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 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.
KeywordsBayesian updating Nitrogen response Stochastic plateau Winter wheat
- Begiebing, S., Schneider, M., Bach, H., & Wagner, P. (2007). Assessment of in-field heterogeneity for determination of the economic potential of precision farming. In J. V. Stafford (Ed.), Proceedings of the 6th European conference on precision agriculture (pp. 811–818). Wageningen, The Netherlands: Wageningen Academic Publishers.Google Scholar
- Boyer, C. N., Lambert, D. M., Velandia, M., English, B. C., Roberts, R. K., Larson, J. A., et al. (2016). Cotton producer awareness and participation in cost-sharing programs for precision nutrient-management technology. Journal of Agricultural and Resource Economics, 41, 81–96.Google Scholar
- Bullock, D., & Mieno, T. (2017). An assessment of the value of information from on-farm field trials. Unpublished Working Paper, University of Illinois, Champaign, IL.Google Scholar
- Byerlee, D. R., & Anderson, J. R. (1982). Risk, utility and the value of information in farmer decision making. Review of Marketing and Agricultural Economics, 50, 231–246.Google Scholar
- Doye, D., Sahs, R., & Kletke, D. (2014). Oklahoma Farm and Ranch Custom Rates, 2013–2014. Stillwater, OK, USA: Oklahoma Cooperative Extension Service Fact Sheet CR-205 0214 Rev.Google Scholar
- Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification. New York, NY, USA: Wiley.Google Scholar
- El-Hout, N. M., & Blackmer, A. M. (1990). Nitrogen status of corn after alfalfa in 29 Iowa fields. Journal of Soil and Water Conservation, 45, 115–117.Google Scholar
- Erickson, B., & Widmar, D. A. (2015). 2015 precision agricultural services dealership survey results. West Lafayette, IN, USA: Department of Agricultural Economics and Department of Agronomy, Purdue University. Retrieved September 14, 2016, from http://agribusiness.purdue.edu/files/resources/2015-crop-life-purdue-precision-dealer-survey.pdf.
- Havránková, J., Rataj, V., Godwin, R. J., & Wood, G. A. (2007). The evaluation of ground based remote sensing systems for canopy nitrogen management in winter wheat—Economic efficiency. Agricultural Engineering International: The CIGR Ejournal. Manuscript CIOSTA 07 002, 9.Google Scholar
- Huang, W., McBride, W., & Vasavada, U. (2009, March). Recent volatility in U.S. fertilizer prices causes and consequences. Amber Waves, pp. 28–31.Google Scholar
- National Agricultural Statistics Service (NASS). (2017a). Wheat-price received, measured in $/BU. National. US Total 2013. Annual Marketing Year. Retrieved January 3, 2017, from https://quickstats.nass.usda.gov.
- National Agricultural Statistics Service (NASS). (2017b). Price paid. Nitrogen, urea 44–46%—Price paid, measured in $/ton. National. US Total 2013. Retrieved January 3, 2017, from https://quickstats.nass.usda.gov.
- Norwood, F. B., Lusk, J. L., & Brorsen, B. W. (2004). Model selection for discrete dependent variables: Better statistics for better steaks. Journal of Agricultural and Resource Economics, 29, 404–419.Google Scholar
- Oklahoma State University. (2016a). Experiment 222: Long-term application of N, P, and K in continuous winter wheat, est. 1968. Retrieved June 28, 2016, from http://www.nue.okstate.edu/Long_Term_Experiments/E222.htm.
- Oklahoma State University. (2016b). Experiment 502: Wheat grain yield response to nitrogen, phosphorus, and potassium fertilization. Lahoma, OK. Retrieved June 28, 2016, from http://nue.okstate.edu/Long_Term_Experiments/E502.htm.
- Ouédraogo, F. B., Brorsen, B. W., & Arnall, D. B. (2016). Changing nitrogen levels in cotton. Journal of Cotton Science, 20, 18–25.Google Scholar
- Pautsch, G. R., Babcock, B. A., & Breidt, F. J. (1999). Optimal information acquisition under a geostatistical model. Journal of Agricultural and Resource Economics, 24, 342–366.Google Scholar
- Rodriguez, D. G. P., & Bullock, D. S. (2015). An empirical investigation of the Stanford’s “1.2 Rule” for nitrogen fertilizer recommendation. Selected Paper. San Francisco, CA, USA: Agricultural and Applied Economics Association.Google Scholar
- Schimmelpfennig, D., & Ebel, R. (2016). Sequential adoption and cost savings from precision agriculture. Journal of Agricultural and Resource Economics, 41, 97–115.Google Scholar
- Zellner, A. (1971). An introduction to Bayesian inference in econometrics. New York, NY, USA: Wiley.Google Scholar