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Nitrogen fertilizer recommendations based on plant sensing and Bayesian updating

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Abstract

Methods are available to predict nitrogen needs of winter wheat based on plant sensing, but adoption rates by producers are low. Current algorithms that provide nitrogen recommendations based on plant sensing implicitly assume that parameters are estimated without error. A Bayesian updating method was developed that can incorporate precision plant sensing information and is simple enough that it could be computed on-the-go. The method can consider producers prior information and can account for parameter uncertainty. Bayesian updating gives higher nitrogen recommendations than plant sensing recommendations using a plug-in method. These recommendations increase net returns over the previous recommendations, but not enough to make plant sensing profitable in this scenario.

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Notes

  1. Note that this practice is an expected profit maximizing strategy since in some years, the nitrogen will be needed. The marginal revenue of applying nitrogen in years that it is needed is roughly 6–10 times its marginal cost and thus it pays to apply nitrogen that is only needed once every 6–10 years.

  2. The algorithm to apply a different amount to each square meter is adjusted to apply little or no fertilizer to areas of the field with little plant growth and so it may be more beneficial than the model used here. It also has a nonlinear yield function rather than a linear function. The commercial algorithm also applies more nitrogen.

  3. Note that the current commercial implementation of NFOA recognizes that the plug-in approach leads to under-application of N. To correct this problem, current models use a lower value of β 1 in order to get closer to the optimal level. Since the NFOA assumes no error, no application cost and the β 1 is high enough that it pays to apply nitrogen, the optimal solution with NFOA does not depend on prices.

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Acknowledgements

The research was partially funded by the Oklahoma Agricultural Experiment Station and USDA National Institute of Food and Agriculture, Hatch Project number OKL02939.

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Authors and Affiliations

  1. Department of Food and Resource Economics, University of Florida, P.O. Box 110240, Gainesville, FL, 32611, USA

    Brandon R. McFadden

  2. Department of Agricultural Economics, Oklahoma State University, Stillwater, OK, 74078-6026, USA

    B. Wade Brorsen

  3. Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK, 74078, USA

    William R. Raun

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  1. Brandon R. McFadden
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Correspondence to B. Wade Brorsen.

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McFadden, B.R., Brorsen, B.W. & Raun, W.R. Nitrogen fertilizer recommendations based on plant sensing and Bayesian updating. Precision Agric 19, 79–92 (2018). https://doi.org/10.1007/s11119-017-9499-4

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