IDEAS home Printed from https://ideas.repec.org/p/ags/aaea13/150421.html
   My bibliography  Save this paper

A Bayesian Analysis of GPS Guidance System in Precision Agriculture: The Role of Expectations

Author

Listed:
  • Khanal, Aditya R.
  • Mishra, Ashok K.
  • Lambert, Dayton M.
  • Paudel, Krishna P.

Abstract

Farmer’s post adoption responses about technology are important in continuation and diffusion of a technology in precision agriculture. We studied farmer’s frequency of application decisions of GPS guidance system, after adoption. Using a Cotton grower’s precision farming survey in the U.S. and Bayesian approaches, our study suggests that ‘meeting expectation’ plays an important positive role. Farmer’s income level, farm size, and farming occupation are other important factors in modeling GPS guidance system adoption and application.

Suggested Citation

  • Khanal, Aditya R. & Mishra, Ashok K. & Lambert, Dayton M. & Paudel, Krishna P., 2013. "A Bayesian Analysis of GPS Guidance System in Precision Agriculture: The Role of Expectations," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150421, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea13:150421
    DOI: 10.22004/ag.econ.150421
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/150421/files/precision_bayesian_Khanal.pdf
    Download Restriction: no

    File URL: https://libkey.io/10.22004/ag.econ.150421?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Paxton, Kenneth W. & Mishra, Ashok K. & Chintawar, Sachin & Roberts, Roland K. & Larson, James A. & English, Burton C. & Lambert, Dayton M. & Marra, Michele C. & Larkin, Sherry L. & Reeves, Jeanne M. , 2011. "Intensity of Precision Agriculture Technology Adoption by Cotton Producers," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 40(1), pages 1-12, April.
    2. Koop,Gary & Poirier,Dale J. & Tobias,Justin L., 2007. "Bayesian Econometric Methods," Cambridge Books, Cambridge University Press, number 9780521671736, June.
    3. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February.
    4. Chib, Siddhartha, 2001. "Markov chain Monte Carlo methods: computation and inference," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 57, pages 3569-3649, Elsevier.
    5. Ebel, Robert M. & Schimmelpfennig, David E., 2012. "Production Cost and the Sequential Adoption of Precision Technology," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124393, Agricultural and Applied Economics Association.
    6. Walton, Jonathan C. & Lambert, Dayton M. & Roberts, Roland K. & Larson, James A. & English, Burton C. & Larkin, Sherry L. & Martin, Steven W. & Marra, Michele C. & Paxton, Kenneth W. & Reeves, Jeanne , 2008. "Adoption and Abandonment of Precision Soil Sampling in Cotton Production," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 33(3), pages 1-21.
    7. J.J. Heckman & E.E. Leamer (ed.), 2001. "Handbook of Econometrics," Handbook of Econometrics, Elsevier, edition 1, volume 5, number 5.
    8. D'Antoni, Jeremy M. & Mishra, Ashok K. & Powell, Rebekah R. & Martin, Steven W., 2012. "Farmers’ Perception of Precision Technology: The Case of Autosteer Adoption by Cotton Farmers," 2012 Annual Meeting, February 4-7, 2012, Birmingham, Alabama 119734, Southern Agricultural Economics Association.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michels, Marius & von Hobe, Cord-Friedrich & Mußhoff, Oliver, 2020. "Understanding the Adoption of Drones in German Agriculture," 60th Annual Conference, Halle/ Saale, Germany, September 23-25, 2020 305579, German Association of Agricultural Economists (GEWISOLA).
    2. Barnes, A.P. & Soto, I. & Eory, V. & Beck, B. & Balafoutis, A. & Sánchez, B. & Vangeyte, J. & Fountas, S. & van der Wal, T. & Gómez-Barbero, M., 2019. "Exploring the adoption of precision agricultural technologies: A cross regional study of EU farmers," Land Use Policy, Elsevier, vol. 80(C), pages 163-174.
    3. Michels, Marius & von Hobe, Cord-Friedrich & Mußhoff, Oliver, 2020. "Understanding the Adoption of Drones in German Agriculture," 60th Annual Conference, Halle/ Saale, Germany, September 23-25, 2020 305579, German Association of Agricultural Economists (GEWISOLA).
    4. Marcin Błażejowski & Paweł Kufel & Jacek Kwiatkowski, 2020. "Model simplification and variable selection: A replication of the UK inflation model by Hendry (2001)," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 645-652, August.
    5. Blazejowski, Marcin & Kwiatkowski, Jacek, 2020. "Bayesian Model Averaging for Autoregressive Distributed Lag (BMA_ADL) in gretl," MPRA Paper 98387, University Library of Munich, Germany.
    6. Ouysse, Rachida & Kohn, Robert, 2010. "Bayesian variable selection and model averaging in the arbitrage pricing theory model," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3249-3268, December.
    7. Roman Horvath & Marek Rusnak & Katerina Smidkova & Jan Zapal, 2014. "The dissent voting behaviour of central bankers: what do we really know?," Applied Economics, Taylor & Francis Journals, vol. 46(4), pages 450-461, February.
    8. Gabriele Fiorentini & Enrique Sentana & Neil Shephard, 2004. "Likelihood-Based Estimation of Latent Generalized ARCH Structures," Econometrica, Econometric Society, vol. 72(5), pages 1481-1517, September.
    9. Bin Jiang & Anastasios Panagiotelis & George Athanasopoulos & Rob Hyndman & Farshid Vahid, 2016. "Bayesian Rank Selection in Multivariate Regression," Monash Econometrics and Business Statistics Working Papers 6/16, Monash University, Department of Econometrics and Business Statistics.
    10. Badi H. Baltagi & Georges Bresson & Anoop Chaturvedi & Guy Lacroix, 2022. "Robust Dynamic Space-Time Panel Data Models Using ε-contamination: An Application to Crop Yields and Climate Change," Center for Policy Research Working Papers 254, Center for Policy Research, Maxwell School, Syracuse University.
    11. Eicher, Theo S. & Helfman, Lindy & Lenkoski, Alex, 2012. "Robust FDI determinants: Bayesian Model Averaging in the presence of selection bias," Journal of Macroeconomics, Elsevier, vol. 34(3), pages 637-651.
    12. Pradeep K. Chintagunta & Junhong Chu & Javier Cebollada, 2012. "Quantifying Transaction Costs in Online/Off-line Grocery Channel Choice," Marketing Science, INFORMS, vol. 31(1), pages 96-114, January.
    13. Danilo Leiva-Leon & Luis Uzeda, 2023. "Endogenous Time Variation in Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 105(1), pages 125-142, January.
    14. Doğan, Osman & Taşpınar, Süleyman, 2014. "Spatial autoregressive models with unknown heteroskedasticity: A comparison of Bayesian and robust GMM approach," Regional Science and Urban Economics, Elsevier, vol. 45(C), pages 1-21.
    15. Horváth, Roman, 2013. "Does trust promote growth?," Journal of Comparative Economics, Elsevier, vol. 41(3), pages 777-788.
    16. Shang, Linmei & Heckelei, Thomas & Gerullis, Maria K. & Börner, Jan & Rasch, Sebastian, 2021. "Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction," Agricultural Systems, Elsevier, vol. 190(C).
    17. Shinya Sugawara & Yasuhiro Omori, 2017. "An Econometric Analysis of Insurance Markets with Separate Identification for Moral Hazard and Selection Problems," Computational Economics, Springer;Society for Computational Economics, vol. 50(3), pages 473-502, October.
    18. Luis Uzeda, 2022. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 25-53, Emerald Group Publishing Limited.
    19. Jean‐Pierre Dubé & Günter J. Hitsch & Peter E. Rossi, 2010. "State dependence and alternative explanations for consumer inertia," RAND Journal of Economics, RAND Corporation, vol. 41(3), pages 417-445, September.
    20. Yari Vecchio & Marcello De Rosa & Gregorio Pauselli & Margherita Masi & Felice Adinolfi, 2022. "The leading role of perception: the FACOPA model to comprehend innovation adoption," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 10(1), pages 1-19, December.

    More about this item

    Keywords

    Farm Management; Research and Development/Tech Change/Emerging Technologies;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aaea13:150421. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/aaeaaea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.