Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-23T15:36:57.500Z Has data issue: false hasContentIssue false
  • English
  • Français

ECONOMIC AND STOCHASTIC EFFICIENCY COMPARISON OF EXPERIMENTAL TILLAGE SYSTEMS IN CORN AND SOYABEAN UNDER RISK

Published online by Cambridge University Press:  26 January 2011

EIHAB M. FATHELRAHMAN ,
JAMES C. ASCOUGH II ,
DANA L. HOAG ,
ROBERT W. MALONE ,
PHILIP HEILMAN ,
LORI J. WILES  and
RAMESH S. KANWAR
EIHAB M. FATHELRAHMAN
Affiliation:
Department of Agricultural and Resource Economics, Colorado State University, Fort Collins, CO 80523USA
JAMES C. ASCOUGH II*
Affiliation:
USDA-ARS, Agricultural Systems Research Unit, Fort Collins, CO 80526USA
DANA L. HOAG
Affiliation:
Department of Agricultural and Resource Economics, Colorado State University, Fort Collins, CO 80523USA
ROBERT W. MALONE
Affiliation:
USDA-ARS, Agricultural Land and Watershed Management Research Unit, Ames, IA 50011USA
PHILIP HEILMAN
Affiliation:
USDA-ARS, Southwest Watershed Research Center, Tucson, AZ 85719USA
LORI J. WILES
Affiliation:
USDA-ARS, Water Management Research Unit, Fort Collins, CO 80526USA
RAMESH S. KANWAR
Affiliation:
Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA 50011USA
*
§Corresponding author: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

There are many reasons why agricultural researchers carefully evaluate approaches to experimental data analysis. Agricultural experiments are typically highly complex, with many types of variables often collected at a wide range of temporal and spatial scales. Furthermore, research in the developing world is often conducted on-farm where simple and conventional experimental designs are often unsuitable. Recently, a variant of stochastic dominance called stochastic efficiency with respect to a function (SERF) has been developed and used to analyse long-term experimental data. Unlike traditional stochastic dominance approaches, SERF uses the concept of certainty equivalents (CEs) to rank a set of risk-efficient alternatives instead of finding a subset of dominated alternatives. This study evaluates the efficacy of the SERF methodology for analysing conventional and conservation tillage systems using 14 years (1990–2003) of economic budget data collected from 36 experimental plots at the Iowa State University Northeast Research Station near Nashua, IA, USA. Specifically, the SERF approach is used to examine which of two different tillage systems (chisel plough and no-till) on continuous corn (Zea mays) and corn/soyabean (Glycine max) rotation cropping systems are the most risk-efficient in terms of maximizing economic profitability (gross margin and net return) by crop across a range of risk aversion preferences. In addition to the SERF analysis, we also conduct an economic analysis of the tillage system alternatives using mean-standard deviation and coefficient of variation for ranking purposes. Decision criteria analysis of the economic measures alone provided somewhat contradictive and non-conclusive rankings, e.g. examination of the decision criteria results for gross margin and net return showed that different tillage system alternatives were the highest ranked depending on the criterion and the cropping system (e.g. individual or rotation). SERF analysis results for the tillage systems were also dependent on the cropping system (individual, rotation or whole-farm combined) and economic outcome of interest (gross margin or net return) but only marginally on the level of risk aversion. For the individual cropping systems (continuous corn, rotation corn and rotation soyabean), the no-till tillage and rotation soyabean system was the most preferred and the chisel plough tillage and continuous corn system the least preferred across the entire range of risk aversion for both gross margin and net return. The no-till tillage system was preferred to the chisel plough tillage system when ranking within the continuous corn and the corn-soyabean rotation cropping systems for both gross margin and net return. Finally, when analysing the tillage system alternatives on a whole-farm basis (i.e. combined continuous corn and corn-soybean rotation), the no-till tillage system was clearly preferred to the chisel plough tillage system for both gross margin and net return. This study indicates that the SERF 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.

Type
Research Article
Information
Experimental Agriculture , Volume 47 , Issue 1 , January 2011 , pp. 111 - 136
Creative Commons
This is a work of the U.S. Government and is not subject to copyright protection in the United States.
Copyright
Copyright © Cambridge University Press 2011. This is a work of the U.S. Government and is not subject to copyright protection in the United States.

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Anderson, J. R. and Dillon, J. L. (1992). Risk Analysis in Dryland Farming Systems. Farming Systems Management Series No. 2. FAO, Rome.Google Scholar
Archer, D. W. and Reicosky, D. C. (2009). Economic performance of alternative tillage systems in the northern corn belt. Agronomy Journal 101:296–304.CrossRefGoogle Scholar
Arrow, K. J. (1965). Aspects of the Theory of Risk-Bearing. Helsinki: Yrjö Jahnssonin Säätiö.Google Scholar
Bakhsh, A., Kanwar, R. S., Karlen, D. L., Cambardella, C. A., Colvin, T. S., Moorman, T. B. and Bailey, T. B. (2000). Tillage and nitrogen management effects on crop yield and residual soil nitrate. Transactions of the American Society of Agricultural Engineers 44:1589–1595.CrossRefGoogle Scholar
Burgess, M. S., Mehuys, G. R. and Madramootoo, C. A. (1996). Tillage and crop residue effects on corn production in Quebec. Agronomy Journal 88:792–797.CrossRefGoogle Scholar
Chase, C. A. and Duffy, M. D. (1991). An economic analysis of the Nashua tillage study: 1978–1987. Journal of Production Agriculture 4:91–98.CrossRefGoogle Scholar
DeVuyst, E. A. and Halverson, A. D. (2004). Economics of annual cropping versus crop-fallow in the Northern Great Plains as influenced by tillage and nitrogen. Agronomy Journal 96:148–153.CrossRefGoogle Scholar
Grandy, A. S., Robertson, G. P. and Thelen, K. D. (2006). Do productivity and environmental trade-offs justify periodically cultivating no-till cropping systems? Agronomy Journal 98:1377–1383.CrossRefGoogle Scholar
Grove, B. (2006). Stochastic efficiency optimization of alternative agricultural water use strategies. Agrekon 45:406–420.CrossRefGoogle Scholar
Grove, B., Nel, F. and Maluleke, H. H. (2006). Stochastic efficiency analysis of alternative water conservation strategies. Agrekon 45:50–59.CrossRefGoogle Scholar
Grove, B. and Oosthuizen, L. K. (2010). Stochastic efficiency analysis of deficit irrigation with standard risk aversion. Agriculture Water Management 97:792–800.CrossRefGoogle Scholar
Hadar, J. and Russell, W. R. (1969). Rules for ordering uncertain prospects. American Economic Review 49:25–34.Google Scholar
Hardaker, J. B., Huirne, R. B. M., Anderson, J. R. and Lien, G. (2004a). Coping With Risk in Agriculture (2nd Ed.). CABI Publishers, Wallingford.CrossRefGoogle Scholar
Hardaker, J. B., Richardson, J. W., Lien, G. and Schumann, K. D. (2004b). Stochastic efficiency analysis with risk aversion bounds: a simplified approach. Australian Journal of Agricultural and Resource Economics 48:253–270.CrossRefGoogle Scholar
Hanoch, G. and Levy, H. (1969). Efficiency analysis of choices involving risk. Review of Economic Studies 36:335–345.CrossRefGoogle Scholar
Karlen, E., Berry, E. C. and Colvin, T. S. (1991). Twelve year tillage and crop rotation effects on yields and soil chemical properties in northeast Iowa. Communications in Soil Science and Plant Analysis 22:1985–2003.CrossRefGoogle Scholar
Keeney, R. L. and Raiffa, H. (1976). Decisions with Multiple Objectives: Preferences and Value Tradeoffs. New York: Wiley.Google Scholar
Klemme, R. (1985). A stochastic dominance comparison of reduced tillage systems in corn and soybean production under risk. American Journal of Agricultural Economics 67:550–557.CrossRefGoogle Scholar
Larson, J. A., Roberts, R. K., Tyler, D. D., Duck, B. N. and Slinsky, S. P. (1998). Stochastic dominance analysis of winter cover crop and nitrogen fertilizer systems for no-tillage corn. Journal of Soil and Water Conservation 53:285–288.Google Scholar
Lee, J., Brown, D. J. and Lovejoy, S. (1985). Stochastic efficiency versus mean-variance criteria as predictors of adoption of reduced tillage. American Journal of Agricultural Economics 67:839–845.CrossRefGoogle Scholar
Lien, G., Stordal, S., Hardaker, J. B. and Asheim, L. J. (2007a). Risk aversion and optimal forest replanting: a stochastic efficiency study. European Journal of Operational Research 181:1584–1592.CrossRefGoogle Scholar
Lien, G., Hardaker, J. B. and Flaten, O. (2007b). Risk and economic sustainability of crop farming systems. Agricultural Systems 94:541–552.CrossRefGoogle Scholar
Lockwood, R.C. (1987). Diffusion and adoption of new technology at the farm level. Canadian Journal of Agricultural Economics 34: 147–150.Google Scholar
Malone, R. W., Ma, L., Heilman, P., Karlen, D. L., Kanwar, R. S. and Hatfield, J. L. (2007). Simulated N management effects on corn yield and tile-drainage nitrate loss. Geoderma 140: 272–283.CrossRefGoogle Scholar
Pendell, D. L., Williams, J. R., Rice, C. W., Nelson, R. G. and Boyles, S. B. (2006). Economic feasibility of no-tillage and manure for soil carbon sequestration in corn production in Northeastern Kansas. Journal of Environmental Quality 35:1364–1373.CrossRefGoogle ScholarPubMed
Pendell, D. L., Williams, J. R., Boyles, S. B., Rice, C. W. and Nelson, R. G. (2007). Soil carbon sequestration strategies with alternative tillage and nitrogen sources under risk. Review of Agricultural Economics 29:247–268.CrossRefGoogle Scholar
Quincke, J. A., Wortmann, C. S., Mamo, M., Franti, T., Drijber, R. A. and García, J. P. (2007). One-time tillage of no-till systems: soil physical properties, phosphorus runoff, and crop yield. Agronomy Journal 99:1104–1110.CrossRefGoogle Scholar
Ribera, L. A., Hons, F. M. and Richardson, J. W. (2004). An economic comparison between conventional and no-tillage farming systems in Burleson County, Texas. Agronomy Journal 96: 415–424.CrossRefGoogle Scholar
Richardson, J. W., Klose, S. L. and Gray, A. 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 332: 299–315.CrossRefGoogle Scholar
Richardson, J. W., Schumann, K. D. and Feldman, P. A. (2006). Simulation and Econometrics to Analyze Risk: Simetar© Inc. 2006 User Manual. College Station, Texas.Google Scholar
Schumann, K. D., Richardson, J. W., Lien, G. and Hardaker, J. B. (2004). Stochastic efficiency analysis using multiple utility functions. Paper presented at the 2004 American Agricultural Economics Association Annual Meeting, Denver, CO, USA, August 1–4.Google Scholar
Systat. (2006). SigmaStat 3.11 for Windows. Chicago, Ill.: Systat, Inc.Google Scholar
Voy, K. D. (1995). Soil Survey of Floyd County, Iowa. USDA-SCS in cooperation with Iowa Agriculture and Home Economics Experimental Station Cooperative Extension Service, Iowa State University, Ames, Iowa.Google Scholar
Watkins, K. B., Hill, J. L. and Anders, M. M. (2008). An economic risk analysis of no-till management and rental arrangements in Arkansas rice production. Journal of Soil and Water Conservation 63:242–250.CrossRefGoogle Scholar
Williams, J. R., Johnson, O. S. and Gwin, R. E. (1987). Tillage systems for wheat and sorghum: an economic and risk analysis. Journal of Soil and Water Conservation 42:120–123.Google Scholar
Williams, J. R. (1988). A stochastic dominance analysis of tillage and crop insurance practices in a semiarid region. American Journal of Agricultural Economics 70:112–120.CrossRefGoogle Scholar
Williams, J. R., Llewelyn, R. V., Pendell, D. J., Schlegel, A. and Dumler, T. (2010). A risk analysis of converting conservation reserve program acres to a wheat-sorghum-fallow rotation. Agronomy Journal 102:612–622.CrossRefGoogle Scholar
Yiridoe, E. K., Weerksink, A., Hooker, D. C., Vyn, T. J. and Swanton, C. (2000). Income risk analysis of alternative tillage systems for corn and soybean production on clay soils. Canadian Journal of Agricultural Economics 48: 161–174.CrossRefGoogle Scholar