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摘要
Many soil science laboratories are now equipped with technology platforms in portable visible near infrared (vis-NIR) and X-ray fluorescence (XRF) spectrometers. These technologies have complementary capabilities, where XRF is known to accurately measure the soil's inorganic element concentration, and vis-NIR has the ability to estimate the soil's organic component and mineralogy suites. In this study data mining was used to estimate soil properties from the vis-NIR spectra, and in a novel way from the XRF spectral data. The prediction outcomes were combined into a single prediction outcome, using formal methods called model averaging procedures. Combining model outcomes derived from spectra using model averaging techniques improves or maintains the prediction status (as ratio of performance to inter-quartile distance) of vis-NIR and XRF models fora wide range of soil properties of agronomic importance. Overall, the relative improvement in %RMSE ranged from 4 to 44%. Weight preference in model averaging was related to the inference of soil chemical and physical properties from vis-NIR and XRF spectra. For example, the weights preference the vis-NIR predictions for soil pH, soil carbon (C), clay, and XRF predictions for most of the elemental soil properties. Based on both the relative improvement in RMSE and RPIQ status, model averaging was found to be suitable for soil pH, soil C (soil organic C (SOC) and total C (TC)), soil texture (sand and clay), CEC and total elements K, Mg, Co, Cr and Mn. Optimum prediction performance for total elements Cu and Zn is achieved by XRF alone. The unreliable RPIQ status for total elements P, S, Mo, Se and exchangeable Ca, Mg, K, and Na derived from vis-NIR and XRF predictions in this study did not improve with model averaging. Overall, Granger-Ramanathan averaging produced similar or better outcomes compared to variance weighted averaging. This model averaging approach is more simple to compute requiring only to fit a simple multiple linear regression model, unlike the VWA approach in which the weighting is estimated for each soil property, and thus in the interests of parsimony is recommended as the model averaging technique to be adopted as protocol. More conventional use of portable XRF in soil analysis is to employ the elemental concentrations measured by the XRF device to predict other soil properties, generally applying multiple linear regression models. When XRF is used in a conventional way to determine elemental concentrations it was demonstrated to be highly reliable for elemental concentrations present in high concentrations, but predictions of elemental content derived from XRF spectra was more effective for elements present in low concentrations. This in turn reduces the capacity of XRF elemental concentrations to be used in the prediction of other soil properties by inference.
- 出版日期2016-10-1
