Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-02T23:35:54.875Z Has data issue: false hasContentIssue false
  • English
  • Français

Identifying associations among site properties and weed species abundance. I. Multivariate analysis

Published online by Cambridge University Press:  20 January 2017

J. Anita Dieleman ,
David A. Mortensen ,
Douglas D. Buhler ,
Cynthia A. Cambardella  and
Thomas B. Moorman
David A. Mortensen
Affiliation:
Department of Agronomy, University of Nebraska, Lincoln, NE 68583
Douglas D. Buhler
Affiliation:
U.S. Department of Agriculture, Agricultural Research Service, National Soil Tilth Laboratory, Ames, IA 50011
Cynthia A. Cambardella
Affiliation:
U.S. Department of Agriculture, Agricultural Research Service, National Soil Tilth Laboratory, Ames, IA 50011
Thomas B. Moorman
Affiliation:
U.S. Department of Agriculture, Agricultural Research Service, National Soil Tilth Laboratory, Ames, IA 50011
Get access Rights & Permissions [Opens in a new window]

Abstract

Site properties and weed species abundance are known to vary spatially across fields. The extent to which they covary is not well understood. The objective of this research was to assess how canonical correlation analysis could be used to identify associations among site properties and weed species abundance within an agricultural field. A farmer-managed field rotated between Zea mays and Glycine max in Boone County, IA, was grid-sampled for site properties in 1992 and for weed species abundance between 1994 and 1997. Twelve site properties were considered in relation to five weed species that were identified and counted after all weed control operations were completed. Site properties such as total nitrogen, Bray-1 P, percent organic carbon, and texture were spatially variable. Weed species abundance was also spatially variable such that most weeds were found in patches and much of the field was weed-free. Canonical correlation analysis identified one to four significant correlations between linear combinations of site properties and weed species abundance. The first and second pairs of linear combinations explained the majority of variation in the data and were used to identify associations among site properties and weed species abundance. In years with Z. mays, the first pair of linear combinations described an association between herbicide activity and weed presence, and the second described topography and soil texture associations with weed presence. In years with G. max, the single observed association described a link between soil texture and presence of Setaria species and Polygonum coccineum. Several consistent associations were identified across years, indicating that site properties can influence weed abundance. However, annual variation in the associations may be attributed to differences in agronomic and weed management practices for each crop, as well as temporal weather variation influencing weed abundance from year to year. This multivariate technique is an important tool to identify associations between site properties and weed abundance that could help explain observed patchy patterns of weed abundance. These associations are an important first step in the generation of hypotheses to be tested at the whole field scale.

Keywords

Abutilon theophrasti Medicus ABUTH, velvetleafAmaranthus spp., pigweedCyperus esculentus L. CYPES, yellow nutsedgePolygonum coccineum Muhl. ex Willd. POLCC, swamp smartweedSetaria spp., foxtailZea mays L., cornGlycine max (L.) Merr., soybeanCanonical correlation analysisgrid-samplingsoil propertiesspatial heterogeneityABUTHAMASSCYPESPOLCCSETSS
Type
Research Article
Information
Weed Science , Volume 48 , Issue 5 , October 2000 , pp. 567 - 575
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

Almekinders, C.J.M., Fresco, L. O., and Struik, P. C. 1995. The need to study and manage variation in agro-ecosystems. Neth. J. Agric. Sci. 43:127–142.Google Scholar
Andreasen, C., Jensen, J. E., and Streibig, J. C. 1991a. Soil properties and plant nutrients affecting the occurrence of Poa annua, Stellaria media, and Viola arvensis on arable land. Pages 395–402 In Proceedings of the British Crop Protection Conference—Weeds. Brighton, Great Britain: British Crop Protection Council.Google Scholar
Andreasen, C., Streibig, J. C., and Haas, H. 1991b. Soil properties affecting the distribution of 37 weed species in Danish fields. Weed Res. 31:181–187.Google Scholar
Blackshaw, R. E., Moyer, J. R., and Kozub, G. C. 1994. Efficacy of downy brome herbicides as influenced by soil properties. Can. J. Plant Sci. 74:177–183.Google Scholar
Blumhorst, M. R., Weber, J. B., and Swain, L. R. 1990. Efficacy of selected herbicides as influenced by soil properties. Weed Technol. 4:279–283.Google Scholar
Braak, C.J.F. ter. 1987. The analysis of vegetation-environment relationships by canonical correspondence analysis. Vegetatio 69:69–77.Google Scholar
Cambardella, C. A., Moorman, T. B., Novak, J. M., Parkin, T. B., Karlen, D. L., Turco, R. F., and Konopka, A. E. 1994. Field-scale variability of soil properties in Central Iowa soils. Soil Sci. Soc. Am. J. 58:1501–1511.Google Scholar
Cardina, J., Sparrow, D. H., and McCoy, E. L. 1995. Analysis of spatial distribution of common lambsquarters (Chenopodium album) in notill soybean (Glycine max). Weed Sci. 43:258–269.Google Scholar
Cousens, R. and Mortimer, M. 1995. Dynamics of Weed Populations. New York: Cambridge University Press, pp. 86–216.CrossRefGoogle Scholar
Dale, M.R.T., Thomas, A. G., and John, E. A. 1992. Environmental factors including management practices as correlates of weed community composition in spring seeded crops. Can. J. Bot. 70:1931–1939.Google Scholar
Derksen, D. A., Thomas, A. G., Lafond, G. P., Loeppky, H. A., and Swanton, C. J. 1994. Impact of agronomic practices on weed communities: fallow within tillage systems. Weed Sci. 42:184–194.CrossRefGoogle Scholar
Dieleman, J. A., Mortensen, D. A., Buhler, D. D., and Ferguson, R. B. 2000. Identifying associations among site properties and weed species abundance. II. Hypothesis generation. Weed Sci. 48:576–587.Google Scholar
Dieleman, J. A., Mortensen, D. A., and Young, L. J. 1999. Predicting within-field weed species occurrence based on field-site attributes. Pages 517–528 In Stafford, J.V., ed. 2nd European Conference on Precision Agriculture, 11–15 July 1999, Odense, Denmark. Sheffield, UK: Sheffield Academic Press.Google Scholar
Dolédec, S. and Chessel, D. 1994. Co-inertia analysis: an alternative method for studying species-environment relationships. Freshw. Biol. 31:277–294.Google Scholar
Gittins, R. 1985. Canonical Analysis: A Review with Applications in Ecology. Berlin: Springer-Verlag, pp. 13–36.CrossRefGoogle Scholar
Harper, J. L. 1977. Population Biology of Plants. New York: Academic Press, pp. 151–194, 305–345.Google Scholar
Hausler, A. and Nordmeyer, H. 1995. Impact of soil properties on weed distribution. Pages 186–189 In Olesen, S. E., ed. Proceedings, Seminar on Site Specific Farming. Tjele: Danish Institute of Plant and Soil Science, SP-report 26.Google Scholar
James, F. C. and McCulloch, C. E. 1990. Multivariate analysis in ecology and systematics: panacea or Pandora's box? Annu. Rev. Ecol. Syst. 21:129–166.Google Scholar
Johnson, G. A., Mortensen, D. A., and Gotway, C. A. 1996. Spatial and temporal analysis of weed seedling populations using geostatistics. Weed Sci. 44:704–710.Google Scholar
Johnson, G. A., Mortensen, D. A., Young, L. J., and Martin, A. R. 1995. The stability of weed seedling population models and parameters in eastern Nebraska corn (Zea mays) and soybean (Glycine max) fields. Weed Sci. 43:604–611.Google Scholar
Johnson, R. A. and Wichern, D. W. 1992. Applied Multivariate Statistical Analysis. 3rd ed. Englewood Cliffs, NJ: Prentice Hall, pp. 459–486.Google Scholar
Mortensen, D. A., Johnson, G. A., and Young, L. J. 1993. Weed distribution in agricultural fields. Pages 113–124 In Robert, P. C. and Rust, R. H., eds. Soil Specific Crop Management. Madison, WI: Agronomy Society of America.Google Scholar
Mulla, D. J. 1993. Mapping and managing spatial patterns in soil fertility and crop yield. Pages 15–26 In Robert, P. C. and Rust, R. H., eds. Soil Specific Crop Management. Madison, WI: Agronomy Society of America.Google Scholar
Novak, J. M., Moorman, T. B., and Cambardella, C. A. 1997. Atrazine sorption at the field scale in relation to soils and landscape position. J. Environ. Qual. 26:1271–1277.Google Scholar
Post, B. J. 1988. Multivariate analysis in weed science. Weed Res. 28:425–430.Google Scholar
Pyšek, P. and Lepš, J. 1991. Response of a weed community to nitrogen fertilizer: a multivariate analysis. J. Veg. Sci. 2:237–244.Google Scholar
Rao, P.S.C. and Wagenet, R. J. 1985. Spatial variability of pesticides in field soils: methods for data analysis and consequences. Weed Sci. 33 (suppl. 2): 18–24.Google Scholar
[SAS] Statistical Analysis Systems. 1990. SAS/STAT User's Guide. Version 6, 4th ed., Volume 1. Cary, NC: Statistical Analysis Systems Institute, pp. 367–385.Google Scholar