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Predicting Organic Matter Content, Total Nitrogen and pH Value of Lime Concretion Black Soil Based on Visible and Near Infrared Spectroscopy

Abstract

Lime concretion black soil is a kind of ancient cultivated soil and exhibits highly-localized distributions in China’s important commodity grain base and fruit/vegetable production base. However, lime concretion black soil is currently poor in fertility and crop output. Visible and near-infrared (VIS/NIR) diffuse reflectance spectroscopy appears as an effective tool of gaining in-depth knowledge of soil properties. This study focused on lime concretion black soil samples, collected their VIR/NIR spectra and correlated the spectral characteristics with the measured organic matter content (OMC), total nitrogen (N) and pH value using chemical methods for establishing the prediction models of soil properties. Different spectral pretreatment, sample selection and wavelength optimization methods were applied for improving the accuracy and robustness of the prediction models. Results show that, after appropriate spectral processing and selection of representative samples, the established principal component regression genetic algorithm (PCR-GA) models can accurately predict the contents of OMC and N as well as pH values of lime concrete black soil samples, with the correlation coefficients of 0.91, 0.97 and 0.92, respectively. The present study proved great potential of VIS/NIRS in real-time detection of lime concretion black soil properties.

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REFERENCES

  1. 1

    J. M. Bremner, “Determination of nitrogen in soil by the Kjeldahl method,” J. Agric. Sci. 55 (1), 11–33 (1960). https://doi.org/10.1017/S0021859600021572

    Article  Google Scholar 

  2. 2

    A. H. Cambule, D. G. Rossiter, J. J. Stoorvogel, and E. M. A. Smaling, “Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique,” Geoderma 183, 41–48 (2012). https://doi.org/10.1016/j.geoderma.2012.03.011

    Article  Google Scholar 

  3. 3

    X. Chu, H. Yuan, Y. Wang, and W. Lu, “Variable selection for partial least squares modeling by genetic algorithms,” Chin. J. Anal. Chem. 29 (4), 437–442 (2001). https://doi.org/10.1002/bmc.50

    Article  Google Scholar 

  4. 4

    X. L. Chu, H. F. Yuan, Y. B. Wang, and W. Z. Lu, “Developing robust near infrared calibration models,” Spectrosc. Spec. Anal. 24 (6), 666–671 (2004)

    Google Scholar 

  5. 5

    R. K. H. Galvão, A. Mário César Ugulino, J. Gledson Emídio, P. Marcio José Coelho, E. C. Silva, and T. C. B. Saldanha, “A method for calibration and validation subset partitioning,” Talanta 67 (4), 736–740 (2005). https://doi.org/10.1016/j.talanta.2005.03.025

    Article  Google Scholar 

  6. 6

    Z. Gong, Z. Chen, G. Luo, G. Zhang, and W. Zhao, “Reference of Chinese soil taxonomy,” Soil 92, 57–63 (1999). https://doi.org/CNKI:SUN:TURA.0.1999-02-000

    Google Scholar 

  7. 7

    Y. He, M. Huang, G. Annia, H. Antihus, and H. Song, “Prediction of soil macronutrients content using near-infrared spectroscopy,” Comput. Electron. Agric. 58 (2), 144–153 (2007). https://doi.org/10.1016/j.compag.2007.03.011

    Article  Google Scholar 

  8. 8

    X.-Y. Hu, “Application of visible/near-Infrared spectra in modeling of soil total phosphorus,” Pedosphere 23 (4), 417–421 (2013). https://doi.org/CNKI:SUN:TRQY.0.2013-04-003

    Article  Google Scholar 

  9. 9

    Y. Hu, A. Erxleben, A. G. Ryder, and P. McArdle, “Quantitative analysis of sulfathiazole polymorphs in ternary mixtures by attenuated total reflectance infrared, near-infrared and Raman spectroscopy,” J. Pharm. Biomed. Anal. 53 (3), 412–420 (2010). https://doi.org/10.1016/j.jpba.2010.05.002

    Article  Google Scholar 

  10. 10

    D. Jouan-Rimbaud, D. L. Massart, R. Leardi, and O. E. De Noord, “Genetic algorithms as a tool for wavelength selection in multivariate calibration,” Anal. Chem. 67 (23), 4295–4301 (1995). https://doi.org/10.1021/ac00119a015

    Article  Google Scholar 

  11. 11

    J. Zhang, L. Xi, X. Yang, X. Xu, W. Guo, T. Cheng, and X. Ma, “Construction of hyperspectral estimation model for organic matter content in Shajiang black soil,” Trans. Chin. Soc. Agric. Eng. 36 (17), 135–141 (2020). https://doi.org/10.11975/j.issn.1002-6819.2020.17.016

    Article  Google Scholar 

  12. 12

    R. Leardi, “Application of genetic algorithm-PLS for feature selection in spectral data sets,” J. Chemom. 14 (6), 643–655 (2010). https://doi.org/10.1002/1099-128X(200009/12)14:5/6<643::AID-CEM621>3.0.CO;2-E

    Article  Google Scholar 

  13. 13

    D. C. Li, G. L. Zhang, and Z. T. Gong, “On taxonomy of Shajiang black soils in China,” Soils 43 (4), 623–629 (2011). https://doi.org/0253-9829(2011)43:4<623:WGSJHT> 2.0.TX;2-Y

  14. 14

    L. J. Li, X. S. Guo, D. Z. Wang, Y. X. Sun, and P. P. Wu, “State and spatial variability of nutrient of lime concretion black soil in Huaibei Plain,” J. Anhui Agric. Sci. 34 (4), 722–723 (2006). https://doi.org/10.1360/yc-006-1280

    Article  Google Scholar 

  15. 15

    A. M. Mouazen, B. Kuang, J. D. Baerdemaeker, and H. Ramon, “Comparison among principal component, partial least squares and back propagation neural network analyses for accuracy of measurement of selected soil properties with visible and near infrared spectroscopy,” Geoderma 158 (1–2), 23–31 (2010). https://doi.org/10.1016/j.geoderma.2010.03.001

    Article  Google Scholar 

  16. 16

    G. J. Postma, W. J. Melssen, and L. M. C. Buydens, “Selecting a representative training set for the classification of demolition waste using remote NIR sensing,” Anal. Chim. Acta 392 (1), 67–75 (1999). https://doi.org/10.1016/S0003-2670(99)00193-2

    Article  Google Scholar 

  17. 17

    R. A. V. Rossel, R. N. Mcglynn, and A. B. Mcbratney, “Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy,” Geoderma 137 (1–2), 70–82 (2006). https://doi.org/10.1016/j.geoderma.2006.07.004

    Article  Google Scholar 

  18. 18

    R. A. V. Rossel, D. J. J. Walvoort, A. B. Mcbratney, L. J. Janik, and J. O. Skjemstad, “Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties,” Geoderma 131 (1–2), 59–75 (2006). https://doi.org/10.1016/j.geoderma.2005.03.007

    Article  Google Scholar 

  19. 19

    Y. Wang, T. Huang, J. Liu, Z. Lin, S. Li, R. Wang, and Y. Ge, “Soil pH value, organic matter and macronutrients contents prediction using optical diffuse reflectance spectroscopy,” Comput. Electron. Agric. 111, 69–77 (2015). https://doi.org/10.1016/j.compag.2014.11.019

    Article  Google Scholar 

  20. 20

    Y. Wang, C. Lu, L. Wang, L. Song, and Y. Ge, “Prediction of soil organic matter content using VIS/NIR soil sensor,” Sens. Transducers J. 168 (4), 113–119 (2014)

    Google Scholar 

  21. 21

    L. W. Liu, “Formation and evolution of vertisols in Huaibei Plain,” Pedosphere 1 (1), 3–15 (1991).

    Google Scholar 

  22. 22

    Z. Yan, Calcic Black Soils Classified in Chinese Soil Taxonomy and the Soil Series Established in Henan Province (College of Water Conservancy and Environment Engineering, Zhengzhou University, Zhengzhou, 2012).

    Google Scholar 

  23. 23

    H. Yang and J. Irudayaraj, “Rapid determination of vitamin C by NIR, MIR and FT-Raman techniques,” J. Pharm. Pharmacol. 54 (9), 1247–1255 (2002). https://doi.org/10.1211/002235702320402099

    Article  Google Scholar 

  24. 24

    Z. Yuan, “Countermeasures of Mengcheng sand ginger black soil improvement and quality promotion,” Ningxia J. Agric. For. Sci. Technol. 54 (3), 50–51 (2013). https://doi.org/10.3969/j.issn.1002-204X.2013.03.024

    Article  Google Scholar 

  25. 25

    Z. Lin, R. Wang, Y. Wang, L. Wang, and C. Lu, “Accurate and rapid detection of soil and fertilizer properties based on visible/near-infrared spectroscopy,” Appl. Opt. 57 (18), D69–D73 (2018). https://doi.org/10.1364/AO.57.000D69

    Article  Google Scholar 

Download references

Funding

This research was funded by National Key Research and Development Program of China, grant number no. 2017YFD0700501, Key Technologies Research and Development Program of Anhui Province, China, grant number nos. 201904b11020012, 18030701201, the Youth Foundation of Natural Science Foundation of Anhui Province, grant number no. 1908085QE202, and the Dean Foundation of Hefei Institutes of Physical Science, Chinese Academy of Sciences, grant number no. YZJJ2019QN14.

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Correspondence to He Huang or Xiangyu Chen.

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Yubing Wang, Huang, H. & Chen, X. Predicting Organic Matter Content, Total Nitrogen and pH Value of Lime Concretion Black Soil Based on Visible and Near Infrared Spectroscopy. Eurasian Soil Sc. (2021). https://doi.org/10.1134/S1064229321110144

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Keywords:

  • visible and near infrared spectroscopy
  • principal component analysis (PCA)
  • genetic algorithm (GA)
  • variable-rate fertilization
  • soil nutrients prediction