first 5 years after conversion attwo of three selected farms. Rapid increases in Olsen P were observed, with soils reaching their optimum agronomic range within 3-5 years after conversion, at two of three farms. A decreasing C:N ratio with time since conversion reflects improved fertility status, and implies that in initial years of pasture establishment, N losses are reduced due to its immobilisation into soil organic matter. These research findings suggest that land-use change from plantation forest to pastoral farm, with inputs of N, P, K and S to soils, allows significant soil C and N sequestration for at least 5 years after conversion. This rate of C sequestration could be used as an offset for forest C sink loss in future emissions trading systems. Further research is required to at least 0.3 m depth to confirm this preliminary study.

The drought tolerant phosphate solubilizing bacteria is needed to dissolve inorganic phosphate (P) which is usually low in the availability at dry land. This study is aimed to obtain a combination of drought tolerant phosphate solubilizing bacteria which has high potential in dissolving P-inorganic. An experimental study which has 4 treatments of bacterial combinations has been conducted in a laboratory. The first treatment was the combination between Pseudomonas azotoformans (A) and Acinetobacter baumannii (B). The second treatment was the combination of A and Bacillus paramycoides (C). The third treatment was B and C, then the fourth treatment was A, B, and C. The potential of the bacterial combination in dissolving P-inorganic was measured qualitatively using phosphate solubilizing index (PSI) on pikovskaya solid medium. While, the potential of the bacterial combination in dissolving P-inorganic was measured quantitatively by measuring dissolved P using the ascorbic acid method in pikovskaya liquid medium containing 0.5% Ca3(PO4)2. The results showed that the combination of those three bacteria (A B and C) has the best potential to dissolve P-inorganic compared to other combinations. In addition, the three bacteria combination also resulted in the highest dissolved P with 0.29% potential dissolution of the total Ca3(PO4)2 contained in the pikovskaya liquid medium, followed by combination of B and C (0.19%), and A and C (0.18%), respectively. Thus, before the combination of these three bacteria is applied in soil, it is needed further experiment of the potential of the bacteria in dissolving soil P-inorganic.

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This paper reports the development and evaluation of a field technique for in situ measurement of root density using a portable spectroradiometer. The technique was evaluated at two sites in permanent pasture on contrasting soils (an Allophanic and a Fluvial Recent soil) in the Manawatu region, New Zealand. Using a modified soil probe, reflectance spectra (350-2500 nm) were acquired from horizontal surfaces at three depths (15, 30 and 60 mm) of an 80-mm diameter soil core, totalling 108 samples for both soils. After scanning, 3-mm soil slices were taken at each depth for root density measurement and soil carbon (C) and nitrogen (N) analysis. The two soils exhibited a wide range of root densities from 1.53 to 37.03 mg dry root g −1 soil. The average root density in the Fluvial soil (13.21 mg g −1 ) was twice that in the Allophanic soil (6.88 mg g −1 ). Calibration models, developed using partial least squares regression (PLSR) of the first derivative spectra and reference data, were able to predict root density on unknown samples using a leave-one-out cross-validation procedure. The root density predictions were more accurate when the samples from the two soil types were separated (rather than grouped) to give sub-populations (n = 54) of spectral data with more similar attributes. A better prediction of root density was achieved in the Allophanic soil (r 2 = 0.83, ratio prediction to deviation (RPD) = 2.44, root mean square error of cross-validation (RMSECV) = 1.96 mg g −1 ) than in the Fluvial soil (r 2 = 0.75, RPD = 1.98, RMSECV = 5.11 mg g −1 ). It is concluded that pasture root density can be predicted from soil reflectance spectra acquired from field soil cores. Improved PLSR models for predicting field root density can be produced by selecting calibration data from field data sources with similar spectral attributes to the validation set. Root density and soil C content can be predicted independently, which could be particularly useful in studies examining potential rates of soil organic matter change.