Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-15T13:21:06.019Z Has data issue: false hasContentIssue false
  • English
  • Français

A Thermal Time Model to Predict Corn Poppy (Papaver rhoeas) Emergence in Cereal Fields

Published online by Cambridge University Press:  20 January 2017

Jordi Izquierdo ,
José L. González-Andújar ,
Fernando Bastida ,
Juan A. Lezaún  and
María J. Sánchez del Arco
Jordi Izquierdo
Affiliation:
Departament d'Enginyeria Agroalimentària i Biotecnologia, Universitat Politècnica de Catalunya, Avenida Canal Olímpic s/n, 08860 Castelldefels, Spain
José L. González-Andújar
Affiliation:
Instituto de Agricultura Sostenible (CSIC), Avenida Alameda del Obispo s/n, Apdo 4084, 14080 Córdoba, Spain
Fernando Bastida
Affiliation:
Departamento de Ciencias Agroforestales, Universidad de Huelva, Campus de La Rábida, Carretera Palos de La Frontera s/n, 21819 Palos de La Frontera, Huelva, Spain
Juan A. Lezaún
Affiliation:
Departamento de Protección de Cultivos, Instituto Técnico y de Gestión Agraria, Avenida Serapio Huici, 20-22, 31610 Villava, Navarra, Spain
María J. Sánchez del Arco
Affiliation:
Instituto Madrileño de Investigación y Desarrollo Rural, Agrario y Alimentario, Finca El Encín, Apdo 127, 28871 Alcalá de Henares, Spain
Get access Rights & Permissions [Opens in a new window]

Abstract

Corn poppy is the most abundant broad-leaved weed in winter cereals of Mediterranean climate areas and causes important yield losses in wheat. Knowledge of the temporal pattern of emergence will contribute to optimize the timing of control measures, thus maximizing efficacy. The objectives of this research were to develop an emergence model on the basis of soil thermal time and validate it in several localities across Spain. To develop the model, monitoring of seedling emergence was performed weekly during the growing season in a cereal field located in northeastern Spain, during 3 yr. Cumulative thermal time from sowing date was used as the independent variable for predicting cumulative emergence. The Gompertz model was fitted to the data set of emergences. A base temperature of 1.0 C was estimated through iteration for maximum fit. The model accounted for 91% of the variation observed. Model validation in several localities and years showed general good performance in predicting corn poppy seedling emergence ( values ranging from 0.64 to 0.99 and root-mean-square error from 4.4 to 24.3). Ninety percent emergence was accurately predicted in most localities. Results showed that the model performs with greater reliability when significant rainfall (10 mm) occurs within 10 d after crop sowing. Complemented with in-field scouting, it may be a useful tool to better timing control measures in areas that are homogeneous enough regarding climate and crop management.

Keywords

Corn poppy, Papaver rhoeas L.Soil temperatureGompertz functiongrowing degree daysvalidationweed control
Type
Weed Management
Information
Weed Science , Volume 57 , Issue 6 , December 2009 , pp. 660 - 664
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.)

References

Literature Cited

Barralis, G., Chadoeuf, R., and Lonchamp, J. P. 1988. Longévité des semences de mauvaises herbes annuelles dans un sol cultivé. Weed Res. 28:407–418.Google Scholar
Baskin, C. C., Milberg, P., Anderson, L., and Baskin, J. M. 2002. Non-deep simple morphological dormancy in seeds of the weedy facultative winter annual Papaver rhoeas . Weed Res. 42:194–202.Google Scholar
Bradford, K. J. 2002. Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Sci. 50:248–260.CrossRefGoogle Scholar
Chancellor, R. J. 1986. Decline of arable weed seeds during 20 years in soil under grass and the periodicity of seedling emergence after cultivations. J. Appl. Ecol. 23:631–637.Google Scholar
Cirujeda, A., Recasens, J., and Taberner, A. 2006. Dormancy cycle and viability of buried seeds of Papaver rhoeas . Weed Res. 46:327–334.Google Scholar
Cirujeda, A., Recasens, J., Torra, J., and Taberner, A. 2008. A germination study of herbicide resistant field poppies in Spain. Agron. Sustain. Dev. 28:207–220.CrossRefGoogle Scholar
Dorado, J., Sousa, E., Calha, I. M., González-Andújar, J. L., and Fernández-Quintanilla, C. 2009. Predicting weed emergence in maize crops under two contrasting climatic conditions. Weed Res. 49:251–260.CrossRefGoogle Scholar
Eizenberg, H., Colquhoun, J., and Mallory-Smith, C. 2004. A predictive degree-days model for small broomerape (Orobanche minor) parasitism in red clover in Oregon. Weed Sci. 53:37–40.Google Scholar
Finch-Savage, W. E. 2004. The use of population-based threshold models to describe and predict the effects of seedbed environment on germination and seedling emergence of crops. Pages 51–95. In Benech-Arnold, R. and Sánchez, R. A. Handbook of Seed Physiology, Applications to Agriculture. New York Food Products Press.Google Scholar
Forcella, F., Benech-Arnold, R. L., Sánchez, R., and Ghersa, C. M. 2000. Modeling seedling emergence. Field Crop Res. 67:123–139.CrossRefGoogle Scholar
Froud-Williams, R. J., Chancellor, R. J., and Drennan, D. S. H. 1984. The effect of seed burial and soil disturbance on emergence and survival of arable weeds in relation to minimum cultivation. J. Appl. Ecol. 21:629–641.CrossRefGoogle Scholar
Grundy, A. C. 2003. Predicting weed emergence: a review of approaches and future challenges. Weed Res. 43:1–11.CrossRefGoogle Scholar
Grundy, A. C., Phelps, K., Reader, J., and Burston, S. 2000. Modeling the germination of Stellaria media using the concept of hydrothermal time. New Phytol. 148:433–444.Google Scholar
Heap, I., Glick, H., Glasgow, L., and Vencill, W. 2008. The International Survey of Herbicide Resistant Weeds. http://www.weedscience.org. Accessed: June 6, 2009.Google Scholar
Leblanc, M. L., Cloutier, D. C., Stewart, K., and Hamel, C. 2003. The use of thermal time to model common lambsquarters (Chenopodium album) seedling emergence in corn. Weed Sci. 51:718–724.Google Scholar
Leguizamón, E. S., Fernández-Quintanilla, C., Barroso, J., and González-Andújar, J. L. 2005. Using thermal and hydrothermal time to model seedling emergence of Avena sterilis ssp. ludoviciana in Spain. Weed Res. 45:149–156.Google Scholar
Lutman, P. J. W., Cussans, G. W., Wright, K. J., Wilson, B. J., Wright, G. M., and Lawson, H. M. 2002. The persistence of seeds of 16 weed species over six years in two arable fields. Weed Res. 42:231–241.Google Scholar
Martinson, K., Durgan, D., Forcella, F., Wiersma, J., Spokas, K., and Archer, D. 2007. An emergence model for wild oat (Avena fatua). Weed Sci. 55:584–591.CrossRefGoogle Scholar
Milberg, P. and Anderson, L. 1997. Seasonal variation in dormancy and light sensitivity in buried seeds of eight annual weed species. Can. J. Bot. 75:1998–2004.Google Scholar
Riba, F., Recasens, J., and Taberner, A. 1990. Flora arvense de los cereales de invierno de Catalunya (I). Pages 239–246. in. Proceedings 1990 Sociedad Española de Malherbología. Madrid, Spain Spanish Weed Science Society.Google 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
Torra, J. and Recasens, J. 2008. Demography of corn poppy (Papaver rhoeas) in relation to emergence time and crop competition. Weed Sci. 56:826–833.CrossRefGoogle Scholar
Vincent, E. M. and Roberts, E. H. 1977. The interaction of light, nitrate and alternating temperature in promoting the germination of dormant seeds of common weed species. Seed Sci. Technol. 5:659–670.Google Scholar
Weber, E. and Gut, D. 2005. A survey of weeds that are increasingly spreading in Europe. Agron. Sustain. Dev. 25:109–121.Google Scholar
Wilson, B. J., Peters, N. C. B., Wright, K. J., and Arkins, A. K. 1988. The influence of crop competition on the seed production of Lamium purpureum, Viola arvensis and Papaver rhoeas in winter wheat. Asp. Appl. Biol. 18:71–80.Google Scholar
Wilson, B. J., Wright, K. J., Brain, P., Clements, M., and Stephens, E. 1995. Predicting the competitive effects of weed and crop density on weed biomass, weed seed production and crop yield in wheat. Weed Res. 35:265–278.CrossRefGoogle Scholar
Zadoks, J. C., Chang, T. T., and Konzak, C. F. 1974. A decimal code for the growth stages of cereals. Weed Res. 14:415–421.Google Scholar