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A Single-Time Survey Method to Predict the Daily Weed Density for Weed Control Decision-Making

  • Roberta Masin (a1), Vasileios P. Vasileiadis (a2), Donato Loddo (a1), Stefan Otto (a2) and Giuseppe Zanin (a1)...

Abstract

Decision-making processes must indicate if, how, and when weed control should be practiced. So far, Decision Support Systems (DSSs) for weed control to prevent crop yield losses can guide decisions on “if” and “how.” Experience shows that farmers need a DSS that can also guide when to treat, but this can only be done if the actual weed density observed in the field is known during the crop cycle. Emergence models allow the prediction of daily density, but precision depends on the survey date. This study focuses on the estimation of the date of the survey for the best prediction of the daily density throughout the crop cycle. The predicted daily density of each species can be used by DSSs without any further survey, saving time and money and improving the use of the DSSs. Results showed that the best date is when the actual density of each weed reaches or exceeds 50% emergence, and this is earlier than the critical point date, supporting the validity of the date estimation method. The possibility to provide specific advice for farmers considering a proper mortality rate of weed seedlings is then discussed. The ability to optimize the date of sampling can improve the reliability of decision-making tools for integrated weed management, in agreement with the European Union goal of sustainable use of pesticides and more environmentally sustainable cropping systems through the use of integrated pest management.

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Corresponding author

Corresponding author's E-mail: roberta.masin@unipd.it

References

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Anderson, R. L. 2008. Weed seedling emergence and survival as affected by crop canopy. Weed Technol. 22:736740.
Berti, A., Bravin, F., and Zanin, G. 2003. Application of decision-support software for postemergence weed control. Weed Sci. 51:618627.
Berti, A. and Zanin, G. 1997. GESTINF: a decision model for post-emergence weed management in soybean (Glycine max (L.) Merr.). Crop Prot. 16:109116.
Berti, A., Zanin, G., Baldoni, G., Grignani, C., Mazzoncini, M., Mintemurro, P., Tei, F., Vazzana, C., and Viggiani, P. 1992. Frequency-distribution of weed counts and applicability of a sequential sampling method to integrated weed management. Weed Res. 32:3944.
Buhler, D. D., Liebman, M., and Obrycki, J. J. 2000. Theoretical and practice challenges to an IPM approach to weed management. Weed Sci. 48:274280.
Colbach, N., Chauvel, B., Gauvrit, C., and Munier-Jolain, N. M. 2007. Construction and evaluation of ALOMYSYS modelling the effects of cropping systems on the blackgrass life-cycle: from seeding to seed production. Ecol. Model. 201:283300.
Cousens, R. and Mortimer, M. 1995. Dynamics of Weed Populations. Cambridge Cambridge University Press.
Donald, C. M. 1981. Competitive plants, communal plants, and yield in wheat crops. Pages 223247 in Evans, L. T., and Peacock, W. J., eds. Wheat Science—Today and Tomorrow. London Cambridge University Press.
European Parliament. 2009. Directive 2009/128/EC of the European Parliament and of the Council. http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2009:309:0071:0086:EN:PDF. Accessed: January 21, 2011.
Fernandez-Quintanilla, C., Quadranti, M., Kudsk, P., and Barberi, P. 2008. Which future for weed science. Weed Res. 48:297301.
Forcella, F., Benech Arnold, R. L., Sanchez, R., and Ghersa, C. M. 2000. Modeling seedling emergence. Field Crops Res. 67:123139.
Grundy, A. C. 2003. Predicting weed emergence: a review of approaches and future challenges. Weed Res. 43:111.
Harper, J. L. 1977. Population Biology of Plants. London Academic Press. 892 p.
Jones, R. E. and Medd, R. W. 2005. A methodology for evaluating risk and efficacy of weed management technologies. Weed Sci. 53:505514.
Masin, R., Cacciatori, G., Zuin, M. C., and Zanin, G. 2010. AlertInf: emergence predictive model for weed control in maize in Veneto. Ital. J. Agrometeor. 1:59.
Mohler, C. L. and Calloway, M. B. 1992. Effects of tillage and mulch on the emergence and survival of weeds in sweet corn. J. Appl. Ecol. 29:2134.
Otto, S., Masin, R., Casari, G., and Zanin, G. 2009. Weed-corn competition parameters in late-winter sowing in Northern Italy. Weed Sci. 57:194201.
Rainbolt, C. R., Thill, D. C., Yenish, J. P., and Ball, D. A. 2004. Herbicide-resistant grass weed development in imidazoline-resistant wheat: weed biology and herbicide rotation. Weed Technol. 18:860868.
Rydahl, P., Been, T., Berti, A., Evans, N., Gouache, D., Gutsche, V., Jensen, J. E., Kapsa, J., Levay, N., Munier-Jolain, N., Nibouche, S., and Raynal, M. 2009. Review of New Technologies Critical to Effective Implementation of Decision Support Systems and Farm Management Systems. http://www.endure-network.eu/about_endure/all_the_news/dss_helping_farmers_make_smart_decisions. Accessed: January 21, 2011.
Slaughter, D. C., Giles, D. K., and Downey, D. 2008. Autonomous robotic weed control systems: a review. Comput. Electron. Agric. 61:6378.
Swanton, C. J., Mahoney, K. J., Chandler, K., and Gulden, R. H. 2008. Integrated weed management: knowledge-based weed management systems. Weed Sci. 56:168172.
Watkinson, A. R. 1980. Density-dependence in single-species populations of plants. J. Theor. Biol. 83:345–57.
Wilkerson, G. G., Wiles, L. J., and Bennett, A. C. 2002. Weed management decision models: pitfalls, perceptions, and possibilities of the economic threshold approach. Weed Sci. 50:411424.
Zanin, G. and Berti, A. 2001. Malerbe componente persistente degli agroecosistemi. Pages 125145 in Catizone, P., and Zanin, G., eds. Malerbologia. Bologna Patron Editore.
Zanin, G., Berti, A., and Riello, L. 1998. Incorporation of weed spatial variability into the weed control decision-making process. Weed Res. 38:107118.
Zanin, G. and Sattin, M. 1988. Threshold level and seed production of velvetleaf (Abutilon theophrasti Medicus) in maize. Weed Res. 28:347352.

Keywords

A Single-Time Survey Method to Predict the Daily Weed Density for Weed Control Decision-Making

  • Roberta Masin (a1), Vasileios P. Vasileiadis (a2), Donato Loddo (a1), Stefan Otto (a2) and Giuseppe Zanin (a1)...

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