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Characterizing spatial stability of weed populations using interpolated maps

Published online by Cambridge University Press:  12 June 2017

Roland Gerhards ,
Dawn Y. Wyse-Pester ,
David Mortensen  and
Gregg A. Johnson
Dawn Y. Wyse-Pester
Affiliation:
Department of Agronomy, University of Nebraska, Lincoln, NE 68583-0915
David Mortensen
Affiliation:
Department of Agronomy, University of Nebraska, Lincoln, NE 68583-0915
Gregg A. Johnson
Affiliation:
University of Minnesota, Southern Experimental Station, Waseca, MN 56093-4521
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Abstract

Intensive surveys were conducted in 2 fields in eastern Nebraska to determine the spatial stability of common sunflower, velvetleaf, green and yellow foxtail, and hemp dogbane over 4 yr (1992 to 1995). The 1st field was planted to soybean in 1992 and corn in 1993, 1994, and 1995. The 2nd field was planted to corn in 1992 and 1994 and soybean in 1993 and 1995. Weed density was sampled prior to postemergence herbicide application at approximately 800 locations per year in each field on a regular 7 m grid. The same locations were sampled every year. Weed density at locations between the sample sites was determined by linear triangulation interpolation. Weed seedling distribution was significantly aggregated, with large weed-free areas in both fields. Common sunflower, velvetleaf, and hemp dogbane patches were very persistent in diameter in the east-west and north-south directions and in location and area over 4 yr in the 1st field. Foxtail distribution and density continuously increased in each of the 4 yr in the first field and decreased in the 2nd field. A geographic information system was used to overlay maps from each year for a species. This showed that 36% of the sampled area was continuously free of common sunflower, 62.5% was free of hemp dogbane, and 11.5% was free of velvetleaf in the 1st field, but only 1% was free of velvetleaf in the 2nd field. The persistence of broadleaf weed patches suggests that weed seedling distributions mapped in one year are good predictors of future seedling distributions. Improved and more efficient sampling methods are needed.

Keywords

Linear interpolationsite-specific weed controlintegrated weed managementABUTHAPCCAHELANSETLUSETVIVelvetleaf, Abutilon theophrasti Medikus. ABUTHHemp dogbane, Apocynum cannabinum L. APCCACommon sunflower, Helianthus annuus L. HELANYellow foxtail, Setaria glauca (L.) Beauv. SETLUGreen foxtail, Setaria viridis (L.) Beauv. SETVISoybean, Glycine max (L.) Merr. Corn, Zea mays L.
Type
Weed Biology and Ecology
Information
Weed Science , Volume 45 , Issue 1 , February 1997 , pp. 108 - 119
Copyright
Copyright © 1997 by the Weed Science Society of America 

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References

Literature Cited

Bender, J. 1995. Change: goals and obstacles. in Bender, J., ed. Future Harvest—Pesticide-Free Farming. Lincoln, NE: University of Nebraska Press, pp. 1–8.Google Scholar
Burnside, O. C., Wilson, R. G., Weisenberg, S., and Hubbard, K. G. 1996. Seed longevity of 41 weed species buried 17 years in eastern and western Nebraska. Weed Sci. 44: 74–86.Google Scholar
Colliver, C. T., Maxwell, B. D., Tyler, D. A., Roberts, D., and Long, D. 1996. Georeferencing wild oat infestations in small grain: using current map information to predict future weed spatial distributions. in Robert, P. and Rust, R. H., eds. Precision Agriculture. Madison, WI: Agronomic Society of America. In press.Google Scholar
Dieleman, J. A. and Mortensen, D. A. 1996. Influence of weed biology and ecology on development of reduced dose strategies for integrated weed management system. Adv. Soil Sci. In press.Google Scholar
Gerhards, R., Hayer, R., Sökefeld, M., Schulze-Lohne, K., Kühbauch, W., Buchner, W., and Graff, M. 1995. Ein Verfahren zur teilschlaggerechten Unkrautkontrolle in Winterweizen. Mitt. Ges. Pfl. Bauwissenschaften 8: 172–175.Google Scholar
Gerhards, R., Nabout, A., Sökefeld, M., Kühbauch, W., and Nour-Eldin, H. 1993. Automatische Erkennung von acht Unkrautarten mit Hilfe digitaler Bildverarbeitung und Fouriertransformation. J. Agron. Crop Sci. 171: 321–328.Google Scholar
Gerhards, R., Sökefeld, M., Knuf, D., and Kühbauch, W. 1996. Kartierung und geostatistische Analyse der Unkrautverteilung in Zuckerr übenschlägen als Grundlage für eine teilschlagspezifische Bekämpfung. J. Agron. Crop Sci. 176: 259–266.Google Scholar
Gotway, C. A., Ferguson, R. B., Hergert, G. W., and Peterson, T. A. 1996. A comparison of kriging and inverse-distance methods for mapping soil nitrate and organic matter for variable rate nitrogen application. Soil Sci. In press.Google Scholar
Hofmeister, H. and Garve, E. 1986. Lebensraum Acker—Pflanzen der Äcker und ihre Ökologie. Hamburg: Paul Parey Verlag, pp. 112–266.Google Scholar
Isaaks, E. H. and Srivastava, R. M. 1989. Applied Geostatistics. New York: Oxford University Press, pp. 249–322.Google Scholar
Johnson, G. A., Mortensen, D. A., and Martin, A. R. 1995. A simulation of herbicide use based on weed spatial distribution. Weed Res. 35: 197–205.Google Scholar
Knuf, D. 1994. Räumliche Verteilung von Unkräutern auf Zuckerübenflächen, unveröffentl. Bonn: Diplomarbeit der Universität Bonn. 69 p.Google Scholar
Marshall, E.J.P. 1988. Field-scale estimates of grass populations in arable land. Weed Res. 28: 191–198.Google Scholar
Mortensen, D. A., Johnson, G. A., and Young, L. J. 1993. Weed distributions in agricultural fields. in Robert, P. and Rust, R. H., eds. Soil Specific Crop Management. Madison, WI: Agronomic Society of America, pp. 113–124.Google Scholar
Nelson, T. 1993. Spray Vision, a selective sprayer technology developed in North America. Weed Sci. Soc. Am. Abstr. 33: 43.Google Scholar
Nordmeyer, H. and Niemann, P. 1992. Möglichkeiten der gezielten Teilflächenbehandlung mit Herbiziden auf der Grundlage von Unkrautverteilung und Bodenvariabilität. Z. Pflkrankh. Pflschutz Sonderh. 13: 539–547.Google Scholar
Thompson, J. F., Stafford, J. V., and Miller, P.C.H. 1991. Potential for automatic weed detection and selective herbicide application. Crop Prot. 10: 254–259.Google Scholar
Van Groenendael, J. M. 1988. Patchy distribution of weeds and some implications for modeling population dynamics: a short literature review. Weed Res. 28: 437–441.Google Scholar
Walter, A. M. 1996. Temporal and spatial stability of weeds. Copenhagen: 2nd International Weed Congress, pp. 125–130.Google Scholar
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.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
Winkle, M. E., Leavitt, J.R.C., and Burnside, O. C. 1981. Effect of weed density on herbicide absorption and bioactivity. Weed Sci. 29: 405–409.Google Scholar
Wyse-Pester, D. Y., Mortensen, D. A., and Gotway, C. A. 1995. Statistical methods to quantify spatial stability of weed population. Proc. North Cent. Weed Control Conf. 50: 152.Google Scholar