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Modeling With Limited Data: The Influence of Crop Rotation and Management on Weed Communities and Crop Yield Loss

Published online by Cambridge University Press:  20 January 2017

Stephen R. Canner
Affiliation:
Formerly USDA–Agricultural Research Service, Agricultural Systems Research Unit, 2150 Centre Ave., Bldg. D, Fort Collins, CO 80526
L. J. Wiles*
Affiliation:
USDA–Agricultural Research Service, Water Management Research, 2150 Centre Ave., Bldg. D, Fort Collins, CO 80526
Robert H. Erskine
Affiliation:
USDA–Agricultural Research Service, Agricultural Systems Research Unit, 2150 Centre Ave., Bldg. D, Fort Collins, CO 80526
Gregory S. McMaster
Affiliation:
USDA–Agricultural Research Service, Agricultural Systems Research Unit, 2150 Centre Ave., Bldg. D, Fort Collins, CO 80526
Gale H. Dunn
Affiliation:
USDA–Agricultural Research Service, Agricultural Systems Research Unit, 2150 Centre Ave., Bldg. D, Fort Collins, CO 80526
James C. Ascough II
Affiliation:
USDA–Agricultural Research Service, Agricultural Systems Research Unit, 2150 Centre Ave., Bldg. D, Fort Collins, CO 80526
*
Corresponding author's E-mail: [email protected]

Abstract

Theory and models of crop yield loss from weed competition have led to decision models to help growers choose cost-effective weed management. These models are available for multiple-species weed communities in a single season of several crops. Growers also rely on crop rotation for weed control, yet theory and models of weed population dynamics have not led to similar tools for planning of crop rotations for cost-effective weed management. Obstacles have been the complexity of modeling the dynamics of multiple populations of weed species compared to a single species and lack of data. We developed a method to use limited, readily observed data to simulate population dynamics and crop yield loss of multiple-species weed communities in response to crop rotation, tillage system, and specific weed management tactics. Our method is based on the general theory of density dependence of plant productivity and extensive use of rectangular hyperbolic equations for describing crop yield loss as a function of weed density. Only two density-independent parameters are required for each species to represent differences in seed bank mortality, emergence, and maximum seed production. One equation is used to model crop yield loss and density-dependent weed seed production as a function of crop and weed density, relative time of weed and crop emergence, and differences among species in competitive ability. The model has been parameterized for six crops and 15 weeds, and limited evaluation indicates predictions are accurate enough to highlight potential weed problems and solutions when comparing alternative crop rotations for a field. The model has been incorporated into a decision support tool for whole-farm management so growers in the Central Great Plains of the United States can compare alternative crop rotations and how their choice influences farm income, herbicide use, and control of weeds in their fields.

Type
Weed Management
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Adcock, T. E. and Banks, P. A. 1991. Effects of preeemergence herbicides on the competitivenss of selected weeds. Weed Sci. 39:5456.CrossRefGoogle Scholar
Anderson, R. L. 2005. A multi-tactic approach to manage weed population dynamics in crop rotations. Agron. J. 97:15791583.Google Scholar
Blackshaw, R. E. 1993. Downy brome (Bromus tectorum) density and relative time of emergence affects interference in winter wheat. Weed Sci. 41:551556.CrossRefGoogle Scholar
Blackshaw, R. E. 1994. Differential competitive ability of winter wheat cultivars against downy brome. Agron. J. 86:649654.Google Scholar
Bosnic, A. C. and Swanton, C. J. 1997. Influence of barnyardgrass (Echinochloa crus-galli) time of emergence and density on corn (Zea mays). Weed Sci. 45:276282.CrossRefGoogle Scholar
Canner, S. R., Wiles, L. J., and McMaster, G. S. 2002. Weed reproduction model parameters may be estimated from crop yield loss data. Weed Sci. 50:763772.Google Scholar
Cardina, J., Regnier, E. E., and Sparrow, D. H. 1995. Velvetleaf (Abutilon theophrasti) competition and economic thresholds in conventional- and no-tillage corn (Zea mays). Weed Sci. 43:8187.Google Scholar
Cousens, R. 1985a. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239252.CrossRefGoogle Scholar
Cousens, R. 1985b. An empirical model relating crop yield to weed and crop density and a statistical comparison with other models. J. Agric. Sci. 105:513521.Google Scholar
Cousens, R., Brain, P., O'Donovan, J. T., and O'Sullivan, P. A. 1987. The use of biologically realistic equations to describe the effects of weed density and relative time of emergence on crop yield. Weed Sci. 35:720725.Google Scholar
Cousens, R. and Mortimer, M. 1995. Dynamics of Weed Populations. New York Cambridge University Press. 332.Google Scholar
Cowan, P., Weaver, S. E., and Swanton, C. J. 1998. Interference between pigweed (Amaranthus spp.), barnyardgrass (Echinochloa crus-galli), and soybean (Glycine max). Weed Sci. 46:533539.Google Scholar
Daugovish, O., Lyon, D. J., and Baltensperger, D. D. 1999. Cropping systems to control winter annual grasses in winter wheat (Triticum aestivum). Weed Technol. 13:120126.CrossRefGoogle Scholar
Dekker, J. and Meggitt, W. F. 1983. Interference between velvetleaf (Abutilon theophrasti Medic.) and soybean (Glycine max (L.) Merr.) II. Population dynamics. Weed Res. 23:103107.Google Scholar
Dieleman, A., Hamill, A. S., Weise, S. F., and Swanton, C. J. 1995. Empirical models of pigweed (Amaranthus spp.) interference in soybean (Glycine max). Weed Sci. 43:612618.Google Scholar
Doucet, C., Weaver, S. E., Hamill, A. S., and Zhang, J. 1999. Separating the effects of crop rotation from weed management on weed density and diversity. Weed Sci. 47:729735.Google Scholar
Dunan, C. M., Wiles, L. J., Anderson, R. L., and Westra, P. 1996. A new approach to modeling weed population dynamics in crop rotations. Weed Sci. Soc. Amer. Abstr. 36:82.Google Scholar
Durgan, B. R., Dexter, A. G., and Miller, S. D. 1990. Kochia (Kochia scoparia) interference in sunflower (Helianthus annuus). Weed Technol. 4:5256.Google Scholar
Fausey, J. C., Kells, J. J., Swinton, S. M., and Renner, K. A. 1997. Giant foxtail (Setaria faberi) interference in nonirrigated corn (Zea mays). Weed Sci. 45:256260.Google Scholar
Geier, P. W., Maddux, L. D., Moshier, L. J., and Stahlman, P. W. 1996. Common sunflower (Helianthus annuus) interference in soybean (Glycine max). Weed Technol. 10:317321.Google Scholar
Hall, M. R., Swanton, C. J., and Anderson, G. W. 1992. The critical period of weed control in grain corn (Zea mays). Weed Sci. 40:441447.CrossRefGoogle Scholar
Harris, T. C. and Ritter, R. L. 1987. Giant green foxtail (Setaria viridis var. major) and fall panicum (Panicum dichotomoflorum) competition in soybeans (Glycine max). Weed Sci. 35:663668.Google Scholar
Jasieniuk, M., Maxwell, B. D., Anderson, R. L., Evans, J. O., Lyon, D. J., Miller, S. D., Morishita, D. W., Ogg, A. G. Jr., Seefeldt, S., Stahlman, P. W., Northam, F. E., Westra, P., Kebede, Z., and Wicks, G. A. 1999. Site-to-site and year-to-year variation in Triticum aestivumAegilops cylindrica interference relationships. Weed Sci. 47:529537.Google Scholar
Kettler, T. A., Lyon, D. J., Doran, J. W., Powers, W. L., and Stroup, W. W. 2000. Soil quality assessment after weed-control tillage in a no-till wheat-fallow cropping system. Soil Sci. Soc. Am. J. 64:339346.Google Scholar
Kim, D. S., Brain, P., Marshall, E. J. P., and Caseley, J. C. 2002. Modelling herbicide dose and weed density effects on crop:weed competition. Weed Res. 42:113.Google Scholar
Knake, E. L. and Slife, F. W. 1969. Effect of time of giant foxtail removal from corn and soybeans. Weed Sci. 17:281283.Google Scholar
Knezevic, S. Z., Evans, S. P., Blankenship, E. E., Van Acker, R. C., and Lindquist, J. L. 2002. Critical period for weed control: the concept and data analysis. Weed Sci. 50:773786.Google Scholar
Knezevic, S. Z., Horak, M. J., and Vanderlip, R. L. 1997. Relative time of redroot pigweed (Amaranthus retroflexus L.) emergence is critical in pigweed-sorghum [Sorghum bicolor (L.) Moench] competition. Weed Sci. 45:502508.Google Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus) in corn (Zea mays). Weed Sci. 42:568573.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-velvetleaf interference relationships. Weed Sci. 44:309313.Google Scholar
Lybecker, D. W., Schweizer, E. E., and King, R. P. 1991. Weed management decisions in corn based on bioeconomic modeling. Weed Sci. 39:124129.CrossRefGoogle Scholar
Lyon, D. J. and Baltensperger, D. D. 1995. Cropping systems control winter annual grass weeds in winter wheat. J. Prod. Agric. 8:535539.CrossRefGoogle Scholar
Massee, T. W. 1976. Downy brome control in dryland winter wheat with stubble-mulch fallow and seeding management. Agron J. 68:952955.Google Scholar
McGiffen, M. E. Jr., Forcella, F., Lindstrom, M. J., and Reicosky, D. C. 1997. Covariance of cropping systems and foxtail density as predictors of weed interference. Weed Sci. 45:388396.Google Scholar
Moomaw, R. S. and Martin, A. R. 1984. Cultural practices affecting season-long weed control in irrigated corn. Weed Sci. 32:460467.Google Scholar
O'Donovan, J. T., Remy, E. Ade St, O'Sullivan, P. A., Dew, D. A., and Sharma, A. K. 1985. Influence of relative time of emergence of wild oat (Avena fatua) on yield loss of barley (Hordeum vulgare) and wheat (Triticum aestivum). Weed Sci. 33:498503.CrossRefGoogle Scholar
Pacala, S. W. 1986. Neighborhood models of plant population dynamics. 4. Single-species and multispecies models of annuals with dormant seeds. Am. Nat. 128:859878.Google Scholar
Pacala, S. W. and Silander, J. A. Jr. 1990. Field tests of neighborhood population dynamic models of two annual weed species. Ecol. Monogr. 60:113134.Google Scholar
Scholes, C., Clay, S. A., and Brix-Davis, K. 1995. Velvetleaf (Abutilon theophrasti) effect on corn (Zea mays) growth and yield in South Dakota. Weed Technol. 9:665668.Google Scholar
Schweizer, E. E. and Zimdahl, R. L. 1984. Weed seed decline in irrigated soil after six years of continuous corn (Zea mays) and herbicides. Weed Sci. 32:7683.Google Scholar
Smith, B. S., Murray, D. S., Green, J. D., Wanyahaya, W. M., and Weeks, D. L. 1990. Interference of three annual grasses with grain sorghum (Sorghum bicolor). Weed Technol. 4:245249.Google Scholar
Stahlman, P. W. and Miller, S. D. 1990. Downy brome (Bromus tectorum) interference and economic thresholds in winter wheat (Triticum aestivum). Weed Sci. 38:224228.Google Scholar
Van Acker, R. C., Swanton, C. J., and Weise, S. F. 1993. The critical period of weed control in soybean [Glycine max (L.) Merr.]. Weed Sci. 41:194200.Google Scholar
VanGessel, M. J., Schweizer, E. E., Garrett, K. A., and Westra, P. 1995. Influence of weed density and distribution on corn (Zea mays) yield. Weed Sci. 43:215218.Google Scholar
Watkinson, A. R. 1981. Interference in pure and mixed populations of Agrostemma githago L. J. Appl. Ecol. 18:967976.Google Scholar
Weiner, J. 1982. A neighborhood model of annual-plant interference. Ecology. 63:12371241.Google Scholar
Wiles, L. J., King, R. P., Schweizer, E. E., Lybecker, D. W., and Swinton, S. M. 1996. GWM: General Weed Management Model. Agric. Syst. 50:355376.Google Scholar
Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: Decision Model for Postemergence Weed Control in Soybean. Agron. J. 83:413417.Google Scholar
Wilkerson, G., Wiles, L., and Bennett, A. 2002. Weed management decision models: pitfalls, perceptions, and possibilities of the economic threshold approach. Weed Sci. 50:411424.Google Scholar
Willey, R. W. and Heath, S. B. 1969. The quantitative relationships between plant population and crop yield. Adv. Agron. 21:281321.Google Scholar
Wilson, R. G. 1993. Effect of preplant tillage, postplant cultivation, and herbicides on weed density in corn (Zea mays). Weed Technol. 7:728734.Google Scholar
Wilson, R. G. and Westra, P. 1991. Wild proso millet (Panicum miliaceum) interference in corn (Zea mays). Weed Sci. 39:217220.Google Scholar
Woolley, B. L., Michaels, T. E., Hall, M. R., and Swanton, C. J. 1993. The critical period of weed control in white bean (Phaseolus vulgaris). Weed Sci. 41:180184.Google Scholar
Zanin, G. and Sattin, M. 1988. Threshold level and seed production of velvetleaf (Abutilon theophrasti Medicus) in maize. Weed Res. 28:347352.Google Scholar