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Covariance of cropping systems and foxtail density as predictors of weed interference

Published online by Cambridge University Press:  12 June 2017

Frank Forcella
Affiliation:
USDA-ARS, North Central Soil Conservation Research Laboratory, Morris, MN 56267
Michael J. Lindstrom
Affiliation:
USDA-ARS, North Central Soil Conservation Research Laboratory, Morris, MN 56267
Donald C. Reicosky
Affiliation:
USDA-ARS, North Central Soil Conservation Research Laboratory, Morris, MN 56267

Abstract

Regression models of the effect of weed density on crop yield can form the basis of weed management programs by helping growers decide whether weed control is economically justified. However, few studies have examined whether one regression model can be used across a wide range of tillage systems and crop rotations. We used a nonlinear analysis of covariance to examine experiments conducted in 1990 and 1991 on the interaction of weed interference with conventional, fall chisel, and no-till systems, and rotations of corn, soybean, and wheat on a clay loam soil. Corn and soybean suffered heavy losses due to interference by green foxtail (a mixed population of robust purple and robust white varieties). Both tillage system and crop rotation altered the relationship between weed density and yield for corn in 1990 and 1991, but tillage was not a factor for soybean in 1991. Companion experiments on a sandy loam soil found no relationship between weed density and dryland corn yield in the drought year 1990, but weed density greatly decreased yield in irrigated corn. In 1991, the same model fit both dryland and irrigated corn grown in sandy loam soil. Foxtail density did not affect average weight per foxtail plant in any of our experiments, which indicates a lack of intraspecific competition. Competitiveness of corn better explained variation in dry weight per foxtail than did weather. Economic thresholds for foxtail interference are not constant but vary with weather, cropping system, and soil type.

Type
Weed Biology and Ecology
Copyright
Copyright © 1997 by the Weed Science Society of America 

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References

Literature Cited

Barrentine, W. L. 1974. Common cocklebur competition in soybeans. Weed Sci. 22: 600603.Google Scholar
Bauer, T. A., Mortensen, D. A., Wicks, G. A., Hayden, T. A., and Martin, A. R. 1991. Environmental variability associated with economic thresholds for soybeans. Weed Sci. 39: 564569.Google Scholar
Buhler, D. D. 1991. Influence of tillage systems on weed population dynamics and control in the northern corn belt of the United States. Adv. Agron. 1: 5160.Google Scholar
Cardina, J., Regnier, E., and Sparrow, D. 1995. Velvetleaf (Abutilon theoprasti) competition and economic thresholds in conventional- and no-tillage corn (Zea mays). Weed Sci. 43: 8187.Google Scholar
Coble, H. D. and Mortensen, D. A. 1992. The threshold concept and its application to weed science. Weed Technol. 6: 191195.Google Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107: 239252.Google Scholar
Dexter, A. G. and Evans, R. R. 1985. Environmental factors affecting weed number threshold. Weed Sci. Soc. Am. Abstr. 25: 59.Google Scholar
Draper, N. R. and Smith, H. 1981. Applied Regression Analysis. New York: J. Wiley, pp. 141192.Google Scholar
Eaton, B. J., Russ, O. G., and Feltner, K. C. 1976. Competition of velvetleaf, prickly sida, and Venice mallow in soybeans. Weed Sci. 24: 224228.CrossRefGoogle Scholar
Firbank, L. G., Cousens, R., Mortimer, A. M., and Smith, R.G.R. 1990. Effects of soil type on crop yield-weed density relationships between winter wheat and Bromus sterilis . J. Appl. Ecol. 27: 308318.Google Scholar
Forcella, F., Buhler, D. D., and McGiffen, M. E. 1994. Pest management and crop residues. in Hatfield, J. L. and Steward, B. A., eds. Advances in Soil Science: Crop Residue Management. Ann Arbor, MI: Lewis Publishers, pp. 173189.Google Scholar
Forcella, F. and Burnside, O. C. 1994. Pest management-weeds. in Hatfield, J. L. and Karlen, D. L., eds. Sustainable Agriculture Systems. Ann Arbor, MI: Lewis Publishers, pp. 157197.Google Scholar
Forcella, F. and Lindstrom, M. J. 1988. Weed seed populations in ridge and conventional tillage. Weed Sci. 36: 500503.Google Scholar
Forcella, F., Oskoui, K. E., and Wagner, S. W. 1993. Application of weed seedbank ecology to low-input crop management. Ecol. Appl. 3: 7483.Google Scholar
Goldfield, S. M. and Quandt, R. E. 1965. Some tests for homoscedasticity. J. Am. Stat. Assoc. 60: 539547.Google Scholar
Lindquist, J. L., Mortensen, D. A., Clay, S. A., Schmenk, R., Kells, J. J., Howatt, K., and Westra, P. 1996. Stability of corn (Zea mays)-velvetleaf (Abutilon theophrasti) interference relationships. Weed Sci. 44: 309313.Google Scholar
Lindstrom, M. J. and Forcella, F. 1988. Tillage and residue management effects on crop production in the northwestern Corn Belt. in Unger, P. W., Sneed, T. V., Jordan, W. R., and Jensen, R., eds. Challenges in Dryland Agriculture. College Station, TX: Texas Agricultural Experiment Station, pp. 565567.Google Scholar
Little, T. M. and Hills, F. J. 1978. Agricultural Experimentation. New York: J. Wiley, pp. 285293.Google Scholar
Mead, R., Curnow, R. N., and Hasted, A. M. 1993. Statistical Methods in Agricultural and Experimental Biology. 2nd ed. London: Chapman and Hall, pp. 213240.Google Scholar
Milliken, G. A. and DeBruin, R. L. 1978. A procedure to test hypotheses for nonlinear models. Commun. Stat. Theor. Meth. A7: 6579.Google Scholar
Morin, C., Blanc, D., and Darmency, H. 1993. Limits of a simple weed model to predict yield losses in maize. Weed Res. 33: 261268.Google Scholar
Mumford, J. D. and Norton, G. A. 1984. Economics of decision making in pest management. Ann. Rev. Entomol. 29: 157174.Google Scholar
Oliver, L. R., Chandler, J. M., and Buchanan, G. A. 1991. Influence of geographic region on jimsonweed (Datura stramonium) interference in soybeans (Glycine max) and cotton Gossypium hirsutum). Weed Sci. 39: 585589.Google Scholar
Orwick, P. L. and Schreiber, M. M. 1979. Interference of redroot pigweed (Amaranthus retroflexus) and robust foxtail (Setaria viridis var. robusta-alba or var. robust-purpurea) in soybeans (Glycine max). Weed Sci. 27: 665674.Google Scholar
Ramanathan, R. 1989. Introductory Econometrics with Applications. New York: Harcourt Brace Javanovich. 613 p.Google Scholar
Ricklefs, R. E. 1979. Ecology. New York: Chiron Press, pp. 548588.Google Scholar
[SAS] Statistical Analysis Systems. 1993. SAS/ETS User&s Guide. Version 6, 2nd ed. Cary, NC: Statistical Analysis Systems Institute, pp. 509684.Google Scholar
Searle, S. R., Speed, F. M., and Milliken, G. A. 1980. Population marginal means in the linear model: an alternative to least squares means. Am. Stat. 34: 216221.Google Scholar
Sokal, R. R. and Rohlf, F. J. 1969. Biometrics. San Francisco: W.H. Freeman, pp. 404493.Google Scholar
Spitters, C.J.T. and Aerts, R. 1983. Simulation for light and water in crop-weed associations. Aspects Appl. Biol. 4: 467475.Google Scholar
Wiley, R. W. and Heath, S. B. 1969. The quantitative relationships between plant population and crop yield. Adv. Agron. 21: 281321.Google Scholar
Williams, J. L. Jr., and Wicks, G. A. 1978. Weed control problems associated with crop residue systems. in Crop Residue Management. Madison, WI: American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, pp. 165172.Google Scholar