Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-29T02:13:39.980Z Has data issue: false hasContentIssue false
  • English
  • Français

Using a sensitivity analysis of a weed dynamics model to develop sustainable cropping systems. I. Annual interactions between crop management techniques and biophysical field state variables

Published online by Cambridge University Press:  20 March 2012

N. COLBACH  and
D. MÉZIÈRE
N. COLBACH*
Affiliation:
INRA, UMR1347 Agroécologie, ECOLDUR, BP 86510, F-21000 Dijon, France
D. MÉZIÈRE
Affiliation:
INRA, UMR1347 Agroécologie, ECOLDUR, BP 86510, F-21000 Dijon, France
*
*To whom all correspondence should be addressed. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

Environmental problems mean that herbicide applications must be drastically reduced and optimized. Models that quantify the effects of crop management techniques on weed dynamics are valuable tools for designing weed management strategies. Indeed, the techniques to be optimized are numerous and diverse, and their effects vary considerably with environmental conditions and the state of the weed flora. In the present study, a mechanistic weed dynamics model, AlomySys, was used to carry out in silico experiments in order to: (1) rank crop management components according to the resulting decrease in weed infestation, and (2) study the sensitivity of the major component effects to biophysical field state variables in order to identify indicators and thresholds that could serve for future decision-rules for farmers. The various results were compiled into rules for optimizing timing and other options (tillage tools, herbicide types) for the different crop management techniques. The rules were based on a series of biophysical field state variables, i.e. cumulated rainfall, thermal time, soil moisture and weed densities prior to the operation, in the previous and pre-previous crops. For instance, the first tillage should be delayed until the cumulated rainfall since harvest exceeds 50 mm and be carried out in moist conditions. Mouldboard ploughing is advised if the infestation of the previous crop exceeds 20 weeds/m2 and particularly if this exceeds 0·3 times that of the pre-previous crop. Ploughing should occur when the cumulated rainfall since harvest reaches 100–200 mm. The effects of crop succession and long-term effects of management techniques have been studied in a companion paper (Colbach et al. 2012).

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 151 , Issue 2 , April 2013 , pp. 229 - 245
Copyright
Copyright © Cambridge University Press 2012

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

REFERENCES

Aubertot, J. N., Lescourret, F., Bonato, O., Colbach, N., Debaeke, P., Doré, T., Fargues, J., Lô-Pelzer, E., Loyce, C. & Sauphanor, B. (in press). How to improve pest management in cropping systems. Effects of cultural practices on pest development. A review. Agronomy for Sustainable Development, in press.Google Scholar
Aubry, C., Papy, F. & Capillon, A. (1998). Modelling decision-making processes for annual crop management. Agricultural Systems 56, 45–65.CrossRefGoogle Scholar
Bohan, D. A., Powers, S. J., Champion, G., Haughton, A. J., Hawes, C., Squire, G., Cussans, J. & Mertens, S. K. (2011). Modelling rotations: can crop sequences explain arable weed seedbank abundance? Weed Research 51, 422–432.CrossRefGoogle Scholar
Chauvel, B. (1996). Variabilité de la production de semences chez le vulpin (Alopecurus myosuroides Huds.) en fonction de la culture. In Proceedings of the Xe Congrès International sur la Biologie des Mauvaises Herbes (Ed. INRA), pp. 43–50. Dijon, France: INRA.Google Scholar
Chauvel, B., Angonin, C. & Colbach, N. (1996). Black-grass (Alopecurus myosuroides Huds) development and seed production in wheat. In Proceedings of the 4th ESA Congress (Eds van Ittersum, M. K., Venner, G. E. G. T. & van de Geijn, T. H.), pp. 528–529. Veldhoven-Wageningen: European Society for Agronomy.Google Scholar
Chauvel, B., Guillemin, J. P., Colbach, N. & Gasquez, J. (2001). Evaluation of cropping systems for management of herbicide-resistant populations of blackgrass (Alopecurus myosuroides Huds.). Crop Protection 20, 127–137.CrossRefGoogle Scholar
Cirujeda, A. & Taberner, A. (2009). Cultural control of herbicide-resistant Lolium rigidum Gaud. populations in winter cereal in Northeastern Spain. Spanish Journal of Agricultural Research 7, 146–154.CrossRefGoogle Scholar
Colbach, N. (2010). Modelling cropping system effects on crop pest dynamics: how to compromise between process analysis and decision aid. Plant Science 179, 1–13.CrossRefGoogle Scholar
Colbach, N. & Debaeke, P. (1998). Integrating crop management and crop rotation effects into models of weed population dynamics: a review. Weed Science 46, 717–728.CrossRefGoogle Scholar
Colbach, N. & Dürr, C. (2003). Effects of seed production and storage conditions on blackgrass (Alopecurus myosuroides Huds.) germination and shoot elongation with time. Weed Science 51, 708–717.CrossRefGoogle Scholar
Colbach, N., Dürr, C., Roger-Estrade, J. & Caneill, J. (2005). How to model the effects of farming practices on weed emergence. Weed Research 45, 2–17.CrossRefGoogle Scholar
Colbach, N., Busset, H., Yamada, O., Dürr, C. & Caneill, J. (2006 a). ALOMYSYS: modelling black-grass (Alopecurus myosuroides Huds.) germination and emergence, in interaction with seed characteristics, tillage and soil climate. II. Evaluation. European Journal of Agronomy 24, 113–128.CrossRefGoogle Scholar
Colbach, N., Chauvel, B., Gauvrit, C. & Munier-Jolain, N. M. (2007). Construction and evaluation of ALOMYSYS, modelling the effects of cropping systems on the blackgrass life-cycle. From seedling to seed production. Ecological Modelling 201, 283–300.CrossRefGoogle Scholar
Colbach, N., Dürr, C., Roger-Estrade, J., Chauvel, B. & Caneill, J. (2006 b). ALOMYSYS: modelling blackgrass (Alopecurus myosuroides Huds.) germination and emergence, in interaction with seed characteristics, tillage and soil climate. I. Construction. European Journal of Agronomy 24, 95–112.CrossRefGoogle Scholar
Colbach, N., Gardarin, A. & Munier-Jolain, N. M. (2010 a). FLORSYS: a mechanistic model of cropping system effects on weed flora based on functional relationships with species traits. In 15th International EWRS Symposium Kaposvár, Hungary (Eds Bastiaans, L., Bohren, C., Christensen, S., Gerowitt, B., Hatcher, P., Krähmer, H., Kudsk, P., Melander, B., Pannacci, E., Rubin, B., Streibig, J., Tei, F., Thompson, A., Torresen, K. & Vurro, M.), pp. 157–158. Budapest, Hungary: Asszisztencia Congress Bureau Ltd.Google Scholar
Colbach, N., Granger, S. & Mézière, D. (2012). Using a sensitivity analysis of a weed dynamics model to develop sustainable cropping systems. II. Long-term effect of past crops and management techniques on weed infestation. Journal of Agricultural Science, Cambridge. Published online doi:10.1017/S0021859612000160.Google Scholar
Colbach, N., Granger, S. & Munier-Jolain, N. M. (2009). Using weed dynamics models for evaluating and developing integrated cropping systems. In XIIIème Colloque International sur la Biologie des Mauvaises Herbes, 8–10 September 2009, Dijon, France, pp. 195–205. Dijon, France: INRA.Google Scholar
Colbach, N., Kurstjens, D. A. G., Munier-Jolain, N. M., Dalbiès, A. & Doré, T. (2010 b). Assessing non-chemical weeding strategies through mechanistic modelling of blackgrass (Alopecurus myosuroides Huds.) dynamics. European Journal of Agronomy 32, 205–218.CrossRefGoogle Scholar
Colbach, N., Molinari, N. & Clermont-Dauphin, C. (2004). Sensitivity analyses for a model simulating demography and genotype evolutions with time. Application to GENESYS modelling gene flow between rape seed varieties and volunteers. Ecological Modelling 179, 91–113.CrossRefGoogle Scholar
Colbach, N., Schneider, A., Ballot, R. & Vivier, C. (2010 c). Diversifying cereal-based rotations to improve weed control. Evaluation with the ALOMYSYS model quantifying the effect of cropping systems on a grass weed. Oleagineux Corps Gras Lipides 17, 292–300.CrossRefGoogle Scholar
Délye, C., Menchari, Y., Guillemin, J. P., Matéjicek, A., Michel, S., Camilleri, C. & Chauvel, B. (2007). Status of blackgrass (Alopecurus myosuroides) resistance to acetyl-coenzyme A carboxylase inhibitors in France. Weed Research 47, 95–105.CrossRefGoogle Scholar
European Union (2006). Regulation (EC) no 1907/2006 of the European Parliament and of the Council of 18 December 2006 concerning the Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH), establishing a European Chemicals Agency, amending Directive 1999/45/EC and repealing Council Regulation (EEC) No 793/93 and Commission Regulation (EC) No 1488/94 as well as Council Directive 76/769/EEC and Commission Directives 91/155/EEC, 93/67/EEC, 93/105/EC and 2000/21/EC. Official Journal of the European Union L396 30.12.2006, 1–849.Google Scholar
Gardarin, A., Dürr, C. & Colbach, N. (in press). Modelling weed seed bank dynamics and emergence with species traits. Ecological Modelling.Google Scholar
Gressel, J. (2011). Global advances in weed management. Journal of Agricultural Science, Cambridge 149 (Suppl. 1), 47–53.CrossRefGoogle Scholar
IFEN (2007). Les Pesticides dans les Eaux – Données 2005. Orléans: Institut Français de l'Environnement.Google Scholar
Kurstjens, D. A. G. & Perdok, U. D. (2000). The selective soil covering mechanism of weed harrows on sandy soil. Soil and Tillage Research 55, 193–206.CrossRefGoogle Scholar
Kurstjens, D. A. G., Perdok, U. D. & Goense, D. (2000). Selective uprooting by weed harrowing on sandy soils. Weed Research 40, 431–447.CrossRefGoogle Scholar
Lonchamp, J. P., Chadoeuf, R., Barralis, G. & Bourlier, M. (1984). Évolution de la capacité de germination des semences de mauvaises herbes enfouies dans le sol. Agronomie 4, 671–682.CrossRefGoogle Scholar
Macé, K., Morlon, P., Munier-Jolain, N. & Quéré, L. (2007). Time scales as a factor in decision-making by French farmers on weed management in annual crops. Agricultural Systems 93, 115–142.Google Scholar
Mézière, D., Granger, S., Boissinot, F. & Colbach, N. (2011). Maîtriser les adventices graminées automnales sans herbicide: Quel est le Poids de l'histoire culturale? Evaluation avec un modèle de dynamique d'adventices. In AFPP – Quatrième Conférence Internationale sur les Méthodes Alternatives en Protection des Cultures, Lille, France, 8–10 Mars 2011, pp. 774–784. Paris, France: AFPP.Google Scholar
Miyazawa, K., Tsuji, H., Yamagata, M., Nakano, H. & Nakamoto, T. (2004). Response of weed flora to combinations of reduced tillage, biocide application and fertilization practices in a 3-year crop rotation. Weed Biology and Management 4, 24–34.CrossRefGoogle Scholar
Moss, S. R. & Clarke, J. H. (1994). Guidelines for the prevention and control of herbicide-resistant black-grass (Alopecurus myosuroides Huds.). Crop Protection 13, 230–234.CrossRefGoogle Scholar
Moss, S. R., Perryman, S. A. M. & Tatnell, L. V. (2007). Managing herbicide-resistant blackgrass (Alopecurus myosuroides): theory and practice. Weed Technology 21, 300–309.Google Scholar
Naylor, R. E. L. (1972 a). Aspects of the population dynamics of the weed Alopecurus myosuroides Huds. in winter cereal crops. Journal of Applied Ecology 9, 127–139.CrossRefGoogle Scholar
Naylor, R. E. L. (1972 b). The nature and consequence of interference by Alopecurus myosuroides Huds. on the growth of winter wheat. Weed Research 12, 137–143.CrossRefGoogle Scholar
Pleasant, J. M. & Schlather, K. J. (1994). Incidence of weed seed in cow (bos sp) manure and its importance as a weed source for cropland. Weed Technology 8, 304–310.CrossRefGoogle Scholar
Powles, S. B. & Yu, Q. (2010). Evolution in action: plants resistant to herbicides. In Annual Review of Plant Biology, Vol. 61 (Eds Merchant, S., Briggs, W. R. & Ort, D.), pp. 317–347. Palo Alto: Annual Reviews.Google Scholar
Rodenburg, J., Meinke, H. & Johnson, D. E. (2011). Challenges for weed management in African rice systems in a changing climate. Journal of Agricultural Science, Cambridge 149, 427–435.CrossRefGoogle Scholar
Rossing, W. A. H., Meynard, J. M. & van Ittersum, M. K. (1997). Model-based explorations to support development of sustainable farming systems: case studies from France and the Netherlands. European Journal of Agronomy 7, 271–283.CrossRefGoogle Scholar
Saltelli, A., Chan, K. & Scott, E. M. E. (2000). Sensitivity Analysis. Chichester, UK: John Wiley & Sons.Google Scholar
Schneider, W., Walter, H., Koch, W. & Kemmer, A. (1984 ). Möglichkeiten und Probleme der Integration acker-baulicher Maßnahmen zur Unkrautbekämpfung im realen Betrieb – Beispiel aus dem Unterland, Baden-Württemberg. Zeitschrift für Pflanzenkrankheiten und Pflanzenschutz, Sonderheft 10, 241–257.Google Scholar
Swain, A. J., Hughes, Z. S., Cook, S. K. & Moss, S. R. (2006). Quantifying the dormancy of Alopecurus myosuroides seeds produced by plants exposed to different soil moisture and temperature regimes. Weed Research 46, 470–479.CrossRefGoogle Scholar
Tricault, Y., Darmency, H. & Colbach, N. (2009). Identifying key components of weed beet management using sensitivity analyses of the GeneSys-Beet model in sugar beet. Weed Research 49, 581–591.CrossRefGoogle Scholar
Van Himme, M. & Bulcke, R. (1975). Distribution, extension et importance d‘Alopecurus myosuroides Huds. en Europe. In Proceedings of the European Weed Research Society Symposium on the Status, Biology and Control of Grassweeds in Europe, pp. 23–54. Doorwerth, The Netherlands: European Weed Research Society.Google Scholar
Supplementary material: File

Colbach supplementary material

Appendix

Download Colbach supplementary material(File)
File 1.9 MB