Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-23T05:59:00.191Z Has data issue: false hasContentIssue false
  • English
  • Français

Estimating the Life of Short-lived, Cyclic Weeds with Markov Processes

Published online by Cambridge University Press:  12 June 2017

L. Allen Torell ,
Kirk C. McDaniel  and
Kent Williams
L. Allen Torell
Affiliation:
Dep. Agric. Econ. and Agric. Bus., new Mex. State Univ., Las Cruces, NM 88003
Kirk C. McDaniel
Affiliation:
Dep. Range and Animal Sci., New Mex. State Univ., Las Cruces, NM 88003
Kent Williams
Affiliation:
Ext. Agent, Mont. State Univ., Malta, MT 59538
Get access Rights & Permissions [Opens in a new window]

Abstract

Some annual weeds, perennial herbs, and suffrutescent half shrubs are unique in that their populations are cyclic in nature and relatively short-lived. This variability presents a confounding factor that makes management decisions about control of these weeds difficult. This paper presents a procedure, using Markov processes, that gives additional information for managing cyclic weed populations. The Markov chain model is used to evaluate the interyear patterns of broom snakeweed survival when growing on New Mexico rangeland.

Keywords

Broom snakeweed, Gutierrezia sarothrae [Pursh] Britt. and Rusby ♯ GUESARangeland brush and weed controltreatment lifestochastic processesweed population dynamicsGUESA
Type
Research
Information
Weed Technology , Volume 6 , Issue 1 , March 1992 , pp. 62 - 67
Copyright
Copyright © 1990 by the Weed Science Society of America 

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

Literature Cited

1. Bernardo, D. J. 1989. A dynamic model for determining optimal range improvement programs. West. J. Agric. Econ. 14:223–234.Google Scholar
2. Bernardo, D. J., and Conner, J. R. 1990. Contemporary methodologies in range economics research. p. 24–34 in Obermiller, F. W., ed. Current Issues in Rangeland Resource Economics. Oregon State Univ. Coop. Ext. Serv. Spec. Rep. 852.Google Scholar
3. Bhat, U. N. 1972. Elements of Applied Stochastic Processes. Wiley & Sons, Inc. New York, N.Y. Google Scholar
4. Ethridge, D. E., Roy, S. K., and Myers, D. W. 1985. A Markov chain analysis of structural changes in the Texas High Plains cotton ginning industry. South. J. Agric. Econ. 17:11–20.Google Scholar
5. Garoian, L., Mjelde, J. W., and Conner, J. R. 1990. Optimal strategies for marketing calves and yearlings from rangeland. Am. J. Agric. Econ. 72:604–613.CrossRefGoogle Scholar
6. Hallberg, M. C. 1969. Predicting the size distribution of agricultural firms–an application of a Markov process with non-stationary transition probabilities. Am. J. Agric. Econ. 51:289–302.CrossRefGoogle Scholar
7. Hillier, F. S., and Lieberman, G. J. 1986. Markovian decision processes and applications. p. 704–746 in Introduction to Operations Research. Holden-Day, Inc., Oakland, Calif. Google Scholar
8. Iosifescu, M. 1980. Finite Markov Processes and Their Applications. Wiley & Sons, Inc. New York, N.Y. Google Scholar
9. Karp, L., and Pope, A. III. 1984. Range management under uncertainty. Am. J. Agric. Econ. 66:437–446.CrossRefGoogle Scholar
10. Kemp, W. P. 1987. Probability of outbreak for rangeland grasshoppers (Orthoptera: Acrididae) in Montana: application of Markovian principles. Econ. Entomol. 80:1100–1105.CrossRefGoogle Scholar
11. Lee, T. C., Judge, G. G., and Zellner, A. 1970. Estimating the Parameters of the Markov Probability Model from Aggregate Time Series Data. North-Holland Publishing Co., New York, N.Y. Google Scholar
12. McDaniel, K. C. 1984. Snakeweed control with herbicides. New Mex. Agric. Exp. Stn., Bull. 706, Las Cruces, New Mex.Google Scholar
13. McDaniel, K. C. 1990. Snakeweed populations in New Mexico, 1979–1989. p. 13–25 in Huddleston, E. W. and Pieper, R. D., ed. Snakeweed: Problems and Perspectives. New Mex. Agric. Exp. Stn., Bull. 751, Las Cruces, New Mex.Google Scholar
14. Mjelde, J. W., Harris, W. D., Schnitkey, G. D., Conner, J. R., Glover, M. K., and Garoian, L. 1991. Application of Stochastic Processes to Summarize Dynamic Programming Solutions. Texas A&M Univ., Dep. of Agric. Econ. Tech. Rep. DTR 91-1.Google Scholar
15. Rodriquez, A., and Taylor, R. G. 1988. Stochastic modeling of short-term cattle operations. Am. J. Agric. Econ. 70:121–132.CrossRefGoogle Scholar
16. Scrifres, C. J. 1980. Brush Management. Texas A&M Univ. Press, College Station, TX 360 p.Google Scholar
17. Torell, L. A., Williams, K., and McDaniel, K. C. 1990. Economics of broom snakeweed control. p. 113–139. in Huddleston, E. W. and Pieper, R. D., ed. Snakeweed: Problems and Perspectives. New Mex. Agric. Exp. Stn., Bull. 751, Las Cruces, New Mex.Google Scholar
18. Wight, J. R., ed. SPUR-Simulation of Production and Utilization of Rangelands: Documentation and Users Guide. ARS Publication No. ARS-63. 372 p.Google Scholar