Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-29T19:05:55.368Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling Soybean Growth and Canopy Apportionment in Weed-Soybean (Glycine max) Competition

Published online by Cambridge University Press:  12 June 2017

David R. Pike ,
Edward W. Stoller  and
Loyd M. Wax
David R. Pike
Affiliation:
Dep. Agron., Univ. Illinois
Edward W. Stoller
Affiliation:
Crop Prot. Res. Unit, U.S. Dep. Agric., Agric. Res. Serv.;
Loyd M. Wax
Affiliation:
Dep. Agron., 1102 S. Goodwin, Urbana, IL 61801
Get access Rights & Permissions [Opens in a new window]

Abstract

Field studies using area-of-influence techniques were conducted in 1987 and 1988 to evaluate soybean growth and yield, and to predict soybean yield losses from photographs of jimsonweed and common cocklebur canopies. Differences in weed competitiveness within the 100-cm area of influence were induced by dates of soybean planting, locations, weed species, and years. Soybean yield losses within the first 20-cm interval from weeds correlated well with yield of all soybean plants within 100 cm of weeds (r2 = 0.86). Soybean growth responses as a function of distance from weeds were best described by complex polynomials, but simple linear functions, based on a data point from soybean plants nearest a weed and from the average of plants 60 to 100 cm from a weed, closely approximately actual yield losses (r2 = 0.96). Soybean yield losses were highly correlated (r2 = 0.84) with leaf area of weeds as viewed from directly above the weed-crop canopy. Weed canopy diameter, measured from overhead photographs 8 weeks after soybean emergence, also correlated well with soybean yield losses (r2= 0.82), but correlation with actual weed leaf area was not significant (r2 = 0.31).

Keywords

Jimsonweed, Datura stramonium L. # DATSTcommon cocklebur, Xanthium strumarium L. # XANSTsoybean, ‘Williams 82’Shadinginterferenceleaf areaarea of influenceimage analysis
Type
Weed Biology and Ecology
Information
Weed Science , Volume 38 , Issue 6 , November 1990 , pp. 522 - 527
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. Aldrich, R. J. 1987. Predicting crop yield reductions from weeds. Weed Technol. 1:199–206.CrossRefGoogle Scholar
2. Fisher, R. A. and Miles, R. E. 1973. The role of spatial pattern in the competition between crop plants and weeds. A theoretical analysis. Mathematical Biosciences 18:335–350.CrossRefGoogle Scholar
3. Govindaraju, D. R. 1986. Environmental variation and the relationship among correlated quantitative traits in jackpine. Can. J. Bot. 66:183–188.CrossRefGoogle Scholar
4. Hagood, E. S. Jr., Bauman, T. T., Williams, J. L. Jr., and Schreiber, M. M. 1981. Growth analysis of soybeans (Glycine max) in competition with jimsonweed (Datura stramonium). Weed Sci. 29:500–504.CrossRefGoogle Scholar
5. Mitchell-Olds, T. 1987. Analysis of local variation in plant size. Ecol. 68:82–87.CrossRefGoogle Scholar
6. Regnier, E. E. and Stoller, E. W. 1989. The effects of soybean (Glycine max) interference on the canopy architecture of common cocklebur (Xanthium strumarium), jimsonweed (Datura stramonium), and velvetleaf (Abutilon theophrasti). Weed Sci. 38:187–195.CrossRefGoogle Scholar
7. Roush, M. L. and Radosevich, S. R. 1985. Relationship between growth and competitiveness of four annual weeds. J. Appl. Ecol. 22:1–11.CrossRefGoogle Scholar
8. Smith, M.A.L., Spomer, L. A., and Cowen, R.K.D. 1988. Use of image analysis to quantify the expression of an unstable allele. J. Hered. 79:147–150.CrossRefGoogle Scholar
9. Spitters, C.J.T. and Aerts, R. 1983. Simulation of competition for light and water in crop-weed associations. Aspects Appl. Biol. 4. Pages 467–483.Google Scholar
10. Spomer, L. A. and Smith, M.A.L. 1988. Image analysis for biological research: Camera influence on measurement accuracy. Intelligent Instruments & Computers. Jul/Aug. Pages 201–216.Google Scholar
11. Stoller, E. W. and Woolley, J. T. 1985. Competition for light by broadleaf weeds in soybeans (Glycine max). Weed Sci. 33:199–202.CrossRefGoogle Scholar
12. Wahua, T.A.T. and Miller, D. A. 1978. Effects of shading on the N2-fixation, yield, and plant composition of field-grown soybeans. Agron. J. 70:387–392.CrossRefGoogle Scholar
13. Weiner, J. 1986. How competition for light and nutrients affects size variability in Ipomoea tricolor populations. Ecology 67:1425–1427.CrossRefGoogle Scholar