Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-26T02:23:44.731Z Has data issue: false hasContentIssue false
  • English
  • Français

A New Approach To Determine When to Control Weeds

Published online by Cambridge University Press:  12 June 2017

Antonio Berti ,
Claudio Dunan ,
Maurizio Sattin ,
Giuseppe Zanin  and
Philip Westra
Antonio Berti
Affiliation:
Centro di Studio sulla Biologia ed il Controllo delle Piante Infestanti-C.N.R., AGRIPOLIS, 35020 Legmare (Padova), Italy
Claudio Dunan
Affiliation:
Dep. Plant Pathology and Weed Science, Colorado State University, Fort Collins, CO 80523
Maurizio Sattin
Affiliation:
Centro di Studio sulla Biologia ed il Controllo delle Piante Infestanti-C.N.R., AGRIPOLIS, 35020 Legmare (Padova), Italy
Giuseppe Zanin
Affiliation:
Univ. Padova, AGRIPOLIS, 35020 Legmare (Padova), Italy
Philip Westra
Affiliation:
Dep. Plant Pathology and Weed Science, Colorado State University, Fort Collins, CO 80523
Get access Rights & Permissions [Opens in a new window]

Abstract

A methodological approach to determine the optimum time to control weeds that integrates aspects of weed biology, weed-crop competition, and economics is presented. The approach is based on the concept of Time Density Equivalent: this is defined as the density of weed plants that germinate with the crop and compete until harvest that causes the same yield loss caused by a group of weeds with a given density, time of emergence, and time of removal. A model was developed that accounts for pattern of weed emergence and permits determination of timing of weed control that minimizes economic loss due to weeds emerging both before and after treatments. The outcomes of the model are presented with two examples: corn in competition with velvetleaf and soybean in competition with Amaranthus cruentus. For both crops, six different weed control strategies involving preemergence, chemical, and mechanical postemergence treatments are considered. The results obtained with the model are compared with the calculation of net margin based on assumptions of simultaneous emergence of crop and weeds and no effect of different times of control. Different control strategies are compared considering not only maximum net margin but also its dependence on time of control, because a strategy with a lower value of maximum net margin, but a flatter net margin curve, allows more flexibility of time of control.

Keywords

Dicamba, 3,6,dichloro-2-methoxybenzoic acidfomesafen, 5-[2-chloro-4-(trifluoromethyl)phenoxy]-N-(methylsulfonyl)-2-nitrobenzamidelinuron, N′-(3,4-dichlorophenyl)-N-methoxy-N-methylureaterbuthylazine, 6-chloro-N-(1,1-dimethylethyl)-N′-ethyl-1,3,5-triazine-2,4-diamineAmaranthus cruentus L. ♯ AMACRvelvetleaf, Abutilon theophrasti Mcdicus, ABUTHcorn, Zea mays L.'Dekalb XL72A'soybean, Glycine max(L.) Merrill ‘Hodgson.’Modelscompetitioneconomic critical periodtime density equivalentweed emergence
Type
Weed Biology and Ecology
Information
Weed Science , Volume 44 , Issue 3 , September 1996 , pp. 496 - 503
Copyright
Copyright © 1996 by the 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

1. Adcock, T. E. and Banks, P. A. 1991. Effects of preemergence herbicides on the competitiveness of selected weeds. Weed Sci. 39: 54–56.Google Scholar
2. Berti, A. and Zanin, G. 1994. Density Equivalent: a method for forecasting yield loss caused by mixed weed populations. Weed Res. 34: 327–332.Google Scholar
3. Berti, A., Sattin, M., and Zanin, G. 1990. Soybean—Amaranthus cruentus L.: effects of the period of duration of competition. Session 5:P01 in Proc. First Congress of the European Society of Agronomy. Paris, France.Google Scholar
4. Capri, E., Trevisan, M., Bergamaschi, E., and Del Re, A.A.M. 1993. Dissipation of acetanilide and triazine herbicides in Italian soils. Field data set. Pages 795–800 in Proc. Brighton Crop Prot. Conf — Weeds. Brighton, U.K.Google Scholar
5. Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107: 239–252.CrossRefGoogle Scholar
6. Cousens, R., Brain, P., O'Donovan, J. T., and O'Sullivan, P. A. 1987. The use of biologically realistic equations to describe the effect of weed density and elative time of emergence on crop yield. Weed Sci. 35: 720–725.CrossRefGoogle Scholar
7. Cousens, R. 1988. Misinterpretations of results in weed research through inappropriate use of statistics. Weed Res. 28: 281–289.Google Scholar
8. Dunan, C. M., Westra, P., Schweizer, E. E., Lybecker, D. W., and Moore, F. D. 1995. The concept and application of Early Economic Period threshold: the case of DCPA in onions (Allium cepa). Weed Sci. 43: 634–639.Google Scholar
9. Hall, M. R., Swanton, C. J., and Anderson, G. 1992. The critical period of weed control in grain corn (Zea mays). Weed Sci. 40: 441–447.Google Scholar
10. Nieto, J. H., Brando, M. A., and Gonzales, J. T. 1968. Critical periods of the crop growth cycle for competition from weeds. Pest Artic. News Summ. 14: 159.Google Scholar
11. Norris, R. F. 1992. Have ecological and biological studies improved weed control strategies? Pages 7–33 in Proc. First Int. Weed Control Congress, Vol I, Monash Univ., Melbourne, Australia.Google Scholar
12. Sattin, M., Zanin, G., and Berti, A. 1992. Case history for weed competition/population ecology: velvetleaf (Abutilon theophrasti) in corn (Zea mays). Weed Technol. 6: 213–219.Google Scholar
13. Söderqvist, T. 1994. The cost of meeting a drinking water quality standard: the case of atrazine in Italy. Pages 151–171 in Bergman, L. and Pugh, D. M., eds. Environmental toxicology, economics and institutions. The atrazine case study. Kluwer Academic Publishers, Dordrecht, The Netherlands.Google Scholar
14. Van Acker, R. C., Swanton, C. J., and Weise, S. F. 1993. The critical period of weed control in soybeans. Weed Sci. 41: 194–200.Google Scholar
15. Walker, A. 1987. Herbicide persistance in soil. Rev. Weed Sci. 3: 1–17.Google Scholar
16. Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: decision model for postemergence weed control in soybean. Agron. J. 83: 413–417.Google Scholar
17. Wilson, B. J. 1986. Yield responses of winter cereals to the control of broadleaved weeds. Pages 75–82 in Proc. European Weed Research Society Symp., Economic Weed Control, Stuttgart, Germany.Google Scholar
18. Wooley, B. L., Micharls, T., Michael, M. R., and Swanton, C. J. 1993 The critical period of weed control in white bean (Phaseolus vulgaris). Weed Sci. 41: 180–184.CrossRefGoogle Scholar
19. Zanin, G., Berti, A. and Sattin, M. 1989. Mais (Zea mays L.)—Abutilon theophrasti Medicus: Effetto della durata e del periodo di competizione. Riv. Agron. 23: 185–192.Google Scholar
20. Zimdahl, R. L. 1988. The concept and application of the critical weed-free period. Pages 145–155 in Altieri, M. and Liebman, M., eds. Weed Management in Agroecosystems: Ecological Approaches. CRC Press, Boca Raton, FL.Google Scholar