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dc.contributor.authorKusumo, Bambang Hari
dc.date.accessioned2009-10-02T01:16:22Z
dc.date.availableNO_RESTRICTIONen_US
dc.date.available2009-10-02T01:16:22Z
dc.date.issued2009
dc.identifier.urihttp://hdl.handle.net/10179/1015
dc.description.abstractThe objectives of this research were to develop and evaluate a field method for in situ measurement of soil properties using visible near-infrared reflectance spectroscopy (Vis-NIRS). A probe with an independent light source for acquiring soil reflectance spectra from soil cores was developed around an existing portable field spectrometer (ASD FieldSpecPro, Boulder, CO, USA; 350-2500 nm). Initial experiments tested the ability of the acquired spectra to predict plant root density, an important property in soil carbon dynamics. Reflectance spectra were acquired from soil containing ryegrass roots (Lolium multiflorum) grown in Allophanic and Fluvial Recent soils in a glasshouse pot trial. Differences in root density were created by differential nitrogen and phosphorus fertilization. Partial least squares regression (PLSR) was used to calibrate spectral data (pre-processed by smoothing and transforming spectra to the first derivative) against laboratory-measured root density data (wet-sieve technique). The calibration model successfully predicted root densities (r2 = 0.85, RPD = 2.63, RMSECV = 0.47 mg cm-3) observed in the pots to a moderate level of accuracy. This soil reflectance probe was then tested using a soil coring system to acquire reflectance spectra from two soils under pasture (0-60 mm soil depths) that had contrasting root densities. The PLSR calibration models for predicting root density were more accurate when soil samples from the two soils were separated rather than grouped. A more accurate prediction was found in Allophanic soils (r2 = 0.83, RPD = 2.44, RMSECV = 1.96 mg g-1) than in Fluvial Recent soils (r2 = 0.75, RPD = 1.98, RMSECV = 5.11 mg g-1). The Vis-NIRS technique was then modified slightly to work on a soil corer that could be used to measure root contents from deeper soil profiles (15- 600 mm depth) in arable land (90-day-old maize crop grown in Fluvial Recent soils). PLSR calibration models were constructed to predict the full range of maize root densities (r2 = 0.83, RPD = 2.42, RMSECV = 1.21 mg cm-3) and also soil carbon (C) and nitrogen (N) concentrations that had been determined in the laboratory (LECO FP- 2000 CNS Analyser; Leco Corp., St Joseph, MI, USA). Further studies concentrated on improving the Vis-NIRS technique for prediction of total C and N concentrations in differing soil types within different soil orders in the field. The soil coring method used in the maize studies was evaluated in permanent and recent pastoral soils (Pumice, Allophanic and Tephric Recent in the Taupo-Rotorua Volcanic Zone, North Island) with a wide range of soil organic matter contents resulting from different times (1-5 years) since conversion from forest soils. Without any sample preparation, other than the soil surface left after coring, it was possible to predict soil C and N concentrations with moderate success (C prediction r2 = 0.75, RMSEP = 1.23%, RPD = 1.97; N prediction r2 = 0.80, RMSEP = 0.10%, RPD = 2.15) using a technique of acquiring soil reflectance spectra from the horizontal cross-section of a soil core (H method). The soil probe was then modified to acquire spectra from the curved vertical wall of a soil core (V method), allowing the spectrometer’s field of view to increase to record the reflectance features of the whole soil sample taken for laboratory analysis. Improved predictions of soil C and N concentrations were achieved with the V method of spectral acquisition (C prediction r2 = 0.97, RMSECV = 0.21%, RPD = 5.80; N prediction r2 = 0.96, RMSECV = 0.02%, RPD = 5.17) compared to the H method (C prediction r2 = 0.95, RMSECV = 0.27%, RPD = 4.45; N prediction r2 = 0.94, RMSECV = 0.03%, RPD = 4.25). The V method was tested for temporal robustness by assessing its ability to predict soil C and N concentrations of Fluvial Recent soils under permanent pasture in different seasons. When principal component analysis (PCA) was used to ensure that the spectral dimensions (which were responsive to water content) of the data set used for developing the PLSR calibration model embraced those of the “unknown” soil samples, it was possible to predict soil C and N concentrations in “unknown” samples of widely different water contents (in May and November), with a high level of accuracy (C prediction r2 = 0.97, RMSEP = 0.36%, RPD = 3.43; N prediction r2 = 0.95, RMSEP = 0.03%, RPD = 3.44). This study indicates that Vis-NIRS has considerable potential for rapid in situ assessment of soil C, N and root density. The results demonstrate that field root densities in pastoral and arable soil can be predicted independently from total soil C, which will allow researchers to predict C sequestration from root production. The recommended “V” technique can be used to assess spatial and temporal variability of soil carbon and nitrogen within soil profiles and across the landscape. It can also be used to assess the rate of C sequestration and organic matter synthesis via root density prediction. It reduces the time, labour and cost of conventional soil analysis and root density measurement.en_US
dc.language.isoenen_US
dc.publisherMassey Universityen_US
dc.rightsThe Authoren_US
dc.subjectSoil propertiesen_US
dc.subjectSoil carbonen_US
dc.subjectRoot densityen_US
dc.subjectin-situ assessmenten_US
dc.subject.otherFields of Research::300000 Agricultural, Veterinary and Environmental Sciences::300100 Soil and Water Sciences::300101 Soil physicsen_US
dc.titleDevelopment of field techniques to predict soil carbon, soil nitrogen and root density from soil spectral reflectance : a thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Soil Science at Massey University, Palmerston North, New Zealanden_US
dc.typeThesisen_US
thesis.degree.disciplineSoil Scienceen_US
thesis.degree.grantorMassey Universityen_US
thesis.degree.levelDoctoralen_US
thesis.degree.levelDoctoralen
thesis.degree.nameDoctor of Philosophy (Ph. D.)en_US


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