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Sampling weed spatial variability on a fieldwide scale

Published online by Cambridge University Press:  12 June 2017

Sharon A. Clay ,
G. Jason Lems ,
David E. Clay ,
Frank Forcella ,
Michael M. Ellsbury  and
C. Gregg Carlson
G. Jason Lems
Affiliation:
Department of Plant Science, South Dakota State University, Brookings, SD 57007
David E. Clay
Affiliation:
Department of Plant Science, South Dakota State University, Brookings, SD 57007
Frank Forcella
Affiliation:
USDA-ARS North Central Soil Conservation Research Laboratory, Morris, MN 56267
Michael M. Ellsbury
Affiliation:
USDA-ARS Northern Grain Insect Research Laboratory, Brookings, SD 57006
C. Gregg Carlson
Affiliation:
Department of Plant Science, South Dakota State University, Brookings, SD 57007
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Abstract

Site-specific weed management recommendations require knowledge of weed species, density, and location in the field. This study compared several sampling techniques to estimate weed density and distribution in two 65-ha no-till Zea mays–Glycine max rotation fields in eastern South Dakota. The most common weeds (Setaria viridis, Setaria glauca, Cirsium arvense, Ambrosia artemisiifolia, and Polygonum pensylvanicum) were counted by species in 0.1-m2 areas on a 15- by 30-m (1,352 points in each field) or 30- by 30-m (676 points in each field) grid pattern, and points were georeferenced and data spatially analyzed. Using different sampling approaches, weed populations were estimated by resampling the original data set. The average density for each technique was calculated and compared with the average field density calculated from the all-point data. All weeds had skewed population distributions with more than 60% of sampling points lacking the specific weed, but very high densities (i.e., > 100 plants m−2) were also observed. More than 300 random samples were required to estimate densities within 20% of the all-point means about 60% of the time. Sampling requirement increased as average density decreased. The W pattern produced average species densities that often were similar to the field averages, but information on patch location was absent. Weed counts taken on the 15- by 30-m grid were dependent spatially and weed contour maps were developed. Kriged maps presented both density and location of weed patches and could be used to establish management zones. However, grid-sampling production fields on a small enough scale to obtain spatially dependent data may have limited usefulness because of time, cost, and labor constraints.

Keywords

Ambrosia artemisiifolia L. AMBEL, common ragweedCirsium arvense (L.) Scop. CIRAR, Canada thistlePolygonum pensylvanicum L. POLPY, Pennsylvania smartweedSetaria glauca (L.) Beauv. SETLU, yellow foxtailS. viridis (L.) Beauv. SETVI, green foxailGlycine max (L.) Merr., soybeanZea mays L., cornMappingprecision farmingsite-specific weed managementAMBELCIRARPOLPYSETLUSETVI
Type
Weed Biology and Ecology
Information
Weed Science , Volume 47 , Issue 6 , December 1999 , pp. 674 - 681
Copyright
Copyright © 1999 by the Weed Science Society of America 

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References

Literature Cited

Brown, R. B. and Steckler, J.P.G.A. 1995. Prescription maps for spatially variable herbicide application in no-till corn. Trans. Am. Soc. Agric. Eng. 38:1659–1666.CrossRefGoogle Scholar
Cardina, J., Sparrow, D., 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
Clark, S. J., Perry, J. N., and Marshall, E.J.P. 1996. Estimating Taylor's power law parameters for weeds and the effect of spatial scale. Weed Res. 36:405–417.Google Scholar
Clay, D, Carlson, E. C. G., Holman, P. W., Schumacher, T. E., and Clay, S. A. 1995. Banding nitrogen fertilizer influence on inorganic nitrogen distribution. J. Plant Nutr. 18:331–334.CrossRefGoogle Scholar
Conn, J. S., Proctor, C. H., and Skroch, W. H. 1982. Selection of sampling methods to determine weed abundance in apple (Malus domestica) orchards. Weed Sci. 30:35–40.Google Scholar
Gerhards, R. and Wyse-Pester, D. Y. 1997. Characterizing spatial stability of weed populations using interpolated maps. Weed Sci. 45:108–119.Google Scholar
Heisel, T., Andreasen, C., and Ersboll, A. K. 1996. Annual weed distributions can be mapped with kriging. Weed Res. 36:325–337.Google Scholar
Isaaks, E. H. and Srivastava, R. M. 1989. An Introduction to Applied Geostatistics. Oxford: Oxford University Press. pp. 369–457.Google Scholar
Johnson, G. A., Mortensen, D., Young, L. J., and Martin, A. 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, G. A., Mortensen, D. A., Young, L. J., and Martin, A. R. 1996. Parametric sequential sampling based on multistage estimation of the negative binomial parameter k . Weed Sci. 44:555–559.CrossRefGoogle Scholar
Lems, J. 1998. Weed spatial variability and management on a field-wide scale. . South Dakota State University, Brookings, SD. 105 p.Google Scholar
Marshall, E.J.P. 1988. Field-scale estimate of grass weed populations in arable land. Weed Res. 28:191–198.Google Scholar
Mortensen, D. A., Johnson, G. A., Wyse, D. Y., and Martin, A. R. 1995. Managing spatially variable weed populations. pp. 397–415 in Proceedings of ASA-CSSA-SSSA, Soil Specific Management for Agricultural Systems. Madison, WI: ASA.Google Scholar
Ott, L. 1977. An Introduction to Statistical Methods and Data Analysis. North Scituate, MA: Wadsworth, pp. 625–638.Google Scholar
Thomas, A. G. 1985. Weed survey system in Saskatchewan for cereal and oilseed crops. Weed Sci. 33:34–43.CrossRefGoogle Scholar
Trangmar, B. B., Yost, R. S., and Uehara, G. 1985. Application of geostatistics to spatial studies of soil properties. Adv. Agron. 38:45–93.Google Scholar
Wiles, L. J., Oliver, G. W., York, A. C., Gold, H., and Wilkerson, G. 1992. Spatial distribution of broadleaf weeds in North Carolina soybean (Glycine max) fields. Weed Sci. 40:554–557.CrossRefGoogle Scholar
Wilson, B. J. and Brain, P. 1991. Long term stability of Alopecus myosuroides Huds. within cereal fields. Weed Res. 31:367–373.Google Scholar