Hostname: page-component-cd9895bd7-dzt6s Total loading time: 0 Render date: 2024-12-22T19:32:23.816Z Has data issue: false hasContentIssue false
  • English
  • Français

Validation of a Management Program Based on A Weed Cover Threshold Model: Effects on Herbicide Use and Weed Populations

Published online by Cambridge University Press:  20 January 2017

Marie-Josée Simard ,
Bernard Panneton ,
Louis Longchamps ,
Claudel Lemieux ,
Anne Légère  and
Gilles D. Leroux
Marie-Josée Simard*
Affiliation:
Agriculture and Agri-Food Canada (AAFC), Soils and Crops Research and Development Centre, 2560 Boul. Hochelaga, QC G1V 2J3, Canada
Bernard Panneton
Affiliation:
AAFC, Horticulture Research and Development Centre, 430 Gouin Boulevard, Saint-Jean-sur-Richelieu, QC J3B 3E6, Canada
Louis Longchamps
Affiliation:
Département de phytologie, Université Laval, QC G1V 0A6, Canada
Claudel Lemieux
Affiliation:
Agriculture and Agri-Food Canada (AAFC), Soils and Crops Research and Development Centre, 2560 Boul. Hochelaga, QC G1V 2J3, Canada
Anne Légère
Affiliation:
AAFC, Saskatoon Research Centre, 107 Science Place, Saskatoon, SK S7N 0X2, Canada
Gilles D. Leroux
Affiliation:
Département de phytologie, Université Laval, QC G1V 0A6, Canada
*
Corresponding author's E-mail: [email protected] © Her Majesty the Queen in Right of Canada, as represented by the Minister of Agriculture and Agri-Food Canada.
Get access Rights & Permissions [Opens in a new window]

Abstract

Weed management decisions based on weed threshold models offer the opportunity to reduce herbicide use by allowing the possibility of forgoing treatment or lowering rates. Weed thresholds based on a relative leaf-cover model were tested during a 4-yr period at two locations. Two 1.62-ha fields, planted to conventional and glyphosate-resistant corn (2004, 2005, 2007) or soybean (2006), were divided in 900 m2 sections. Herbicides were applied postemergence to each of these sections with either variable rates based on weed thresholds, or constant full rates. Variable herbicide rates included: no application, half rate, or full rate. Relative weed cover values of 0.2 and 0.4 (corn) or 0.1 and 0.3 (soybean) served as thresholds for incremental rates. Digital images were used to evaluate the relative weed cover. Weed density was assessed before and after herbicide application. Weed seed production was estimated for two species in 2004 and 2005. No difference in crop yield, relative weed cover, weed density, or weed seed production was observed between conventional and glyphosate-resistant cropping systems. During the first year, herbicide use reduction was obtained (−85.4%) with marginal crop yield loss (5 to 15%). In the subsequent 3 yr, preherbicide weed densities increased and concomitant increases in relative weed cover values did not allow more than a 10% overall reduction in herbicide use. This threshold model designed to maintain crop yields within a given year did not allow significant reduction in herbicide use during the following 3 yr. Residual weed populations most likely replenished the seed bank to levels that allowed weed densities to increase afterward. Increased weed density over time in plots treated with full rates of herbicide every year also indicated that a single postemergence herbicide treatment was not sufficient to contain weed populations at low levels every year in this corn–soybean rotation.

Keywords

Glyphosatecorn, Zea mays L.soybean, Glycine max (L.) MerrDecision support systemimage analysiscrop yield losssite-specific weed managementresidual weed population
Type
Weed Management
Information
Weed Science , Volume 57 , Issue 2 , April 2009 , pp. 187 - 193
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Berti, A. and Sattin, M. 1996. Effect of weed position on yield loss in soyabean and a comparison between relative weed cover and other regression models. Weed Res. 36:249–258.CrossRefGoogle Scholar
Blackshaw, R. E., O'Donovan, J. T., Harker, K. N., Clayton, G. W., and Stougaard, R. N. 2006. Reduced herbicide doses in field crops: a review. Weed Biol. Manag. 6:10–17.CrossRefGoogle Scholar
Blanco-Moreno, J. M., Chamorro, L., Masalles, R. M., Recasens, J., and Sans, F. X. 2004. Spatial distribution of Lolium rigidum seedlings following seed dispersal by combine harvesters. Weed Res. 44:375–387.Google Scholar
Buhler, D. D., Doll, J. D., Proost, R. T., and Visocky, M. R. 1995. Integrating mechanical weeding with reduced herbicide use in conservation tillage corn production systems. Agron. J. 87:507–512.CrossRefGoogle Scholar
Bussan, A. J., Boerboom, C. M., and Stoltenberg, D. E. 2001. Response of velvetleaf demographic processes to herbicide rate. Weed Sci. 49:22–30.CrossRefGoogle Scholar
Cardina, J., Regnier, E., and Sparrow, D. 1995. Velvetleaf (Abutilon theophrasti) competition and economic thresholds in conventional- and no-tillage corn (Zea mays). Weed Sci. 43:81–87.Google Scholar
Cousens, R. 1985a. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.Google 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. 105:513–521.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. 35:720–725.Google Scholar
Evans, S. P., Knezevic, S. Z., Lindquist, J. L., Shapiro, C. A., and Blankenship, E. E. 2003. Nitrogen application influences the critical period for weed control in corn. Weed Sci. 51:408–417.CrossRefGoogle Scholar
Forcella, F. 2000. Rotary hoeing substitutes for two-thirds rate of soil-applied herbicide. Weed Technol. 14:298–303.Google Scholar
Gressel, J. 2002. Molecular Biology of Weed Control. London, UK Taylor & Francis. 504.CrossRefGoogle 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. 40:441–447.CrossRefGoogle Scholar
Hamill, A. S., Weaver, S. E., Sikkema, P. H., Swanton, C. J., Tardif, F. J., and Ferguson, G. M. 2004. Benefits and risks of economic vs. efficacious approaches to weed management in corn and soybean. Weed Technol. 18:723–732.Google Scholar
Harland, J. and Wilkinson, T. T. 1882. Lancashire Legends, Traditions, Pageants, Sports, &c. London, UK John Heywood. 283.Google Scholar
Knezevic, S. Z., Evans, S. P., Blankenship, E. E., VanAcker, R. C., and Lindquist, J. L. 2002. Critical period of weed control: the concept and data analysis. Weed Sci. 50:773–786.Google Scholar
Knezevic, S. Z., Evans, S. P., and Mainz, M. 2003. Row spacing influences critical time of weed removal in soybean. Weed Technol. 17:666–673.Google Scholar
Knezevic, S. Z., Wiese, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus) in corn (Zea mays). Weed Sci. 42:568–573.Google Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1995. Comparison of empirical models depicting density of Amaranthus retroflexus L. and relative leaf area as predictors of yield loss in maize (Zea mays L.). Weed Res. 35:207–214.CrossRefGoogle Scholar
Kropff, M. J. and Spitters, C. J. T. 1991. A simple model of crop loss by weed competition from early observations on relative leaf area of the weeds. Weed Res. 31:97–105.Google Scholar
Lemieux, C., Vallée, L., and Vanasse, A. 2003. Predicting yield loss in maize fields and developing decision support for post-emergence herbicide applications. Weed Res. 43:323–332.Google Scholar
Lotz, L. A. P., Christensen, S., and Cloutier, D. 1996. Prediction of the competitive effects of weeds on crop yields based on relative leaf area of weeds. Weed Res. 36:93–101.Google Scholar
Lotz, L. A. P., Kropff, M. J., Wallinga, J., Bos, H. J., and Groeneveld, R. M. W. 1994. Techniques to estimate relative leaf area and cover of weeds in crops for yield loss prediction. Weed Res. 34:167–175.CrossRefGoogle Scholar
Neve, P. and Powles, S. B. 2005a. High survival frequencies at low herbicide use rates in populations of Lolium rigidum result in rapid evolution of herbicide resistance. Heredity. 95:485–492.Google Scholar
Neve, P. and Powles, S. B. 2005b. Recurrent selection with reduced herbicide rates results in the rapid evolution of herbicide resistance in Lolium rigidum . Theor. Appl. Genet. 110:1154–1166.Google Scholar
Ngouajio, M., Lemieux, C., Fortier, J-J., Careau, D., and Leroux, G. D. 1998. Validation of an operator-assisted module to measure weed and crop leaf cover by digital image analysis. Weed Technol. 12:446–453.Google Scholar
Ngouajio, M., Lemieux, C., and Leroux, G. D. 1999a. Prediction of corn (Zea mays) yield loss from early observations of the relative leaf area and the relative leaf cover of weeds. Weed Sci. 47:297–304.Google Scholar
Ngouajio, M., Leroux, G. D., and Lemieux, C. 1999b. A flexible sigmoidal model relating crop yield to weed relative leaf cover and its comparison with nested models. Weed Res. 39:329–343.Google Scholar
Norris, R. F. 1999. Ecological implications of using thresholds for weed management. J. Crop Prod. 2:31–58.Google Scholar
Roggenkamp, G. J., Mason, S. C., and Martin, A. R. 2000. Velvetleaf (Abutilon theophrasti) and green foxtail (Setaria viridis) response to corn (Zea mays) hybrid. Weed Technol. 14:304–311.Google Scholar
Spitters, C. J. T., Kropff, M. J., and De Groot, W. 1989. Competition between maize and Echinochloa crus-galli analysed by a hyperbolic regression model. Ann. Appl. Biol. 115:541–551.CrossRefGoogle Scholar
Swanton, C. J., Weaver, S., Cowan, P., Van Acker, R., Deen, W., and Shreshta, A. 1999. Weed thresholds: theory and applicability. J. Crop Prod. 2:9–29.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. 41:194–200.Google Scholar
Wallinga, J., Groeneveld, R. M. W., and Lotz, L. A. P. 1998. Measures that describe weed spatial patterns at different levels of resolution and their applications for patch spraying of weeds. Weed Res. 38:351–359.Google Scholar
Zhang, J., Weaver, S. E., and Hamill, A. S. 2000. Risks and reliability of using herbicides at below-labeled rates. Weed Technol. 14:106–115.Google Scholar