Precision Agriculture

, Volume 11, Issue 3, pp 291–305 | Cite as

Within-field nitrogen response in corn related to aerial photograph color

  • J. D. WilliamsEmail author
  • N. R. Kitchen
  • P. C. Scharf
  • W. E. Stevens


Precise management of nitrogen (N) using canopy color in aerial imagery of corn (Zea mays L.) has been proposed as a strategy on which to base the rate of N fertilizer. The objective of this study was to evaluate the relationship between canopy color and yield response to N at the field scale. Six N response trials were conducted in 2000 and 2001 in fields with alluvial, claypan and deep loess soil types. Aerial images were taken with a 35-mm slide film from ≥1100 m at the mid- and late-vegetative corn growth stages and processed to extract green and red digital values. Color values of the control N (0 kg N ha−1) and sufficient N (280 kg N ha−1 applied at planting) treatments were used to calculate the relative ratio of unfertilized to fertilized and relative difference color values. Other N fertilizer treatments included side-dressed applications in increments of 56 kg N ha−1. The economic optimal N rate was weakly related (R 2 ≤ 0.34) or not related to the color indices at both growth stages. For many sites, delta yield (the increase in yield between control N and sufficient N treatments) was related to the color indices (R 2 ≤ 0.67) at the late vegetative growth stage; the best relationship was with green relative difference. The results indicate the potential for color indices from aerial photographs to be used for predicting delta yield from which a site-specific N rate could be determined.


Aerial photography Remote sensing Nitrogen Variable-rate Corn (Zea mays L.) 


  1. Arregui, L. M., & Quemada, M. (2008). Strategies to improve nitrogen use efficiency in winter cereal crops under rainfed conditions. Agronomy Journal, 100, 277–284.CrossRefGoogle Scholar
  2. Arslan, S., & Colvin, T. S. (2002). An evaluation of the response of yield monitors and combines to varying yields. Precision Agriculture, 3, 107–122.CrossRefGoogle Scholar
  3. Bausch, W. C., & Duke, H. R. (1996). Remote sensing of plant nitrogen status in corn. Transactions of ASAE, 39, 1869–1875.Google Scholar
  4. Birrell, S. J., Sudduth, K. A., & Borgelt, S. C. (1996). Comparison of sensors and techniques for crop yield mapping. Computers and Electronics in Agriculture, 14, 215–233.CrossRefGoogle Scholar
  5. Blackmer, T. M., & Schepers, J. S. (1996). Aerial photography to detect nitrogen stress in corn. Journal of Plant Physiology, 148, 440–444.Google Scholar
  6. Blackmer, T. M., Schepers, J. S., & Varvel, G. E. (1994). Light reflectance compared with other nitrogen stress measurements in corn leaves. Agronomy Journal, 86, 934–938.Google Scholar
  7. Blackmer, T. M., Schepers, J. S., Varvel, G. E., & Meyer, G. E. (1996). Analysis of aerial photography for nitrogen stress within corn fields. Agronomy Journal, 88, 729–733.Google Scholar
  8. Bundy, L. G., & Andraski, T. W. (1995). Soil yield potential effects on performance of soil nitrate tests. Journal of Production Agriculture, 8, 561–568.Google Scholar
  9. Flowers, M., Weisz, R., Heiniger, R., Tarleton, B., & Meijer, A. (2003). Field validation of a remote sensing technique for early nitrogen application decisions in wheat. Agronomy Journal, 95, 167–176.Google Scholar
  10. Franzen, D. W., Cihacek, L. J., & Hofman, V. L. (1996). Variability of soil nitrate and phosphate under different landscapes. In P. C. Roberts, R. H. Rust, & W. E. Larson (Eds.), Proceedings of third international conference on precision agriculture (pp. 521–529). Madison, WI: American Society of Agronomy.Google Scholar
  11. Hong, N., Scharf, P. C., Davis, J. G., Kitchen, N. R., & Sudduth, K. A. (2007). Economically optimal nitrogen rate reduces soil residual nitrate. Journal of Environmental Quality, 36, 354–362.CrossRefPubMedGoogle Scholar
  12. Kitchen, N. R., & Goulding, K. W. T. (2001). On-farm technologies and practices to improve nitrogen use efficiency. In R. F. Follet & J. L. Hatfield (Eds.), Nitrogen in the environment: Sources, problems, and management (pp. 335–369). Amsterdam, The Netherlands: Elsevier Science B.V.CrossRefGoogle Scholar
  13. Long, D. S., Neilsen, G. A., & Carlson, G. R. (1989). Use of aerial photography for improving layout of field research plots. Applied Agricultural Research, 4, 96–100.Google Scholar
  14. Lory, J. A., & Scharf, P. C. (2003). Yield goal versus delta yield for predicting fertilizer nitrogen need in corn. Agronomy Journal, 95, 994–999.Google Scholar
  15. Mullen, R. W., Freeman, K. W., Raun, W. R., Johnson, G. V., Stone, M. L., & Solie, J. B. (2003). Identifying an in-season response index and the potential to increase wheat yield with nitrogen. Agronomy Journal, 95, 347–351.CrossRefGoogle Scholar
  16. Pinter, P. J., Hatfield, J. L., Schepers, J. S., Barnes, E. M., Moran, M. S., Daughtry, C. S. T., et al. (2003). Remote sensing for crop management. American Society for Photogrammetry and Remote Sensing, 69, 647–664.Google Scholar
  17. Puckett, L. J., Zamora, C., Essaid, H., Wilson, J. T., Johnson, H. M., Brayton, M. J., et al. (2008). Transport and fate of nitrate at the ground-water/surface-water interface. Journal of Environmental Quality, 37, 1034–1050.CrossRefPubMedGoogle Scholar
  18. Rice, C. W., & Havlin, J. L. (1994). Integrating mineralizable nitrogen indices into fertilizer nitrogen recommendations. In J. L. Havlin & J. S. Jacobsen (Eds.), Soil testing: Prospects for improving nutrient recommendations (pp. 1–13). Madison, WI: Soil Science Society of America.Google Scholar
  19. Ritchie, S. W., Hanway, J. J., & Benson, G. O. (1993). How a corn plant develops. Special Report, 48. Ames: Iowa State University.Google Scholar
  20. Sawyer, J., Nafziger, E., Randall, G., Bundy, L., Rehm, G., & Joern, B. (2006). Concepts and rationale for regional nitrogen rate guidelines for corn. Iowa State University Extension Publication, PM 2015.Google Scholar
  21. Scharf, P. C., Brouder, S. M., & Hoeft, R. G. (2006a). Chlorophyll meter readings can predict nitrogen need and yield response of corn in the north-central USA. Agronomy Journal, 98, 655–665.CrossRefGoogle Scholar
  22. Scharf, P. C., Kitchen, N. R., Sudduth, K. A., & Davis, J. G. (2006b). Spatially variable corn yield is a weak predictor of optimal nitrogen rate. Soil Science Society of America Journal, 70, 2154–2160.CrossRefGoogle Scholar
  23. Scharf, P. C., Kitchen, N. R., Sudduth, K. A., Davis, J. G., Hubbard, V. C., & Lory, J. A. (2005). Field-scale variability in economically-optimal N fertilizer rate for corn. Agronomy Journal, 97, 452–461.CrossRefGoogle Scholar
  24. Scharf, P. C., & Lory, J. A. (2002). Calibrating corn color from aerial photographs to predict sidedress N need. Agronomy Journal, 94, 397–404.CrossRefGoogle Scholar
  25. Schepers, J. S., Blackmer, T. M., Wilhelm, W. W., & Resende, M. (1996). Transmittance and reflectance measurements of corn leaves from plants with different nitrogen and water supply. Journal of Plant Physiology, 148, 523–529.Google Scholar
  26. Schepers, J. S., Frank, K. D., & Bourg, C. (1986). Effect of yield goal and residual soil nitrogen concentration on N fertilizer recommendations for irrigated maize in Nebraska. Journal of Fertilizer Issues, 3, 133–139.Google Scholar
  27. Schepers, J. S., & Mosier, A. R. (1991). Accounting for nitrogen in nonequilibrium soil-crop systems. In R. F. Follett, D. R. Keeney, & R. M. Cruse (Eds.), Managing nitrogen for groundwater quality and farm profitability (pp. 125–128). Madison, WI: Soil Science Society of America.Google Scholar
  28. Shanahan, J. F., Schepers, J. S., Francis, D. D., Varvel, G. E., Wilhelm, W. W., Tringe, J. M., et al. (2001). Use of remote sensing imagery to estimate corn grain yield. Agronomy Journal, 93, 583–589.CrossRefGoogle Scholar
  29. Sripada, R. P., Heiniger, R. W., White, J. G., & Meijer, A. D. (2006). Aerial color infrared photography for determining early in-season nitrogen requirements in corn. Agronomy Journal, 98, 968–977.CrossRefGoogle Scholar
  30. Stone, M. L., Solie, J. B., Raun, W. R., Whitney, R. W., Taylor, S. L., & Ringer, J. D. (1996). Use of spectral radiance for correcting in-season fertilizer nitrogen deficiencies in winter wheat. Transactions of the ASAE, American Society of Agricultural Engineers, 39, 1623–1631.Google Scholar
  31. Sudduth, K. A., Birrel, S. J., & Krumpelman, M. J. (2000). Field evaluation of corn population sensor. In P. C. Roberts, R. H. Rust, & W. E. Larson (Eds.), Proceedings of the fifth international conference on precision agriculture (paper 252). Madison, WI: American Society of Agronomy. CD format only.Google Scholar
  32. Sudduth, K. A., & Drummond, S. T. (2007). Yield editor: Software for removing errors from crop yield maps. Agronomy Journal, 99, 1471–1482.CrossRefGoogle Scholar
  33. Teal, R. K., Tubana, B., Girma, K., Freeman, K. W., Arnall, D. B., Walsh, O., et al. (2006). In-season prediction of corn grain yield potential using normalized difference vegetation index. Agronomy Journal, 98, 1488–1494.CrossRefGoogle Scholar
  34. USEPA. (Aug 2002). National water quality inventory: 2000 report. EPA-841-R-02-001, Washington, DC.Google Scholar
  35. USDA ERS. (2005). Farm business and household survey data: Summaries from ARMS. 2005 Survey.
  36. Williams, J. D., Crozier, C. R., Crouse, D. A., White, J. G., Bang, J., & Duffera, M. (2005). Spatial relationships between soil amino sugar nitrogen, soil properties, and landscape attributes. In J. V. Stafford (Ed.), Proceedings of the fifth European conference on precision agriculture (pp. 303–309). Wageningen: Wageningen Academic Scientific Publishers.Google Scholar
  37. Wolfe, D. W., Henderson, D. W., Hsaio, T. C., & Alvino, A. (1988). Interactive water and nitrogen effects on senescence of maize: II. Photosynthetic decline and longevity of individual leaves. Agronomy Journal, 80, 865–870.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • J. D. Williams
    • 1
    Email author
  • N. R. Kitchen
    • 2
  • P. C. Scharf
    • 3
  • W. E. Stevens
    • 4
  1. 1.Agribusiness, Plant and Animal DepartmentBrigham Young University, IdahoRexburgUSA
  2. 2.USDA-ARS, Cropping Systems and Water Quality Research UnitColumbiaUSA
  3. 3.Plant Science DivisionUniversity of MissouriColumbiaUSA
  4. 4.Plant Science DivisionUniversity of MissouriPortagevilleUSA

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