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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

Published online by Cambridge University Press:  20 March 2012

N. COLBACH ,
S. GRANGER  and
D. MÉZIÈRE
N. COLBACH*
Affiliation:
INRA, UMR1347 Agroécologie, ECOLDUR, BP 86510, F-21000 Dijon, France
S. GRANGER
Affiliation:
AgroSup Dijon, 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]
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Summary

Both scientists and farmers are confronted by a similar question: which current and past cropping system components will influence the present weed flora, and how? This information is necessary to optimize both cropping systems for weed control, and quality and cost in surveys and monitoring schemes. The present study addressed these questions with a sensitivity analysis to input variables of a cropping system model, AlomySys, that predicts weed dynamics in interaction with pedo-climatic conditions. The study ranked cropping system components according to their impact on weed infestation in winter wheat, showing for instance that though crop succession was crucial, current and past tillage strategies influenced grass weed densities even more. Crops were not only ranked as a function of the resulting weed risk but the latter was also linked to crop species traits, i.e. crop type, usual sowing period and emergence speed. A previous winter v. spring crop thus increased weed density by 72% in the following winter wheat; a late-sown v. early sown winter crop by 26%, a slow v. fast-emerging winter crop by 17%, and a lower competitive ability by 9%. Similarly, the characteristics of each crop management technique (tillage, catch crop, secondary crop, mowing, mechanical weeding, herbicides, nitrogen fertilizer, manure and harvest) were quantified. For instance, the timing of the first tillage operation was crucial prior to the analysed winter wheat crop while the choice of the tool used even 5 years previously still influenced weed infestation in the current year; a catch crop prior to previous spring sown crops reduced the current infestation regardless of catch crop sowing dates and densities, but the reductive effect could be lost if the field was tilled several times to destroy the catch crop. The advice synthesized here and in a companion paper (Colbach & Mézière 2012). will be valuable to design innovative, integrated cropping systems, indicating (1) which cropping system components to modify to produce the largest effect, (2) for how long past practices must be considered when choosing current options and (3) the optimal options for the different management techniques. Points (1) and (2) are also valuable to identify data to record in surveys, though still resulting in a total of 232 variables. In a second step, these detailed variables were therefore simplified and aggregated to determine a smaller set of 22 synthetic variables easily recorded in surveys, such as the proportion of winter and spring crops during the last 10 years (instead of the actual crop sequence), the proportion of crops sown in summer, early autumn, late autumn, early spring and late spring during the last 5 years (instead of exact sowing dates), the ploughing frequency (instead of ploughing dates and characteristics), the mean number of herbicide sprayings per year (instead of dates), etc. This reduced survey list will reduce the cost of surveys as well as increase the number and quality of surveys as more farmers will be ready to participate and there will be fewer uncertainties in the answers.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 151 , Issue 2 , April 2013 , pp. 247 - 267
Copyright
Copyright © Cambridge University Press 2012

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References

REFERENCES

Andersson, T. N. & Milberg, P. (1998). Weed flora and the relative importance of site, crop, crop rotation, and nitrogen. Weed Science 46, 30–38.Google Scholar
Ball, D. A., Frost, S. M., Fandrich, L., Tarasoff, C. & Mallory-Smith, C. (2008). Biological attributes of rattail fescue (Vulpia myuros). Weed Science 56, 26–31.Google Scholar
Bàrberi, P. & Lo Cascio, B. (2001). Long-term tillage and crop rotation effects on weed seedbank size and composition. Weed Research 41, 325–340.CrossRefGoogle Scholar
Bergez, J. E., Colbach, N., Crespo, O., Garcia, F., Jeuffroy, M. H., Justes, E., Loyce, C., Munier-Jolain, N. & Sadok, W. (2010). Designing crop management systems by simulation. European Journal of Agronomy 32, 3–9.CrossRefGoogle Scholar
Blackshaw, R. E. (1993). Downy brome (Bromus tectorum) density and relative-time of emergence affects interference in winter wheat (Triticum aestivum). Weed Science 41, 551–556.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
Brisson, N., Mary, B., Ripoche, D., Jeuffroy, M. H., Ruget, F., Nicoullaud, B., Gate, P., Devienne-Barret, F., Antonioletti, R., Dürr, C., Richard, G., Beaudoin, N., Recous, S., Tayot, X., Plenet, D., Cellier, P., Machet, J. M., Meynard, J. M. & Delecolle, R. (1998). STICS: a generic model for the simulation of crops and their water and nitrogen balances. I. Theory and parameterization applied to wheat and corn. Agronomie 18, 311–346.CrossRefGoogle Scholar
Burnside, O. C., Wilson, R. G., Weisberg, S. & Hubbard, K. G. (1996). Seed longevity of 41 weed species buried 17 years in eastern and western Nebraska. Weed Science 44, 74–86.CrossRefGoogle Scholar
Cardina, J., Herms, C. P. & Doohan, D. J. (2002). Crop rotation and tillage system effects on weed seedbanks. Weed Science 50, 448–460.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 Colloque 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., 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. & 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., Roger-Estrade, J., Chauvel, B. & Caneill, J. (2006 b). ALOMYSYS: modelling black-grass (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., 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., 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. & Mézière, D. (2012). 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. Journal of Agricultural Science, Cambridge. Published online doi: 10.1017/S0021859612000159.Google 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., Kurstjens, D. A. G., Munier-Jolain, N. M., Dulout-Dalbiès, A. & Doré, T. (2010 b). Assessing non-chemical weeding strategies through a modelling approach applied to blackgrass (Alopecurus myosuroides Huds.) dynamics. European Journal of Agronomy 32, 205–218.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
Colbach, N., Chauvel, B., Darmency, D. & Tricault, Y. (2011). Influence of life traits on emergence and dynamics of an annual spring weed in cropping systems. A sensitivity analysis. Journal of Agricultural Science, Cambridge 149, 679–700.CrossRefGoogle Scholar
Conn, J. S., Beattie, K. L. & Blanchard, A. (2006). Seed viability and dormancy of 17 weed species after 19·7 years of burial in Alaska. Weed Science 54, 464–470.CrossRefGoogle Scholar
Davidson, R. M. (1990). Management of herbicide resistant annual ryegrass, Lolium rigidum, in crops and pastures. In Proceedings of the 9th Australian Weeds Conference (Ed. Heap, J. W.), pp. 230–233. Adelaide: Crop Science Society of South Australia Inc.Google Scholar
Délye, C. (2005). Weed resistance to acetyl coenzyme A carboxylase inhibitors: an update. Weed Science 53, 728–746.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
Délye, C., Michel, S., Bérard, A., Chauvel, B., Brunel, D., Guillemin, J. P., Dessaint, F. & Le Corre, V. (2010). Geographical variation in resistance to acetyl-coenzyme A carboxylase-inhibiting herbicides across the range of the arable weed Alopecurus myosuroides Huds (black-grass). New Phytologist 186, 1005–1017.Google Scholar
Donatelli, M. & Marchetti, R. (1994). A multi-crop submodel to predict emergence time: model definition and preliminary testing. In 3rd ESA Congress, pp. 350–351. Abano-Padova, Italy: ESA.Google Scholar
Douglas, A. & Peltzer, S. C. (2004). Managing herbicide resistant annual ryegrass (Lolium rigidum Gaud.) in no-till systems in Western Australia using occasional inversion ploughing. In Weed Management: Balancing People, Planet, Profit. 14th Australian Weeds Conference, Wagga Wagga, New South Wales, Australia, 6–9 September 2004: Papers and Proceedings (Eds Sindel, B. M. & Johnson, S. B.), pp. 300–303. Wahroonga, NSW: Weed Society of New South Wales.Google Scholar
Doyle, C. J. (1997). A review of the use of models of weed control in Integrated Crop Protection. Agriculture, Ecosystems and Environment 64, 165–172.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, 1–849.Google Scholar
Fried, G., Chauvel, B. & Reboud, X. (2009). A functional analysis of large-scale temporal shifts from 1970 to 2000 in weed assemblages of sunflower crops in France. Journal of Vegetation Science 20, 49–58.CrossRefGoogle Scholar
Fried, G., Norton, L. R. & Reboud, X. (2008). Environmental and management factors determining weed species composition and diversity in France. Agriculture, Ecosystems and Environment 128(1–2), 68–76.CrossRefGoogle Scholar
Fried, G. & Reboud, X. (2007). Shift in oilseed rape weed community composition as influenced by agronomic practices. Oleagineux, Corps Gras, Lipides 14, 130–138.CrossRefGoogle 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 149(Supp. 1), 47–53.Google Scholar
Hole, D. G., Perkins, A. J., Wilson, J. D., Alexander, I. H., Grice, P. V. & Evans, A. D. (2005). Does organic farming benefit biodiversity? Biological Conservation 122, 113–130.CrossRefGoogle Scholar
Holst, N., Rasmussen, I. A. & Bastiaans, L. (2007). Field weed population dynamics: a review of model approaches and applications. Weed Research 47, 1–14.CrossRefGoogle Scholar
IFEN (2007). Les Pesticides dans les Eaux – Données 2005. Orléans: Institut Français de l'Environnement.Google Scholar
McCloskey, M., Firbank, L. G., Watkinson, A. R. & Webb, D. J. (1996). The dynamics of experimental arable weed communities under different management practices. Journal of Vegetation Science 7, 799–808.CrossRefGoogle Scholar
Menalled, F. D., Gross, K. L. & Hammond, M. (2001). Weed aboveground and seedbank community responses to agricultural management systems. Ecological Applications 11, 1586–1601.CrossRefGoogle 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
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.CrossRefGoogle Scholar
Murdoch, A. J. & Ellis, R. H. (2000). Dormancy, viability and longevity. In Seeds: the Ecology of Regeneration in Plant Communities (Ed. Fenner, M.), pp. 183–214. Wallingford, UK: Cabi.Google Scholar
Naylor, R. E. L. (1972). The nature and consequence of interference by Alopecurus myosuroides Huds. on the growth of winter wheat. Weed Research 12, 137–143.CrossRefGoogle Scholar
Orson, J. H. & Livingston, D. B. F. (1987). Field trials on the efficacy of herbicides on resistant black-grass (Alopecurus myosuroides) in different cultivation regimes. In Proceedings 1987 British Crop Protection Conference, Weeds, pp. 887–899. Surrey, UK: BCPC.Google 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
Puricelli, E. & Tuesca, D. (2005). Weed density and diversity under glyphosate-resistant crop sequences. Crop Protection 24, 533–542.CrossRefGoogle Scholar
Recasens, J., Planes, J., Bosque, J. L., Briceno, R. & Taberner, A. (2001). Management strategies for herbicide-resistant Lolium rigidum Gaud. populations. In Actas Congreso 2001 Sociedad Espanola de Malherbologia, Leon Spain, 20, 21 y22 de noviembre de 2001, pp. 117–122. Sociedad Espanola de Malherbologia (Spanish Weed Science Society).Google Scholar
Reddy, K. N. (2003). Impact of rye cover crop and herbicides on weeds, yield, and net return in narrow-row transgenic and conventional soybean (Glycine max). Weed Technology 17, 28–35.CrossRefGoogle 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
Saltelli, A., Chan, K. & Scott, E. M. E. (2000). Sensitivity Analysis. Chichester, UK: John Wiley and 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 X, 241–257.Google 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 Elsen, T. (2000). Species diversity as a task for organic agriculture in Europe. Agriculture Ecosystems and Environment 77, 101–109.Google 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