Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-23T19:09:22.992Z Has data issue: false hasContentIssue false
  • English
  • Français

Spatial Relationships Between Seedbank and Seedling Populations of Common Lambsquarters (Chenopodium album) and Annual Grasses

Published online by Cambridge University Press:  12 June 2017

John CardiNa ,
Denise H. Sparrow  and
Edward L. McCoy
John CardiNa
Affiliation:
Dep. Hortic. and Crop Sci., Ohio State Univ., Wooster, OH 44691
Denise H. Sparrow
Affiliation:
Dep. Hortic. and Crop Sci., Ohio State Univ., Wooster, OH 44691
Edward L. McCoy
Affiliation:
Sch. Nat. Res., Oh. Agric. Res. and Dev. Ctr., Ohio State Univ., Wooster, OH 44691
Get access Rights & Permissions [Opens in a new window]

Abstract

Predictions of weed seedling populations from seedbank data should characterize the spatial distribution as well as the composition and abundance of weeds. The spatial distribution of seedbank and seedling populations of common lambsquarters and annual grasses (giant foxtail, large crabgrass, and fall panicum) were described in moldboard plow and no-tillage soybean fields from 1990 to 1993. Spearman rank correlations between seedbank and seedling densities were significant for common lambsquarters in both tillages and all years, but for annual grasses correlations were significant only in no-tillage. Semivariograms showed spatial autocorrelation in seedbank and seedling populations of common lambsquarters in all years in no-till, but less often in the moldboard plow field. Annual grass seed and seedling populations were autocorrelated in the no-till field every year except 1993, and in the moldboard plow field in 1992 and 1993 only. Cross-semivariograms showed spatial continuity between seedbank and seedling population densities in 3 of 4 yr in no-till for common lambsquarters, and in all years of no-till and 1 yr of moldboard plow for annual grasses. Grey-scale field maps of common lambsquarters seedbanks corresponded visually to maps of seedling populations and could have been used to target control efforts, but visual correspondence between annual grass seedbank and seedling maps was poor. Seedbank and seedling mapping may be useful for site-specific management, but additional information is needed to understand the variation in the relationships between these two populations over time and space.

Keywords

Common lambsquarters, Chenopodium album L. ♯ CHEALsoybean, Glycine max (L.) Merr. ‘Century 84.’Autocorrelationgeostatisticssite-specific managementspatial dependencespatial pattern
Type
Weed Biology and Ecology
Information
Weed Science , Volume 44 , Issue 2 , June 1996 , pp. 298 - 308
Copyright
Copyright © 1996 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. Baker, H. G. 1989. Some aspects of the natural history of seed banks. Pages 9–21 in Leek, M. A., Parker, M. A. V. T., and Simpson, R. L. (eds.) Ecology of Soil Seed Banks. Academic Press, New York.CrossRefGoogle Scholar
2. Ball, D. A. and Miller, S. D. 1989. Acomparison of techniques for estimation of arable soil seedbanks and their relationship to weed flora. Weed Res. 29: 365–373.Google Scholar
3. Barralis, G., Chadoeuf, R., and Gouet, J. P. 1986. Essai de determination de la taille de l'echantillon pour l'etude du potentiel semencier d'un sol. Weed Res. 26: 291–297.CrossRefGoogle Scholar
4. Benoit, D. L. 1986. Methods of sampling seed banks in arable soils with special reference to Chenopodium spp. , University of Western Ontario, London, Canada.Google Scholar
5. Bigwood, D. B. and Inouye, D. W. 1988. Spatial pattern analysis of seed banks: an improved method and optimized sampling. Ecology 69: 497–507.Google Scholar
6. Cantrell, R. S. and Cosner, C. 1991. The effects of spatial heterogeneity in population dynamics. J. Math. Biol. 29: 484–498.CrossRefGoogle Scholar
7. Cardina, J. and Sparrow, D. H. 1996. A comparison of methods to predict weed seedling populations from the soil seedbank. Weed Sci. (In Press).Google Scholar
8. Cardina, J., Sparrow, D. H., and McCoy, E. L. 1995. Analysis of spatial distribution of common lambsquarters (Chenopodium album) in no-till soybean (Glycine max). Weed Sci. 43: 258–268.Google Scholar
9. Chauvel, B., Gasquez, J., and Darmency, H. 1989. Changes of weed seed bank parameters according to species, time and environment. Weed Res. 29: 213–219.CrossRefGoogle Scholar
10. Donald, W. W. 1994. Geostatistics for mapping weeds, with a Canada thistle (Cirsium arvense) patch as a case study. Weed Sci. 42: 648–657.CrossRefGoogle Scholar
11. Dutilleul, P. 1993. Spatial heterogeneity and the design of ecological field experiments. Ecology 74: 1646–1658.Google Scholar
12. Forcella, F. 1992. Prediction of weed seedling densities from buried seed reserves. Weed Res. 32: 29–38.CrossRefGoogle Scholar
13. Forcella, F., Wilson, R. G., Renner, K. A., Dekker, J., Harvey, R. G., Aim, D. A., Buhler, D. D., and Cardina, J. 1992. Weed seedbanks of the U.S. corn belt: Magnitude, variation, emergence, and application. Weed Sci. 40: 636–644.CrossRefGoogle Scholar
14. Goyeau, H. and Fablet, G. 1982. Etude du stock de semences de mauvaises herbes dans le sol: Le problem de l'echantillonnage. Agronornie 2: 542–551.Google Scholar
15. Isaaks, E. H. and Srivastava, R. M. 1989. Spatial description. Pages 40–66 in Isaaks, E. H. and Srivastava, R. M., Applied Geostatistics. Oxford Univ. Press, New York.Google Scholar
16. Johnson, G. A. 1994. Model parameterization, parametric sequential sampling, and geostatistical analysis of weed seedling populations. , University of Nebraska, Lincoln.Google Scholar
17. Kolasa, J. and Picket, S.T.A. 1991. Ecological Heterogeneity. Springer-Verlag, New York, NY.Google Scholar
18. Legendre, P. and Fortin, M.-J. 1989. Spatial pattern and ecological analysis. Vegetatio 80: 107–138.Google Scholar
19. Ludwig, J. A. and Reynolds, J. F. 1988. Interspecific covariation. Pages 145–155 in Ludwig, J. A. and Reynolds, J. F. Statistical Ecology. John Wiley & Sons, Inc., New York.Google Scholar
20. Lybecker, D. W., Schweizer, E. E., and King, R. P. 1991. Weed management decisions based on bioeconomic modeling. Weed Sci. 39: 124–129.Google Scholar
21. Moloney, K. A. 1988. Fine-scale spatial and temporal variation in the demography of a perennial bunchgrass. Ecol. 69: 1588–1598.CrossRefGoogle Scholar
22. Rossi, R. E., Mulla, D. J., Journel, A. G., and Franz, E. H. 1992. Geostatistical tools for modeling and interpreting ecological spatial dependence. Ecol. Monographs 62: 277–314.Google Scholar
23. Streibig, J. C., Gottschau, A., Dennis, B., Haas, H., and Polgaard, P. 1984. Soil properties affecting weed distribution. 7th Int'l Symp. Weed Biol., Ecol, and Syst. 7: 147–154.Google Scholar
24. 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
25. Thompson, K. 1986. Small-scale heterogeneity in the seed bank of an acidic-grassland. J. Ecol. 74: 733–738.CrossRefGoogle Scholar
26. 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
27. Trangmar, B. B., Yost, R. S., and Uehara, G. 1985. Application of geostatistics to spatial studies of soil properties. Adv. Agron. 38: 45–94.Google Scholar
28. Vauclin, M., Vieira, S. R., Vauchaud, G., and Nielsen, D. R. 1983. The use of cokriging with limited field soil observations. Soil Sci. Soc. Am. J. 47: 175–184.Google Scholar
29. Wiles, L. J., Oliver, G. W., York, A. C., Gold, H. J., and Wilkerson, G. G. 1992. Spatial distribution of broadleaf weeds in North Carolina soybean (Glycine max) fields. Weed Sci. 40: 554–557.CrossRefGoogle Scholar
30. Wiles, L. J., Wilkerson, G. G., Gold, H. J., and Coble, H. D. 1992. Modeling weed distribution for improved postemergence control decisions. Weed Sci. 40: 546–553.Google Scholar
31. Wilson, B. J. and Brain, P. 1991. Long-term stability of distribution of Alopercurus myosuroides Huds. within cereal fields. Weed Res. 31: 367–373.Google Scholar
32. Wilson, R. G., Kerr, E. D., and Nelson, L. A. 1985. Potential for using weed seed content in the soil to predict future weed problems. Weed Sci. 33: 171–175.Google Scholar