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Continuum of Risk Analysis Methods to Assess Tillage System Sustainability at the Experimental Plot Level

  • Eihab M. Fathelrahman

    (USDA-APHIS, 2150 Centre Ave., Bldg. B, Mail Stop 2E3, Fort Collins, CO 80526, USA)

  • James C. Ascough II

    ()

    (USDA-ARS, Agricultural Systems Research Unit, 2150 Centre Ave, Bldg. D, Suite 200, Fort Collins, CO 80526, USA)

  • Dana L. Hoag

    (Department of Agricultural and Resource Economics, B330 Clark Bldg., Colorado State University, Fort Collins, CO 80523, USA)

  • Robert W. Malone

    (USDA-ARS, National Laboratory for Agriculture and the Environment, Agroecosystems Management Research Unit, 2110 University Boulevard, Ames, IA 50011, USA)

  • Philip Heilman

    (USDA-ARS, Southwest Watershed Research Center, 2000 E. Allen Rd., Tucson, AZ 85719, USA)

  • Lori J. Wiles

    (USDA-ARS, Water Management Research Unit, 2150 Centre Ave, Bldg. D, Suite 320, Fort Collins, CO 80526, USA)

  • Ramesh S. Kanwar

    (Department of Agricultural and Biosystems Engineering, 104 Davidson Hall, Iowa State University, Ames, IA 50011, USA)

This study applied a broad continuum of risk analysis methods including mean-variance and coefficient of variation (CV) statistical criteria, second-degree stochastic dominance (SSD), stochastic dominance with respect to a function (SDRF), and stochastic efficiency with respect to a function (SERF) for comparing income-risk efficiency sustainability of conventional and reduced tillage systems. Fourteen years (1990–2003) of economic budget data derived from 35 treatments on 36 experimental plots under corn ( Zea mays L.) and soybean ( Glycine max L.) at the Iowa State University Northeast Research Station near Nashua, IA, USA were used. In addition to the other analyses, a visually-based Stoplight or “probability of target value” procedure was employed for displaying gross margin and net return probability distribution information. Mean-variance and CV analysis of the economic measures alone provided somewhat contradictive and inconclusive sustainability rankings, i.e ., corn/soybean gross margin and net return showed that different tillage system alternatives were the highest ranked depending on the criterion and type of crop. Stochastic dominance analysis results were similar for SSD and SDRF in that both the conventional and reduced tillage system alternatives were highly ranked depending on the type of crop and tillage system. For the SERF analysis, results were dependent on the type of crop and level of risk aversion. The conventional tillage system was preferred for both corn and soybean for the Stoplight analysis. The results of this study are unique in that they highlight the potential of both traditional stochastic dominance and SERF methods for distinguishing economically sustainable choices between different tillage systems across a range of risk aversion. This study also indicates that the SERF risk analysis method appears to be a useful and easily understood tool to assist farm managers, experimental researchers, and potentially policy makers and advisers on problems involving agricultural risk and sustainability.

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Article provided by MDPI, Open Access Journal in its journal Sustainability.

Volume (Year): 3 (2011)
Issue (Month): 7 (July)
Pages: 1035-1063

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Handle: RePEc:gam:jsusta:v:3:y:2011:i:7:p:1035-1063:d:13251
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  1. Richardson, James W. & Klose, Steven L. & Gray, Allan W., 2000. "An Applied Procedure For Estimating And Simulating Multivariate Empirical (Mve) Probability Distributions In Farm-Level Risk Assessment And Policy Analysis," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 32(02), August.
  2. Lien, Gudbrand & Brian Hardaker, J. & Flaten, Ola, 2007. "Risk and economic sustainability of crop farming systems," Agricultural Systems, Elsevier, vol. 94(2), pages 541-552, May.
  3. Meyer, Jack, 1977. "Choice among distributions," Journal of Economic Theory, Elsevier, vol. 14(2), pages 326-336, April.
  4. Babcock, Bruce A. & Choi, E. Kwan & Feinerman, Eli, 1993. "Risk and Probability Premiums for Cara Utility Function," Staff General Research Papers 596, Iowa State University, Department of Economics.
  5. Grove, Bennie & Oosthuizen, Lukas Klopper, 2010. "Stochastic efficiency analysis of deficit irrigation with standard risk aversion," Agricultural Water Management, Elsevier, vol. 97(6), pages 792-800, June.
  6. Dustin L. Pendell & Jeffery R. Williams & Scott B. Boyles & Charles W. Rice & Richard G. Nelson, 2007. "Soil Carbon Sequestration Strategies with Alternative Tillage and Nitrogen Sources under Risk," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 29(2), pages 247-268.
  7. Alfons Weersink & Michael Walker & Clarence Swanton & Jim Shaw, 1992. "Economic Comparison of Alternative Tillage Systems under Risk," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 40(2), pages 199-217, 07.
  8. Hardaker, J. Brian & Richardson, James W. & Lien, Gudbrand D. & Schumann, Keith D., 2004. "Stochastic efficiency analysis with risk aversion bounds: a simplified approach," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 48(2), June.
  9. Grove, Bennie & Nel, F. & Maluleke, H.H., 2006. "Stochastic efficiency analysis of alternative water conservation strategies," Agrekon, Agricultural Economics Association of South Africa (AEASA), vol. 45(1), March.
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