Precision Agriculture

, Volume 17, Issue 4, pp 470–487 | Cite as

Evaluation of mid-season sensor based nitrogen fertilizer recommendations for winter wheat using different estimates of yield potential

  • Jacob T. Bushong
  • Jeremiah L. Mullock
  • Eric C. Miller
  • William R. Raun
  • D. Brian Arnall


Optical sensors, coupled with mathematical algorithms, have proven effective at determining more accurate mid-season nitrogen (N) fertilizer recommendations in winter wheat. One parameter required in making these recommendations is in-season grain yield potential at the time of sensing. Four algorithms, with different methods for determining grain yield potential, were evaluated for effectiveness to predict final grain yield and the agronomic optimum N rate (AONR) at 34 site-years. The current N fertilizer optimization algorithm (CNFOA) outperformed the other three algorithms at predicting yield potential with no added N and yield potential with added N (R2 = 0.46 and 0.25, respectively). However, no differences were observed in the amount of variability accounted for among all four algorithms in regards to predicting the AONR. Differences were observed in that the CNFOA and proposed N fertilizer optimization algorithm (PNFOA), under predicted the AONR at approximately 75 % of the site-years; whereas, the generalized algorithm (GA) and modified generalized algorithm (MGA) recommended N rates under the AONR at about 50 % of the site-years. The PNFOA was able to determine N rate recommendations within 20 kg N ha−1 of the AONR for half of the site-years; whereas, the other three algorithms were only able recommend within 20 kg N ha−1 of the AONR for about 40 % of the site-years. Lastly, all four algorithms reported more accurate N rate recommendations compared to non-sensor based methodologies and can more precisely account for the year to year variability in grain yields due to environment.


Nitrogen recommendations Optical sensors Yield potential Winter wheat 



Agronomic optimum N rate


Current N fertilizer optimization algorithm


Days of potential growth


Generalized algorithm


Growing degree day


In-season estimate of yield


Modified generalized algorithm


Preplant nitrate test


Proposed N fertilizer optimization algorithm


Normalized difference vegetation index


Response index


Stress index



The authors would like to thank the Oklahoma Soil Fertility Research and Education Advisory Board for funding this research project and their continued financial support of soil fertility research at Oklahoma State University. The authors would also like to express their gratitude to all the current and former soil fertility graduate students who aided in the data collection and maintenance of research sites.

Compliance with ethical standards

Conflict of Interest

The mention of any trademarked products or equipment utilized in this experiment was for research purposes only and does not act as an endorsement by Oklahoma State University. The authors and Oklahoma State University have no direct financial relation with any of the named manufacturers, thus the authors declare there is no conflict of interest regarding the publication of this manuscript.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Jacob T. Bushong
    • 1
  • Jeremiah L. Mullock
    • 1
  • Eric C. Miller
    • 1
  • William R. Raun
    • 1
  • D. Brian Arnall
    • 1
  1. 1.Department of Plant and Soil SciencesOklahoma State UniversityStillwaterUSA

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