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A Dynamic Decision Model of Technology Adoption under Uncertainty: Case of Herbicide-Resistant Rice

Published online by Cambridge University Press:  28 April 2005

Mamane M. Annou ,
Eric J. Wailes  and
Michael R. Thomsen
Mamane M. Annou
Affiliation:
Department of Agricultural Economics and Agribusiness, Division of Agriculture, University of Arkansas, Fayetteville, AR
Eric J. Wailes
Affiliation:
Department of Agricultural Economics and Agribusiness, Division of Agriculture, University of Arkansas, Fayetteville, AR
Michael R. Thomsen
Affiliation:
Department of Agricultural Economics and Agribusiness, Division of Agriculture, University of Arkansas, Fayetteville, AR
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Abstract

Herbicide-resistant (HR) rice technology is a potential tool for control of red rice in commercial rice production. Using an ex ante mathematical programming framework, this research presents an empirical analysis of HR rice technology adoption under uncertainty. The analysis accounts for stochastic germination of red rice and sheath blight to model a profit maximization problem of crop rotation among HR rice, regular rice, and soybeans. The results demonstrate that risk attitudes and technology efficiency determine adoption rates and optimal rotation patterns.

Keywords

biotechnologyherbicide resistancemathematical programmingprofit maximizationriceriskrotationtechnology adoptionQ16Q18O33C61
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 37 , Issue 1 , April 2005 , pp. 161 - 172
Copyright
Copyright © Southern Agricultural Economics Association 2005

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References

Alston, G.M., Norton, G.W., and Pardey, P.G.. Science under Scarcity: Principles and Practice for Agricultural Research Evaluation and Priority Setting. Ithaca, NY: Cornell University Press, 1995.Google Scholar
Annou, M., Thomsen, M., and Wailes, E.. “Impacts of Herbicide-Resistant Rice Technology on Rice-Soybeans Rotation.” AgBio-Forum 4(2001):79–86.Google Scholar
BASF Corporation. Internet site: www.agproducts.basf.com (Accessed October 2002).Google Scholar
Boisvert, R.N., and McCarl, B.A.. “Agricultural Risk Modeling Using Mathematical Programming.” Southern Cooperative Series Bulletin 356(1990). Cornell University, New York.Google Scholar
BU Growers, Ltd. Internet site: www.bugrowers. com/seedsales.htm (Accessed October 2002).Google Scholar
Cartwright, R., and Lee, F.. “Management of Sheath Blight and Blast in Arkansas.” Internet site: http://ipm.uaex.edu/Diseases/Rice/Shethblt/sheathBL.htm (Accessed February 2002).Google Scholar
Collender, R.N., and Zilberman, D.. “Land Allocation under Uncertainty for Alternative Specifications of Returns Distributions.” American Journal of Agricultural Economics 67(1985): 779–86.10.2307/1241817Google Scholar
Cousens, R. “An Empirical Model Relating Crop Yield to Weed and Crop Density and a Statistical Comparison with Other Models.” Journal of Agricultural Science 105(1985):513–21.10.1017/S0021859600059396Google Scholar
Delta Farm Press. “Horizon Ag: Clearfield Rice Acreage Up in 2003, Seed Supply Increases.” Internet site: http://deltafarmpress.com/mag/farming.horizon.ag.clearfield (Accessed September 2003).Google Scholar
Dillon, C.R. “Microeconomic Effects of Reduced Yield Variability Cultivars of Soybeans and Wheat.” Southern Journal of Agricultural Economics 24(1992):121–33.Google Scholar
El-Nazer, T., and McCarl, B.A.. “The Choice of Crop Rotation: A Modeling Approach and Case Study.” American Journal of Agricultural Economics 68(1986): 127–36.10.2307/1241657Google Scholar
Falck-Zepeda, J., Traxler, G., and Nelson, R.. “Rent Creation and Distribution From Biotechnology Innovations: The Case of Bt Cotton and Herbicide-Tolerant Soybeans.” Transition in Ag-BioTech: Economics of Strategy and Policy, NE 165 Conference, Washington, DC, June 24-25, 1999.Google Scholar
Feder, G., Just, R.E., and Zilberman, D.. “Adoption of Agricultural Innovations in Developing Countries: A Survey.” Economic Development and Cultural Change 33(1984):255–98.Google Scholar
General Algebraic Modeling System, GAMS Development Corp., 1217 Potomac St NW, Washington, DC, 1988.Google Scholar
Hazell, P.B.R. “A Linear Alternative to Quadratic and Semivariance Programming for Farm Planning under Uncertainty.” American Journal of Agricultural Economics 53(1971):55–62.Google Scholar
Kislev, Y, and Shchori-Bachrach, N.. “The Process of an Innovation Cycle.” American Journal of Agricultural Economics 55(1973):28–37.10.2307/1238658Google Scholar
Kropff, M.J. “Modeling the Effects of Weeds on Crop Production.” Weed Research 28(1988): 465–71.10.1111/j.1365-3180.1988.tb00829.xGoogle Scholar
Kwon, S.L., Smith, R.J., and Talbert, R.E.. “Interference of Red Rice (Oryza sativa) Densities in Rice (O. sativa).” Weed Science 39(1991):169–74.Google Scholar
Noldin, J.A., Chandler, J.M., Ketchersid, M.L., and McCauley, G.N.. “Red Rice (Oryza sativa) Biology. II. Ecotype Sensitivity to Herbicides.” Weed Technology 13(1998): 19–24.Google Scholar
Norton, G.W., and Davis, J.S.. “Evaluating Returns to Agricultural Research: A Review.” American Journal of Agricultural Economics 63(1981): 685–99.10.2307/1241211Google Scholar
Pantone, D.J., and Baker, B.. “Reciprocal Yield Analysis of Red Rice (Oryza sativa) Competition in Cultivated Rice.” Weed Science 39(1991):42–47.Google Scholar
Smith, R.J. Jr. “Control of Red Rice in Water-Seeded Rice.” Weed Science 29(1988):663–66.Google Scholar
University of Arkansas Cooperative Extension Service. Crop Budgets. Internet site: www.aragriculture.org/farmplanning/default.asp (Accessed February 2002).Google Scholar
University of Arkansas Cooperative Extension Service. Internet site: www.aragricultoe.org/weedinsctdis/diseasemgmt/rice/sheathblight/default.asp (Accessed February 2002).Google Scholar
van Groenendael, J.M. “Patchy Distribution of Weeds and Some Implications for Modeling Population Dynamics: A Short Literature Review.” Weed Research 28(1988):437–44.10.1111/j.1365-3180.1988.tb00825.xGoogle Scholar
Yassour, J., Zilberman, D., and Rausser, G.C.. “Optimal Choices among Alternative Technologies with Stochastic Yields.” American Journal of Agricultural Economics 63(1981): 718–24.10.2307/1241217Google Scholar
Zepeda, L. “Predicting Bovine Somatropin Use by California Dairy Producers.” Western Journal of Agricultural Economics 15(1990): 55–62.Google Scholar