Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-26T05:50:03.131Z Has data issue: false hasContentIssue false
  • English
  • Français

Production Practice Alternatives for Income and Suitable Field Day Risk Management

Published online by Cambridge University Press:  28 April 2015

Carl R. Dillon
Carl R. Dillon*
Affiliation:
Department of Agricultural Economics, University of Kentucky, Lexington, Kentucky
Get access Rights & Permissions [Opens in a new window]

Abstract

Production risk includes yield and days suitable for fieldwork variability. Both were modeled using biophysical simulation and a mean-variance, chance-constrained mathematical programming formulation representing a Kentucky corn, soybean, and wheat producer. While crop diversification, planting date, and maturity group can be used to reduce the types of risk considered, interaction between the two influences how production practices are used to manage risk. For the conditions studied, plant population alterations were less effective for risk reduction of either component. The study provides evidence of the importance of the consideration of both elements of production risk in whole farm planning.

Keywords

days suitable for fieldworkmathematical programmingrisk management
Type
Invited Paper Sessions
Information
Journal of Agricultural and Applied Economics , Volume 31 , Issue 2 , August 1999 , pp. 247 - 261
Copyright
Copyright © Southern Agricultural Economics Association 1999

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

Acharya, B.P., Hayes, J.C., and Brown, L.C.. “Available Working Days in the Mid-South.” Paper presented at the American Society of Agricultural Engineering meeting, Chicago, Illinois, 1983.Google Scholar
Anderson, J.R., Dillon, J.L., and Hardaker, J.B.. Agricultural Decision Analysis. Ames, IA: Iowa State University Press, 1977.Google Scholar
Apland, J., Barne, R.N.. and Justus, F.. The Farm Lease: “An Analysis of Owner-Tenant and Landlord Preferences under Risk.” American Journal of Agricultural Economics 66(1984): 376–384.CrossRefGoogle Scholar
Apland, J.D. and Kaiser, H.. “Discrete Stochastic Sequential Programming: A Primer.” Staff Papers Series, Paper P84–8. Institute of Agriculture, Forestry and Home Economics. St. Paul, MN: University of Minnesota, 1984.Google Scholar
Babeir, A.S., Colvin, A.S., and Marley, S.J.. “Predicting Field Tractability with a Simulation Model.” Paper presented at the American Society of Agricultural Engineering meeting, East Lansing, Michigan, 1985.Google Scholar
Boggess, W.G. and Amerling, C.B.. “A Bioeconomic Simulation Analysis of Irrigation Investments.” Southern Journal of Agricultural Economics 15:2(1983):85–91.Google Scholar
Boggess, W.G. and Ritchie, J.T.. “Economic and Risk Analysis of Irrigation Decisions in Humid Regions.” Journal of Production Agriculture 1:2(1988):116–122.CrossRefGoogle Scholar
Boisvert, R.N. and Jensen, H.. “A Method for Planning Under Uncertain Weather Conditions, with Applications to Corn-Soybean Farming in Southern Minnesota.” University of Minnesota Agricultural Experiment Station Technical Bulletin No. 292, 1973.Google Scholar
Boisvert, R.N. and McCarl, B.A.. “Agricultural Risk Modeling Using Mathematical Programming.” Southern Cooperative Series Bulletin No. 356. Cornell University, New York. July 1990.Google Scholar
Charnes, A. and Cooper, W.W. “Chance Constrained Programming.” Management Science 6(1959):73–79.CrossRefGoogle Scholar
Cocks, K.D. “Discrete Stochastic Programming” Management Science 15(1968):72–79.CrossRefGoogle Scholar
Danok, A.B., McCarl, B.A., and White, T.K.. “Machinery Selection Modeling: Incorporation of Weather Variability.” American Journal of Agricultural Economics 62(1980):700–708.CrossRefGoogle Scholar
Dillon, C.R. “Microeconomic Effects of Reduced Yield Variability Cultivars of Soybeans and Wheat.” Southern Journal of Agricultural Economics 24:1(1992): 121–133.Google Scholar
Dillon, C.R., Mjeld, J.W.. and McCarl, B.A.. “Biophysical Simulation in Support of Crop Production Decisions: A Case Study in the Blacklands Region of Texas.” Southern Journal of Agricultural Economics 21(1989):73–86.Google Scholar
Donald, W.W and Prato, T.. “Efficiency and Economics of Herbicides for Canada Thistle (Cirsium arvense) Control in No-Till Spring Wheat (Triticum aestivum).” Weed Science 40:2(1992): 233–240.CrossRefGoogle Scholar
Dubman, R.W., Gunte, L.F.. and Miller, B.R.. “Revenue and Cost Uncertainty, Generalized Mean-Variance and the Linear Complementarity Problem: Comment.” American Journal of Agricultural Economics 61(1989):806–809.CrossRefGoogle Scholar
Elliott, R.L., Lembke, W.D., and Hunt, D.R.. “A Simulation Model for Predicting Available Days for Soil Tillage.” Transactions American Society of Agricultural Engineering 20(1981): 288-291, 295.Google Scholar
Epplin, F.M. and Al-Sakkaf, G.A.. “Risk-Efficient Tillage Systems and Program Participation Strategies for Land Subject to Conservation. Review of Agricultural Economics 17(1995): 242, 311–321.CrossRefGoogle Scholar
Etyang, M.N., Preckel, P.V., Binkley, J.K., and Doster, D.H.. “Field Time Constraints for Farm Planning Models.” Agricultural Systems 58:1(1998): 25–37.CrossRefGoogle Scholar
Freund, R.J. “The Introduction of Risk into a Programming Model.” Econometrica 24(1956): 253–264.CrossRefGoogle Scholar
Gerloff, D.C. and Maxey, L.. Field Crop Budgets for 1998. Tennessee Agricultural Extension Service. AE and RD#30. January 1998.Google Scholar
Gibson, C.D.Enterprise Analysis, Ohio Valley Farm Analysis Group, 1993–1997. University of Kentucky Cooperative Extension Service. 1998.Google Scholar
Grisley, W. “Farmer-to-Farmer Transfer of New Crop Varieties: An Empirical Analysis on Small Farms in Uganda.” Agricultural Economics 11,1(1994):43–49.Google Scholar
Hardaker, J.B., Hiurne, R.B.M., and Anderson, J.R.. Coping with Risk in Agriculture. CAB International. 1997.Google Scholar
Harrigan, T.M., Bickert, W.G., and Rozt, C.A.. “Simulation of Dairy Manure Management and Cropping Systems.” Applied Engineering in Agriculture 12:5(1996):563–574.CrossRefGoogle Scholar
Harris, T.R. and Mapp, H.R. “A Stochastic Dominance Comparison of Water-Conserving Irrigation Strategies.” American Journal of Agricultural Economics 68:2(1986):298–305.CrossRefGoogle Scholar
Hurd, B.H. “Yield Response and Production Risk: An Analysis Integrated Pest Management in Cotton.” Journal of Agricultural Resources Economics 19:2(1994):313–326.Google Scholar
Krause, M.A. and Black, J.R.. “Optimal Adoption Strategies for No-Till Technology in Michigan.” Review of Agricultural Economics 17:3(1995):299–310.CrossRefGoogle Scholar
Kentucky Agricultural Statistics Service. Kentucky Agricultural Statistics. Various years. National Agricultural Statistics Service of USDA and Kentucky Department of Agriculture.Google Scholar
Lapan, H. and Moschini, G.. “Futures Hedging Under Price, Basis and Production Risk.” American Journal of Agricultural Economics 76:3(1994):465–477.CrossRefGoogle Scholar
Larson, J.A. and Mapp, H.P.. “Cotton Cultivar, Planting, Irrigating and Harvesting Decisions Under Risk.” Journal of Agricultural Resources Economics 22:1(1997):157–173.Google Scholar
Larson, J.A., Mapp, H.P., Verhale, L.M.. and Banks, J.C.. “Adapting a Cotton Model for Decision Analyses: A Yield-Response Evaluation.” Agricultural Systems 50:2(1996): 145–167.CrossRefGoogle Scholar
Leatham, D.J. and Baker, T.G.. “Farmers' Choice of Fixed and Adjustable Interest Rate Loans.” American Journal of Agricultural Economics 70(1988):803–812.CrossRefGoogle Scholar
Markowitz, H. “Portfolio Selection.” Journal of Finance., 12(1952):77–91.Google Scholar
McCarl, B.A. and Bessler, D.. “Estimating an Upper Bound on the Pratt Risk Aversion Coefficient When the Utility Function is Unknown.” Australian Journal of Agricultural Economes 33(1989):56–63.CrossRefGoogle Scholar
Meyer, J. “Two-Moment Decision Models and Expected Utility Maximization.” American Economics Review 77(1987):421–430.Google Scholar
Misra, S.K. and Spurlock, S.R.. “Incorporating the Impacts of Uncertain Fieldwork Time on Whole-Farm Risk-Return Levels: A Target MO-TAD Approach.” Southern Journal of Agricultural Economics 23:2(1991):9–17.Google Scholar
Mjelde, J.W, Cothren, J.T., Rister, M.E.. Hons, F.M., Coffman, C.G., Shumway, C.R. and Lemon, R.G.. “Integrating Data From Various Field Experiments: The Case of Corn in Texas.” Journal of Production Agriculture 4:1(1991):139–147.CrossRefGoogle Scholar
Morgan, R.D. The Kentucky Farm Business Management Program 1997 Annual Summary. University of Kentucky cooperative Expension service. Agricultural Economics–Extension Series No. 98-03. 1998.Google Scholar
Olson, K.D. and Eidman, V.R.. “A Farmer's Choice of Weed Control Method and the Impacts of Policy and Risk.” Review of Agricultural Economics 14:1(1992):125–137.CrossRefGoogle Scholar
Olson, K.D., Martin, N.P., Hick, D.R.. and Schmidt, M.A.. “Economic Analysis of Including an Annual Forage in a Corn-Soybean Farming System.” Journal of Production Agriculture 4:4(1991):599–606.CrossRefGoogle Scholar
Oriade, C.A., Dillon, C.R. and Keisling, T.C.. “Economics of Wheat Residue Management in Doublecrop Soybean.” Journal of Production Agriculture. Forthcoming.Google Scholar
Pannell, D.J. “Optimal Herbicide Strategies for Weed Control Under Risk Aversion.” Review of Agricultural Economics 17:3(1995):243, 337–350.CrossRefGoogle Scholar
Paris, Q. “Revenue and Cost Uncertainty, Generalized Mean-Variance, and the Linear Complementarity Problem.” American Journal of Agricultural Economics 61(1979):268–275.CrossRefGoogle Scholar
Paris, Q. “Revenue and Cost Uncertainty, Generalized Mean-Variance and the Linear Complementarity Problem: Reply.” American Journal of Agricultural Economics 61(1989):810–812.CrossRefGoogle Scholar
Pingali, P.L., Hossain, M., Pandey, S. and Price, L.L.. “Economics of Nutrient Management in Asian Rice Systems: Towards Increasing Knowledge Intensity.” Field-Crops-Research 56:1,2(1998): 157–176.CrossRefGoogle Scholar
Polito, T.A. and Voss, R.D.. “Corn Yield Response to Varied Producer Controlled Factors and Weather in High Yield Environments.” Journal of Producation Agriculture 4:1(1991):51–57.CrossRefGoogle Scholar
Rae, A.N. “An Empirical Application and Evaluation of Discrete Stochastic Programming in Farm Management.” American Journal of Agricultural Economics 53(1971):625–38.CrossRefGoogle Scholar
Ritchie, J.T. and Otter, S.. “Description and Performance of CERES—Wheat: A User Oriented Wheat Yield Mode,” pp 159–175. ARS Wheat Yield Project, Willis, W.O.. ed. Washington, D.C.:USDA ARS-38, June 1985.Google Scholar
Robinson, L.J. and Barry, P.J.. The Competitive Firm's Response to Risk. Macmillan, New York. 1987.Google Scholar
Rosenberg, S.E., Rotz, C.A., Black, J.R.. and Muhtar, H.. “Prediction of Suitable Days for Field Work.” Agricultural Economics Staff Paper, Michigan State University. June 27–30, 1982. (82-56) 17 p.Google Scholar
Simalenga, T.E. and Have, H.. “Estimation of Soil Tillage Workdays in a Semi-Arid Area.” Journal of Agricultural Engineering Research 51:2(1992):81–89.CrossRefGoogle Scholar
Szmedra, P.I., Wetzstei, M.E.. and Mc-Clendon, R.W.. “Partial Adoption of Divisible Technologies in Agriculture.” Journal of Agricultural Economics Research 42:3(1990):20–26.Google Scholar
Spurlock, S.R., Buehrin, N.W.. and Caillavet, D.F.. “Days suitable for Fieldwork in Mississippi.” Proc-Beltwide-Cotton-Conference. Memphis, Tennessee. 1995. v. 1 p. 383–387.Google Scholar
Stapper, M. and Arkin, G.F.. “CORNF: A Dynamic Growth and Development Model for Maize (Zea mays L.).” Texas Agricultural Experiment Station, Program and Model Documentation No. 80-2. December 1980.Google Scholar
Seeeney, D.W., Granad, G.V.. and Burton, R.O. Jr. “Early and Traditionally Maturing Soybean Varieties Grown in Two Planting Systems.” Journal of Production Agriculture 8:3(1995):373–379.CrossRefGoogle Scholar
Teague, P.W. and Lee, J.G.. “Risk Efficient Perennial Crop Selection: A MOTAD Approach to Citrus Production.” Southern Journal of Agricultural Economics 20:2(1988):145–152.Google Scholar
Traxler, G.Falck-Zepeda, J., Ortiz-Monasterio, J. I. and Sayre, K.. “Production Risk and the Evolution of Varietal Technology.” American Journal of Agricultural Economics 77:1(1995):1–7.CrossRefGoogle Scholar
Vandeveer, L.R., Paxton, K.W, and Lavergne, D.R.. “Irrigation and Potential Diversification Benefits in Humid Climates.” Southern Journal of Agricultural Economics 21:2(1989):167–174.Google Scholar
Weisensei, W.P. and Schoney, R.A.. “An Analysis of the Yield-Price Risk Associated with Specialty Crops.” Western Journal of Agricultural Economics 14:2(1989):293–299.Google Scholar
Whitson, R.E., Kay, R.D., Le Pori, W.A. and Rister, M.E.. “Machinery and Crop Selection with Weather Risk.” Transactions American Society of Agricultural Engineering 24(1981):288-91, 295.Google Scholar
Wilkerson, G.G., Jones, J.W, Boote, K.T., Ingram, K.T.. and Mishoe, J.W.. “Modeling Soybean Growth for Management.” Transactions American Society of Agricultural Engineering 26(1983):63–73.CrossRefGoogle Scholar
Williams, J.R., Gross, L.K., Claassen, M.M.. and Llewelyn, R.V.. “Economic Analysis of Tillage for Corn and Soybean Rotations with Government Commodity Programs.” Journal of Production Agriculture 3:3(1990):308–316.CrossRefGoogle Scholar
Wilson, P.N. and Gunderson, C.E.. “Financial Risk in Cotton Production.” Southern Journal of Agricultural Economics 17:2(1985): 199–206.Google Scholar
Zacharias, T.P. and Grube, A.H.. “An Economic Evaluation of Weed Control Methods Used in Combination with Crop Rotation: A Stochastic Dominance Approach.” North Central Journal of Agricultural Economics 6:1(1984):113–120.CrossRefGoogle Scholar