Precision Agriculture

, Volume 11, Issue 3, pp 215–238 | Cite as

Estimating the demand and willingness-to-pay for cotton yield monitors

  • Michele C. Marra
  • Roderick M. Rejesus
  • Roland K. Roberts
  • Burton C. English
  • James A. Larson
  • Sherry L. Larkin
  • Steve Martin
Article

Abstract

Survey data from cotton farmers in six southeastern states of the USA were used to estimate the demand and willingness-to-pay (WTP) for either retrofitting yield monitors onto cotton pickers or to purchase a yield monitor as an option with a new cotton picker. ‘Don’t know’ responses were either omitted, combined with ‘no’ responses or included as a separate category for comparing WTP and estimates of the price elasticity of demand. Our results suggest that treating the ‘don’t know’ response as a separate category provides WTP estimates that are more consistent with expectations than the other approaches. The estimated price elasticities and demand curves indicate that previous users of precision technology are more responsive to changes in price of cotton yield monitors and would be more likely to adopt them when the price decreases. These demand and WTP estimates provide important information that can be used by those who sell cotton yield monitors, as well as policy-makers who may wish to subsidize this technology. Referendum contingent valuation was useful for evaluating the demand for any new technology.

Keywords

Contingent valuation Ordered probit Site-specific farming Yield monitor Willingness-to-pay (WTP) 

References

  1. Cameron, T. A., & Huppert, D. D. (1991). Referendum contingent valuation estimates: sensitivity to the assignment of offered values. Journal of the American Statistical Association, 86, 910–918.CrossRefGoogle Scholar
  2. Cameron, T. A., & James, M. D. (1987). Estimating willingness to pay from survey data: An alternative pre-test market evaluation procedure. Journal of Marketing Research, 24, 389–395.CrossRefGoogle Scholar
  3. Cameron, T. A., Poe, G. L., Ethier, R. G., & Schultze, W. D. (2002). Alternative non-market value-elicitation methods: Are the underlying preferences the same? Journal of Environmental Economics and Management, 44, 371–557.CrossRefGoogle Scholar
  4. Carson, R. T., Hanemann, W. M., Kopp, R. J., Krosnick, J. A., Mitchell, R. C., Presser, S., et al. (1998). Referendum design and contingent valuation: The NOAA panel’s no-vote recommendation. Review of Economics and Statistics, 80, 335–338.CrossRefGoogle Scholar
  5. Champ, P. A., Alberini, A., & Correas, I. (2005). Using contingent valuation to value a noxious weed control program: The effects of including an unsure response category. Ecological Economics, 55, 47–60.CrossRefGoogle Scholar
  6. Champ, P. A., & Brown, T. C. (1997). A comparison of contingent and actual voting behavior. In J. E. Englin (Ed.), Proceedings from the W-133 Benefits and Cost Transfer in Natural Resource Planning 10th Interim Report (pp. 77–98). Reno, NV: University of Nevada - Reno.Google Scholar
  7. Daberkow, S. G., Fernandez-Cornejo, J., & Padgitt, M. (2002). Precision agriculture technology diffusion: current status and future prospects. In P. C. Robert, R. H. Rust, & Larson W. E. (Eds.), Proceedings of the 6th International Conference on Precision Agriculture, Minneapolis, MN. July 15–18, 2002. Madison, WI: ASA/CSSA/SSSA.Google Scholar
  8. Durrence, J. S., Thomas, D. L., Perry, C. D., & Vellidis, G. (1999). Preliminary evaluation of commercial cotton yield monitors: the 1998 season in south Georgia. In The National Cotton Council (Ed.), Proceedings of the Beltwide Cotton Conference (pp. 366–372). Orlando, FL, January 3–7, 1999. Memphis, TN: National Cotton Council of America.Google Scholar
  9. Freeman, A. M. (1993). The measurement of environmental and resource values: Theory and methods. Washington, DC: Resources for the Future Press.Google Scholar
  10. Giraud, K. S., Loomis, J. B., & Johnson, R. L. (1999). Internal and external scopt in willingness-to-pay estimates for threatened and endangered wildlife. Journal of Environmental Management, 56, 221–229.CrossRefGoogle Scholar
  11. Green, D., Jacowitz, K. E., Kahneman, D., & McFadden, D. (1998). Referendum contingent valuation, anchoring, and willingness to pay for public goods. Resource and Energy Economics, 20, 85–116.CrossRefGoogle Scholar
  12. Greene, W. (2000). Econometric analysis (4th ed.). Upper Saddle River, NJ: Prentice-Hall, Inc.Google Scholar
  13. Griffin, T. W., Lowenberg-DeBoer, J. M., Lambert, D. M., Peone, J., Payne, T., & Daberkow, S. J. (2004). Precision farming: adoption, profitability, and making better use of data. In The North Central Farm Management Extension Committee (Ed.), Triennial north central farm management conference, Lexington, KY (July 14–16, 2004). Lexington, KY: University of Kentucky.Google Scholar
  14. Hanemann, W. M. (1984). Welfare evaluation in contingent valuation experiments with discrete responses. American Journal of Agricultural Economics, 66, 332–341.CrossRefGoogle Scholar
  15. Hanemann, M., Loomis, J., & Kanninen, B. (1991). Statistical efficiency of double-bounded dichotomous choice contingent valuation. American Journal of Agricultural Economics, 73, 1255–1263.CrossRefGoogle Scholar
  16. Hite, D., Hudson, D., & Intarapapong, W. (2002). Willingness to pay for water quality improvements: The case of precision technology. Journal of Agricultural and Resource Economics, 27, 433–449.Google Scholar
  17. Hole, A. R. (2007). A comparison of approaches to estimating confidence intervals for willingness-to-pay measures. Health Economics, 16, 827–840.CrossRefPubMedGoogle Scholar
  18. Hubbell, B., Marra, M., & Carlson, G. (2000). Estimating the demand for a new technology: Bt cotton and insecticide policies. American Journal of Agricultural Economics, 82, 118–132.CrossRefGoogle Scholar
  19. Hudson, D., & Hite, D. (2003). Producer willingness to pay for precision application technology: Implications for government and the technology industry. Canadian Journal of Agricultural Economics, 51, 39–53.CrossRefGoogle Scholar
  20. Isik, M., Khanna, M., & Winter-Nelson, A. (2001). Investment in site-specific crop management under uncertainty: Implications for nitrogen pollution control and environmental policy. Agricultural Economics, 24, 9–21.Google Scholar
  21. Khanna, M. (2001). Sequential adoption of site-specific technologies and its implication for nitrogen productivity: A double selectivity model. American Journal of Agricultural Economics, 83, 35–51.CrossRefGoogle Scholar
  22. Krinsky, I., & Robb, A. L. (1986). On approximating the statistical properties of elasticities. Review of Economics and Statistics, 68, 715–719.CrossRefGoogle Scholar
  23. Larson, J. A., Roberts, R. K., English, B. C., Cochran, R. L., & Wilson, B. S. (2005). A computer decision aid for the cotton yield monitor investment decision. Computers and Electronics in Agriculture, 48, 216–234.CrossRefGoogle Scholar
  24. Larson, J. A., Roberts, R. K., English, B. C., Larkin, S. L., Marra, M. C., Martin, S. W., et al. (2008). Factors affecting farmer adoption of remotely sensed imagery for precision management in cotton production. Precision Agriculture, 9, 195–208.CrossRefGoogle Scholar
  25. Lechner, W., & Baumann, S. (2000). Global navigation satellite systems. Computers and Electronics in Agriculture, 25, 67–85.CrossRefGoogle Scholar
  26. Lowenberg-DeBoer, J. (2000). Estimating precision farming benefits. In J. Lowenberg-DeBoer & K. Erickson (Eds.), Precision farming profitability (pp. 5–11). West Lafayette, IN: Purdue University.Google Scholar
  27. Park, T., Loomis, J. B., & Creel, M. (1991). Confidence interval for evaluating benefit estimates from dichotomous choice contingent valuation studies. Land Economics, 67, 64–73.CrossRefGoogle Scholar
  28. Perry, C. D., Vellidis, G., Wells, N., & Kvien, C. (2001). Simultaneous evaluation of multiple commercial yield monitors in Georgia. In The National Cotton Council (Ed.), Proceedings of the Beltwide Cotton Conference (pp. 328–339). Anaheim, CA. January 9–13, 2001. Memphis, TN: National Cotton Council of America.Google Scholar
  29. Ready, R. C., Whitehead, J. C., & Blomquist, G. C. (1995). Contingent valuation when respondents are ambivalent. Journal of Environmental Economics and Management, 29, 181–196.CrossRefGoogle Scholar
  30. Roades, J. P., Beck, A. D., & Searcy, S. W. (2000). Cotton yield mapping: Texas experiences in 1999. In The National Cotton Council (Ed.), Proceedings of the Beltwide Cotton Conference (pp. 404–407). San Antonio, TX. January 4–8, 2000. Memphis, TN: National Cotton Council of America.Google Scholar
  31. Roberts, R. K., English, B. C., Larson, J. A., Cochran, R. L., Goodman, W. R., Larkin, S. L., et al. (2002). Precision farming by cotton producers in six southern states: Results from the 2001 precision farming survey. Department of Agricultural Economics Research Series 03–02. Knowxville, TN: University of Tennessee.Google Scholar
  32. Roberts, R. K., English, B. C., Larson, J. A., Cochran, R. L., Goodman, W. R., Larkin, S. L., et al. (2004). Adoption of site-specific information and variable-rate technologies in cotton precision farming. Journal of Agricultural and Applied Economics, 30, 143–158.Google Scholar
  33. Sassenrath-Cole, G. F., Thomson, S. J., Williford, J. R., Hood, K. N., Thomasson, J. A., Willams, J., & Woodard, D. (1998). Field testing of cotton yield monitors. In The National Cotton Council (Ed.), Proceedings of the Beltwide Cotton Conference (pp. 364–366). San Diego, CA. January 5–9, 1998. Memphis, TN: National Cotton Council of America.Google Scholar
  34. Searcy, S. W., & Roades, J. P. (1998). Evaluation of cotton yield mapping. In The National Cotton Council (Ed.), Proceedings of the Beltwide Cotton Conference (pp. 33–35). San Diego, CA. January 5–9, 1998. Memphis, TN: National Cotton Council of America.Google Scholar
  35. Valco, T. D., Nichols, R. L., & Lalor, W. F. (1998). Adopting precision farming technology for cotton nutrition. In The National Cotton Council (Ed.), Proceedings of the Beltwide Cotton Conference (pp. 627–629). San Diego, CA. January 5–9, 1998. Memphis, TN: National Cotton Council of America.Google Scholar
  36. Vossler, C. A., & Kerkvliet, J. (2003). A criterion validity test of the contingent valuation method: Comparing hypothetical and actual voting behavior for a public referendum. Journal of Environmental Economics and Management, 45, 631–649.CrossRefGoogle Scholar
  37. Vossler, C. A., Kerkvliet, J., Polasky, S., & Gainutdinova, O. (2003). Externally validating contingent valuation: an open-space survey and referendum in Corvallis, Oregon. Journal of Economic Behavior and Organization, 51, 261–277.CrossRefGoogle Scholar
  38. Wang, H. (1997). Treatment of ‘don’t know’ responses in contingent valuation surveys: A random valuation approach. Journal of Environmental Economics and Management, 32, 219–232.CrossRefGoogle Scholar
  39. Wolak, F. J., Khalilian, A., Dodd, R. B., Han, Y. J., Keshkin, M., Lippert, R. M., & Hair, W. (1999). Cotton yield monitor evaluation, South Carolina – year 2. In The National Cotton Council (Ed.), Proceedings of the Beltwide Cotton Conference (pp. 361–364). Orlando, FL, January 3–7, 1999. Memphis, TN: National Cotton Council of America.Google Scholar
  40. York, A. (2003). Department of Crop Science, North Carolina State University, Raleigh, NC, personal communication.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Michele C. Marra
    • 1
  • Roderick M. Rejesus
    • 1
  • Roland K. Roberts
    • 2
  • Burton C. English
    • 2
  • James A. Larson
    • 2
  • Sherry L. Larkin
    • 3
  • Steve Martin
    • 4
  1. 1.Department of Agriculture and Resource EconomicsNorth Carolina State UniversityRaleighUSA
  2. 2.Department of Agricultural EconomicsUniversity of TennesseeKnoxvilleUSA
  3. 3.Department of Food and Resource EconomicsUniversity of FloridaGainesvilleUSA
  4. 4.Department of Agricultural Economics and Delta Research & Extension CenterMississippi State UniversityStonevilleUSA

Personalised recommendations