Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-24T16:24:48.375Z Has data issue: false hasContentIssue false
  • English
  • Français

Biophysical Simulation in Support of Crop Production Decisions: A Case Study in the Black-lands Region of Texas

Published online by Cambridge University Press:  05 September 2016

Carl R. Dillon ,
James W. Mjelde  and
Bruce A. McCarl
Carl R. Dillon
Affiliation:
Department of Agricultural Economics, Texas A&M University
James W. Mjelde
Affiliation:
Department of Agricultural Economics, Texas A&M University
Bruce A. McCarl
Affiliation:
Department of Agricultural Economics, Texas A&M University
Get access Rights & Permissions [Opens in a new window]

Abstract

Economic feasibility of Texas Blacklands corn production in relation to sorghum, wheat, and cotton is studied. Biophysical simulation generated yield data are integrated with an economic decision model using quadratic programming. Given the various scenarios analyzed, corn is economically feasible for the Blacklands. A crop mix of half corn and half cotton production is selected under risk neutrality with wheat entering if risk aversion is present. Corn and grain sorghum production are highly substitutable. Profit effects attributed to changing corn planting dates are more pronounced than profit changes resulting from altering corn population or maturity class.

Keywords

biophysical simulationproduction managementriskquadratic programming
Type
Submitted Articles
Information
Journal of Agricultural and Applied Economics , Volume 21 , Issue 1 , July 1989 , pp. 73 - 86
Copyright
Copyright © Southern Agricultural Economics Association 1989

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 Amer. Soc. Agr. Eng. meeting, Chicago, Illinois, 1983.Google Scholar
Ahmed, J., van Bavel, C.H.M., and Hiler, E.A.. “Optimization of Crop Irrigation Strategy under a Stochastic Weather Regime: A Simulation Study.” Water Resour. Res., 12, 6(1976):1241–1247.Google Scholar
Apland, J., McCarl, B.A., and Miller, W.. “Risk and the Demand for Supplemental Irrigation: A Case Study in the Corn Belt.” Amer. J. Agr. Econ., 62:1(1980):142–145.CrossRefGoogle Scholar
Arkin, G.F., Vanderlip, R.L., and Ritchie, J.T.. “A Dynamic Grain Sorghum Growth Model.” Transactions Amer. Soc. Agr. Eng., 19(1976):622-26,630.Google Scholar
Babeir, A.S., Colvin, T.S., and Marley, S.J., “Predicting Field Tractability with a Simulation Model.” Paper presented at the Amer. Soc. Agr. Eng. meeting, East Lansing, Michigan, 1985.Google Scholar
Baier, W. “Crop-Weather Analysis Model: Review and Model Development.” J. Appl. Meteorology, 12(M73):937–947.Google Scholar
Bernardo, D.J., Whittlesey, N.K., Saxton, K.E., and Bassett, D.L.. “An Irrigation Model for Management of Limited Water Supplies.” West. J. Agr. Econ., 12(1987):164–173.Google Scholar
Boggess, W.G. “Discussion: Use of Biophysical Simulation in Production Economics.” So. J. Agr. Econ., 16, 1(1984):87–89.Google Scholar
Boggess, W.G., and Amerling, C.B.. “A Bioeconomic Simulation Analysis of Irrigation Investments.” So. J. Agr. Econ., 15, 1(1983):85–92.Google Scholar
Boggess, W.G., Lynne, G.D., Jones, J.W., and Swaney, D.P.. “Risk Return Assessment of Irrigation Decisions in Humid Regions.” So. J. Agr. Econ., 15, 1(1983):35–44.Google Scholar
Coffman, C. G. Agronomist-Corn and Sorghum Specialist, Texas Agricultural Extension Service. Personal Communication. 1987.Google Scholar
Cothren, J.T. Associate Professor, Department of Soil and Crop Sciences, Texas A&M University. Personal Communication. 1987.Google Scholar
Dillon, C.R. “An Economic Study of Tactical Crop Production Decisions in the Blacklands: The Melding of Biophysical Simulation and Economic Decision Models.” Master's Thesis, Texas A&M University, 1987.Google Scholar
Dillon, C.R., Mjelde, J. W., McCarl, B.A., Cothren, J.T., Martin, J.R., Rister, M.E., and Stockle, C.. “Blacklands Corn Production: A Study of Economic Feasibility.” Texas Agricultural Experiment Station Publication Draft Paper, 1988.Google Scholar
Elliott, R.L., Lembke, W.D., and Hunt, D.R.. “A Simulation Model for Predicting Available Days for Soil Tillage.” Transactions Amer. Soc. Agri. Eng., 20(1981):288-91, 295.Google Scholar
El-Nazer, T., and McCarl, B.A.. “The Choice of Crop Rotation: A Modeling Approach and Case Study.” Amer. J. Agr. Econ., 68(1986):127–136.Google Scholar
Harris, T.R., and Mapp, H.P. Jr. “A Control Theory Approach to Optimal Irrigation Scheduling in the Oklahoma Panhandle.” So. J. Agr. Econ., 12, 1(1980):165–172.Google Scholar
Jackson, B.S., Arkin, G.F., and Hearn, A.B.. “The Cotton Simulation Model ‘COTTAM’: Fruiting Model Calibration and Testing.” Transactions Amer. Soc. Agr. Eng., 31(1988):846–854.Google Scholar
Larsen, G.A. Sensitivity Analysis of the Texas A&M Wheat (TAMW) Model. USDA Statistical Reporting Service, AGES 830712. August 1983.Google Scholar
Maas, S.J., and Arkin, G.F.. “Sensitivity Analysis of SORGF, A Grain Sorghum Model.” Transactions Amer. Soc. Agr. Eng., 28(1980a):671–675.Google Scholar
Maas, S.J., and Arkin, G.F.. TAMW: A Wheat Growth and Development Simulation Model. Texas Agricultural Experiment Station, Program and Model Documentation No. 80-3. October 1980b.Google Scholar
Maas, S.J., and Arkin, G.F.. User's Guide to SORGF: A Dynamic Grain Sorghum Growth Model With Feedback Capacity. Texas Agricultural Experiment Station, Program and Model Documentation No. 78-1. January 1978.Google Scholar
McCarl, B.A., and Bessler, D.. “When the Applicable Utility Function Is Unknown What Is a Good Upper Bound on the Pratt Risk Aversion Coefficient?” Aust. J. of.Agr. Econ., Forthcoming 1988.Google Scholar
Metzer, R.B. Agronomist-Cotton Specialist, Texas Agricultural Extension Service. Personal Communication. 1987.Google Scholar
Miller, F.R. Professor, Department of Soil and Crop Sciences, Texas A&M University. Personal Communication. 1987.Google Scholar
Miller, T.D. Agronomist-Small Grains Specialist, Texas Agricultural Extension Service. Personal Communication. 1987.Google Scholar
Mjelde, J.W., Sonka, S.T., Dixon, B.L., and Lamb, P.J.. “Valuing Forecast Characteristics in a Dynamic Agricultural Production System.” Amer. J. Agr. Econ., 70(1988):674–684.Google Scholar
Morrison, J.E., Gerik, T.J., Chichester, F.W., and Martin, J.R.. “No-tillage Farming System Technologies, Procedures, Performance, and Economics for High-clay Soils.” Draft Paper, USDA and Texas Agricultural Experiment Station, 1988.Google Scholar
Musser, W.N., and Tew, B.V.. “Use of Biophysical Simulation in Production Economics.” So. J. Agr. Econ., 16, 1(1984):77–86.Google Scholar
Parker, M.R., Rister, M.E., Teague, P.W., and Mjelde, J.W.. An Economic Decision Framework for Central Texas Corn Production. Department of Agricultural Economics, Texas A&M University (DIR 86-1 SP-2). April 1986.Google Scholar
Reichelderfer, K.H., and Bender, F.E.. “Application of a Simulative Approach to Evaluating Alternative Methods for the Control of Agricultural Pests.” Amer. J. Agr. Econ., 61(1979):258–267.CrossRefGoogle Scholar
Rosenthal, W. Research Scientist, Texas Agricultural Experiment Station, Blackland Research Center. Personal Communication. 1987.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
Vanderlip, R.C., and Arkin, G.F.. “Simulating Accumulation and Distribution of Dry Matter in Grain Sorghum.” Agronomy J., 69(1977):917–923.CrossRefGoogle Scholar
Whitson, R.E., Kay, R.D. Pori, W.A. Le, and Rister, M.E.. “Machinery and Crop Selection with Weather Risk.” Transactions Amer. Soc. Agr. Eng., 24(1981):288-91, 295.Google Scholar