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

Eihab Fathelrahman, James C. Ascough, Dana Hoag (), Robert W. Malone, Philip Heilman, Lori J. Wiles and Ramesh S. Kanwar
Additional contact information
James C. Ascough: USDA-ARS, Agricultural Systems Research Unit, 2150 Centre Ave, Bldg. D, Suite 200, Fort Collins, CO 80526, 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

Sustainability, 2011, vol. 3, issue 7, 1-29

Abstract: 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.

Keywords: agriculture; tillage systems; stochastic dominance; economic budgeting; risk analysis; sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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