Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-28T05:20:02.188Z Has data issue: false hasContentIssue false
  • English
  • Français

Influence of Early-Season Yield Loss Predictions from WeedSOFT® and Soybean Row Spacing on Weed Seed Production from a Mixed-Weed Community

Published online by Cambridge University Press:  20 January 2017

Andrew A. Schmidt  and
William G. Johnson
Andrew A. Schmidt
Affiliation:
Department of Agronomy, University of Missouri, Columbia, MO 65211
William G. Johnson*
Affiliation:
Purdue University, West Lafayette, IN 47905
*
Corresponding author's E-mail: wgjohnso@purdue.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Seed production from weeds that are missed by herbicide application can affect future weed populations and management decisions. It may be possible to expand the utility of computerized weed management decision aids to include an estimate of weed seed production resulting from selected treatments based on crop yield potential. Field studies were conducted in soybean near Columbia, MO, to determine whether weed control recommendations based on crop yield potential from a computerized weed management decision aid influence weed seed production in two soybean row spacings. At approximately 28 d after planting, weed densities and heights were entered into WeedSOFT® to generate a list of treatments ranked by predicted crop yields. Treatments included: (1) highest predicted crop yield in a glyphosate-resistant system, (2) highest predicted crop yield in a nonglyphosate-resistant system, (3) a 10% yield reduction, (4) a 20% yield reduction, and (5) an untreated control. These treatments were applied to soybean grown in 38- and 76-cm rows. Treatments that provided 90% or higher control of an individual species at 22 d after treatment usually produced less seed than untreated checks. Weed seed production based on early-season herbicide efficacy showed a linear relationship and was relatively predictable (r2 ≥ 0.52) for the predominant weed species. For less dominant weed species, weed seed production was not strongly correlated (r2 ≤ 0.27) to early-season herbicide efficacy but apparently influenced by control of other weed species. Narrow row spacing reduced giant foxtail biomass both years but did not reduce common ragweed and ivyleaf morningglory biomass. Narrow rows did not decrease giant foxtail, common ragweed, and ivyleaf morningglory seed production.

Keywords

Glyphosatecommon ragweed, Ambrosia artemisiifolia (L.) # AMBELgiant foxtail, Setaria faberi (L.) Herrm. # SETFAivyleaf morningglory, Ipomoea hederacea (L.) Jacq. # IPOHEsoybean, Glycine maxDecision support systemherbicide decision aidAP, at plantingDAT, days after treatmentHPMY-G, highest percent maximum yield that used glyphosate-resistant soybeanHPMY-NG, highest percent maximum yield that did not use glyphosate
Type
Research
Information
Weed Technology , Volume 18 , Issue 2 , June 2004 , pp. 412 - 418
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

Allen, J. R., Johnson, W. G., Smeda, R. J., Wiebold, W. J., and Kremer, R. J. 2000. ALS-resistant Helianthus annuus interference in Glycine max . Weed Sci. 48:461–466.CrossRefGoogle Scholar
Anonymous. 2002. Missouri Farm Facts. Jefferson City, MO: Missouri Department of Agriculture and Washington, D.C.: The United States Department of Agriculture. P. 11.Google Scholar
Bararpour, M. T. and Oliver, L. R. 1998. Effect of tillage and interference on common cocklebur (Xanthium strumarium) and sicklepod (Senna obtusifolia) population, seed production, and seedbank. Weed Sci. 46:424–431.CrossRefGoogle Scholar
Brenchley, W. E. 1936. The weed seed population of arable soil, Illinois. The reestablishment of weed species after reduction by fallowing. J. Ecol. 24:479–501.Google Scholar
Buhler, D. D., Hartzler, R. G., and Forcella, F. 1997. Implications of weed seedbank dynamics to weed management. Weed Sci. 45:329–336.CrossRefGoogle Scholar
Burnside, O. C., Moomaw, R. S., Roeth, F. W., Wicks, G. A., and Wilson, R. G. 1986. Weed seed demise in soil in weed-free corn (Zea mays) production across Nebraska. Weed Sci. 34:248–251.Google Scholar
Bussan, A. J., Boerboom, C. M., and Stoltenberg, D. E. 2000. Response of Setaria faberi demographic processes to herbicide rate. Weed Sci. 48:445–453.CrossRefGoogle Scholar
Bussler, B. H., Maxwell, B. D., and Puettmann, K. J. 1995. Using plant volume to quantify interference in corn (Zea mays) neighborhoods. Weed Sci. 43:586–594.Google Scholar
Cardina, J., Regnier, E., and Harrison, K. 1991. Long-term tillage effects on seed banks in three Ohio soils. Weed Sci. 39:186–194.Google Scholar
Chikoye, D. C., Weise, S. F., and Swanton, C. J. 1995. Influence of common ragweed (Ambrosia artemisiifolia) time of emergence and density on white bean (Phaseolus vulgaris). Weed Sci. 43:375–380.Google Scholar
Clements, D. R., Benoit, D. L., Murphy, S. D., and Swanton, C. J. 1996. Tillage effects on weed seed return and seedbank composition. Weed Sci. 44:314–322.Google Scholar
Colquhoun, J. B., Stoltenberg, D. E., Binning, L. K., and Boerboom, C. M. 2001. Phenology of common lambsquarters (Chenopodium album) growth parameters. Weed Sci. 43:375–380.Google Scholar
Conley, S. P., Binning, L. K., Boerboom, C. M., and Stoltenberg, D. E. 2002. Estimating giant foxtail cohort productivity in soybean based on weed density, leaf area, or volume. Weed Sci. 50:72–78.Google Scholar
Dieleman, J. A., Mortensen, D. A., and Martin, A. R. 1999. Influence of velvetleaf (Abutilon theophrasti) and common sunflower (Helianthus annuus) density variation on weed management outcomes. Weed Sci. 47:81–89.Google Scholar
Forcella, F., King, R. P., Swinton, S. M., Buhler, D. D., and Gunsolus, J. L. 1996. Multi-year validation of a decision aid for integrated weed management. Weed Sci. 44:650–661.Google Scholar
Kropff, M. J. and Lotz, L. A. P. 1992. Optimization of weed management systems: the role of ecological models of interplant competition. Weed Technol. 6:462–470.Google Scholar
Martin, A. R., Bills, L. B., Boerboom, C. M., and Johnson, W. G. 2003. WeedSOFT: using a decision support system for regional IPM implementation. Weed Sci. Soc. Am. Abstr. 43:38.Google Scholar
Mickelson, J. A. and Renner, K. A. 1997. Weed control using reduced rates of postemergence herbicides in narrow and wide row soybean. J. Prod. Agric. 10:431–437.CrossRefGoogle Scholar
Mortensen, D. A. and Coble, H. D. 1991. Two approaches to weed control decision aid software. Weed Technol. 5:445–452.Google Scholar
Mosier, D. G. and Oliver, L. R. 1995. Common cocklebur (Xanthium strumarium) and entireleaf morningglory (Ipomoea hederacea var. integriuscula) interference on soybeans (Glycine max). Weed Sci. 43:239–246.Google Scholar
Mulugeta, D. and Boerboom, C. M. 2000. Critical time of weed removal in glyphosate-resistant soybean (Glycine max). Weed Sci. 48:35–42.Google Scholar
Nelson, K. A. and Renner, K. A. 1999. Weed management in wide- and narrow-row glyphosate resistant soybean. J. Prod. Agric. 12:460–465.CrossRefGoogle Scholar
Oriade, C. A., Dillon, C. R., Vories, E. D., and Bohanan, M. E. 1997. An economic analysis of alternative cropping and row spacing systems for soybean production. J. Prod. Agric. 10:619–624.Google Scholar
Oryokot, J. O. E., Murphy, S. D., Thomas, A. G., and Swanton, C. J. 1997. Temperature- and moisture-dependent models of seed germination and shoot elongation in green (Amaranthus powellii) and redroot pigweed (A. retroflexus). Weed Sci. 45:488–496.Google Scholar
Shaw, R. H. and Weber, C. R. 1967. Effects of canopy arrangements on light interception and yield of soybeans. Agron. J. 59:155–159.Google Scholar
Shibles, R. M. and Weber, C. R. 1966. Interception of solar radiation and dry matter production by various soybean planting patterns. Crop Sci. 6:55–59.Google Scholar
Swinton, S. M. and King, R. P. 1994. A bioeconomic model for weed management in corn and soybean. Agric. Syst. 44:313–335.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
Yelverton, F. H. and Coble, H. D. 1991. Narrow row spacing and canopy formation reduces weed resurgence in soybeans (Glycine max). Weed Technol. 5:169–174.Google Scholar