Precision Agriculture

, Volume 20, Issue 1, pp 40–55 | Cite as

Development of field mobile soil nitrate sensor technology to facilitate precision fertilizer management

  • Natalia Rogovska
  • David A. LairdEmail author
  • Chien-Ping Chiou
  • Leonard J. Bond


Precision nitrogen (N) fertilizer management has the potential to improve N fertilizer use efficiency, simultaneously reducing the cost of inputs for farmers and the off-site environmental impact of crop production. Although technology is available to spatially vary sidedress N fertilizer application rates within fields, sensor technology capable of measuring soil nitrate (NO3) levels in-real-time and on-the-go with sufficient accuracy to facilitate precision application of N fertilizers is lacking. The potential of Diamond-Attenuated Total internal Reflectance (D-ATR) Fourier Transform Infrared (FTIR) spectroscopy was evaluated as a soil NO3 sensor. Two independent datasets were tested; (1) the field dataset consisted of 124 GPS registered soil samples collected from four agricultural fields; and (2) the laboratory dataset consisted of five different soils spiked with various amounts of KNO3 (135 samples) and incubated in the laboratory. Spectra were collected using an Agilent 4100 Exoscan FTIR spectrometer equipped with a D-ATR cell and analyzed using partial least squares regression. Calibration R2 values (D-ATR-FTIR predicted versus independently measured soil NO3 concentrations) for the field and laboratory datasets were 0.83 and 0.90 (RMSE = 8.3 and 8.8 mg kg−1), respectively; and robust “leave one field/soil out” cross validation tests yielded R2 values for the field and laboratory datasets of 0.65 and 0.83 (RMSE = 12.5 and 13.3 mg kg−1), respectively. The study demonstrates the potential of using D-ATR-FTIR spectroscopy for rapid field-mobile determination of soil NO3 concentrations.


Soil nitrate sensor Late spring nitrate test Variable rate N fertilization On-the-go nitrate sensing Fourier Transform Infrared spectroscopy 



The study was funded by the Iowa State University College of Agriculture and Life Sciences and by a Grant from the Leopold Center for Sustainable Agriculture.

Compliance with ethical standards

Conflict of interest

Iowa State University Research Foundation has filed a patent application on technology described in this paper and recently several of the authors have formed a startup company, N-Sense, LLC, which is exploring commercial opportunities.


  1. Adamchuk, V. I., Hummel, J. W., Morgan, M. T., & Upadhyaya, S. K. (2004). On-the-go soil sensors for precision agriculture. Computers and Electronics in Agriculture, 44, 71–91.CrossRefGoogle Scholar
  2. Adamchuk, V., Lund, E., Dobermann, A., & Morgan, M. T. (2003). On-the-go mapping of soil properties using ion-selective electrodes. In J. Stafford & A. Werner (Eds.), Precision agriculture. Proceedings of the 3rd European conference on precision agriculture (pp. 27–33). Wageningen, The Netherlands: Wageningen Academic Publishers.Google Scholar
  3. Adsett, J. F., Thottan, J. A., & Sibley, K. J. (1999). Development of an automated on-the-go soil nitrate monitoring system. Applied Engineering in Agriculture, 15, 351–356.CrossRefGoogle Scholar
  4. Bendre, M. R., Thool, R. C., &Thool, V. R. (2015). Big data in precision agriculture: weather forecasting for future farming. In 1st international conference on next generation computing technologies (NGCT) (pp. 744–750). IEEE Xplore,
  5. Binder, D. L., Sander, D. H., & Walters, D. T. (2000). Maize response to time of nitrogen application as affected by level of nitrogen deficiency. Agronomy Journal, 92, 1228–1236.CrossRefGoogle Scholar
  6. Binford, G. D., Blackmer, A. M., & Cerrato, M. E. (1992). Relationship between corn yields and soil nitrate in late spring. Agronomy Journal, 84, 53–59.CrossRefGoogle Scholar
  7. Blackmer, A. M., Pottker, D., Cerrato, M. E., & Webb, J. (1989). Correlation between soil nitrate concentrations in late spring and corn yields in Iowa. Journal of Production Agriculture, 2, 103–109.CrossRefGoogle Scholar
  8. Blackmer, A. M., Voss, R. D., & Mallarino, A. P. (1997). Nitrogen fertilizer recommendations for corn in Iowa. Ames, IA: Iowa State University extension publ. Retrieved May 5, 2018, from
  9. Borenstein, A., Linker, R., Shmulevich, I., & Shaviv, A. (2006). Determination of soil nitrate and water content using attenuated total reflectance spectroscopy. Applied Spectroscopy, 60, 1267–1272.CrossRefGoogle Scholar
  10. Chang, C. W., Laird, D. A., Mausbach, M. J., & Hurburgh, C. J. (2001). Near infrared reflectance spectroscopy-principal component regression analyses of soil properties. Soil Science Society of America Journal, 65, 480–490.CrossRefGoogle Scholar
  11. Environmental Protection Agency. (2011). Reactive nitrogen in the United States: An analysis of inputs, flows, consequences, and management options, EPA Science Advisory Board, U.S. Environmental Protection Agency, EPA-SAB-11-013, Washington, DC.Google Scholar
  12. Fahsi, A., Tsegaye, T., Boggs, J., Tadesse, W., & Coleman, T. L. (1998). Precision agriculture with hyperspectral remotely-sensed data, GIS, and GPS technology: a step toward an environmentally responsible farming. In E. T. Engman (Ed.), Remote sensing for agriculture, ecosystems, and hydrology (pp. 270–276). Barcilona, Spain: EurOpt Series.CrossRefGoogle Scholar
  13. Griffiths, P. R., & De Haseth, J. A. (2007). Fourier transform infrared spectroscopy, second edition (Chapter 15). Hoboken, NJ, USA: Wiley.Google Scholar
  14. Jaynes, D. B., Dinnes, D. L., Meek, D. W., Karlen, D. L., Cambardella, C. A., & Colvin, T. S. (2004). Using the late spring nitrate test to reduce nitrate loss within a watershed. Journal of Environmental Quality, 33, 669–677.CrossRefGoogle Scholar
  15. Khoshhesab Z. M. (2012). Reflectance IR spectroscopy. In T. Theophanides (Ed.). Infrared spectroscopymaterials science, engineering and technology, (Ch. 11). INTECH: Retrieved May 6, 2018, from
  16. Kim, H. J., Hummel, J. W., Sudduth, K. A., & Motavalli, P. P. (2007). Simultaneous analysis of soil macronutrients using ion-selective electrodes. Soil Science Society of America Journal, 71, 1867–1877.CrossRefGoogle Scholar
  17. Laird, D., Rogovska, N., & Chiou, C. P. (2016). Soil nitrate sensing system for precision management of nitrogen fertilizer application. US Patent, 62(263), 788.Google Scholar
  18. Linker, R., Kenny, A., Shaviv, A., Singher, L., & Shmulevich, I. (2004). Fourier transform infrared-attenuated total reflection nitrate determination of soil pastes using principal component regression, partial least squares, and cross-correlation. Applied Spectroscopy, 58, 516–520.CrossRefGoogle Scholar
  19. Linker, R., Shmulevich, I., Kenny, A., & Shaviv, A. (2005). Soil identification and chemometrics for direct determination of nitrate in soils using FTIR-ATR mid-infrared spectroscopy. Chemosphere, 61, 652–658.CrossRefGoogle Scholar
  20. Lobsey, C. R., Viscarra Rossel, R. A., & McBratney, A. B. (2010). Proximal soil nutrient sensing using electrochemical sensors. In R. A. Rossel et al. (Eds.), Proximal soil sensing (pp. 77–88). Dordrecht, The Netherlands: Springer.CrossRefGoogle Scholar
  21. Ma, B. L., & Biswas, D. K. (2015). Precision nitrogen management for sustainable corn production. In E. Lichtfouse & A. Goyal (Eds.), Sustainable agriculture reviews (pp. 33–62). Dordrecht, The Netherlands: Springer.CrossRefGoogle Scholar
  22. Magdoff, F. (1991). Understanding the Magdoff pre-sidedress nitrate test for corn. Journal of Production Agriculture, 4, 297–305.CrossRefGoogle Scholar
  23. Melkonian, J. J., van Es, H. M., DeGaetano, A. T., & Joesph, L. (2008). ADAPT-N: Adaptive nitrogen management for maize using high-resolution climate data and model simulations. In R. Khosla (Ed.), ADAPT-N: Adaptive nitrogen management for maize using high-resolution climate data and model simulations. Proceedings of the 9th international conference on precision agriculture. Monticello, IL, USA: International Society of Precision Agriculture. Retrieved May 6, 2018 from
  24. Pioneer. (2018). Staging corn growth. Retrieved May 5, 2018, from
  25. Agilent 4100 ExoScan FTIR Operation Manual. Retrieved May 6, 2018, from
  26. Rorie, R. L., Purcell, L. C., Mozaffari, M., Karcher, D. E., King, C. A., Marsh, M. C., et al. (2011). Association of “greenness” in corn with yield and leaf nitrogen concentration. Agronomy Journal, 103, 529–535.CrossRefGoogle Scholar
  27. Rossel, R. A. V., Adamchuk, V. I., Sudduth, K. A., McKenzie, N. J., & Lobsey, C. (2011). Proximal soil sensing: An effective approach for soil measurements in space and time. In D. L. Sparks (Ed.), Advances in agronomy (Vol. 113, pp. 237–282). San Diego, CA, USA: Elsevier.Google Scholar
  28. Scharf, P. C., & Lory, J. A. (2002). Calibrating corn color from aerial photographs to predict sidedress nitrogen need. Agronomy Journal, 94, 397–404.CrossRefGoogle Scholar
  29. Scharf, P. C., Shannon, D. K., Palm, H. L., Sudduth, K. A., Drummond, S. T., Kitchen, N. R., et al. (2011). Sensor-based nitrogen applications out performed producer-chosen rates for corn in on-farm demonstrations. Agronomy Journal, 103, 1683–1691.CrossRefGoogle Scholar
  30. Schnetger, B., & Lehners, C. (2014). Determination of nitrate plus nitrite in small volume marine water samples using vanadium(III)chloride as a reduction agent. Marine Chemistry, 160, 91–98.CrossRefGoogle Scholar
  31. Sela, S., van Es, H. M., Moebius-Clune, B. N., Marjerison, R., Melkonian, J., Moebius-Clune, D., et al. (2016). Adapt-N outperforms grower-selected nitrogen rates in northeast and midwestern United States strip trials. Agronomy Journal, 108, 1726–1734.CrossRefGoogle Scholar
  32. Sethuramasamyraja, B., Adamchuk, V. I., Dobermann, A., Marx, D. B., Jones, D. D., & Meyer, G. E. (2008). Agitated soil measurement method for integrated on-the-go mapping of soil pH, potassium and nitrate contents. Computers and Electronics in Agriculture, 60, 212–225.CrossRefGoogle Scholar
  33. Sibley, K. J., Astatkie, T., Brewster, G., Struik, P. C., Adsett, J. F., & Pruski, K. (2009). Field-scale validation of an automated soil nitrate extraction and measurement system. Precision Agriculture, 10, 162–174.CrossRefGoogle Scholar
  34. Sinfield, J. V., Fagerman, D., & Colic, O. (2010). Evaluation of sensing technologies for on-the-go detection of macro-nutrients in cultivated soils. Computers and Electronics in Agriculture, 70, 1–18.CrossRefGoogle Scholar
  35. Verma, P. K., Kundu, A., Puretz, M. S., Dhoonmoon, C., Chegwidden, O. S., Londergan, C. H., et al. (2017). The bend + libration combination band is an intrinsic, collective, and strongly solute-dependent reporter on the hydrogen bonding network of liquid water. Journal of Physical Chemistry B, 122, 2587–2599. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Natalia Rogovska
    • 1
  • David A. Laird
    • 1
    Email author
  • Chien-Ping Chiou
    • 2
  • Leonard J. Bond
    • 2
  1. 1.Department of AgronomyIowa State UniversityAmesUSA
  2. 2.Center for Nondestructive EvaluationIowa State UniversityAmesUSA

Personalised recommendations