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An Economic Assessment Of The Whole-Farm Impact Of Precision Agriculture

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  • Olson, Kent D.
  • Elisabeth, Pascal

Abstract

The full impact of an investment in a management information system (MIS), such as precision agriculture (PA), comes from improved managerial decision making throughout the whole farm and not just from improvements in a specific part of the farm. This study was conducted to determine whether the adoption of PA had a positive impact on whole-farm profitability. To overcome problems of simultaneity and self-selection in the adoption decision of PA, this study used a two stage econometric model using data from farms in Southwest Minnesota. The PA adoption decision was evaluated in the first stage, and the impact of adopting PA was evaluated in the second stage. The whole farm rate of return to assets (ROA) was used to measure the impact of PA. For all 212 farms in the dataset, the adoption of precision agriculture was explained significantly (p<0.1) by two variables: positively by the farmer's self-described soil variability and negatively by the level of non-farm income. For the 63 crop farms in the dataset, adoption of PA was explained significantly (p<0.1) only by the farmer's self-described soil variability. The adoption of PA was estimated to have a significant (p<0.1) negative impact on ROA for the entire group of farms but was not significant when the farms were separated into crop farms only and into size clusters. PA's lack of significance in explaining ROA in the subgroups may be due to small sample size, the variability of ROA itself, the lack of fully implementing PA as an MIS, differences in management ability, and the availability of other farming methods that are just as profitable as PA.

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Bibliographic Info

Paper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2003 Annual meeting, July 27-30, Montreal, Canada with number 22119.

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Date of creation: 2003
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Handle: RePEc:ags:aaea03:22119

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Related research

Keywords: precision agriculture; economics; adoption; profitability; Agribusiness;

References

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  1. Tomaszewski, M. A. & van Asseldonk, M. A. P. M. & Dijkhuizen, A. A. & Huirne, R. B. M., 2000. "Determining farm effects attributable to the introduction and use of a dairy management information system in The Netherlands," Agricultural Economics, Blackwell, vol. 23(1), pages 79-86, June.
  2. Tvedt, Douglas D. & Olson, Kent D. & Hawkins, Douglas M., 1989. "Short-Run Indicators Of Financial Success For Southwest Minnesota Farmers," Staff Papers 13933, University of Minnesota, Department of Applied Economics.
  3. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492 National Bureau of Economic Research, Inc.
  4. Francis Vella, 1998. "Estimating Models with Sample Selection Bias: A Survey," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 127-169.
  5. Fernandez-Cornejo, Jorge & McBride, William D., 2000. "Genetically Engineered Crops For Pest Management In U.S. Agriculture," Agricultural Economics Reports 33931, United States Department of Agriculture, Economic Research Service.
  6. El-Osta, Hisham S. & Mishra, Ashok K., 2001. "Adoption And Economic Impact Of Site-Specific Technologies In U.S. Agriculture," 2001 Annual meeting, August 5-8, Chicago, IL 20593, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  7. Heckman, James J, 1974. "Shadow Prices, Market Wages, and Labor Supply," Econometrica, Econometric Society, vol. 42(4), pages 679-94, July.
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