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    Assessment of soil properties in situ using a prototype portable MIR spectrometer in two agricultural fields

    Access Status
    Fulltext not available
    Authors
    Ji, W.
    Adamchuk, V.
    Biswas, A.
    Dhawale, N.
    Sudarsan, B.
    Zhang, Y.
    Viscarra Rossel, Raphael
    Shi, Z.
    Date
    2016
    Type
    Journal Article
    
    Metadata
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    Citation
    Ji, W. and Adamchuk, V. and Biswas, A. and Dhawale, N. and Sudarsan, B. and Zhang, Y. and Viscarra Rossel, R. et al. 2016. Assessment of soil properties in situ using a prototype portable MIR spectrometer in two agricultural fields. Biosystems Engineering. 152: pp. 14-27.
    Source Title
    Biosystems Engineering
    DOI
    10.1016/j.biosystemseng.2016.06.005
    ISSN
    1537-5110
    School
    School of Molecular and Life Sciences (MLS)
    URI
    http://hdl.handle.net/20.500.11937/74369
    Collection
    • Curtin Research Publications
    Abstract

    Mid-infrared (MIR) soil spectroscopy has shown applicability to predict selected properties through various laboratory studies. However, reports on the successful use of MIR instruments in field conditions (in situ) have been limited. In this study, a small portable prototype MIR (898–1811 cm-1) spectrometer was used to collect soil spectra from two agricultural fields (predominantly organic and mineral soils). Both fields were located at Macdonald Campus of McGill University in Ste-Anne-de-Bellevue, Quebec, Canada. In each of the 120 predefined field locations, in situ spectroscopic measurements were repeated three times and one representative soil sample was analyzed following conventional laboratory procedures. For every soil property, a field-specific partial least squares regression (PLSR) model was developed and evaluated using a leave-one-out cross-validation routine. Each soil property was evaluated in terms of the accuracy and reproducibility of model predictions. Among tested soil properties, soil organic matter, water content, bulk density, cation exchange capacity (CEC), Ca and Mg yielded higher model performance indicators (R2 > 0.50 and RPD > 1.40) as compared to soil pH, Fe, Cu, phosphorus, nitrate-nitrogen, K or Na. In most instances, the error estimate representing the prediction reproducibility was found to be as high as 50% of the overall prediction error. This was due to the combination of optical and electrical noise and soil micro-variability causing soil spectra representing the same field location to yield different predictions.

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