Hostname: page-component-586b7cd67f-t7czq Total loading time: 0 Render date: 2024-11-24T04:01:01.876Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling effects of spatial patterns on the seed bank dynamics of Alopecurus myosuroides

Published online by Cambridge University Press:  12 June 2017

J. L. Gonzalez-Andujar ,
J. N. Perry  and
S. R. Moss
J. N. Perry
Affiliation:
Department of Entomology and Nematology, Rothamsted Experimental Station, Harpenden, Herts., AL5 2JQ, U.K.
S. R. Moss
Affiliation:
Department of Crop and Disease Management, Rothamsted Experimental Station, Harpenden, Herts., AL5 2JQ, U.K.
Get access Rights & Permissions [Opens in a new window]

Abstract

A spatially explicit metapopulation neighborhood model was extended to encompass the seed bank dynamics of the annual weed Alopecurus myosuroides Huds. (blackgrass), growing in U.K. winter wheat crops established after noninversion tillage. The effects of the initial spatial pattern of infestation, herbicide, and combine harvesting on seed bank densities and on expected yield losses of the infested crop were studied within a 634 m2 area. In the absence of herbicide, all seed bank populations were large and typical of values in the literature; those with patchy initial distributions spread quickly over the entire field. The effects of intraspecific competition ensured that even after 10 yr, the average seed bank density from three patterns with the same initial density consistently retained the same rank order: the initially uniform pattern consistently ranked largest; a patchily distributed moderate infestation ranked next; a more patchily distributed heavy infestation ranked least. Expected grain yield losses sometimes exceeded 40%. With the introduction of a herbicide, seed densities declined exponentially, but the rank order with regard to spatial pattern remained. Relatively dense patches occasionally persisted for longer than 10 yr. The economic threshold for treatment was achieved within 2 and 5 yr, depending on the infestation pattern, but the model predicted that it would take many more than 10 yr before infestations could be completely eliminated. The effects of harvesting by combine were modeled. In all cases, the speed of the spread of infestation along a row appeared to be largely due to the combine rather than to natural dispersal or other cultivation practices. The proportion of seeds removed by the combine was an important determinant of the number of years required to drive the population below the economic threshold and of the probability of long-term eradication of the weed metapopulation.

Keywords

Alopecurus myosuroides Huds. ALOMY, blackgrassMetapopulationspatially explicit modelpatchinessdispersionthresholdcultivationcombine
Type
Weed Biology and Ecology
Information
Weed Science , Volume 47 , Issue 6 , December 1999 , pp. 697 - 705
Copyright
Copyright © 1999 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

The National Crop Disease and Weed Survey. 1993. Woodbridge, Suffolk, U.K: 1993 Farmers' Report. NCDWS Maps, 26 Chapel Street. pp. 23–25.Google Scholar
Auld, B. A. and Coote, B. G. 1990. INVADE: Towards the simulation of plant spread. Agric. Ecosyst. Environ. 30:121–128.Google Scholar
Auld, B. A. and Tisdell, C. A. 1988. Influence of the spatial distribution of weeds on crop yield loss. Plant Prot. Q. 33:81.Google Scholar
Ayres, P. 1978. The influence of application method on the control of black-grass (Alopecurus myosuroides Huds.) in winter cereals by post-emergence applications of a range of herbicides. Pages 687–694 in Proceedings of the 1978 British Crop Protection Conference—Weeds. Brighton, U.K.: British Crop Protection Conference.Google Scholar
Ballaré, C. L., Scopel, A. L., Ghersa, C. M., and Sanchez, R. A. 1987a. The population ecology of Datura ferox in soybean crops. A simulation approach incorporating seed dispersal. Agric. Ecosyst. Environ. 19:177–188.Google Scholar
Ballaré, C. L., Scopel, A. L., Ghersa, C. M., and Sanchez, R. A. 1987b. The demography of Datura ferox in soybean crops. Weed Res. 27:91–102.Google Scholar
Brain, P. and Cousens, R. 1990. The effect of weed distribution on prediction of yield loss. J. Appl. Ecol. 27:735–742.CrossRefGoogle Scholar
Christensen, S., Nordbo, E., and Kristensen, A. 1994. Weed cover mapping with spectral reflectance measurements. Asp. Appl. Biol. 37:171–179.Google Scholar
Clark, S. J., Perry, J. N., and Marshall, E.J.P. 1996. Estimating Taylor's power law for weed species and the effect of spatial scale. Weed Res. 36:405–417.Google Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.CrossRefGoogle Scholar
Cussans, G. W. and Moss, S. R. 1982. Population dynamics of annual grass weeds. Pages 91–98 in Proceedings of the 1982 Crop Protection Symposium: Decision Making in the Practice of Crop Protection. Brighton, U.K.: British Crop Protection Conference.Google Scholar
Dent, J. B., Fawcett, R. H., and Thornton, P. K. 1989. Economics of crop protection in Europe with reference to weed control. Pages 917–927 in Proceedings of the 1989 British Crop Protection Conference—Weeds. Brighton, U.K.: British Crop Protection Conference.Google Scholar
Doyle, C. J. 1991. Mathematical models in weed management. Crop Prot. 10:432–444.Google Scholar
Doyle, C. J., Cousens, R., and Moss, S. R. 1986. A model of the economics of controlling Alopecurus myosuroides (Huds.) in winter wheat. Crop Prot. 5:143–156.Google Scholar
Fernandez-Quintanilla, C., Navarrete, L., Torner, C., and Gonzalez-Andujar, J. L. 1987. Influence of herbicide treatments on the population dynamics of Avena sterilis ssp. ludoviciana in winter wheat crops. Weed Res. 27:375–383.Google Scholar
Ghersa, C. M., Martinez-Ghersa, M. M., Satorre, E. H., Van Esso, M. L., and Chichotky, G. 1993. Seed dispersal, distribution and recruitment of seedlings of Sorghum halepense (L.). Weed Res. 33:79–88.Google Scholar
Gill, G. S. 1996. Management of herbicide resistant ryegrass in Western Australia—research and its adoption. Pages 542–545 in Proceedings of the 11th Australian Weeds Conference, Melbourne.Google Scholar
Gill, G. S. and Holmes, J. E. 1997. Efficacy of cultural control methods for combating herbicide-resistant Lolium rigidum . Pestic. Sci. 51:352–358.Google Scholar
Gonzalez-Andujar, J. L. 1996. High control measures cannot produce extinction in weed populations. Ecol. Model. 91:293–294.Google Scholar
Gonzalez-Andujar, J. L. and Perry, J. N. 1995. Models for the herbicidal control of the seed bank of Avena sterilis: the effect of spatial and temporal heterogeneity and of dispersal. J. Appl. Ecol. 32:578–587.CrossRefGoogle Scholar
Hassell, M. P. 1975. Density-dependence in single-species population. J. Anim. Ecol. 44:283–295.CrossRefGoogle Scholar
Hollies, J. D. 1982. A survey of commercially grown high yielding wheat and barley crops from 1977–1981. Pages 609–618 in Proceedings of the 1982 British Crop Protection Conference—Weeds. Brighton, U.K.: British Crop Protection Conference.Google Scholar
Howard, C. L., Mortimer, A. M., Gould, P., Putwain, P. D., Cousens, R., and Cussans, G. W. 1991. The dispersal of weeds: seed movement in arable agriculture. Pages 821–828 in Proceedings of the 1991 British Crop Protection Conference—Weeds. Brighton, U.K.: British Crop Protection Conference.Google Scholar
Hughes, G. 1996. Incorporating spatial pattern of harmful organisms into crop loss models. Crop Prot. 15:407–421.CrossRefGoogle Scholar
Marshall, E.J.P. 1988. Field-scale estimates of grass weed populations in arable land. Weed Res. 28:191–198.Google Scholar
Marshall, E.J.P. 1989. Distribution patterns of plants associated with arable field edges. J. Appl. Ecol. 26:247–257.Google Scholar
Matthews, J. M. and Powles, S. B. 1996. Managing herbicide resistant annual ryegrass, southern Australian research. Pages 537–541 in Proceedings of the 11th Australian Weeds Conference, Melbourne.Google Scholar
Maxwell, B. D. and Ghersa, C. 1992. The influence of weed seed dispersion versus the effect of competition on crop yield. Weed Technol. 6:196–204.Google Scholar
McCanny, S. J. and Cavers, P. B. 1988. Spread of proso millet (Paniceum miliaceum L.) in Ontario, Canada. IE Dispersal by combines. Weed Res. 28:67–72.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
Mortimer, A. M., Sutton, T. J., and Gould, P. 1989. On robust weed population models. Weed Res. 29:229–238.CrossRefGoogle Scholar
Moss, S. R. 1980a. The agro-ecology and control of black-grass, Alopecurus myosuroides Huds., in modern cereal growth systems. Adviser Agricultural Development Service Q. Rev. 38:170–191.Google Scholar
Moss, S. R. 1980b. Some effects of burning cereal straw on seed viability, seedling establishment and control of Alopecurus myosuroides Huds. Weed Res. 20:271–276.Google Scholar
Moss, S. R. 1983. The production and shedding of Alopecurus myosuroides Huds. seeds in winter cereal crops. Weed Res. 23:45–51.Google Scholar
Moss, S. R. 1985. The survival of Alopecurus myosuroides Huds. seeds in soil. Weed Res. 25:201–211.Google Scholar
Moss, S. R. 1990. The seed cycle of Alopecurus myosuroides in winter cereals: a quantitative analysis. Pages 27–36 in Proceedings of the European Weed Resource Society Symp. 1990, Integrated Weed Management in Cereals. Helsinki: European Weed Resource Society.Google Scholar
Naylor, R.E.L. 1970. The prediction of black-grass infestation. Weed Res. 10:296–299.Google Scholar
Naylor, R.E.L. 1972. Aspects of the population dynamics of the weed Alopecurus myosuroides Huds., in winter cereal crops. J. Appl. Ecol. 9:127–139.Google Scholar
Pacala, S. W. and Silander, J. A. 1990. Field tests of neighborhood population dynamic models of two annual weeds species. Ecol. Monogr. 60:113–134.Google Scholar
Perry, J. N. and Gonzalez-Andujar, J. L. 1993. A metapopulation neighbourhood model of an annual plant with a seedbank. J. Ecol. 81:453–463.CrossRefGoogle Scholar
Rew, L. J., Miller, P.C.H., and Paice, M.E.R. 1997. The importance of patch mapping resolution for sprayer control. Asp. Appl. Biol. 48:49–55.Google Scholar
Thill, D. C. and Mallory-Smith, C. A. 1997. The nature and consequence of weed spread in cropping system. Weed Sci. 45:337–342.Google Scholar
Thornton, P. K., Fawcett, R. H., Dent, J. B., and Perkins, T. J. 1990. Spatial weed distribution and economic thresholds for weed control. Crop Prot. 9:337–342 Google Scholar
Thurston, J. M. 1964. Weed studies in winter wheat. Pages 592–598 in Proceedings of the 7th British Weed Control Conference. Brighton, U.K.: British Crop Protection Conference.Google Scholar
Turley, D. B., Bacon, E.T.G., Shepherd, C. E., Peters, N.C.B., Potyka, I., Glen, D. M., Dampney, P.M.R., and Johnson, P. N. 1996. Straw incorporation—rotational ploughing for grass weed control. Asp. Appl. Biol. 47:257–264.Google Scholar
VanGesell, M. J., Schweizer, E. E., Garrett, K. A., and Westra, P. 1995. Influence of weed density and distribution on corn (Zea mays) yield. Weed Sci. 43:215–218.Google Scholar
van Groenendael, J. M. 1988. Patchy distribution of weeds and some implications for modelling population dynamics: a short literature review. Weed Res. 28:437–441.Google Scholar
Weiner, J. 1982. A neighborhood model of annual plant interference. Ecology 63:1237–1241.Google Scholar
Wiles, L. J., Oliver, G. W., York, R. C., Gold, H. J., and Wilkerson, G. G. 1992a. Spatial distribution of broadleaf in North Carolina soybean (Glycina max) fields. Weed Sci. 40:554–557.Google Scholar
Wiles, L. J., Wilkerson, G. G., Gold, H. J., and Coble, H. D. 1992b. Modeling weed distribution for improved postemergence control decisions. Weed Sci. 40:546–553.Google Scholar
Wilson, B. J. and Brain, P. 1991. Long-term stability of distribution of Alopecurus myosuroides Huds. within cereal fields. Weed Res. 31:367–373.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.CrossRefGoogle Scholar