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Nutrient quantification in fresh and dried mixtures of ryegrass and clover leaves using laser-induced breakdown spectroscopy

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Abstract

Laser-induced breakdown spectroscopy (LIBS) is an analytical technique that can be used to facilitate variable rate fertilizer application, potentially increasing yield, reducing costs and reducing environmental side effects of nutrient loss. LIBS can give real-time information about macro and micro nutrients with little to no sample preparation. The study reported in this paper investigated whether LIBS can predict nutrient levels of fresh and dried pelletized pasture and what the limitations are. Spectra were acquired in air and under argon. Partial least square regression was used to build models for each macro and micro nutrient. The best results were for potassium, sodium and manganese with root mean square errors of cross-validation of 0.20, 0.029 and 0.0008 wt%, respectively, coefficient of determination of 0.92, 0.93 and 0.90, limits of detection of 0.99, 0.11 and 0.0027 wt%, and precisions of 0.30, 0.042 and 0.0012 wt%. LIBS can be used to assess nutrient levels of fresh pasture. Reducing the shot-to-shot variation will lead to improved calibrations.

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Acknowledgements

We would like to acknowledge and offer continuing respect and admiration to deceased colleague Dr. Sadhana Talele, who was not able to see this work completed. Financial assistance from the New Zealand Ministry of Business, Innovation and Employment under contract C11X1209 is gratefully acknowledged.

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Authors and Affiliations

  1. School of Engineering, The Dodd Walls Centre for Photonic and Quantum Technologies, The University of Waikato, Hamilton, 3240, New Zealand

    H. Jull & R. Künnemeyer

  2. Plant and Food Research, Hamilton, 3240, New Zealand

    P. Schaare

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  1. H. Jull
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  2. R. Künnemeyer
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  3. P. Schaare
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Correspondence to H. Jull.

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Jull, H., Künnemeyer, R. & Schaare, P. Nutrient quantification in fresh and dried mixtures of ryegrass and clover leaves using laser-induced breakdown spectroscopy. Precision Agric 19, 823–839 (2018). https://doi.org/10.1007/s11119-018-9559-4

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