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Using soil parameters to predict weed infestations in soybean

Published online by Cambridge University Press:  20 January 2017

Case R. Medlin ,
David R. Shaw ,
Michael S. Cox ,
Patrick D. Gerard ,
Melinda J. Abshire  and
Milton C. Wardlaw III
Case R. Medlin
Affiliation:
Department of Plant and Soil Sciences, 117 Dorman Hall, Box 9555, Mississippi State University, Mississippi State, MS 39762
Michael S. Cox
Affiliation:
Department of Plant and Soil Sciences, 117 Dorman Hall, Box 9555, Mississippi State University, Mississippi State, MS 39762
Patrick D. Gerard
Affiliation:
Experimental Statistics, 151 Dorman Hall, Box 9653, Mississippi State University, Mississippi State, MS 39762-9653
Melinda J. Abshire
Affiliation:
Department of Plant and Soil Sciences, 117 Dorman Hall, Box 9555, Mississippi State University, Mississippi State, MS 39762
Milton C. Wardlaw III
Affiliation:
Department of Plant and Soil Sciences, 117 Dorman Hall, Box 9555, Mississippi State University, Mississippi State, MS 39762
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Abstract

An understanding of environmental factors governing patchy weed distribution in fields could prove to be a valuable tool in weed management. The objectives of this research were to investigate the relationships between weed distribution patterns and environmental properties in two Mississippi soybean fields and to construct models based on those relationships to predict weed distribution. Two months before planting, fields were soil sampled on a 60- by 60-m coordinate grid, and samples were analyzed for calcium, magnesium, potassium, sodium, phosphorus, zinc, cation exchange capacity, percent organic matter, and soil pH. The relative elevation of each sample location was also recorded. Approximately 8 wk after planting, weed populations were estimated on a 30- by 30-m grid over the soil sample grid. Punctual kriging was used to estimate environmental values at each weed sample location. Discriminant analysis techniques were used to evaluate the associations between environmental characteristics on weed population densities of sample areas within each field. Generally, as sicklepod and pitted morningglory infestations increased, the prediction accuracy of the discriminant functions also increased; however, horsenettle infestations were not closely correlated to the environmental properties. Discriminant functions reasonably predicted presence or absence of sicklepod and pitted morningglory within the field. However, validation of the functions across years within the same field and with data collected from the other field resulted in poor classification of all species infestations. Prediction of weed infestations with environmental properties was specific for each field, year, and species.

Keywords

Horsenettle, Solanum carolinense L. SOLCApitted morningglory, Ipomoea lacunosa L. IPOLAsicklepod, Senna obtusifolia (L.) Irwin and Barnaby CASOBsoybean, Glycine max (L.) Merr. ‘DPL 3588’, ‘DPL 3519s’Site-specific weed managementspatial variability.
Type
Research Article
Information
Weed Science , Volume 49 , Issue 3 , June 2001 , pp. 367 - 374
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Arshad, M. A., Gill, K. S., Turkington, T. K., and Woods, D. L. 1997. Canola root rot and yield response to liming and tillage. Agron. J. 89:17–22.Google Scholar
Banks, P. A., Santlemann, P. W., and Tucker, B. B. 1976. Influence of long-term soil fertility treatments on weed species in winter wheat. Agron. J. 68:825–827.Google Scholar
Bozsa, R. C. and Oliver, L. R. 1990. Competitive mechanisms of common cocklebur (Xanthium strumarium) and soybean (Glycine max) during seedling growth. Weed Sci. 38:344–350.Google Scholar
Bozsa, R. C., Oliver, L. R., and Driver, T. L. 1989. Intraspecific and interspecific S. obtusifolia (Cassia obtusifolia) interference. Weed Sci. 37:670–673.CrossRefGoogle Scholar
Buchanan, G. A., Hoveland, C. S., and Harris, M. C. 1975. Response of weeds to soil pH. Weed Sci. 23:473–477.CrossRefGoogle 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
Creel, J. M. Jr., Hoveland, C. S., and Buchanan, G. A. 1968. Germination, growth, and ecology of sicklepod. Weed Sci. 16:396–400.Google Scholar
Dieleman, J. A., Mortensen, D. A., and Buhler, D. D. 1997. Multivariate approaches for linking field-scale variability of soil properties and weed populations. Weed Sci. Soc. Am. Abstr. 37:46.Google Scholar
Franz, E., Gebhardt, M. R., and Unklesbay, K. B. 1991. The use of local spectral properties of leaves as an aid for identifying weed seedlings in digital images. Trans. Am. Soc. Agric. Eng. 34:682–687.Google Scholar
Hagood, E. S. Jr., Bauman, T. T., Williams, J. L. Jr., and Scheiber, M. M. 1980. Growth analysis of soybean (Glycine max) in competition with velvetleaf (Abutilon theophrasti). Weed Sci. 28:729–733.Google Scholar
Isaaks, E. H. and Srivastava, R. M. 1989. Spherical models. Pages 369–399 In An Introduction to Applied Geostatistics. New York: Oxford University Press.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
Marshall, E.J.P. 1988. Field-scale estimates of grass weed populations in arable land. Weed Res. 28:191–198.CrossRefGoogle Scholar
Munger, P. H., Chandler, J. M., Cothren, J. T., and Hons, F. M. 1987. Soybean (Glycine max)-velvetleaf (Abutilon threophrasti) interspecific competition. Weed Sci. 35:647–653.Google Scholar
Pettiet, J. V. 1973. An evaluation of potassium fertilizer needs for cotton in the Yazoo-Mississippi Delta. Mississippi State, MS: Mississippi State University, Mississippi Agriculture and Forestry Experiment Station Bull. 66. 16 p.Google Scholar
[SAS] Statistical Analysis Systems. 1992. SAS/STAT User's Guide. Release 6.03 ed. Cary, NC: Statistical Analysis Systems Institute. 1,028 p.Google Scholar
Thornton, P. K., Fawcett, R. H., Dent, J. B., and Perkins, T. J. 1990. Spatial weed distribution and economic thresholds for weed control. Crop Prot. 9:337–342.CrossRefGoogle Scholar
Weaver, S. E. and Hamill, A. S. 1985. Effects of soil pH on competitive ability and leaf nutrient content of corn (Zea mays L.) and three weed species. Weed Sci. 33:447–451.Google Scholar