Hostname: page-component-848d4c4894-5nwft Total loading time: 0 Render date: 2024-05-14T21:08:31.861Z Has data issue: false hasContentIssue false

Integrating crop management and crop rotation effects into models of weed population dynamics: a review

Published online by Cambridge University Press:  12 June 2017

Philippe Debaeke
Affiliation:
Station d'Agronomie, INRA, BP 27, 31326 Castanet-Tolosan Cedex, France

Abstract

Current weed demography models were reviewed to evaluate how the effects of cultural practices on weed dynamics were integrated into the models and to suggest possible ways to improve the simulation of cropping system effects. Several models were chosen to illustrate the interactions between cropping systems and weed dynamics. The first one described the structure of the weed life cycle. The second model integrated the effects of a wide set of cultural practices; the comparison of this example with other models suggested how the integration of cropping system effects could be improved. The last two models introduced the interactions of cultural practices with intraplot weed variability, either spatial variability of weed densities or genetic and phenotypic variability within weed populations. This review indicates some ways to make weed population models more comprehensive, robust, and accurate in order to improve their contribution to the evaluation and management of cropping systems.

Type
Special Topics
Copyright
Copyright © 1998 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

Aarts, H.F.M. 1986. A computerized model for predicting changes in a population of Galium aparine . Pages 277284 in Proceedings of the EWRS Symposium on Economic Weed Control, Stuttgart. Wageningen, The Netherlands: EWRS.Google Scholar
Ball, D. A. and Shaffer, M. J. 1993. Simulating resource competition in multispecies agricultural plant communities. Weed Res. 33: 299310.Google Scholar
Bailaré, C. L., Scopel, A. L., Ghersa, C. M., and Sanchez, R. A. 1987. The population ecology of Datura ferox in soybean crops. A simulation approach to incorporating seed dispersal. Agric. Ecosyst. Environ. 19: 177188.Google Scholar
Blom, C.W.P.M. 1988. The realism of models in plant demography. Acta Bot. Neerl. 37: 421438.Google Scholar
Boiffin, J., Dubrulle, P., Durr, C., and Richard, G. 1994. Modelling sugar-beer seedling emergence and early growth. Pages 11431148 in Proceedings of the 13th International Conference “Soil tillage for crop production and protection of the environment”—International Soil Tillage Research Organization.Google Scholar
Brust, G. E. and House, G. J. 1988. Weed seed destruction by arthropods and rodents in low-input soybean. Agroecosystems 3: 1925.Google Scholar
Chauvel, B., Angonin, C., and Colbach, N. 1996. Black-grass (Alopecurus myosuroides Huds.) development and seed production in wheat. Pages 528529 in Proceedings of the 4th ESA Congress, Veldhoven-Wageningen. Wageningen, The Netherlands: European Society for Agronomy.Google Scholar
Chauvel, B. and Gasquez, J. 1993. Influence des facteurs culturaux sur la dynamique du vulpin (Alopecurus myosuroides Huds.). Pages 4349 in Thomas, J. M., ed. Communications de la 4ème Conférence Internationale IFOAM, Dijon. Quetigny, France: ENITA.Google Scholar
Colbach, N. and Meynard, J. M. 1996. Modelling the influence of cropping system on gene flow for herbicide resistant rapeseed. Presentation of model structure. Pages 223230 in 10ème Colloque International sur la Biologie des Mauvaises Herbes, Dijon. Paris: Association Nationale pour la Protection des Plantes.Google Scholar
Coulomb, I. 1991. Analyse quantitative du comportement du sol au labour: rǒle de l'état structural initial. Thèse de Doctorat de l'Institut National Agronomique Paris-Grignon.Google Scholar
Cousens, R. 1986. The use of populations in the study of the economics of weed control. Pages 269276 in Proceedings of the EWRS Symposium on Economic Weed Control, Stuttgart.Google Scholar
Cousens, R., Cussans, G. W., and Wilson, J. 1987. Modeling weed populations in cereals. Rev. Weed Sci. 3: 93112.Google Scholar
Cousens, R., Doyle, C. J., Wilson, B. J., and Cussans, G. W. 1986. Modelling the economics of controlling Avena fatua in winter wheat. Pestic. Sci. 17: 112.Google Scholar
Cousens, R. and Mortimer, M. 1995. Dynamics of Weed Populations. Cambridge, Great Britain: Cambridge University Press. 332 p.Google Scholar
Cousens, R. and Moss, S. R. 1990. A model of the effects of cultivation on the vertical distribution of weed seeds within the soil. Weed Res. 30: 6170.Google Scholar
Cussans, G. W. and Moss, S. R. 1982. Population dynamics of annual grass weeds. Pages 9198 in Proceedings of the British Crop Protection Symposium ‘Decision making in the practice of crop protection,’ BCPC Farnham. Lavenham: Lavenham Press.Google Scholar
Danuso, F. and Zanin, G. 1989a. Simulazione della dinamica di popolazioni di malerbe annuali in colture erbacee. I. Descrizione del modello ‘WEPOM.’ Riv. Agron. 23: 466476.Google Scholar
Danuso, F. and Zanin, G. 1989b. Simulazione della dinamica di popolazioni di malerbe annuali in colture erbacee. II. Ottimizzazione degli interventi di post-emergenza contro l'Abutilon theophrasti Medicus nel mais. Riv. Agron. 23: 477485.Google Scholar
Debaeke, Ph. 1988. Modélisation de l'évolution à long terme de la flore adventice. II. Application à trois dicotylédones annuelles en un site donné. Agronomie 8: 767777.Google Scholar
Debaeke, Ph. and Barralis, G. 1988. Essai de modélisation de l'évolution du stock semencier: application à une dicotylédone adventice Anagallis arvensis L. sur 3 sites pédoclimatiques. Pages 91102 in 8ème Colloque International sur la Biologie, l'Ecologie et la Systématique des Mauvaises Herbes, Dijon. Paris: ANPP.Google Scholar
Debaeke, Ph. and Sebillotte, M. 1988. Modélisation de l'évolution à long terme de la flore adventice. I. Construction d'un modèle descriptif de l'évolution quantitative du stock de semences de l'horizon travaillé. Agronomie 8: 393403.Google Scholar
Doyle, C. J. 1991. Mathematical models in weed management. Crop Prot. 10: 432444.Google Scholar
Doyle, C. J., Cousens, R., and Moss, S. R. 1986. A model of the economics of controlling Alopecurus myosuroides Huds in winter wheat. Crop Prot. 5: 143150.Google Scholar
Dunan, C. M., Moore, F. D. III, and Westra, P. 1994. A plant process-economic model for wild oats management decisions in irrigated barley. Agric. Syst. 45: 355368.Google Scholar
Fawcett, R. S. and Slife, F. W. 1978. Effects of field applications of nitrate on weed seed germination and dormancy. Weed Sci. 26: 594596.Google Scholar
Fitt, D. L., Gregory, P. H., Todd, A. D., McCartney, H. A., and Macdonald, O. C. 1987. Spore dispersal and plant disease gradients: a comparison between two empirical models. J. Phytopathol. 118: 227242 Google Scholar
Froud-Williams, R. J., Chancellor, R. J., and Drennan, D. S. 1984. The effects of seed burial and soil disturbance on emergence and survival of arable weeds in relation to minimal cultivation. J. Appl. Ecol. 21: 629641.Google Scholar
Gonzalez-Andujar, J. L. and Fernandez-Quintanilla, C. 1991. Modelling the population dynamics of Avena sterilis under dry-land cereal cropping systems. J. Appl. Ecol. 28: 1627.Google Scholar
Gonzalez-Andujar, J. L. and Fernandez-Quintanilla, C. 1993. Strategies for the control of Avena sterilis in winter wheat production systems in central Spain. Crop Prot. 12: 617623.Google Scholar
Graf, B., Gutierrez, A. P., Rakotobe, O., Zahner, P., and Delucchi, V. 1990. A simulation model for the dynamics of rice growth and development. II. The competition with weeds for nitrogen and light. Agric. Syst. 32: 367392.Google Scholar
Grundy, A. C., Froud-Williams, R. J., and Boatman, N. D. 1993. The use of cultivar, crop seed rate and nitrogen level for the suppression of weeds in winter wheat. Proc. Brighton Crop Prot. Conf. Weeds 997–1002.Google Scholar
Grundy, A. C., Mead, A., and Bond, W. 1996. Modelling the effect of weed-seed distribution in the soil profile on seedling emergence. Weed Res. 36: 375384.CrossRefGoogle Scholar
Hofsretter, W. 1986. Untersuchungen zur Schadwirkung und zur Populationsdynamik von Einjährigem Bingelkraur (Mercurialis annua L). Dissertation, Universität Gießen. 207 p.Google Scholar
Holzmann, A. and Niemann, P. 1988. Prognose der Verunkrautung mit Viola arvensis auf der Basis populationsdynamischer Parameter. Z. Pflkrankh. Pflschutz, Sonderheft XI: 9196.Google Scholar
Howard, C. L., Mortimer, A. M., Gould, P, Putwain, P. D., Cousens, R., and Cussans, G. W. 1991. The dispersal of weeds: seed movement in arable agriculture. Proc. Br. Crop Prot. Cont. Weeds, Brighton, pp. 821828.Google Scholar
Jordan, N. 1993. Simulation analysis of weed population dynamics in ridgerilled fields. Weed Sci. 41: 468474.Google Scholar
Jordan, N., Mortensen, D. A., Prenzlow, D. M., and Cox, K. C. 1995. Simulation analysis of crop rotation effects on weed seed banks. Am. J. Bor. 82: 290398.Google Scholar
Kiniry, J. R., Williams, J. R., Gassman, P. W., and Debaeke, Ph. 1992. A general, process-orientated model for two competing plant species. Trans. ASAE 35: 801810.Google Scholar
Kropff, M. J. 1993. Eco-physiological models for crop-weed competition. Pages 2532 in Kropff, M. K. and van Laar, H. H., eds. Modelling Crop—Weed Interactions. Wallingford, CT: CAB International.Google Scholar
Kropff, M. J. and Spitters, C.J.T. 1992. An eco-physiological model for interspecific competition, applied to the influence of Chenopodium album L. on sugar beet. I. Model description and parametrization. Weed Res. 32: 437450.Google Scholar
Kropff, M. J., Wallinga, J., and Lotz, L.A.P. 1996. Weed population dynamics. Pages 314 in Proceedings of the Second International Weed Control Congress, Copenhagen, Denmark, June 25–28, 1996. Slagelse, Denmark: Department of Weed Control and Pesticide Ecology.Google Scholar
Lintell-Smith, G., Watkinson, A. R., and Firbank, L. G. 1991. The effects of reduced nitrogen and weed-weed competition on the populations of three common cereal weeds. Proc. Br. Crop Prot. Conf. Weeds, Brighton 1991: 135140.Google Scholar
Manlove, R. J., Mortimer, A. M., and Putwain, P. C. 1982. Modelling wild oat populations and their control. Proc. Br. Crop Prot. Conf. Weeds, Brighton, pp. 749756.Google Scholar
Maxwell, B. D., Roush, M. L., and Radosevich, S. R. 1990. Predicting the evolution and dynamics of herbicide resistance in weed populations. Weed Technol. 4: 213.Google Scholar
Melander, B. 1993. Population dynamics of Apera spica-venti as influenced by cultural methods. Proc. Br. Crop Prot. Conf. Weeds, Brighton, pp. 107112.Google Scholar
Mortensen, D. A. and Coble, H. D. 1991. Two approaches to weed control decision software. Weed Technol. 5: 445452.Google Scholar
Mortensen, D. A., Martin, A. R., Harvill, T. E., and Bauer, T. A. 1993. The influence of rotational diversity on economic optimum thresholds in soybean. Pages 815823 in Proceedings of the 8th EWRS Symposium ‘Quantitative approaches in weed and herbicide research and their practical application,’ Braunschweig. Wageningen, The Netherlands: EWRS.Google Scholar
Moss, S. R. 1979. The influence of tillage and method of straw disposal on the survival and growth of black-grass, Alopecurus myosuroides, and its control by chlortoluron and isoproturon. Ann. Appl. Biol. 91: 91100.Google Scholar
Moss, S. R. 1980. Some effects of burning straw on seed viability, seedling establishment and control of Alopecurus myosuroides Huds. Weed Res. 20: 271276.Google Scholar
Moss, S. R. 1985. The influence of crop variety and seed rate on Alopecurus myosuroides competition in winter cereals. Proc. Br. Crop Prot. Conf. Weeds, pp. 701708.Google Scholar
Moss, S. R. 1990. The seed cycle of Alopecurus myosuroides Huds. in winter cereals: a quantitative analysis. Pages 2736 in Proceedings of the EWRS Symposium ‘Integrated weed management in cereals,’ Helsinki. Wageningen, The Netherlands: EWRS.Google Scholar
Pollard, F. 1982. A computer model for predicting changes in a population of Bromus sterilis in continuous winter cereals. Proc. Br. Crop. Prot. Conf. Weeds, Brighton, pp. 979979.Google Scholar
Rauber, R. and Koch, W. 1975. Zur Populationsdynamik des Flughafers (Avena fatua L.) unter dem Aspekt der langfristigen Befallsprognose. Pages 113123 in Proceedings of the EWRS Symposium “Status and control of grasseeds in Europe,” Paris. Wageningen, The Netherlands: EWRS.Google Scholar
Roberts, H. A. and Feast, P. M. 1973. Emergence and longevity of seeds of annual weeds in cultivated and undisturbed soil. J. Appl. Ecol. 10: 133143.Google Scholar
Roger-Estrade, J. 1995. Modélisation de l'évolution à long terme de l'état structural des parcelles labourées. Contribution à l'analyse des effets des systèmes de culture. Thèse de Doctorat de I'INA-PG. 185 p.Google Scholar
Röttele, M. 1980. Populationskynamik des Klettenlabkrautes (Galium aparine L.). Dissertation, Universität Hohenheim. 133 p.Google Scholar
Röttele, M. and Koch, W. 1981. Verreilung von Unkrautsamen im Boden und Konsequenzen für die Bestimmung der Samendichte. Z. Pflkrank. Pflschutz, Sonderheft IX: 383391.Google Scholar
Schneider, W., Walter, H., Koch, W., and Kemmer, A. 1984. Möglichkeiten und Probleme der Integration ackerbaulicher Maßnahmen zur Unkrautbekämpfung im realen Betrieb—Beispiel aus dem Unterland, Baden-Würrremberg. Z. Pflkrank. Pflschutz, Sonderheft X: 241257.Google Scholar
Schweizer, E. E., Lybecker, D. W., Wiles, L. J., and Westra, P. 1993. Bio-economic weed management models in crop producrion. Pages 103107 in International Crop Science I. Madison, WI: Crop Science Society of America.Google Scholar
Spitters, C.J.T. and Aerts, R. 1983. Simulation of competition for light and water in crop-weed associations. Aspects Appl. Biol. 4: 467483.Google Scholar
Springensguth, W. 1960. Untersuchungen über die Anwendung von Kalkstickstoff und Kalidüngemitteln zur Bekämpfung des Ackerfuchsschwanzes (Alopecurus agrestis L.). Z. Acker-Pflanzenb. 110: 6981.Google Scholar
Streibig, J. C. 1989. The herbicide dose-response curve and the economics of weed control. Proc. Br. Crop Prot. Conf. Weeds, Brighton, pp. 927935.Google Scholar
Swinton, S. M. and King, R. P. 1994. A bioeconomic model for weed management in corn and soybean. Agric. Syst. 44: 313335.Google Scholar
van der Weide, R. Y. and van Groenendael, J. M. 1990. How useful are population models: an example from Galium aparine L. Z. Pflkrank. Pflschurz, Sonderheft XII: 147155.Google Scholar
van Groenendael, J. M. 1988. Patchy distribution of weeds and some implications for modelling population dynamics: a short literature review. Weed Res. 28: 437441.Google Scholar
Wang, R. L. and Dekker, J. 1995. Weedy adaptation in Setaria spp. III. Variation in herbicide resistance in Setaria spp. Pestic. Biochem. Physiol. 51: 99116.Google Scholar
Watkins, F. B. 1971. Effects of annual dressings of nitrogen fertilizer on wild oat infestations. Weed Res. 11: 292301.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., Jones, J. W., Coble, H. D., and Gunsolus, J. L. 1990. SOYWEED: a simulation model of soybean and common cocklebur growth and competition. Agron. J. 82: 10031010.CrossRefGoogle Scholar
Wilson, B. J., Cousens, R., and Cussans, G. W. 1984. Exercises in modelling populations of Avena fatua L. to aid strategic planning for the long term control of this weed in cereals. Pages 287294 in7ème Colloque International sur la Biologie, l'Ecologie er la Systématique des Mauvaises Herbes, Paris. Paris: COLUMA.Google Scholar
Wilson, B. J. and Wright, K. J. 1991. Effects of cultivation and seed shedding on the population dynamics of Galium aparine in winter wheat crops. Proc. Br. Crop Prot. Conf. Weeds, Brighton, pp. 813820.Google Scholar
Zwerger, P. and Hurle, K. 1988. Simulationsstudien zum Einfluß von Fruchtfolge und Bekämpfungsmaßnahmen auf die Verunkrautung. Z. Pflkrank. Pflschutz, Sonderheft XI: 7182.Google Scholar