[go: up one dir, main page]

Skip to main content

Advertisement

Log in

Active remote sensing and grain yield in irrigated maize

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

Advances in agricultural technology have led to the development of active remote sensing equipment that can potentially optimize N fertilizer inputs. The objective of this study was to evaluate a hand-held active remote sensing instrument to estimate yield potential in irrigated maize. This study was done over two consecutive years on two irrigated maize fields in eastern Colorado. At the six- to eight-leaf crop growth stage, the GreenSeeker™ active remote sensing unit was used to measure red and NIR reflectance of the crop canopy. Soil samples were taken before side-dressing from the plots at the time of sensing to determine nitrate concentration. Normalized difference vegetation index (NDVI) was calculated from the reflectance data and then divided by the number of days from planting to sensing, where growing degrees were greater than zero. An NDVI-ratio was calculated as the ratio of the reflectance of an area of interest to that of an N-rich portion of the field. Regression analysis was used to model grain yield. Grain yields ranged from 5 to 24 Mg ha−1. The coefficient of determination ranged from 0.10 to 0.76. The data for both fields in year 1 were modeled and cross-validated using data from both fields for year 2. The coefficient of determination of the best fitting model for year 1 was 0.54. The NDVI-ratio had a significant relationship with observed grain yield (r 2 = 0.65). This study shows that the GreenSeeker™ active sensor has the potential to estimate grain yield in irrigated maize; however, improvements need to be made.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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., Araus, J. L., Casadesús, J., & Royo, C. (2002). Relationship between growth traits and spectral vegetation indices in Durum Wheat. Crop Science, 42, 1547–1555.

    Article  Google Scholar 

  • Báez-González, A. D., Chen, P., Tiscareño-López, M., & Srinivasan, R. (2002). Using satellite and field data with crop growth modeling to monitor and estimate maize yield in Mexico. Crop Science, 42, 1943–1949.

    Article  Google Scholar 

  • Bausch, W. C., & Duke, H. R. (1996). Remote sensing of plant nitrogen status in corn. Transactions of the American Society Agricultural Engineers, 39(6), 1869–1875.

    Google Scholar 

  • Bausch, W. C., & Diker, K. (2001). Innovative remote sensing techniques to increase nitrogen use efficiency of corn. Communications in Soil Science and Plant Analysis, 32(7 & 8), 1371–1390.

    Article  CAS  Google Scholar 

  • Blackmer, T. M., Schepers, J. S., & Varvel, G. E. (1994). Light reflectance compared with other nitrogen stress measurements in maize leaves. Agronomy Journal, 86, 934–938.

    Article  Google Scholar 

  • Bundy, L. G., & Andraski, T. W. (1993). Soil and plant nitrogen availability tests for corn following alfalfa. Journal of Production Agriculture, 6, 200–206.

    Google Scholar 

  • 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 

  • Campbell, J. B. (2002). Introduction to remote sensing (3rd ed.). New York, USA: The Guilford Press.

    Google Scholar 

  • Dwyer, L. M., Stewart, D. W., Carrigan, L., Ma, B. L., Weaver, P., & Balchini, D. (1999). Guidelines for comparisons among different maize maturity rating systems. Agronomy Journal, 91, 946–949.

    Article  Google Scholar 

  • Ercoli, L., Mariotti, M., Masom, A., & Massantini, F. (1993). Relationship between nitrogen and chlorophyll content and spectral properties in maize leaves. European Journal of Agronomy, 2, 113–117.

    Google Scholar 

  • Gauch, H. G., & Zobel, R. W. (1988). Predictive and postdictive success of statistical analysis of yield trials. Theoretical Genetics, 76, 1–10.

    Article  Google Scholar 

  • Heckman, J. R., Hlubik, W. T., Prostak, D. J., & Paterson, J. W. (1995). Pre-sidedress soil nitrate test for sweet corn. Horticultural Science, 30, 1033–1036.

    Google Scholar 

  • Inman, D., Khosla, R., & Mayfield, T. (2005). On-the-go active remote sensing for efficient crop nitrogen management. Sensor Review Journal, 25(3), 209–214.

    Article  Google Scholar 

  • Large, E. C. (1954). Growth stages in cereals. Journal of Plant Pathology, 3, 128–129.

    Article  Google Scholar 

  • Ma, B. L., Lianne, L. M., Dwyer, M., Costa, C., Cober, E. R., & Morrison, M. J. (2001). Early prediction of soybean yield from canopy reflectance measurements. Agronomy Journal, 93, 1227–1234.

    Article  Google Scholar 

  • Magdoff, F. R., Ross, D., & Amadon, J. (1984). A soil test for nitrogen availability to maize. Soil Science Society of America Journal, 48, 1301–1304.

    Article  Google Scholar 

  • Magdoff, F. R. (1991). Understanding the Magdoff pre-sidedress nitrate test for corn. Journal of Production Agriculture, 4, 297–305.

    Google Scholar 

  • Meisinger, J. J., Bandel, V. A., Angle, J. S., O’Keefe, B. E., & Reynolds, C. M. (1992). Pre-sidedress soil nitrate test evaluation in Maryland. Soil Science Society of America Journal, 56, 1527–1532.

    Article  Google Scholar 

  • Mulvaney, R. L. (1996). Nitrogen-inorganic forms. In D. L. Sparks et al (Eds.), Methods of soil analysis. Part 3, Chemical methods, Chapter 4 (pp 1146–1162). Madison, WI: Soil Science Society of America.

    Google Scholar 

  • Neter, J., Kunter, M. H., Nachtscheim, C. J., & Wasserman, W. (1996). Applied linear regression models (4th ed.). Chicago, Il USA: McGraw-Hill/Irwin publishers.

    Google Scholar 

  • NUE Web 2005. Outline for generating new crop algorithms for N fertilization. Available at: http://www.nue.okstate.edu/Algorithm/Algorithm_Outline.htm. Verified December 12, 2005. Oklahoma State University, Stillwater, OK, USA.

  • Peñuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J., & Field, C. B. (1994). Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sensing of Environment, 48, 135–146.

    Article  Google Scholar 

  • Raun, W. R., Soile, J. B., Johnson, G. V., Stone, M. L., Lukina, E. V., Thomason, W. E., & Schepers, J. S. (2001). In-season prediction of potential grain yield in winter wheat using canopy reflectance. Agronomy Journal, 93, 131–138.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Raun, W. R., Solie, J. B., Stone, M. L., Martin, K. L., Freeman, K. W., Mullen, R. W., Zhang, H., Scheppers, J. S., & Johnson, G. V. (2005). Optical sensor-based algorithm for crop nitrogen fertilization. Communications in Soil Science and Plant Analysis, 36, 2759–2781.

    Article  CAS  Google Scholar 

  • Rozas, H. S., Echeverría, H. E., Studdert, G. A., & Domínguez, G. (2000). Evaluation of the pre-sidedress soil nitrogen test for no-tillage maize fertilized at planting. Agronomy Journal, 92, 1176–1183.

    Article  CAS  Google Scholar 

  • SAS Institute (2001). Statistical analysis software version 8. Cary, NC, USA: SAS Institute.

    Google Scholar 

  • Schepers, J. S., Blackmer, T. M., Wilhelm, W. W., & Resende, M. (1996). Transmittance and reflectance measurements of maize leaves from plants with different nitrogen and water supply. Journal of Plant Physiology, 148, 523–529.

    CAS  Google Scholar 

  • Shanahan, J. F., Schepers, J. S., Francis, D. D., Varvel, G. E., Wilhelm, W. W., Tringe, J. M., Schlemmer, M. R., & Major, D. J. (2001). Use of remote-sensing imagery to estimate corn grain yield. Agronomy Journal, 93, 583–589.

    Article  Google Scholar 

  • Spellman, D. E., Rongni, A., Westfall, D. G., Waskom, R. M., & Soltanpour, P. N. (1996). Pre-sidedress nitrate soil testing to manage nitrogen fertility in irrigated corn in a semi-arid environment. Communications in Soil Science and Plant Analysis, 27(3&4), 561–574.

    Article  CAS  Google Scholar 

  • Stone, M. L., Soile, 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 American Society of Agricultural Engineers, 39(5), 1623–1631.

    Google Scholar 

  • Thenkabail, P. S., Smith, R. B., & DePauw, E. (2000). Hyperspectral Vegetation Indicies and their relationships with agricultural crop characteristics. Remote Sensing of Environment, 71, 158–182.

    Article  Google Scholar 

  • Vleeshouwers, L. M., & Jongschaap, R. E. E. (2001). Chlorophyll and nitrogen relations in maize with regards to spectral properties: forcing methods of remote sensing data in crop growth simulation models. Deliverable D06 EU Croma (EVG1–2000–000027). Available online: http://library.wur.nl/wasp/bestanden/LUWPUBRD_00121626_A502_001.pdf. Verified 08/30/2007.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to R. Khosla.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Inman, D., Khosla, R., Reich, R.M. et al. Active remote sensing and grain yield in irrigated maize. Precision Agric 8, 241–252 (2007). https://doi.org/10.1007/s11119-007-9043-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-007-9043-z

Keywords