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The economic potential of precision nitrogen application with wheat based on plant sensing

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  • Jon T. Biermacher
  • B. Wade Brorsen
  • Francis M. Epplin
  • John B. Solie
  • William R. Raun

Abstract

Plant‐based precision nitrogen fertilizer application technologies have been developed as a way to predict and precisely meet nitrogen needs. Equipment necessary for precision application of nitrogen, based on sensing of growing wheat plants in late winter, is available commercially, but adoption has been slow. This article determines the expected profit from using a plant‐sensing system to determine winter wheat nitrogen requirements. We find that plant‐sensing systems have the potential to be more profitable than traditional nonprecise systems, but the existing system simulated was roughly breakeven with a traditional system.

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  • Jon T. Biermacher & B. Wade Brorsen & Francis M. Epplin & John B. Solie & William R. Raun, 2009. "The economic potential of precision nitrogen application with wheat based on plant sensing," Agricultural Economics, International Association of Agricultural Economists, vol. 40(4), pages 397-407, July.
    • Handle: RePEc:bla:agecon:v:40:y:2009:i:4:p:397-407
    DOI: 10.1111/j.1574-0862.2009.00387.x
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    Cited by:

    1. Tumusiime, Emmanuel & Brorsen, B. Wade & Mosali, Jagadeesh & Johnson, Jim & Locke, James & Biermacher, Jon T., 2011. "Determining Optimal Levels of Nitrogen Fertilizer Using Random Parameter Models," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 43(4), pages 1-12, November.
    2. Harmon, Xavier & Boyer, Christopher N. & Lambert, Dayton M. & Larson, James A., 2017. "Temporal Frequency Of Soil Test Information Effects On Returns To Potassium Fertilization In Cotton Production," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 49(2), pages 251-272, May.
    3. Asmaa Mourhir & Elpiniki I. Papageorgiou & Konstantinos Kokkinos & Tajjeeddine Rachidi, 2017. "Exploring Precision Farming Scenarios Using Fuzzy Cognitive Maps," Sustainability, MDPI, vol. 9(7), pages 1-23, July.
    4. Baveye, Philippe C. & Laba, Magdeline, 2015. "Moving away from the geostatistical lamppost: Why, where, and how does the spatial heterogeneity of soils matter?," Ecological Modelling, Elsevier, vol. 298(C), pages 24-38.
    5. Ng'ombe, John, 2019. "Economics of the Greenseeder Hand Planter, Discrete Choice Modeling, and On-Farm Field Experimentation," Thesis Commons jckt7, Center for Open Science.
    6. Elizabeth Canales & Jason S. Bergtold & Jeffery R. Williams, 2020. "Conservation practice complementarity and timing of on‐farm adoption," Agricultural Economics, International Association of Agricultural Economists, vol. 51(5), pages 777-792, September.
    7. Späti, Karin & Huber, Robert & Finger, Robert, 2021. "Benefits of Increasing Information Accuracy in Variable Rate Technologies," Ecological Economics, Elsevier, vol. 185(C).
    8. Wang, Tong & Jin, Hailong & Sieverding, Heidi & Kumar, Sandeep & Miao, Yuxin & Rao, Xudong & Obembe, Oladipo & Mirzakhani Nafchi, Ali & Redfearn, Daren & Cheye, Stephen, 2023. "Understanding farmer views of precision agriculture profitability in the U.S. Midwest," Ecological Economics, Elsevier, vol. 213(C).
    9. Wang, Tong & Jin, Hailong & Sieverding, Heidi L. & Rao, Xudong & Miao, Yuxin & Kumar, Sandeep & Redfearn, Daren & Nafchi, Ali, 2022. "Understanding farmer perceptions of precision agriculture profitability in the U.S. Midwest," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322502, Agricultural and Applied Economics Association.

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