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Infestation and spatial dependence of weed seedling and mature weed populations in corn

Published online by Cambridge University Press:  20 January 2017

Dawn Y. Wyse-Pester ,
Lori J. Wiles  and
Philip Westra
Dawn Y. Wyse-Pester
Affiliation:
Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO 80523
Philip Westra
Affiliation:
Department of Bioagricultural Sciences and Pest Management, Colorado State University, Fort Collins, CO 80523
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Abstract

Knowing the distribution of weed seedlings in farmer-managed fields could help researchers develop reliable distribution maps for site-specific weed management. With a knowledge of the spatial arrangement of a weed population, cost effective sampling programs and management strategies can be designed, so inputs can be selected and applied to specific field areas where management is warranted. In 1997 and 1998, weeds were sampled at 612 to 682 sites in two center pivot irrigated corn fields (71 and 53 ha) in eastern Colorado. Weeds were enumerated when corn reached the two-leaf, four-leaf, and physiological maturity stages in a 76.2- by 76.2-m grid, a random-directed grid where sites were established at intervals of 76.2 m, and a star configuration based on a 7.62- by 7.62-m grid within three 23,225 m2 areas. Directional correlograms were calculated for 0, 30, 60, 90, 120, and 150° from the crop row. Fifteen weed species were observed across fields. Spatial dependence occurred in 7 of the 93 samples (a collection of sampling units for a particular weed species that was detected within a field at a particular sampling time and year) for populations of field sandbur, pigweed species, nightshade species, and common lambsquarters. Correlogram analysis indicated that 18 to 72% of the variation in sample density was a result of spatial dependence over a geographic distance not exceeding 5 to 363 m among the examined data. Because of the lack of spatial correlation for weed seedling distributions in these eastern Colorado corn fields, interpolated density maps should be based on grid sizes (separation distances) less than 7.62 m for weed seedling infestations.

Keywords

Common lambsquarters, Chenopodium album L. CHEALfield sandbur, Cenchrus longispinus (Hack.) Fern CCHPAnightshade spp., Solanum spp.pigweed spp., Amaranthus spp.corn, Zea mays L.Geostatisticsmapsamplingspatial correlogramspatial structureweed distribution
Type
Research Article
Information
Weed Science , Volume 50 , Issue 1 , February 2002 , pp. 54 - 63
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Anscombe, F. J. 1949. The statistical analysis of insect counts based on the negative binomial distribution. Biometrics 5:165–173.Google Scholar
Audsley, E. 1993. Operational research analysis of patch spraying. Crop Prot. 12:111–119.Google Scholar
Audsley, E. and Beaulah, S. A. 1996. Combining weed maps to produce a treatment map for patch spraying. Asp. Appl. Biol. 46:111–117.Google Scholar
Buntin, G. D. 1994. Developing a primary sampling program. Page 104 In Pedigo, L. P. and Buntin, G. D., eds. Handbook of Sampling Methods for Arthropods in Agriculture. Boca Raton, FL: CRC Press.Google Scholar
Cardina, J., Johnson, G. A., and Sparrow, D. H. 1997. The nature and consequence of weed spatial distribution. Weed Sci. 45:364–373.Google Scholar
Cardina, J., Sparrow, D. H., and McCoy, E. L. 1996. Spatial relationships between seedbank and seedling populations of common lambsquarters (Chenopodium album) and annual grasses. Weed Sci. 44:298–308.Google Scholar
Donald, W. W. 1994. Geostatistics for mapping weeds, with a Canada thistle (Cirsium arvense) patch as a case study. Weed Sci. 42:648–657.Google Scholar
Dotzenko, A. D., Ozkan, M., and Storer, K. R. 1969. Influence of crop sequence, nitrogen fertilizer, and herbicides on weed seed populations in sugar beet fields. Agron. J. 61:34–37.Google Scholar
Gerhards, R., Wyse-Pester, D. Y., Mortensen, D., and Johnson, G. A. 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 distribution can be mapped with kriging. Weed Res. 36:325–337.CrossRefGoogle Scholar
Howard, C. L., Morimer, A. M., Gould, P., Putwain, P. D., Cousens, R., and Cussans, G. W. 1991. The dispersal of weeds: seed movement in arable agriculture. Pages 821–828 In Proc. 1991 Brighton Crop Prot. Conf.—Weeds.Google Scholar
Isaaks, E. H. and Srivastava, R. M. 1989. An Introduction to Applied Geostatistics. New York: Oxford University Press. pp. 50–62, 142–148, 170–172Google Scholar
Iwao, S. 1968. A new regression method for analyzing the aggregation pattern of animal populations. Res. Popul. Ecol. 10:1–20.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., and Martin, A. R. 1995. A simulation of herbicide use based on weed spatial distribution. Weed Res. 35:197–205.Google Scholar
Liebhold, A. M., Rossi, R. E., and Kemp, W. P. 1993. Geostatistics and geographic information systems in applied insect ecology. Annu. Rev. Entomol. 38:303–327.Google Scholar
Midgarden, D. G., Youngman, R. R., and Fleischer, S. J. 1993. Spatial analysis of counts of western corn rootworm (Coleoptera: Chrysomelidae) adults on yellow sticky traps in corn: geostatistics and dispersion indices. Environ. Entomol. 22:1,124–1,133.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., Rust, R. H., and Larson, W. E., eds. Soil Specific Crop Management. Madison, WI: ASAA, CSSA, SSA.Google Scholar
Mulugeta, D. and Boerboom, C. M. 1999. Seasonal abundance and spatial pattern of Setaria faberi, Chenopodium album, and Abutilon theophrasti in reduced-tillage soybeans. Weed Sci. 47:95–106.Google Scholar
Nordbo, E., Christensen, S., Kristensen, K., and Walter, M. 1994. Patch spraying of weeds in cereal crops. Asp. Appl. Biol. 40:325–334.Google Scholar
Pedigo, L. P. 1994. Introduction to sampling arthropod populations. Page 9 In Pedigo, L. P. and Buntin, G. D., eds. Handbook of Sampling Methods for Arthropods in Agriculture. Boca Raton, FL: CRC Press.Google Scholar
Rossi, R. E., Mulla, D. J., Journel, A. G., and Franz, E. H. 1992. Geostatistical tools for modeling and interpreting ecological spatial dependence. Ecol. Monogr. 62:277–314.Google Scholar
Schweizer, E. E., Westra, P., and Lybecker, D. W. 1994. Controlling weeds in corn (Zea mays) rows with an in-row cultivator versus decisions made by a computer model. Weed Sci. 42:593–600.Google Scholar
Taylor, L. R. 1961. Aggregation, variance and the mean. Nature 189:732–735.Google Scholar
Turechek, W. W. and Madden, L. V. 1999. Spatial pattern analysis of strawberry leaf blight in perennial production systems. Phytopathology 89:421–433.Google Scholar
Wallace, M. K. and Hawkins, D. M. 1994. Applications of geostatistics in plant nematology. J. Nematol. 26:626–634.Google Scholar
Walter, A. M. 1996. Temporal and spatial stability of weeds. Pages 125–130 In Brown, H., Cussans, G. W., Devine, M. D., Duke, S. O., Fernandez-Quintanilla, C., Helweg, A., Labrada, R. E., Landes, M., Kudsk, P., and Streibig, J. C., eds. Proceedings of the Second International Weed Control Congress. Corvalllis, OR: Oregon State University.Google Scholar
Weisz, R., Fleischer, S., and Smilowitz, Z. 1995. Site-specific integrated pest management for high value crops: sample units for map generation using the Colorado potato beetle (Coleoptera: Chrysomelidae) as a model system. J. Econ. Entomol. 88:1,069–1,080.Google Scholar