Precision Agriculture

, Volume 12, Issue 6, pp 892–904 | Cite as

Evaluation of two crop canopy sensors for nitrogen variability determination in irrigated maize

Article
First Online:

Abstract

Advances in precision agriculture technology have led to the development of ground-based active remote sensors that can determine normalized difference vegetation index (NDVI). Studies have shown that NDVI is highly related to leaf nitrogen (N) content in maize (Zea mays L.). Remotely sensed NDVI can provide valuable information regarding in-field N variability and significant relationships between sensor NDVI and maize grain yield have been reported. While numerous studies have been conducted using active sensors, none have focused on the comparative effectiveness of these sensors in maize under semi-arid irrigated field conditions. Therefore, the objectives of this study were (1) to determine the performance of two active remote sensors by determining each sensor’s NDVI relationship with maize N status and grain yield as driven by different N rates in a semi-arid irrigated environment and, (2) to determine if inclusion of ancillary soil or plant data (soil NO3 concentration, leaf N concentration, SPAD chlorophyll and plant height) would affect these relationships. Results indicated that NDVI readings from both sensors had high r 2 values with applied N rate and grain yield at the V12 and V14 maize growth stages. However, no single or multiple regression using soil or plant variables substantially increased the r 2 over using NDVI alone. Overall, both sensors performed well in the determination of N variability in irrigated maize at the V12 and V14 growth stages and either sensor could be an important tool to aid precision N management.

Keywords

Crop canopy sensors Remote sensing NDVI 
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References

  1. Alchanatis, V. Z., Schmilovitch, Z., & Meron, M. (2005). In-field assessment of single leaf nitrogen status by spectral reflectance measurements. Precision Agriculture, 6, 25–39.CrossRefGoogle Scholar
  2. Aparicio, N., Villegas, D., Araus, J. L., Casadesus, J., & Royo, C. (2002). Relationship between growth traits and spectral vegetation indices in durum wheat. Crop Science, 42, 1547–1555.CrossRefGoogle Scholar
  3. Campbell, J. B. (2002). Introduction to remote sensing (3rd ed.). New York, USA: The Guilford Press. New York.Google Scholar
  4. Chang, J., Clay, D. E., Dalsted, K., Clay, S., & O’Neill, M. (2003). Corn (Zea mays L.) yield prediction using multispectral and multidate reflectance. Agronomy Journal, 95, 1447–1453.CrossRefGoogle Scholar
  5. Dellinger, A. E., Schmidt, J. P., & Beegle, D. B. (2008). Developing nitrogen fertilizer recommendations for corn using an active sensor. Agronomy Journal, 100, 1546–1552.CrossRefGoogle Scholar
  6. Ercoli, L., Mariotti, M., Masom, A., & Massantini, F. (1993). Relationship between nitrogen and chlorophyll content and spatial properties in maize leaves. European Journal of Agronomy, 2(2), 113–117.Google Scholar
  7. Freeman, K. W., Girma, K., Arnall, D. B., Mullen, R. W., Martin, K. L., Teal, R. K., et al. (2007). By-plant prediction of corn forage biomass and nitrogen uptake at various growth stages using remote sensing and plant height. Agronomy Journal, 99, 530–536.CrossRefGoogle Scholar
  8. Holland Scientific Crop Circle™ ACS-210 Plant Canopy Reflectance Sensor (2005) Instruction manual (revision 1.0). Holland Scientific, Inc., 5001 South 73rd Street, Lincoln, NE 68516, USA.Google Scholar
  9. Inman, D., Khosla, R., & Mayfield, T. (2005). On-the-go active remote sensing for efficient crop nitrogen management. Sensor Review, 25(3), 209–216.CrossRefGoogle Scholar
  10. Inman, D., Khosla, R., Reich, R. M., & Westfall, D. G. (2007). Active remote sensing and grain yield in irrigated maize. Precision Agriculture, 8, 241–252.CrossRefGoogle Scholar
  11. Kitchen, N. (2006). Variable-rate nitrogen fertilizer application in corn using in-field sensing of leaves or canopy. Agronomy technical note MO-35. Columbia, MO: USDA.Google Scholar
  12. Martin, K. L., Girma, K., Freeman, K. W., Teal, R. K., Tubana, B., Arnall, D. B., et al. (2007). Expression of variability in corn as influenced by growth stage using optical sensor measurements. Agronomy Journal, 99, 384–389.CrossRefGoogle Scholar
  13. Mulvaney, R. L. (1996). Nitrogen-inorganic forms. In D. L. Sparks (Ed.), Methods of soil analysis. Part 3. Soil science society of America book series: 5 (pp. 1155–1158). Madison, WI: Soil Science Society of America.Google Scholar
  14. NTech Industries. (2005). Model 505 GreenSeeker ™ hand held optical sensor unit operating manual (Rev. G). NTech Industries, Inc. 740 South State Street, Ukiah, CA 95482, USA.Google Scholar
  15. Osborne, S. L., Schepers, J. S., Francis, D. D., & Schlemmer, M. R. (2002). Detection of phosphorus and nitrogen deficiencies in corn using spectral radiance measurements. Agronomy Journal, 94, 1215–1221.CrossRefGoogle Scholar
  16. Penuelas, J., Gamon, J. A., Fredenn, A. L., Merino, J., & Field, C. B. (1994). Reflectance indices associated with physiological changes in nitrogen and water-limited sunflower leaves. Remote Sensing of the Environment, 48, 135–146.CrossRefGoogle Scholar
  17. Raun, W. R., Solie, J. B., Martin, K. L., Freeman, K. W., Stone, M. L., Johnson, G. V., et al. (2005). Growth stage, development, and spatial variability in corn evaluated using optical sensor readings. Journal of Plant Nutrition, 28, 173–182.CrossRefGoogle Scholar
  18. Ritchie, S. W., Hanway, J. J., & Benson, G. O. (1986). How a corn plant develops. Special report 48. Ames, IA: Iowa State Cooperative Extension Service.Google Scholar
  19. SAS Institute. (2006). SAS user’s guide. Cary, NC: SAS Inst.Google Scholar
  20. 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, 96, 583–589.CrossRefGoogle Scholar
  21. Solari, F., Shanahan, J., Ferguson, R., Schepers, J., & Gitelson, A. (2008). Active sensor reflectance measurements of corn nitrogen status and yield potential. Agronomy Journal, 100, 571–579.CrossRefGoogle Scholar
  22. Sripada, R. P., Heiniger, R. W., White, J. G., & Weisz, R. (2005). Aerial color infrared photography for determining late-season nitrogen requirements in corn. Agronomy Journal, 97, 1443–1451.CrossRefGoogle Scholar
  23. Staff, Soil Survey. (1980). Soil survey of Larimer County, Colorado. Washington D.C., USA: Gov. Print Office.Google Scholar
  24. 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
  25. 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.CrossRefGoogle Scholar
  26. Varvel, G. E., Willhelm, W. W., Shanahan, J. F., & Schepers, M. J. S. (2007). An algorithm for corn nitrogen recommendations using a chlorophyll meter based sufficiency index. Agronomy Journal, 99, 701–706.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Agronomy and HorticultureUniversity of Nebraska-Lincoln, WCRECNorth PlatteUSA
  2. 2.Department of Soil and Crop SciencesColorado State UniversityFort CollinsUSA
  3. 3.Precision Ag Research CenterKing Saud UniversityRiyadhSaudi Arabia

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