Hostname: page-component-586b7cd67f-2brh9 Total loading time: 0 Render date: 2024-11-22T12:10:46.015Z Has data issue: false hasContentIssue false
  • English
  • Français

Value of Temperature-Activated Polymer-Coated Seed in the Northern Corn Belt

Published online by Cambridge University Press:  28 April 2015

David W. Archer  and
Russ W. Gesch
David W. Archer
Affiliation:
U.S. Department of Agriculture, Agricultural Research Service, North Central Soil Conservation Research Laboratory, Morris, MN
Russ W. Gesch
Affiliation:
U.S. Department of Agriculture, Agricultural Research Service, North Central Soil Conservation Research Laboratory, Morris, MN
Get access Rights & Permissions [Opens in a new window]

Abstract

The value of an innovative seed technology is estimated in a discrete stochastic programming framework for a representative farm in the northern Corn Belt. Temperature-activated polymer-coated seed has the potential to increase net returns by increasing yields due to early planting and use of longer season varieties, as well as reducing yield loss due to delayed planting. A biophysical simulation model was used to estimate die impact of polymer-coated seed on corn and soybean yields and on field day availability for five planting periods, three crop varieties, and two tillage systems on two different soils under varying weather conditions.

Keywords

biophysical simulationcornmathematical programmingsoybeanQ12C61
Type
Articles
Information
Journal of Agricultural and Applied Economics , Volume 35 , Issue 3 , December 2003 , pp. 625 - 637
Copyright
Copyright © Southern Agricultural Economics Association 2003

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

Charnes, A., and Cooper, W.W.. “Chance-constrained Programming.” Management Science 6(1958):73–79.CrossRefGoogle Scholar
Cocks, K.D. “Discrete Stochastic Programming.” Management Science 15(September 1968):72–79.CrossRefGoogle Scholar
Dillon, C.R., Mjelde, 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
Dillon, C.R., Shearer, S.A., and Mueller, T.. “A Mixed Integer, Nonlinear Programming Model of Innovative Variable Rate Planting Date with Polymer Seed Coatings.” Paper presented at the American Agricultural Economics Association Annual Meeting, Chicago, IL, August 5-8, 2001.Google Scholar
Etyang, M.N., Preckel, P.V., Binkley, J.K., and Doster, D.H.. “Field Time Constraints for Farm Planning Models.” Agricultural Systems 58(September 1998):25–37.CrossRefGoogle Scholar
Gesch, R.W., Sharrat, B.S., Archer, D.W., and Balachander, N.. “Performance of Early Planted Polymer-Coated Maize Seed.” Agronomy Abstracts, 2001.Google Scholar
Grooms, L. “Coated Seed Holds Promise For Earlier No-Tilling.” No-Till Farmer, November 12, 2001.Google Scholar
Lazarus, W.Minnesota Farm Machinery Cost Estimates For 2001. St. Paul, MN: University of Minnesota Extension Service, FO-6696, October 4, 2001.Google Scholar
McCoy, S.M., Vyn, T.J., and West, T.D.. “Effect of Acrylic Polymer Seed Coatings on the Feasibility of Relay Intercropping in Indiana.” Agronomy Abstracts (2000):132.Google Scholar
Preckel, P.V., and DeVuyst, E.. “Efficient Handling of Probability Information for Decision Analysis Under Risk.” American Journal of Agricultural Economics 74(August 1992):655–662.CrossRefGoogle Scholar
Sharpley, A.N., and Williams, J.R.. EPIC-Erosion/ Productivity Impact Calculator: 1. Model Documentation, Washington, DC: U.S. Department of Agriculture Technical Bulletin No. 1768, 1990.Google Scholar
U.S. Department of Agriculture, National Agricultural Statistics Service. Census of Agriculture. Internet site: http://www.nass.usda.gov/census/ (Accessed May 16, 2003).Google Scholar