Nitrogen fertilizer recommendations based on plant sensing and Bayesian updating
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 w...
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| Veröffentlicht in: | Precision agriculture 2018-02, Vol.19 (1), p.79-92 |
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| creator | McFadden, Brandon R. Brorsen, B. Wade Raun, William R. |
| description | 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. |
| doi_str_mv | 10.1007/s11119-017-9499-4 |
| format | Article |
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| source | SpringerLink Journals - AutoHoldings |
| subjects | Agriculture Atmospheric Sciences Bayesian analysis Biomedical and Life Sciences Chemistry and Earth Sciences Computer Science Detection Error detection Life Sciences Nitrogen Parameter estimation Parameter uncertainty Physics Remote Sensing/Photogrammetry Soil Science & Conservation Statistics for Engineering Wheat Winter wheat |
| title | Nitrogen fertilizer recommendations based on plant sensing and Bayesian updating |
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