Hostname: page-component-848d4c4894-2pzkn Total loading time: 0 Render date: 2024-06-03T05:31:50.370Z Has data issue: false hasContentIssue false
  • English
  • Français

Weed reproduction model parameters may be estimated from crop yield loss data

Published online by Cambridge University Press:  20 January 2017

Stephen R. Canner ,
Lori J. Wiles  and
Gregory S. McMaster
Lori J. Wiles
Affiliation:
Water Management Research Unit, U.S. Department of Agriculture, Agricultural Research Service, Agricultural Engineering Research Center, Colorado State University, Fort Collins, CO 80523-1325
Gregory S. McMaster
Affiliation:
Great Plains Systems Research Unit, U.S. Department of Agriculture, Agricultural Research Service, Room 353, 301 South Howes Street, Fort Collins, CO 80522
Get access Rights & Permissions [Opens in a new window]

Abstract

Studies quantifying weed seed production as a function of weed density are expensive and difficult, and lack of these data is a common limitation in modeling weed population dynamics over time. Observed empirical and theoretical relationships between crop yield loss curves and weed seed production curves led us to the hypothesis that there should be a strong relationship between the shapes of these two curves. Data from literature sources were evaluated to test this hypothesis for hyperbolic curves and to determine if the data describing the crop yield loss caused by weeds could provide estimates of the shape parameter of a hyperbolic equation for describing density dependence in weed reproduction. For each of 162 data sets, a shape parameter (N50) and a scale parameter (U) were estimated for an increasing hyperbolic model both for absolute crop yield loss as a function of weed density (N50YL, UYL) and for weed yield (either total biomass yield or seed yield) as a function of weed density (N50WY, UWY). N50YL was strongly correlated with N50WY across all data sets, with an apparent 1:1 relationship between the two. This relationship suggests that the shape parameter of the yield loss model may substitute for the shape parameter of a hyperbolic model describing the density-dependence of weed seed production. This substitution will be most useful in weed population modeling situations where data describing crop yield loss as a function of weed density are already available, but data describing weed seed production as a function of weed density are not available.

Keywords

black medic, Medicago lupulina L. MEDLUcatchweed bedstraw, Galium aparine L. GALAPcorn poppy, Papaver rhoeas L. PAPRHdowny brome, Bromus tectorum L. BROTEfield violet, Viola arvensis Murr. VIOARgiant foxtail, Setaria faberi Herrm. SETFAkochia, Kochia scoparia (L.) Schrad. KCHSCround-leaved mallow, Malva pusilla L. MALNEwild oat, Avena fatua L. AVEFAbarley, Hordeum vulgare L.chili, Capsicum annuum L.dry bean, Phaseolus vulgaris L.field pea, Pisum sativum L.maize, Zea mays L.peanut, Arachis hypogaea L.rye, Secale cereale L.safflower, Carthamus tinctorius L.soybean, Glycine max L.sugarbeet, Beta vulgaris L.sunflower, Helianthus annuus L.tomato, Lycopersicon esculentum Mill.wheat, Triticum aestivum L.Competitionhyperbolic modelsinterferencepopulation dynamicsseed productionyield loss
Type
Research Article
Information
Weed Science , Volume 50 , Issue 6 , December 2002 , pp. 763 - 772
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

Afentouli, C. G. and Eleftherohorinos, I. G. 1998. Littleseed canarygrass (Phalaris minor) and short-spiked canarygrass (Phalaris brachystachys) interference in wheat and barley. Weed Sci. 44:560–565.CrossRefGoogle Scholar
Askew, S. D. and Wilcut, J. W. 2001. Tropic croton interference in cotton. Weed Sci. 49:184–189.Google Scholar
Baziramakenga, R. and Leroux, G. D. 1998. Economic and interference threshold densities of quackgrass (Elytrigia repens) in potato (Solanum tuberosum). Weed Sci. 46:176–180.Google Scholar
Blackshaw, R. E. 1993a. Downy brome (Bromus tectorum) interference in winter rye (Secale cereale). Weed Sci. 41:557–562.Google Scholar
Blackshaw, R. E. 1993b. Safflower (Carthamus tinctorius) density and row spacing effects on competition with green foxtail (Setaria viridis). Weed Sci. 41:403–408.Google Scholar
Blackshaw, R. E. 1993c. Downy brome (Bromus tectorum) density and relative time of emergence affects interference in winter wheat. Weed Sci. 41:551–556.Google Scholar
Blackshaw, R. E. 1994. Differential competitive ability of winter wheat cultivars against downy brome. Agron. J. 86:649–654.Google Scholar
Blackshaw, R. E. and Schaalje, G. B. 1993. Density and species proportion effects on interference between redstem filaree (Erodium cicutarium) and round-leaved mallow (Malva pusilla). Weed Sci. 41:594–599.Google Scholar
Bosnic, A. C. and Swanton, C. J. 1997. Influence of barnyardgrass (Echinochloa crus-galli) time of emergence and density on corn (Zea mays). Weed Sci. 45:276–282.Google Scholar
Bridges, D. C., Brecke, B. J., and Barbour, J. C. 1992. Wild poinsettia (Euphorbia heterophylla) interference with peanut (Arachis hypogaea). Weed Sci. 40:37–42.CrossRefGoogle Scholar
Canner, S. R., Wiles, L. J., Dunan, C. R., and Erskine, R. H. 1998. A new approach for modeling long-term multi-species weed population dynamics. Proc. West. Soc. Weed Sci. 51:30.Google Scholar
Cardina, J., Regnier, E. E., and Sparrow, D. H. 1995. Velvetleaf (Abutilon theophrasti) competition and economic thresholds in conventional- and no-tillage corn (Zea mays). Weed Sci. 43:81–87.Google Scholar
Charles, G. W., Murison, R. D., and Harden, S. 1998. Competition of noogoora burr (Xanthium occidentale) and fierce thornapple (Datura ferox) with cotton (Gossypium hirsutum). Weed Sci. 46:442–446.Google Scholar
Chikoye, D., 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
Clewis, S. B., Askew, S. D., and Wilcut, J. W. 2001. Common ragweed interference in peanut. Weed Sci. 49:768–772.Google 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
Cousens, R. 1985a. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.Google Scholar
Cousens, R. 1985b. An empirical model relating crop yield to weed and crop density and a statistical comparison with other models. J. Agric. Sci. 105:513–521.Google Scholar
Cousens, R. 1987. Theory and reality of weed control thresholds. Plant Prot. Q. 2:13–20.Google Scholar
Cousens, R. and Mortimer, M. 1995. Dynamics of Weed Populations. Cambridge, Great Britain: Cambridge University Press. pp. 86–167; pp. 283–294.Google Scholar
Durgan, B. R., Dexter, A. G., and Miller, S. D. 1990. Kochia (Kochia scoparla) interference in sunflower (Helianthus annuus). Weed Technol. 4:52–56.Google Scholar
Fausey, J. C., Kells, J. J., Swinton, S. M., and Renner, K. A. 1997. Giant foxtail (Setaria faberi) interference in nonirrigated corn (Zea mays). Weed Sci. 45:256–260.Google Scholar
Harrison, S. R. 1990. Regression of a model on real-system output: an invalid test of model validity. Agric. Syst. 34:183–190.Google Scholar
Holland, J. F. and MacNamara, D. W. 1982. Weed control and row spacing in dry-land sorghum in northern New South Wales. Aust. J. Exp. Agric. Anim. Husb. 22:310–316.Google Scholar
Jordan, N., Mortensen, D. A., Prenzlow, D. M., and Cox, K. C. 1995. Simulation analysis of crop rotation effects on weed seedbanks. Am. J. Bot. 82:390–398.Google Scholar
King, C. A. and Purcell, L. C. 1997. Interference between hemp sesbania (Sesbania exaltata) and soybean (Glycine max) in response to irrigation and nitrogen. Weed Sci. 45:91–97.Google Scholar
Makowski, R.M.D. 1995. Round-leaved mallow (Malva pusilla) interference in spring wheat (Triticum aestivum) and lentil (Lens culinaris) in Saskatchewan. Weed Sci. 43:381–388.Google Scholar
Malik, V. S., Swanton, C. J., and Michaels, T. E. 1993. Interaction of white bean (Phaseolus vulgaris L.) cultivars, row spacing, and seeding density with annual weeds. Weed Sci. 41:62–68.Google Scholar
Maxwell, B. D. and Sheley, R. L. 1997. Noxious weed population dynamics education model. Weed Technol. 11:182–188.Google Scholar
McGiffen, M. E. Jr., Masiunas, J. B., and Hesketh, J. D. 1992. Competition for light between tomatoes and nightshades (Solanum nigrum or S. ptycanthum). Weed Sci. 40:220–226.Google Scholar
Norris, R. F. 1992. Case history for weed competition/population ecology: barnyardgrass (Echinochloa crus-galli) in sugarbeets (Beta vulgaris). Weed Technol. 6:220–227.CrossRefGoogle Scholar
O’Donovan, J. T. and Blackshaw, R. E. 1997. Effect of volunteer barley (Hordeum vulgare L.) interference on field pea (Pisum sativum L.) yield and profitability. Weed Sci. 45:249–255.Google Scholar
Pacala, S. W. 1986. Neighborhood models of plant population dynamics. 4. Single-species and multispecies models of annuals with dormant seeds. Am. Nat. 128:859–878.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, G. S. and Oliver, L. R. 1998. Influence of planting date on common cocklebur (Xanthium strumarium) interference in early-maturing soybean (Glycine max). Weed Sci. 46:99–104.CrossRefGoogle Scholar
[SAS] Statistical Analysis Systems. 1996. SAS Release 6.12. Cary, NC: Statistical Analysis Systems Institute.Google Scholar
Schroeder, J. 1993. Late-season interference of spurred anoda in chile peppers. Weed Sci. 41:172–179.CrossRefGoogle Scholar
Shinozaki, K. and Kira, T. 1956. Intraspecific competition among higher plants. VII. Logistic theory of the C-D effect. J. Inst. Polytech. Osaka City Univ. D 7:35–72.Google Scholar
Spitters, C.J.T. and Aerts, R. 1983. Simulation of competition for light and water in crop-weed associations. Asp. Appl. Biol. 4:467–483.Google Scholar
Toler, J. E., Guice, J. B., and Murdock, E. C. 1996. Interference between johnsongrass (Sorghum halepense), smooth pigweed (Amaranthus hybridus) and soybean (Glycine max). Weed Sci. 44:331–338.Google Scholar
Watkinson, A. R. 1981. Interference in pure and mixed populations of Agrostemma githago L. J. Appl. Ecol. 18:967–976.Google Scholar
Watkinson, A. R. 1986. Plant population dynamics. Pages 137–184 In Crawley, M. J., ed. Plant Ecology. Oxford: Blackwell.Google Scholar
Weiner, J. 1982. A neighborhood model of annual-plant interference. Ecology 63:1237–1241.CrossRefGoogle Scholar
Wille, M. J., Thill, D. C., and Price, W. J. 1998. Wild oat (Avena fatua) seed production in spring barley (Hordeum vulgare) is affected by the interaction of wild oat density and herbicide rate. Weed Sci. 46:336–343.Google Scholar
Willey, R. W. and Heath, S. B. 1969. The quantitative relationships between plant population and crop yield. Adv. Agron. 21:281–321.Google Scholar
Wilson, B. J. and Wright, K. J. 1990. Predicting the growth and competitive effects of annual weeds in wheat. Weed Res. 30:201–211.Google Scholar
Wilson, B. J., Wright, K. J., Brain, P., Clements, M., and Stephens, E. 1995. Predicting the competitive effects of weed and crop density on weed biomass, weed seed production and crop yield in wheat. Weed Res. 35:265–278.CrossRefGoogle Scholar
Wilson, R. G. 1993. Wild proso millet (Panicum miliaceum) interference in dry bean (Phaseolus vulgaris). Weed Sci. 41:607–610.CrossRefGoogle Scholar
Wilson, R. G. and Westra, P. 1991. Wild proso millet (Panicum miliaceum) interference in corn (Zea mays). Weed Sci. 39:217–220.Google Scholar
Zanin, G. and Sattin, M. 1988. Threshold level and seed production of velvetleaf (Abutilon theophrasti Medicus) in maize. Weed Res. 28:347–352.CrossRefGoogle Scholar