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Weed–Corn Competition Parameters in Late-Winter Sowing in Northern Italy

Published online by Cambridge University Press:  20 January 2017

Stefan Otto ,
Roberta Masin ,
Giorgio Casari  and
Giuseppe Zanin
Stefan Otto*
Affiliation:
National Research Council (CNR), Institute of Agro-environmental and Forest Biology, Viale dell'Università, 16-35020 Legnaro (PD), Italy
Roberta Masin
Affiliation:
Department of Environmental Agronomy and Crop Science, Padova University, Viale dell'Università, 16-35020 Legnaro (PD), Italy
Giorgio Casari
Affiliation:
Du Pont de Nemours, Via Volta 16, 20093 Cologno (MI), Italy
Giuseppe Zanin
Affiliation:
Department of Environmental Agronomy and Crop Science, Padova University, Viale dell'Università, 16-35020 Legnaro (PD), Italy
*
Corresponding author's E-mail: [email protected]
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Abstract

In recent years, interest has increased in late-winter sowing of corn in northern Italy because of many agronomic advantages. However, cold and rainy weather slows initial crop growth, which can favor weed infestation. There is, therefore, a need for appropriate timing of weed control tactics based on an understanding of the competitive relationship and dynamics between crop and weeds. Five experiments were conducted over 4 yr, with a series of treatments increasing either duration of interference or length of weed-free period. Yield data were fitted with sigmoidal equations to find the critical point (CP) and critical period of weed control (CPWC). Although the CP is determined only by the competition between weed and crop, the CPWC is also market dependent. To quantify the effect of weed flora on the CP, a multiple regression model was tested, taking into account weed density, inflection point, and slope parameter of the Gompertz model of the cumulated infestation. The results confirmed that the late-winter sowing date increases the importance of late winter– and early spring–emerging weeds. In general, the precompetitive period was longer in the late winter–sown corn than in traditional midspring-sown corn. The delayed start of the CPWC makes control more difficult with a preemergence herbicide, which raises questions on the utility of this agronomic technique. Multiple regression analysis showed that the position of CP can be estimated with the density, earliness, and competitiveness of the infestation. Furthermore, the slope/inflection point ratio of the Gompertz model appears to be independent of sowing date. Results suggest that the weed–crop competition mechanism can be represented with simply the weed flora dynamic parameters and that a combination of crop–weed competition studies and emergence prediction models can predict the position of CP and give useful information about the CPWC and weed management.

Keywords

Corn, Zea mays L. cv. ‘PR33 J24’Novel sowing techniqueweed–crop competitionweed emergence dynamicsmodeling
Type
Weed Management
Information
Weed Science , Volume 57 , Issue 2 , April 2009 , pp. 194 - 201
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Bedmar, F., Manetti, P., and Monterubbianesi, G. 1999. Determination of the critical period of weed control in corn using a thermal basis. Pesq. Agropec. Bras. 34:187–193.CrossRefGoogle Scholar
Berti, A., Dunan, C., Sattin, M., Zanin, G., and Westra, P. 1996. A new approach to determine when to control weeds. Weed Sci. 44:496–503.CrossRefGoogle Scholar
Blandino, M., Reyneri, A., Vanara, F., Pascale, M., Haidukowski, M., and Saporiti, M. 2008. Effect of sowing date and insecticide application against European corn borer (Lepidoptera: Crambidae) on fumonisin contamination in maize kernels. Crop Prot. 27:1432–1436.CrossRefGoogle Scholar
Bosnic, A. C. and Swanton, C. J. 1997. Influence of barnyardgrass (Echinochloa crus-galli) time of emergence and density on corn (Zea mays). Weed Sci. 45:276–282.CrossRefGoogle Scholar
Buhler, D. D. and Gunsolus, J. L. 1996. Effect of date of preplant tillage and planting on weed populations and mechanical weed control in soybean (Glycine max). Weed Sci. 44:373–379.CrossRefGoogle Scholar
Bukun, B. 2004. Critical periods for weed control in cotton in Turkey. Weed Res. 44:404–412.CrossRefGoogle Scholar
Chikoye, D., Weise, S. F., and Swanton, C. J. 1995. Influence of common ragweed (Ambrosia artemisiifolia) time of emergence and density on white bean (Phaseolus vulgaris). Weed Sci. 43:375–380.CrossRefGoogle Scholar
Deen, W., Cousens, R., Warringa, J., et al. 2003. An evaluation of four crop: weed competition models using a common data set. Weed Res. 43:116–129.CrossRefGoogle Scholar
Dogan, M. N., Unay, A., Boz, O., and Albay, F. 2004. Determination of optimum weed control timing in maize (Zea mays L.). Turk. J. Agric. For. 28:349–354.Google Scholar
Evans, S. P., Knezevic, S. Z., Lindquist, J. L., Shapiro, C. A., and Blankenship, E. E. 2003. Nitrogen application influences the critical period for weed control in corn. Weed Sci. 51:408–417.CrossRefGoogle Scholar
Ferrero, A., Scanzio, M., and Acutis, M. 1996. Critical period of weed interference in maize 171–176. in. Proceedings of the Second International Weed Control Congress, Copenhagen.Google Scholar
Forcella, F. 1998. Real-time assessment of seed dormancy and seedling growth for weed management. Seed Sci. Res. 8:201–209.CrossRefGoogle Scholar
Ghosheh, H. Z., Holshouser, D. L., and Chandler, J. M. 1996. The critical period of johnsongrass (Sorghum halepense) control in field corn (Zea mays). Weed Sci. 44:944–947.CrossRefGoogle Scholar
Gower, S. A., Loux, M. M., Cardina, L., and Harison, S. K. 2002. Effect of planting date, residual herbicide, and postemergence application timing on weed control and grain yield in glyphosate-tolerant corn (Zea mays). Weed Technol. 16:488–494.CrossRefGoogle Scholar
Grundy, A. C. 2003. Predicting weed emergence: a review of approaches and future challenges. Weed Res. 43:1–11.CrossRefGoogle Scholar
Hall, M. R., Swanton, C. J., and Anderson, G. W. 1992. The critical period of weed control in grain corn (Zea mays). Weed Sci. 40:441–447.CrossRefGoogle Scholar
Horvath, D. P., Guiden, R., and Clay, S. A. 2006. Microarray analysis of late-season velvetleaf (Abutilon theophrasti) effect on corn. Weed Sci. 54:983–994.CrossRefGoogle Scholar
Isik, D., Mennen, H., Bukun, B., Oz, A., and Ngouajio, M. 2006. The critical period for weed control in corn in Turkey. Weed Technol. 20:867–872.CrossRefGoogle Scholar
Knezevic, S. Z., Evans, S. P., Blankenship, E. E., Van Acker, R. C., and Lindquist, J. L. 2002. Critical period for weed control: the concept and data analysis. Weed Sci. 50:773–786.CrossRefGoogle Scholar
Knezevic, S. Z., Horak, M. J., and Vanderlip, R. L. 1997. Relative time of redroot pigweed (Amaranthus retroflexus) emergence is critical in pigweed-sorghum (Sorghum bicolor) competition. Weed Sci. 45:502–508.CrossRefGoogle Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus) in corn (Zea mays). Weed Sci. 42:568–573.CrossRefGoogle Scholar
Leguizamon, E. S., Fernandez-Quintanilla, C., Barroso, J., and Gonzalez-Andujar, J. L. 2005. Using thermal and hydrothermal time to model seedling emergence of Avena sterilis ssp. ludoviciana in Spain. Weed Res. 45:149–156.CrossRefGoogle Scholar
Littell, R. C., Milliken, G. A., Stroup, W. W., and Wolfinger, R. D. 1996. SAS System For Mixed Models. Cary, NC SAS Institute.Google Scholar
López-Ovejero, R. F., Garcia y Garcia, A., Pinto de Carvalho, S. J., Christoffoleti, P. J., Dourado Neto, D., Martins, F., and Nicolai, M. 2005. Using thermal units for estimating critical period of weed competition in off-season maize crop. J. Environ. Sci. Health Part B. 40:1–11.Google ScholarPubMed
Martin, S. G., Van Acker, R. C., and Friesen, L. F. 2001. Critical period of weed control in spring canola. Weed Sci. 49:326–333.CrossRefGoogle Scholar
Masin, R., Zuin, M. C., Archer, D. W., Forcella, F., and Zanin, G. 2005. WeedTurf: a predictive model to aid control of annual summer weeds in turf. Weed Sci. 53:193–201.CrossRefGoogle Scholar
McDonald, A. J., Riha, S. J., and Mohlerl, C. L. 2004. Mining the record: historical evidence for climatic influences on maize–Abutilon theophrasti competition. Weed Res. 44:439–445.CrossRefGoogle Scholar
McMaster, G. S. and Wilhelm, W. W. 1997. Growing degree-days: one equation, two interpretations. Agric. Forest Meteor. 87:291–300.CrossRefGoogle Scholar
Mohler, C. L. 2001. Enhancing the competitive ability of crops. Pages 269–321. in Liebman, M., Mohler, C., and Staver, C. Ecological Management of Agricultural Weeds. Cambridge University Press.CrossRefGoogle Scholar
Mulugeta, D. and Boerboom, C. M. 2000. Crtitical time of weed removal in glyphosate-resistan Glicine max . Weed Sci. 48:35–42.CrossRefGoogle Scholar
Norsworthy, J. K. and Oliveira, M. J. 2004. Composition of the critical period for weed control in wide- and narrow-row corn. Weed Sci. 52:802–807.CrossRefGoogle Scholar
Onofri, A. 2001. BIOASSAY97: A New EXCEL VBA Macro to Perform Statistical Analyses on Pesticide Dose–Response Data. http://www.agr.unipg.it/disaprov/bioassay97/bioassay97.htm. Accessed: July 23, 2007.Google Scholar
Otto, S., Masin, R., Chistè, G., and Zanin, G. 2007. Modelling the correlation between plant phenology and weed emergence for improving weed control. Weed Res. 47:488–498.CrossRefGoogle Scholar
Rácz, F., Illés, O., Pók, I., Szöke, C., and Zsubori, Z. 2003. Role of sowing time in maize production. Acta Agraria Debreceniensis. 11:36–39. http://www.date.hu/acta-agraria/2003-11i/racz.pdf. Accessed: November 16, 2008.CrossRefGoogle Scholar
Singh, M., Saxena, M. C., Abu-Irmaileh, B. E., Al-Thalabi, S. A., and Haddad, N. I. 1996. Estimation of critical period of weed control. Weed Sci. 44:273–283.CrossRefGoogle Scholar
Vianello, M., Vischetti, C., Scarponi, L., and Zanin, G. 2005. Herbicide losses in runoff events from a field with a low slope: role of a vegetative filter strip. Chem. 61:717–725.Google ScholarPubMed
Vitta, J. I. and Satorre, E. H. 1999. Validation of a weed:crop competition model. Weed Res. 39:259–269.CrossRefGoogle Scholar
Williams, M. M. I. I. 2006. Planting date influences critical period of weed control in sweet corn. Weed Sci. 54:928–933.CrossRefGoogle Scholar
Zanin, G., Berti, A., and Sattin, M. 1989. Mais (Zea mays L.)—Abutilon theophrasti Medicus: effetto della durata e del periodo di competizione. Riv. di Agron. 23:185–192. [In Italian, abstract in English].Google Scholar
Zanin, G., Mosca, G., and Catizone, P. 1992. A profile of the potential flora in maize fields of the Po valley. Weed Res. 32:407–418.CrossRefGoogle Scholar
Zanin, G., Otto, S., Riello, L., and Borin, M. 1997. Ecological interpretation of weed flora dynamics under different tillage systems. Agric. Ecosyst. Environ. 66:177–188.CrossRefGoogle Scholar
Zimdahl, R. L. 1993. Fundamentals of Weed Science. San Diego, CA Academic. 128.Google Scholar