Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-03T00:33:02.341Z Has data issue: false hasContentIssue false
  • English
  • Français

Validation of Weed Competitive Indices for Predicting Peanut Yield Losses in Oklahoma

Published online by Cambridge University Press:  20 January 2017

John B. Willis ,
Don S. Murray  and
Shea W. Murdock
John B. Willis
Affiliation:
Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK 74078
Don S. Murray*
Affiliation:
Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK 74078
Shea W. Murdock
Affiliation:
Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK 74078
*
Corresponding author's E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Weed interference experiments have not been extensively conducted in Oklahoma peanut. Research was conducted in three environments to evaluate usefulness of single-weed density experiments with the use of several weeds to measure their relative competitive abilities with a crop. These data can be used to validate current competitive indices (CIs) used by a model to predict peanut yield loss due to weeds. This model is used by the Herbicide Application Decision Support System (HADSS) and Pesticide Economic and Environmental Tradeoffs (PEET), two decision-support systems (DSSs) available for Oklahoma peanut. Six weeds common in Oklahoma peanut were used: crownbeard, eclipta, ivyleaf morningglory, johnsongrass, Palmer amaranth, and prickly sida plus two others, barnyardgrass and common cocklebur, as benchmark species. Each weed was planted into peanut uniformly at eight weeds/10 m of row. Dry weed biomass accounted for 77 to 90% of variation in in-shell peanut yield loss; however, model parameters only allow for weed number. Yield losses from these experiments were compared to those predicted by the model to test original CI accuracy. Treatment means were compared to the prediction model with the use of protected LSD. Several significant differences were noted, and the CIs for those weed species were adjusted accordingly. Adjusting CIs improved actual yield data goodness of fit to model predictions specific to environment in question, but not necessarily in different environments. The CI changed at Ft. Cobb were eclipta from 1.8 to 4.5 and ivyleaf morningglory from 3.4 to 5.0. CI adjustments at Perkins were common cocklebur from 10.0 to 5.8 and johnsongrass from 3.0 to 4.6. Collecting data for several weed species at uniform density in a crop provides a more time-efficient method for obtaining accurate relative weed interference data; this method is useful in validating or establishing CI lists in areas and/or crops with limited data.

Keywords

Barnyardgrass, Echinochloa crus-galli (L.) Beauv. #3 ECHCGcommon cocklebur, Xanthium strumarium L. # XANSTcrownbeard, Verbesina encelioides (Cav.) Benth. & Hook. f. ex Gray # VEEENeclipta, Eclipta prostrata L. # ECLALivyleaf morningglory, Ipomoea hederacea (L.) Jacq. # IPOHEjohnsongrass, Sorghum halepense (L.) Pers. # SORHAPalmer amaranth, Amaranthus palmeri S. Wats. # AMAPAprickly sida, Sida spinosa L. # SIDSPpeanut, Arachis hypogaea L. ‘Tamspan 90’Competitiondecision-support systeminterferenceyield-loss prediction modelTCLtotal competitive load
Type
Research Article
Information
Weed Technology , Volume 20 , Issue 3 , September 2006 , pp. 688 - 694
Copyright
Copyright © 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

Albers-Nelson, M. R., Murray, D. S., Verhalen, L. M., and Goad, C. L. 2000. Establishment techniques for common cocklebur (Xanthium strumarium). Weed Technol. 14:463–470.CrossRefGoogle Scholar
Bennet, A. C., Price, A. J., Sturgill, M. C., Buol, G. S., and Wilkerson, G. G. 2003. HADSS, Pocket HERB, and WebHADSS: decision aids for field crops. Weed Technol. 17:412–420.Google Scholar
Castner, E. P., Murray, D. S., Hackett, N. M., Verhalen, L. M., Weeks, D. L., and Stone, J. F. 1989. Interference of hogpotato (Hoffmanseggia glauca) with cotton (Gossypium hirsutum). Weed Sci. 37:688–694.CrossRefGoogle Scholar
Coble, H. D. and Mortensen, D. A. 1992. The threshold concept and its application to weed science. Weed Technol. 6:191–195.Google Scholar
Farris, R. L. and Murray, D. S. 2003. Influence of crownbeard (Verbesina encelioides) densities on peanut (Arachis hypogaea) yield. Proc. South. Weed Sci. Soc. 56:179.Google Scholar
Green, J. D., Murray, D. S., and Verhalen, L. M. 1987. Full-season interference of silverleaf nightshade (Solanum elaeagnifolium) with cotton (Gossypium hirsutum). Weed Sci. 35:813–818.Google Scholar
Hackett, N. M., Murray, D. S., and Weeks, D. L. 1987a. Interference of silverleaf nightshade (Solanum elaeagnifolium) on Spanish peanuts (Arachis hypogaea). Peanut Sci. 14:39–41.Google Scholar
Hackett, N. M., Murray, D. S., and Weeks, D. L. 1987b. Interference of horsenettle (Solanum carolinense) with peanuts (Arachis hypogaea). Weed Sci. 35:780–784.Google Scholar
Hill, L. V. and Santelmann, P. W. 1969. Competitive effects of annual weeds on Spanish peanuts. Weed Sci. 17:1–2.Google Scholar
Mercer, K. L., Murray, D. S., and Verhalen, L. M. 1987. Interference of unicorn-plant (Proboscidea louisianica) with cotton (Gossypium hirsutum). Weed Sci. 35:807–812.Google Scholar
Monks, C. D., Bridges, D. C., Woodruff, J. W., Murphy, T. R., and Berry, D. J. 1995. Expert system evaluation and implementation for soybean (Glycine max) weed management. Weed Technol. 9:535–540.Google Scholar
Mortensen, D. A. and Coble, H. D. 1991. Two approaches to weed control decision-aid software. Weed Technol. 5:445–452.Google Scholar
Murdock, S. W. 2002. Adaptation and Validation of a Computerized Decision Support System for Cotton and Peanut Production in Oklahoma. Ph.D. dissertation. Oklahoma State University, Stillwater, OK. 88 p.Google Scholar
Murdock, S. W. and Murray, D. S. 2000. Adaptation of a computer decision support system (DSS) to Oklahoma peanut production. Proc. South. Weed Sci. Soc. 53:153.Google Scholar
Murdock, S. W., Murray, D. S., Verhalen, L. M., and Medlin, C. R. 2004. Adaptation and validation of HADSS for cotton production in Oklahoma. J. Cotton Sci. 8:42–54. Web page: http://journal.cotton.org. Accessed: April 28, 2004.Google Scholar
Nofziger, D. L., Hornsby, A. G., and Hoag, D. L. 1998. Pesticide economic and environmental tradeoffs: developer's perspective. in El-Swaify, S. A. and Yakowitz, D. S., eds. Multiple Objective Decision Making for Land, Water, and Environmental Management. Boca Raton, FL: Lewis Publishers. Pp. 83–92.Google Scholar
Pawlak, J. A., Murray, D. S., and Smith, B. S. 1990. Influence of capsule age on germination of nondormant jimsonweed (Datura stramonium) seed. Weed Technol. 4:31–34.CrossRefGoogle Scholar
Rankins, A. Jr., Shaw, D. R., and Byrd, J. D. Jr. 1998. HERB and MSU-HERB field validation for soybean (Glycine max) weed control in Mississippi. Weed Technol. 12:88–96.Google Scholar
Rogers, J. B., Murray, D. S., Verhalen, L. M., and Claypool, P. L. 1996. Ivyleaf morningglory (Ipomoea hederacea) interference with cotton (Gossypium hirsutum). Weed Technol. 10:107–114.Google Scholar
Rowland, M. W., Murray, D. S., and Verhalen, L. M. 1999. Full-season Palmer amaranth (Amaranthus palmeri) interference with cotton (Gossypium hirsutum). Weed Sci. 47:305–309.Google Scholar
Royal, S. S., Brecke, B. J., and Colvin, D. L. 1997. Common cocklebur (Xanthium strumarium) interference with peanut (Arachis hypogaea). Weed Sci. 45:38–43.Google Scholar
Rushing, D. W., Murray, D. S., and Verhalen, L. M. 1985a. Weed interference with cotton (Gossypium hirsutum). I. Buffalobur (Solanum rostratum). Weed Sci. 33:810–814.Google Scholar
Rushing, D. W., Murray, D. S., and Verhalen, L. M. 1985b. Weed interference with cotton (Gossypium hirsutum). II. Tumble pigweed (Amaranthus albus). Weed Sci. 33:815–818.Google Scholar
[SAS] Statistical Analysis Systems. 1999–2001. Software version 8e. Cary, NC: Statistical Analysis Systems Institute.Google Scholar
Smith, B. S., Murray, D. S., Green, J. D., Wanyahaya, W. M., and Weeks, D. L. 1990a. Interference of three annual grasses with grain sorghum (Sorghum bicolor). Weed Technol. 4:245–249.Google Scholar
Smith, B. S., Murray, D. S., and Weeks, D. L. 1990b. Velvetleaf (Abutilon theophrasti) interference with cotton (Gossypium hirsutum). Weed Technol. 4:799–803.Google Scholar
Sturgill, M. C., Wilkerson, G. G., Price, A. J., Bennett, A. C., and Buol, G. S. 2002. HADSS user's manual. Crop Sci. Dept. Res. Bull. 198, North Carolina State University, Raleigh, NC. Web page: http://www.hadss.com/HADSS2002%20-Manual.htm. Accessed: February 16, 2004.Google Scholar
Webster, T. M. 2001. Weed survey—Southern states, 2001: Broadleaf crops subsection (cotton, peanut, soybean, tobacco, and forestry). Proc. South. Weed Sci. Soc. 54:244–259.Google Scholar
White, A. D. and Coble, H. D. 1997. Validation of HERB for use in peanut (Arachis hypogaea). Weed Technol. 11:573–579.Google Scholar
Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: Decision model for postemergence weed control in soybean. Agron. J. 83:413–417.Google Scholar
Wood, M. L., Murray, D. S., Banks, J. C., Verhalen, L. M., Westerman, R. B., and Anderson, K. B. 2002. Johnsongrass (Sorghum halepense) density effects on cotton (Gossypium hirsutum) harvest and economic value. Weed Technol. 16:495–501.Google Scholar
Wood, M. L., Murray, D. S., Westerman, R. B., Verhalen, L. M., and Claypool, P. L. 1999. Full-season interference of Ipomoea hederacea with Gossypium hirsutum . Weed Sci. 47:693–696.Google Scholar