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An economic evaluation of site-specific input application Rx maps: evaluation framework and case study

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Abstract

Commercial consultants frequently sell site-specific crop input management recommendation maps (Rxs) to their farmer-clients. This study proposes a method to empirically evaluate the efficacy of commercial Rxs. The method takes three steps: (1) it uses precision agriculture technology to conduct randomized on-farm precision experiments; (2) it estimates yield response functions for the Rx’s management zones using the data; and (3) it conducts economic analysis to test the hypothesis that implementing the Rx is an economically optimal strategy. The method is illustrated using data from a 2018 on-farm nitrogen and seed rate precision experiment on a 31-ha Ohio field, for which nitrogen and seed Rxs were created by the farmer’s professional consultant. The study demonstrates the promise of improving input management through on-farm precision experimentation and data analysis. Future research must conduct trials over multiple years to account for weather. A call is made for the development of public and private research infrastructure to lower the costs on-farm precision experimentation and data analysis.

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References

  • Bock, B. R., & Sikora, F. J. (1990). Modified-quadratic/plateau model for describing plant responses to fertilizer. Soil Science Society of America Journal, 54(6), 1784–1789.

    Article  CAS  Google Scholar 

  • Boyer, C. N., Larson, J. A., Roberts, R. K., McClure, A. T., Tyler, D. D., & Zhou, V. (2013). Stochastic corn yield response functions to nitrogen for corn after corn, corn after cotton, and corn after soybeans. Journal of Agricultural and Applied Economics, 45, 669–681.

    Article  Google Scholar 

  • Bullock, D. S., Puntel, L., Montesdeoca, K., Johannsen, K., & Schreiber, D. Notes on I-CORPS interviews for NSF USDA I-FAST ‘Data-Intensive Farm Management Project.’ Unpublished document, University of Illinois Department of Agricultural and Consumer Economics, July 20, 2020, Available upon request.

  • Bullock, D. S., Boerngen, M., Tao, H., Maxwell, B., Luck, J. D., Shiratsuchi, L., et al. (2019). The data-intensive farm management project: Changing agronomic research through on-farm precision experimentation. Agronomy Journal, 111(6), 2736–2746.

    Article  Google Scholar 

  • Bullock, D. G., & Bullock, D. S. (1994). Quadratic and quadratic-plus-plateau models for predicting optimal nitrogen rate of corn: A comparison. Agronomy Journal, 86(1), 191–195.

    Article  Google Scholar 

  • Cerrato, M. E., & Blackmer, A. M. (1990). Comparison of models for describing; corn yield response to nitrogen fertilizer. Agronomy Journal, 82(1), 138–143.

    Article  Google Scholar 

  • Colaço, A. F., & Bramley, R. G. V. (2018). Do crop sensors promote improved nitrogen management in grain crops? Field Crops Research, 218, 126–140.

    Article  Google Scholar 

  • Coyos, T., Borrás, L., & Gambin, B. L. (2018). Site-specific covariates affecting yield response to nitrogen of late-sown maize in Central Argentina. Agronomy Journal, 110(4), 1544–1553.

    Article  Google Scholar 

  • Heady, E. O., Pesek, J. T., & Brown, W. G. (1955). Crop response surfaces and economic optima in fertilizer use. Iowa Agriculture and Home Economics Experiment Station Research Bulletin, 32(424), 1.

    Google Scholar 

  • Heege, H. J. (2013). Sensing of natural soil properties. In: H. Heege (Ed.) Precision in crop farming. Dordrecht: Springer. https://doi.org/10.1007/978-94-007-6760-7_5.

    Chapter  Google Scholar 

  • Marenya, P. P., & Barrett, C. B. (2009). State-conditional fertilizer yield response on Western Kenyan farms. American Journal of Agricultural Economics, 91(4), 991–1006.

    Article  Google Scholar 

  • Ockello-Ogutu, C., Paris, Q., & Williams, W. A. (1985). Testing a von Liebig crop response function against polynomial specifications. American Journal of Agricultural Economics, 67(4), 873–888.

    Article  Google Scholar 

  • Paris, Q. (1992). The von Liebig hypothesis. American Journal of Agricultural Economics, 74(4), 1019–1028.

    Article  Google Scholar 

  • Pennington, D. (2015). Yield monitor calibration procedure. Michigan State University Extension. https://www.canr.msu.edu/news/yield_monitor_calibration_procedure.

  • Pya, N., & Wood, S. N. (2015). Shape constrained additive models. Statistics and Computing, 25(3), 543–559.

    Article  Google Scholar 

  • Scharf, P. C., Wiebold, W. J., & Lory, J. A. (2002). Corn yield response to nitrogen fertilizer timing and deficiency level. Agronomy Journal, 94(3), 435–441.

    Article  Google Scholar 

  • Spillman, W. J. (1923). Application of the law of diminishing returns to some fertilizer and feed data. Journal of Farm Economics, 5(1), 36–52.

    Article  Google Scholar 

  • Spillman, W. J. (1933). Use of the exponential yield curve in fertilizer experiments. United States Department of Agriculture, Technical Bulletin 348, April.

  • Stauber, M. S., Burt, O. R., & Linse, F. (1975). An economic evaluation of nitrogen fertilization of grasses when carry-over is significant. American Journal of Agricultural Economics, 57(3), 463–471.

    Article  Google Scholar 

  • Stecker, J. A., Buchholz, D. D., Hanson, R. G., Wollenhaupt, N. C., & McVay, K. A. (1995). Tillage and rotation effects on corn yield response to fertilizer nitrogen on aqualf soils. Agronomy Journal, 87(3), 409–415.

    Article  Google Scholar 

  • Tembo, G., Wade Brorsen, B., Epplin, F. M., & Tostão, E. (2008). Crop input response functions with stochastic plateaus. American Journal of Agricultural Economics, 90(2), 424–434.

    Article  Google Scholar 

  • Tumusiime, E., Wade Brorsen, B., Mosali, J., Johnson, J., Locke, J., & Biermacher, J. T. (2011). Determining optimal levels of nitrogen fertilizer using random parameter models. Journal of Agricultural and Applied Economics, 43, 541–552.

    Article  Google Scholar 

  • Vanotti, M. B., & Bundy, L. G. (1994). Corn nitrogen recommendations based on yield response data. Journal of Production Agriculture, 7(2), 249–256.

    Article  Google Scholar 

  • Wood, S. N. (2017). Generalized additive models: An introduction with r. Boca Raton: CRC Press.

    Book  Google Scholar 

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Authors and Affiliations

  1. Kansas State University, Manhattan, USA

    Grant Gardner

  2. University of Nebraska Lincoln, Lincoln, USA

    Taro Mieno

  3. University of Illinois, Urbana-Champaign, USA

    David S. Bullock

Authors
  1. Grant Gardner
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  2. Taro Mieno
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  3. David S. Bullock
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Correspondence to Taro Mieno.

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Gardner, G., Mieno, T. & Bullock, D.S. An economic evaluation of site-specific input application Rx maps: evaluation framework and case study. Precision Agric 22, 1304–1316 (2021). https://doi.org/10.1007/s11119-021-09785-z

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