Development of field mobile soil nitrate sensor technology to facilitate precision fertilizer management
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.
KeywordsSoil 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.
- 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
- 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, https://doi.org/10.1109/ngct.2015.7375220.
- 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 https://www.extension.iastate.edu/waterquality/files/page/files/Nitrogen%20Fertilizer%20Recommendations%20for%20Corn%20in%20Iowa.pdf.
- 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
- 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
- Griffiths, P. R., & De Haseth, J. A. (2007). Fourier transform infrared spectroscopy, second edition (Chapter 15). Hoboken, NJ, USA: Wiley.Google Scholar
- Khoshhesab Z. M. (2012). Reflectance IR spectroscopy. In T. Theophanides (Ed.). Infrared spectroscopy—materials science, engineering and technology, (Ch. 11). INTECH: https://doi.org/10.5772/2055. Retrieved May 6, 2018, from https://www.intechopen.com/books/infrared-spectroscopy-materials-science-engineering-and-technology.
- 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
- 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
- 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 https://cpb-us-e1.wpmucdn.com/blogs.cornell.edu/dist/8/6785/files/2016/06/Prec-Ag-Conf-2008-Melkonian-van-Es-uhaslu.pdf.
- Pioneer. (2018). Staging corn growth. Retrieved May 5, 2018, from https://www.pioneer.com/home/site/us/agronomy/library/staging-corn-growth/#defined.
- Agilent 4100 ExoScan FTIR Operation Manual. Retrieved May 6, 2018, from https://www.agilent.com/cs/library/usermanuals/public/0023-401.pdf.
- 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
- 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. https://doi.org/10.1021/acs.jpcb.7b09641.CrossRefGoogle Scholar