IDEAS home Printed from https://ideas.repec.org/p/ags/saea15/196962.html
   My bibliography  Save this paper

Farmers’ Willingness to Engage in Best Management Practices: an Application of Multiple Imputation

Author

Listed:
  • Zhong, Hua
  • Hu, Wuyang

Abstract

The purpose of this article is to explore how much farmers may engage their lands in Best Management Practices (BMPs) through a water quality trading (WQT) program in Kentucky. We conducted a survey of farmers in the Kentucky River watershed from 2011 to 2012. The questions in our survey are whether and how much farmers may adopt the BMPs (in addition to what they have already used) if they are offered compensation through WQT. About 20% of respondents with respect to five different types of BMPs did not indicate how much they will adopt. We compare three strategies to handle the missing data: deleting the observations with missing value, simple imputation by imputing the mean of the observed value, and Multiple Imputation (MI). Follow these missing data treatments, we estimate the factors affecting how much farmers may engage in BMPs using Tobit model. The results show that increasing the compensation for using BMPs are more likely to encourage farmers to adopt riparian buffers, animal fences and nutrient management. Besides, land area, rent area, farming revenue, percentage of reinvestment to farms from household income, water quality near the farm, participation in government programs, and current experience of BMPs will affect BMPs adoption. The results by using the MI are more promising and reasonable than using the deletion method and mean imputation method.

Suggested Citation

  • Zhong, Hua & Hu, Wuyang, 2015. "Farmers’ Willingness to Engage in Best Management Practices: an Application of Multiple Imputation," 2015 Annual Meeting, January 31-February 3, 2015, Atlanta, Georgia 196962, Southern Agricultural Economics Association.
    • Handle: RePEc:ags:saea15:196962
    DOI: 10.22004/ag.econ.196962
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/196962/files/SAEA%202015.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.196962?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. Shortle, James, 2013. "Economics and Environmental Markets: Lessons from Water-Quality Trading," Agricultural and Resource Economics Review, Cambridge University Press, vol. 42(1), pages 57-74, April.
    2. Horton, Nicholas J. & Kleinman, Ken P., 2007. "Much Ado About Nothing: A Comparison of Missing Data Methods and Software to Fit Incomplete Data Regression Models," The American Statistician, American Statistical Association, vol. 61, pages 79-90, February.
    3. Royston, Patrick & White, Ian R., 2011. "Multiple Imputation by Chained Equations (MICE): Implementation in Stata," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i04).
    4. Patrick Royston, 2004. "Multiple imputation of missing values," Stata Journal, StataCorp LP, vol. 4(3), pages 227-241, September.
    5. Eriko Miyama & Shunsuke Managi, 2014. "Global environmental emissions estimate: application of multiple imputation," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 16(2), pages 115-135, April.
    6. Schenker, Nathaniel & Raghunathan, Trivellore E. & Chiu, Pei-Lu & Makuc, Diane M. & Zhang, Guangyu & Cohen, Alan J., 2006. "Multiple Imputation of Missing Income Data in the National Health Interview Survey," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 924-933, September.
    7. Fowler, Lara B. & Royer, Matthew B. & Colburn, Jamison E., 2013. "Addressing Death by a Thousand Cuts: Legal and Policy Innovations to Address Nonpoint Source Runoff," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 28(3), pages 1-5.
    8. Braden, John B. & Boyle, Kevin J., 2013. "Theme Overview: Innovations in Nonpoint Source Pollution Policy: Introduction," Choices: The Magazine of Food, Farm, and Resource Issues, Agricultural and Applied Economics Association, vol. 28(3), pages 1-2.
    9. Rebecca R. Andridge & Roderick J. A. Little, 2010. "A Review of Hot Deck Imputation for Survey Non‐response," International Statistical Review, International Statistical Institute, vol. 78(1), pages 40-64, April.
    10. King, Gary & Honaker, James & Joseph, Anne & Scheve, Kenneth, 2001. "Analyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation," American Political Science Review, Cambridge University Press, vol. 95(1), pages 49-69, March.
    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. Zhong, Hua & Hu, Wuyang & Penn, Jerrod M., 2018. "Application of Multiple Imputation in Dealing with Missing Data in Agricultural Surveys: The Case of BMP Adoption," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 43(1), January.
    2. Präg, Patrick, 2018. "Nonresponse to Items on Self-Reported Delinquency. A Review and Evaluation of Missing Data Techniques," SocArXiv y9sv7, Center for Open Science.
    3. McDonough, Ian K. & Millimet, Daniel L., 2017. "Missing data, imputation, and endogeneity," Journal of Econometrics, Elsevier, vol. 199(2), pages 141-155.
    4. Jue Yang & Shunsuke Managi & Masayuki Sato, 2015. "The effect of institutional quality on national wealth: an examination using multiple imputation method," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 17(3), pages 431-453, July.
    5. Burns, Christopher & Prager, Daniel & Ghosh, Sujit & Goodwin, Barry, 2015. "Imputing for Missing Data in the ARMS Household Section: A Multivariate Imputation Approach," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205291, Agricultural and Applied Economics Association.
    6. James Honaker & Gary King, 2010. "What to Do about Missing Values in Time‐Series Cross‐Section Data," American Journal of Political Science, John Wiley & Sons, vol. 54(2), pages 561-581, April.
    7. Anne Valentine & Ilhom Akobirshoev & Monika Mitra, 2019. "Intimate Partner Violence among Women with Disabilities in Uganda," IJERPH, MDPI, vol. 16(6), pages 1-13, March.
    8. Mueller, Christoph Emanuel & Keil, Silke Inga & Bauer, Christian, 2017. "Effects of spatial proximity to proposed high-voltage transmission lines: Evidence from a natural experiment in Lower Saxony," Energy Policy, Elsevier, vol. 111(C), pages 137-147.
    9. Ahmad R. Alsaber & Jiazhu Pan & Adeeba Al-Hurban, 2021. "Handling Complex Missing Data Using Random Forest Approach for an Air Quality Monitoring Dataset: A Case Study of Kuwait Environmental Data (2012 to 2018)," IJERPH, MDPI, vol. 18(3), pages 1-25, February.
    10. Rachel Griffith & Rodrigo Lluberas & Melanie Lührmann, 2016. "Gluttony and Sloth? Calories, Labor Market Activity and the Rise of Obesity," Journal of the European Economic Association, European Economic Association, vol. 14(6), pages 1253-1286.
    11. Nengsih Titin Agustin & Bertrand Frédéric & Maumy-Bertrand Myriam & Meyer Nicolas, 2019. "Determining the number of components in PLS regression on incomplete data set," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(6), pages 1-28, December.
    12. Mueller, Christoph Emanuel, 2019. "Effects of spatial proximity to proposed electric power lines on residents' expectations, attitudes, and protest behavior: A replication study," Energy Policy, Elsevier, vol. 130(C), pages 341-346.
    13. R Florez-Lopez, 2010. "Effects of missing data in credit risk scoring. A comparative analysis of methods to achieve robustness in the absence of sufficient data," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(3), pages 486-501, March.
    14. Vartuhí Tonoyan & Robert Strohmeyer & Mohsin Habib & Manfred Perlitz, 2010. "Corruption and Entrepreneurship: How Formal and Informal Institutions Shape Small Firm Behavior in Transition and Mature Market Economies," Entrepreneurship Theory and Practice, , vol. 34(5), pages 803-832, September.
    15. Jan Marcus & Rainer Siegers & Markus M. Grabka, 2013. "Preparation of Data from the New SOEP Consumption Module: Editing, Imputation, and Smoothing," Data Documentation 70, DIW Berlin, German Institute for Economic Research.
    16. Ke-Hai Yuan & Fan Yang-Wallentin & Peter M. Bentler, 2012. "ML Versus MI for Missing Data With Violation of Distribution Conditions," Sociological Methods & Research, , vol. 41(4), pages 598-629, November.
    17. Raymundo M. Campos-Vázquez, 2013. "Efectos de los ingresos no reportados en el nivel y tendencia de la pobreza laboral en México," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 23-54, November.
    18. Scott Gehlbach & Konstantin Sonin & Ekaterina Zhuravskaya, 2010. "Businessman Candidates," American Journal of Political Science, John Wiley & Sons, vol. 54(3), pages 718-736, July.
    19. repec:jss:jstsof:45:i04 is not listed on IDEAS
    20. Filippelli, Raphael & Termansen, Mette & Hasan, Syezlin & Hasler, Berit & Hansen, Line & Smart, James C.R., 2022. "Water quality trading markets – Integrating land and marine based measures under a smart market approach," Ecological Economics, Elsevier, vol. 200(C).
    21. Harald Schoen, 2008. "Identity, Instrumental Self-Interest and Institutional Evaluations," European Union Politics, , vol. 9(1), pages 5-29, March.

    More about this item

    Keywords

    Environmental Economics and Policy; Research Methods/ Statistical Methods;

    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:saea15:196962. 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/saeaaea.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.