4.7 Article

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

期刊

BIOSYSTEMS ENGINEERING
卷 152, 期 -, 页码 14-27

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.biosystemseng.2016.06.005

关键词

Proximal soil sensing; Soil spectroscopy; Mid-infrared (MIR) spectra

资金

  1. National Science and Engineering Research Council of Canada (NSERC) [RGPIN/402298-2011]
  2. Merit Scholarship Program for Foreign Students - Quebec Fund for Research on Nature and Technology

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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 (R-2 > 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. (C) 2016 IAgrE. Published by Elsevier Ltd. All rights reserved.

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