Skip to main content

Advertisement

Log in

A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

Nitrogen (N) fertilizer rates applied spatially according to crop requirements can improve the efficiency of N use. The study compares the performance of two commercial sensors, the Yara N-Sensor/FieldScan (Yara International ASA, Germany) and the GreenSeeker (NTech Industries Inc., Ukiah, California, USA), for assessing the status of N in spring wheat (Triticum aestivum L.) and corn (Zea mays L.). Four experiments were conducted at different locations in Quebec and Ontario, Canada. The normalized difference vegetation index (NDVI) was determined with the two sensors at specific growth stages. The NDVI values derived from Yara N-Sensor/FieldScan correlated with those from GreenSeeker, but only at the early growth stages, where the NDVI values varied from 0.2 to 0.6. Both sensors were capable of describing the N condition of the crop or variation in the stand, but each sensor had its own sensitivity characteristics. It follows that the algorithms developed with one sensor for variable-rate N application cannot be transferred directly to another sensor. The Yara N-Sensor/FieldScan views the crop at an oblique angle over the rows and detects more biomass per unit of soil surface compared to the GreenSeeker with its nadir (top-down) view of the crop. The Yara N-Sensor/FieldScan should be used before growth stage V5 for corn during the season if NDVI is used to derive crop N requirements. GreenSeeker performed well where NDVI values were >0.5. However, unlike GreenSeeker, the Yara N-Sensor/FieldScan can also record spectral information from wavebands other than red and near infrared, and more vegetation indices can be derived that might relate better to N status than NDVI.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Aparicio, N., Villegas, D., Royo, C., Casadesus, J., & Araus, J. L. (2004). Effect of sensor view angle on the assessment of agronomic traits by ground level hyper-spectral reflectance measurements in durum wheat under contrasting Mediterranean conditions. International Journal of Remote Sensing, 25, 1131–1152.

    Article  Google Scholar 

  • Berntsen, J., Thomsen, A., Schelde, K., Hansen, O. M., Knudsen, L., Broge, N., et al. (2006). Algorithms for sensor-based redistribution of nitrogen fertilizer in winter wheat. Precision Agriculture, 7, 65–83.

    Article  Google Scholar 

  • Bowen, T. R., Hopkins, B. G., Ellsworth, J. W., Cook, A. G., & Funk, S.A. (2005). In-season variable rate N in potato and barley production using optical sensing instrumentation. In W. B. Stevens (Ed.), Proceedings of Western Nutrient Management Conference (Vol. 6, pp. 141–148). Salt Lake City, UT, USA: Potash and Phosphate Institute.

  • Carpenter, S. R., Caraco, N. F., Correll, D. L., Howarth, R. W., Sharpley, A. N., & Smith, V. H. (1998). Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecological Applications, 8, 559–568.

    Article  Google Scholar 

  • CRAAQ. (2003). Guide de référence en fertilisation (Fertilization Reference Guide). Quebec: Centre de référence en agriculture et agroalimentaire du Québec (Quebec’s Centre of Reference in Agriculture and Agri-Food). p. 297.

  • Demetriades-Shah, T. H., & Court, M. N. (1987). Oblique view reflectance for assessing nitrogen status of incomplete canopies. International Journal of Remote Sensing, 8, 1049–1055.

    Article  Google Scholar 

  • Evans, J., Fettell, N. A., Coventry, D. R., O’Connor, G. E., Walsgott, D. N., Mahoney, J., et al. (1991). Wheat response after temperate crop legumes in south-eastern Australia. Australian Journal of Agricultural Research, 42, 31–43.

    Article  Google Scholar 

  • Fangmeier, A., Grüters, U., Hertstein, U., Sandhage-Hofmann, A., Vermehren, B., & Jäger, H. J. (1996). Effects of elevated CO2, nitrogen supply and tropospheric ozone on spring wheat. I: Growth and yield. Environmental Pollution, 91, 381–390.

    Article  PubMed  CAS  Google Scholar 

  • Filella, I., Serrano, L., Serra, J., & Peñuelas, J. (1995). Evaluating wheat nitrogen status with canopy reflectance indices and discriminant analysis. Crop Science, 35, 1400–1405.

    Google Scholar 

  • Gates, D. M., Keegan, H. J., Schleter, J. C., & Weidner, V. R. (1965). Spectral properties of plants. Applied Optics, 41, 11–20.

    Article  Google Scholar 

  • Hong, S. D., Schepers, J. S., Francis, D. D., & Schlemmer, M. R. (2007). Comparison of ground-based remote sensors for evaluation of corn biomass affected by nitrogen stress. Communications in Soil Science and Plant Analysis, 38, 2209–2226.

    Article  CAS  Google Scholar 

  • Hossain, M. A., Ishimine, Y., Akamine, H., & Kuramochi, H. (2004). Effect of nitrogen fertilizer application on growth, biomass production, and N-uptake of torpedograss (Panicum repens L.). Weed Biology and Management, 4, 86–94.

    Article  CAS  Google Scholar 

  • Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25, 295–309.

    Article  Google Scholar 

  • Jasper, J., Reusch, S., & Link, A. (2006). N-Sensor ALS-Active sensing of crop N status using optimized wavelength combination. In D. J. Mulla (Ed.), Proceedings of the 8th International Conference on Precision Agriculture and Other Precision Resources Management (July 23–26). St. Paul, MN, USA: Precision Agriculture Center, University of Minnesota.

  • Jordan, C. F. (1969). Derivation of leaf area index from quality of light on the forest floor. Ecology, 50, 663–666.

    Article  Google Scholar 

  • Link, A., Panitzki, M., & Reusch, S. (2002). Hydro N-Sensor: tractor-mounted remote sensing for variable nitrogen fertilization. In P. C. Robert, R. H. Rust, & W. E. Larson (Eds.), Proceedings of the 6th International Conference on Precision Agriculture (pp. 1012–1018). Madison, WI, USA: ASA/CSSA/SSSA.

  • Ma, B. L., Dwyer, L. M., & Gregorich, E. G. (1999). Soil nitrogen amendment effects on nitrogen uptake and grain yield of maize. Agronomy Journal, 91, 650–656.

    Google Scholar 

  • Ma, B. L., Morrison, M. J., & Dwyer, L. M. (1996). Canopy light reflectance and field greenness to assess nitrogen fertilization and yield of maize. Agronomy Journal, 88, 915–920.

    Google Scholar 

  • Ma, B. L., Subedi, K. D., & Costa, C. (2005). Comparison of crop-based indicators with soil nitrate test for corn nitrogen requirement. Agronomy Journal, 97, 462–471.

    Article  CAS  Google Scholar 

  • Maier, N. A., McLaughlin, M. J., Heap, M., Butt, M., & Smart, M. K. (2002). Effect of nitrogen source and calcitic lime on soil pH and potato yield, leaf chemical composition, and tuber cadmium concentrations. Journal of Plant Nutrition, 25, 523–544.

    Article  CAS  Google Scholar 

  • Mehlich, A. (1984). Mehlich 3 soil test extractant: A modification of Mehlich 2 extractant. Communications in Soil Science and Plant Analysis, 15, 1409–1416.

    Article  CAS  Google Scholar 

  • Myneni, R. B., & Williams, D. L. (1994). On the relationship between FAPAR and NDVI. Remote Sensing of Environment, 49, 200–211.

    Article  Google Scholar 

  • Pearce, S. C. (1992). Data analysis in agricultural experimentation. II. Some standard contrasts. Experimental Agriculture, 28, 375–383.

    Article  Google Scholar 

  • Pena-Yewtukhiwa, E. M., Schwab, G. J., & Murdock, L. W. (2006). Univariate distribution analysis to evaluate variable rate fertilization. Agronomy Journal, 98, 554–561.

    Article  Google Scholar 

  • Poss, J. A., Russell, W. B., & Grieve, C. M. (2006). Estimating yields of salt- and water-stressed forages with remote sensing in the visible and near infrared. Journal of Environmental Quality, 35, 1060–1071.

    Article  PubMed  CAS  Google Scholar 

  • Raun, W. R., Solie, J. B., Johnson, G. V., Stone, M. L., Mullen, R. W., Freeman, K. W., et al. (2002). Improving nitrogen use efficiency in cereal grain production with optical sensing and variable rate application. Agronomy Journal, 94, 815–820.

    Google Scholar 

  • Reusch, S. (2005). Optimum waveband selection for determining the nitrogen uptake in winter wheat by active remote sensing. In J. Stafford (Ed.), Precision Agriculture 2005, Proceedings of the 5th European Conference on Precision Agriculture (pp. 261–266). The Netherlands: Wageningen Academic Publishers.

  • Reusch, S., Link, A., & Lammel, J. (2002). Tractor-mounted multispectral scanner for remote field investigation. In P. C. Robert, R. H. Rust, & W. E. Larson (Eds.), Proceedings of the 6th International Conference on Precision Agriculture (pp. 1385–1393). Madison, WI, USA: ASA/CSSA/SSSA.

  • Ritchie, S. W., Hanway, J. J., & Benson, G. O. (1993). How a corn plant develops. Special Report (No. 48). Ames, IA, USA: Iowa State University of Science and Technology Cooperative Extension Service.

  • Rouse, J. W. Jr., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the great plains with ERTS. In S. C. Freden, E. P. Mercanti, & M. A. Becker (Eds.), Third ERTS symposium (Vol. 1, pp. 309–317). NASA, Washington, DC, USA: NASA Special Publication SP-351.

  • SAS Institute Inc. (1990). SAS/STAT user’s guide (Version 6.0). Cary, NC, USA: SAS Inst. Inc.

    Google Scholar 

  • Sharp, T. C., Evans, G., & Salvador, A. (2004). Weekly NDVI relationships to height, nodes and productivity index for low, medium, and high cotton productivity zones. In C. P. Dugger & D. A. Ritcher (Eds.), 2004 Proceedings of the Beltwide Cotton Conferences (pp. 2048). Memphis, TN, USA: National Cotton Council.

  • Thomason, W. E., Phillips, S. B., & Raymond, F. D. (2007). Defining useful limits for spectral reflectance measures in corn. Journal of Plant Nutrition, 30, 1263–1277.

    Article  CAS  Google Scholar 

  • Tóth, V. R., Mészáros, I., Veres, Sz., & Nagy, J. (2002). Effects of the available nitrogen on the photosynthetic activity and xanthophyll cycle pool of maize in field. Journal of Plant Physiology, 159, 627–634.

    Article  Google Scholar 

  • Walburg, G., Bauer, M. E., Daughtry, C. S. T., & Housley, T. L. (1982). Effects of nitrogen nutrition on the growth, yield, and reflectance characteristics of corn canopies. Agronomy Journal, 74, 677–683.

    Google Scholar 

  • Welsh, J. P., Wood, G. A., Godwin, R. J., Taylor, J. C., Earl, R., Blackmore, S., et al. (2003a). Developing strategies for spatially variable nitrogen application in cereals, part I: Winter barley. Biosystems Engineering, 84, 481–494.

    Article  Google Scholar 

  • Welsh, J. P., Wood, G. A., Godwin, R. J., Taylor, J. C., Earl, R., Blackmore, S., et al. (2003b). Developing strategies for spatially variable nitrogen application in cereals, part II: Wheat. Biosystems Engineering, 84, 495–511.

    Article  Google Scholar 

  • Whitelaw, M. A. (2000). Growth promotion of plants inoculated with phosphate-solubilizing fungi. Advances in Agronomy, 69, 99–151.

    Article  CAS  Google Scholar 

  • Wood, C. W., Reeves, D. W., Duffield, R. R., & Edmisten, K. L. (1992). Field chlorophyll measurements for evaluation of corn nitrogen status. Journal of Plant Nutrition, 15, 487–500.

    Article  Google Scholar 

  • Wright, D. L., Rasmussen, V. P., Ramsey, R. D., Baker, D. J., & Ellsworth, J. W. (2004). Canopy reflectance estimation of wheat nitrogen content for grain protein management. GIScience and Remote Sensing, 41(4), 287–300.

    Article  Google Scholar 

  • Zadoks, J. C., Chang, T. T., & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research, 14, 415–421.

    Article  Google Scholar 

  • Zebarth, B. J., Rees, H., Tremblay, N., Fournier, P., & Leblon, B. (2003). Mapping spatial variation in potato nitrogen status using the N Sensor. Acta Horticulture, 627, 267–273.

    Google Scholar 

Download references

Acknowledgments

We would like to thank Marcel Tétreault, Mohammed Yacine Bouroubi, the crew at the L’Acadie Experimental farm and the growers Landry, Bieri and Martel for their cooperation. This work was supported by the GAPS program of Agriculture and Agri-Food Canada.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nicolas Tremblay.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tremblay, N., Wang, Z., Ma, BL. et al. A comparison of crop data measured by two commercial sensors for variable-rate nitrogen application. Precision Agric 10, 145–161 (2009). https://doi.org/10.1007/s11119-008-9080-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-008-9080-2

Keywords

Navigation