Hostname: page-component-7f64f4797f-d87pz Total loading time: 0 Render date: 2025-11-03T23:48:03.485Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling Weed Emergence in Italian Maize Fields

Published online by Cambridge University Press:  20 January 2017

Roberta Masin ,
Donato Loddo ,
Stefano Benvenuti ,
Stefan Otto  and
Giuseppe Zanin
Roberta Masin*
Affiliation:
Dipartimento di Agronomia Ambientale e Produzioni Vegetali, Università di Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
Donato Loddo
Affiliation:
Dipartimento di Agronomia Ambientale e Produzioni Vegetali, Università di Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
Stefano Benvenuti
Affiliation:
Dipartimento di Biologia delle Piante Agrarie, Viale delle Piagge 23, 56100, Pisa, Italy
Stefan Otto
Affiliation:
Istituto di Biologia Agroambientale e Forestale – CNR, Viale dell'Università 16, 35020 Legnaro (PD), Italy
Giuseppe Zanin
Affiliation:
Dipartimento di Agronomia Ambientale e Produzioni Vegetali, Università di Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy
*
Corresponding author's E-mail: roberta.masin@unipd.it
Get access Rights & Permissions [Opens in a new window]

Abstract

A hydrothermal time model was developed to simulate field emergence for three weed species in maize (common lambsquarters, johnsongrass, and velvetleaf). Models predicting weed emergence facilitate well-timed and efficient POST weed control strategies (e.g., chemical and mechanical control methods). The model, called AlertInf, was created by monitoring seedling emergence from 2002 to 2008 in field experiments at three sites located in the Veneto region in northeastern Italy. Hydrothermal time was calculated using threshold parameters of temperature and water potential for germination estimated in previous laboratory studies with seeds of populations collected in Veneto. AlertInf was validated with datasets from independent field experiments conducted in Veneto and in Tuscany (west central Italy). Model validation resulted in both sites in efficiency index values ranging from 0.96 to 0.99. AlertInf, based on parameters estimated in a single region, was able to predict the timing of emergence in several sites located at the two extremes of the Italian maize growing area.

Keywords

common lambsquarters, Chenopodium album L.,CHEALjohnsongrass, Sorghum halepense L. Pers, SORHAvelvetleaf, Abutilon theophrasti Medik., ABUTHHydrothermal timeemergence predictionmodelingweed control

Information

Type
Weed Management
Information
Weed Science , Volume 60 , Issue 2 , June 2012 , pp. 254 - 259
Copyright
Copyright © 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.)

Article purchase

Temporarily unavailable

References

Literature Cited

Alvarado, V. and Bradford, K. J. 2002. A hydrothermal time model explains the cardinal temperatures for seed germination. Plant Cell Env. 25:1061–1069.CrossRefGoogle Scholar
Archer, D. W., Forcella, F., Eklund, J. J., and Gunsolus, J. 2001. WeedCast Version 4.0. http://www.ars.usda.gov/services/software/software.htm. Accessed: February 1, 2011.Google Scholar
Battla, D. and Benech-Arnold, R. L. 2007. Predicting changes in dormancy level in weed seed soil banks: implications for weed management. Crop Prot. 26:189–197.CrossRefGoogle Scholar
Benech-Arnold, R. L., Ghersa, C. M., Sanchez, R. A. and Insausti, P. 1990. A mathematical model to predict Sorghum halepense (L.) Pers. seedlings emergence in relation to soil temperature. Weed Res. 30:90–99.Google Scholar
Bouwmeester, H. J. and Karssen, C. 1993. Seasonal periodicity in germination of seeds of Chenopodium album L. Ann. Bot. 72:463–473.CrossRefGoogle Scholar
Bradford, K. J. 2002. Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Sci. 50:248–260.CrossRefGoogle Scholar
Buhler, D. D., Liebman, M., and Obrycki, J. J. 2000. Theoretical and practice challenges to an IPM approach to weed management. Weed Sci. 48:274–278.CrossRefGoogle Scholar
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. 201:283–300.CrossRefGoogle Scholar
Dorado, J., Sousa, E., Calha, I. M., Gonzalez-Andujar, J. L., and Fernandez-Quintanilla, C. 2009. Predicting weed emergence in maize crops under two contrasting climatic conditions. Weed Res. 49:251–260.CrossRefGoogle Scholar
Forcella, F., Benech-Arnold, R. L., Sanchez, R., and Ghersa, C. M. 2000. Modeling seedling emergence. Field Crop Res. 67:123–139.CrossRefGoogle Scholar
Grundy, A. C. 2003. Predicting weed emergence: a review of approaches and future challenges. Weed Res. 43:1–11.CrossRefGoogle Scholar
Gummerson, R. J. 1986. The effect of constant temperatures and osmotic potential on the germination of sugar beet. J. Exp. Bot. 37:729–741.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
Lemieux, C., Vallee, L., and Vanasse, A. 2003. Predicting yield loss in maize fields and developing decision support for post-emergence herbicide applications. Weed Res. 43:323–332.CrossRefGoogle Scholar
Leon, R. G., Knapp, A. D., and Owen, M. D. K. 2004. Effect of temperature on the germination of common waterhemp (Amaranthus tuberculatus), giant foxtail (Setaria faberi), and velvetleaf (Abutilon theophrasti). Weed Sci. 52:67–73.CrossRefGoogle Scholar
Loague, K. and Green, R. E. 1991. Statistical and graphical methods for evaluating solute transport models: overview and application. J. Cont. Hydrol. 7:51–73.CrossRefGoogle Scholar
Masin, R., Cacciatori, G., Zuin, M. C., and Zanin, G. 2010a. AlertInf: emergence predictive model for weed control in maize in Veneto. It. J. Agromet. 1:5–9.Google Scholar
Masin, R., Loddo, D., Benvenuti, S., Zuin, M. C., Macchia, M., and Zanin, G. 2010b. Temperature and water potential as parameters for modeling weed emergence in central-northern Italy. Weed Sci. 58:216–222.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
Oriade, C. and Forcella, F. 1999. Maximizing efficacy and economics of mechanical weed control in row crops through forecasts of weed emergence. J. Crop Prod. 2:189–205.CrossRefGoogle Scholar
Roman, E. S., Thomas, A. G., Murphy, S. D., and Swanton, C. J. 1999. Modeling germination and seedling elongation of common lambsquarters (Chenopodium album). Weed Sci. 47:149–155.CrossRefGoogle Scholar
Royo-Esnal, A., Torra, J., Antoni Conesa, J., Forcella, F., and Recasens, J. 2010. Modeling the emergence of three arable bedstraw (Galium) species. Weed Sci. 58:10–15.CrossRefGoogle Scholar
Spokas, K. and Forcella, F. 2009. Software tools for weed seed germination modeling. Weed Sci. 57:216–227.CrossRefGoogle Scholar
Spokas, K., Forcella, F., Archer, D., Peterson, D., and Miller, S. 2007. Improving weed germination models by incorporating seed microclimate and translocation by tillage. Proc. Weed Sci. Soc. Am. 44:60.Google Scholar
Swanton, C. J. and Weise, S. F. 1991. Integrated weed management: the rationale and the approach. Weed Technol. 5:657–663.CrossRefGoogle Scholar
Walsh, M., Forcella, F., Aecher, D., and Eklund, J. 2002. WEEDEM: turning information into action. Pages 446–449 in Jacob, H. S., et al. 2002. Proceedings of the 13th Australian Weeds Conference, Perth, Western Australia.Google Scholar