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Seasonal abundance and spatial pattern of Setaria faberi, Chenopodium album, and Abutilon theophrasti in reduced-tillage soybeans

Published online by Cambridge University Press:  12 June 2017

Chris M. Boerboom
Affiliation:
Department of Agronomy, University of Wisconsin, Madison, WI 53706

Abstract

A better understanding of the influence of various crop and weed management practices on spatiotemporal dynamics of weeds could improve the design of integrated weed management systems. We examined the influence of 18- and 76-cm soybean row spacings on emergence pattern and spatial aggregation of giant foxtail, common lambsquarters, and velvetleaf seedling cohorts. In addition, we characterized the soil seedbank and determined the quantitative and spatial relationship between the seedbank and seedling populations. Viable seeds of about 10 weed species and twice as many species of seedlings were identified in the weed community. Giant foxtail and common lambsquarters were the predominant species in the seedling and seedbank population, respectively, each accounting for 60 to 70% of the total weed species density. Emergence of giant foxtail, common lambsquarters, and velvetleaf depleted 12 to 33%, < 2% and 12 to 49% of the seedbank in the upper 10 cm of the soil profile. Peak time and periodicity of weed emergence was not influenced by soybean row spacing, and peak time of emergence of giant foxtail, common lambsquarters, and velvetleaf occurred 3 to 4, 3 to 6, and 3 to 9 weeks after soybean planting (WAP), respectively. Magnitude of giant foxtail emergence 5, 6, and 9 WAP was 98, 96, and 76% greater in 76- than in 18-cm row soybeans only when the population of 76-cm row soybeans was 57% lower than the 18-cm soybeans in 1997. Giant foxtail and common lambsquarters seeds in the seedbank were aggregated in 1996 and 1997 according to the Taylor power law (TPL) and the negative binomial distribution (NBD). The TPL and the NBD were similar in describing the spatial aggregation of giant foxtail and common lambsquarters but not some velvetleaf seedling cohorts. The spatial aggregation of seedlings varied among cohorts for different weed species and was likely due to species-specific biological characteristics that influence seed dispersal, germination, and seedling emergence. Within a 1.5-ha area, aggregation declined with decreasing density. Within a 24-m2 area, the level of aggregation of all weed species decreased as seedling densities increased. These results indicated that soybean row spacing influenced neither weed emergence pattern nor weed spatial aggregation; thus, several management decisions can be similar in 18- and 76-cm row soybeans.

Type
Weed Biology and Ecology
Copyright
Copyright © 1999 by the Weed Science Society of America 

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References

Literature Cited

Auld, B. A. and Tisdell, C. S. 1988. Influence of spatial distribution of weeds on crop yield loss. Plant Prot. Q. 31: 81.Google Scholar
Baskin, J. M. and Baskin, C. C. 1985. The annual dormancy cycle in buried weed seeds: a continuum. BioScience 35: 492498.CrossRefGoogle Scholar
Beckett, T. H., Stoller, E. W., and Wax, L. M. 1988. Interference of four annual weeds in corn (Zea mays). Weed Sci. 36: 764769.Google Scholar
Berti, A., Zanin, G., Baldoni, G., Grignani, C., Mazzoncini, M., Montemurro, P., Tei, F., Vazzana, C., and Viggianti, P. 1992. Frequency distribution of weed counts and applicability of a sequential sampling method to integrated weed management. Weed Res. 32: 3944.CrossRefGoogle Scholar
Bigwood, D. W. and Inouye, D. W. 1988. Spatial pattern analysis of seed banks: an improved method and optimized sampling. Ecology 69: 497507.CrossRefGoogle Scholar
Brain, P. and Cousens, R. 1990. The effect of weed distribution on prediction of yield loss. J. Appl. Ecol. 27: 735742.CrossRefGoogle Scholar
Buhler, D. D. and Mester, T. C. 1991. Effect of tillage systems on the emergence depth of giant (Setaria filbert) and green foxtail (Setaria virdis). Weed Sci. 39: 200203.CrossRefGoogle Scholar
Butts, R. A. and Schaalje, G. B. 1994. Spatial distribution of fall populations of Russian wheat aphid (Homoptera: Aphididae) in winter wheat. J. Econ. Entomol. 87: 12301236.CrossRefGoogle Scholar
Cardina, J., Sparrow, D. H., and McCoy, E. L. 1995. Analysis of spatial distribution of common lambsquarters (Chenopodium album) in no-till soybeans. Weed Sci. 43: 258268.CrossRefGoogle Scholar
Cardina, J., Sparrow, D. H., and McCoy, E. L. 1996. Spatial relationships between seed bank and seedling populations of common lambsquarters (Chenopodium album) and annual grasses. Weed Sci. 44: 298308.Google Scholar
Chauvel, B., Gasquez, J., and Darmency, H. 1989. Changes of weed seed bank parameters according to species, time and environment. Weed Res. 45: 706715.Google Scholar
Clark, S. J., Perry, J. N., and Marshall, E.J.P. 1996. Estimating power law parameters for weeds and the effect of spatial scale. Weed Res. 36: 405417.CrossRefGoogle Scholar
Dessiant, F., Barralis, G., Caixinhas, M. L., Mayor, J. P., Recasens, J., and Zanin, G. 1996. Precision of soil seed bank sampling: how many cores? Weed Res. 36: 143151.Google Scholar
Dessiant, F. and Caussanel, J. P. 1994. Trend surface analysis: a simple tool for modelling spatial patterns of weeds. Crop Prot. 13: 433438.CrossRefGoogle Scholar
Ervio, L. 1971. The effect of intraspecific competition on the development of Chenopodium album . Weed Res. 11: 124134.Google Scholar
Forcella, F., Wilson, R. G., Renner, K. A., Dekker, J., Harvey, R. G., Alm, D. A., Buhler, D. D., and Cardina, J. 1992. Weed seed bank of the U.S. corn belt: magnitude, variation, emergence, and application. Weed Sci. 40: 636644.CrossRefGoogle Scholar
Gates, E. G. and Ethridge, F. G. 1972. A generalized set of discrete frequency distributions with Fortran program. Math. Geol. 4: 124.Google Scholar
Gross, K. L. and Renner, K. A. 1989. A new method for estimating seed numbers in the soil. Weed Sci. 37: 836839.CrossRefGoogle Scholar
Harper, J. L. 1977. Population Biology of Plants. London: Academic Press, pp. 3360.Google Scholar
Harper, J. L., Williams, J. T., and Sager, G. R. 1965. The behavior of seeds in soil. I. The heterogeneity of soil surfaces and its role in determining the establishment of plants from seed. J. Ecol. 53: 273286.Google Scholar
Harrison, S. K. 1990. Interference and seed production by common lambsquarters (Chenopodium album) in soybean (Glycine max). Weed Sci. 38: 113118.Google Scholar
Heisel, T., Andreasen, C., and Ersboll, A. K. 1996. Annual weed distributions can be mapped with kriging. Weed Res. 36: 325337.Google Scholar
Johnson, G. A., Mortensen, D. A., and Martin, A. R. 1995a. A simulation of herbicide use based on weed spatial distribution. Weed Res. 35: 197205.Google Scholar
Johnson, G. A., Mortensen, D. A., Young, L. J., and Martin, A. R. 1995b. The stability of weed seedling population models and parameters in eastern Nebraska corn (Zea mays) and soybean (Glycine max) fields. Weed Sci. 43: 604611.Google Scholar
Johnson, G. A., Mortensen, D. A., Young, L. J., and Martin, A. R. 1996. Parametric sequential sampling based on multistage estimation of negative binomial parameter k. Weed Sci. 44: 555559.Google Scholar
Kovach, D. A., Thill, D. C., and Young, F. L. 1988. A water-spray system for removing seed from soil. Weed Technol. 2: 238341.Google Scholar
Legendre, P. and Fortin, M. 1989. Spatial pattern and ecological analysis. Vegetatio 80: 107138.Google Scholar
Lewis, J. 1973. Longevity of crop and weed seeds. Weed Res. 13: 179191.Google Scholar
Lloyd, M. 1967. Mean crowding. J. Anim. Ecol. 36: 130.Google Scholar
Lyons, N. J. and Hutchenson, K. 1988. Measures of the dispersion of a population based on ranks. Pages 370377 in McDonald, L., Manly, B., Lockwood, J., and Logan, J., eds. Estimation and Analysis of Insect Population. New York: Springer-Verlag.Google Scholar
Madden, L. V., Hughes, G., and Ellis, M. A. 1995. Spatial heterogeneity of the incidence of grape downy mildew. Phytopathology 85: 269275.Google Scholar
Mulugeta, D. and Stoltenberg, D. E. 1997a. Seed bank characterization and emergence of a weed community in a moldboard plow system. Weed Sci. 45: 5460.Google Scholar
Mulugeta, D. and Stoltenberg, D. E. 1997b. Increased weed emergence and seed bank depletion by soil disturbance in a no-tillage system. Weed Sci. 45: 234241.Google Scholar
Mulugeta, D. and Stoltenberg, D. E. 1997c. Weed and weed seed bank management with integrated methods as influenced by tillage. Weed Sci. 45: 706715.Google Scholar
Mulugeta, D. and Stoltenberg, D. E. 1998. Influence of cohorts on Chenopodium album demography. Weed Sci. 46: 6570.Google Scholar
Ogg, A. G. and Dawson, J. H. 1984. Time of emergence of eight weed species. Weed Sci. 32: 327335.CrossRefGoogle Scholar
Routledge, R. D. and Swartz, T. B. 1991. Taylor's power law re-examined. Oikos 60: 107112.Google Scholar
[SAS] Statistical Analysis Systems. 1990. SAS/STAT Users Guide. Version 6, 4th ed. Cary, NC: Statistical Analysis Systems Institute, pp. 120.Google Scholar
Sawyer, A. J. 1989. Inconstancy of Taylor's b: simulated sampling with different quadrat size and spatial distributions. Res. Popul. Ecol. 31: 1124.CrossRefGoogle Scholar
Schweizer, E. E. and Zimdahl, R. L. 1984. Weed seed decline in irrigated soil after rotation of crops and herbicides. Weed Sci. 32: 8489.Google Scholar
Southwood, T.R.E. 1978. Ecological Methods with Particular Reference to the Study of Insect Populations. London: Chapman and Hall, p. 11.Google Scholar
Swinton, S. M. and King, R. P. 1994. A bioeconomic model for weed management in corn and soybean. Agric. Syst. 44: 313335.Google Scholar
Taylor, L. R. 1961. Aggregation, variance and the mean. Nature 189: 732735.Google Scholar
Taylor, L. R. 1984. Assessing and interpreting the spatial distributions of insect populations. Annu. Rev. Entomol. 29: 321357.CrossRefGoogle Scholar
Taylor, L. R., Woiwood, I. P., and Perry, J. N. 1979. The negative binomial as a dynamic ecological model for aggregation and the density dependence of k. J. Anim. Ecol. 48: 289304.Google Scholar
Teasdale, J. R., Beste, C. E., and Potts, W. F. 1991. Response of weeds to tillage and cover crop residue. Weed Sci. 39: 195199.Google Scholar
Thornton, P. K., Fawcett, R. H., Dent, J. B., and Perkings, T. J. 1990. Spatial weed distribution and economic thresholds for weed control. Crop Prot. 9: 337342.Google Scholar
Weaver, S. E., Tan, C. S., and Brain, P. 1988. Effect of temperature and soil moisture on time of emergence of tomatoes and four weed species. Can. J. Plant Sci. 68: 877886.Google Scholar
Wiles, L. J., Oliver, G. W., York, A. C., Gold, H. J., and Coble, H. D. 1992. Modelling weed distribution for improved postemergence control decisions. Weed Sci. 40: 546553.CrossRefGoogle Scholar
Wilkerson, G. A., Modem, S. A., and Coble, H. D. 1991. HERB: decision model for post emergence weed control in soybean. Agron. J. 83: 413417.Google Scholar
Yamamura, K. 1990. Sampling scale dependence of Taylor's power law. Oikos 59: 121125.Google Scholar