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Hydrothermal Emergence Model for Ripgut Brome (Bromus diandrus)

Published online by Cambridge University Press:  20 January 2017

Addy L. García ,
Jordi Recasens ,
Frank Forcella ,
Joel Torra  and
Aritz Royo-Esnal
Addy L. García*
Affiliation:
Dto Hortofruticultura, Botànica i Jardineria, ETSEA, Universitat de Lleida, Lleida, Spain
Jordi Recasens
Affiliation:
Dto Hortofruticultura, Botànica i Jardineria, ETSEA, Universitat de Lleida, Lleida, Spain
Frank Forcella
Affiliation:
USDA-ARS Lab, Morris, Minnesota, USA
Joel Torra
Affiliation:
Fundació Centre UdL-IRTA. Lleida. Spain
Aritz Royo-Esnal
Affiliation:
Dto Hortofruticultura, Botànica i Jardineria, ETSEA, Universitat de Lleida, Lleida, Spain
*
Corresponding author's Email: addylau@hbj.udl.cat
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Abstract

A model that describes the emergence of ripgut brome was developed using a two-season data set from a no-tilled field in northeastern Spain. The relationship between cumulative emergence and hydrothermal time (HTT) was described by a sigmoid growth function (Chapman). HTT was calculated with a set of water potentials and temperatures, iteratively used, to determine the base water potential and base temperature. Emergence of ripgut brome was well described with a Chapman function. The newly-developed function was validated with four sets of data, two of them belonging to a third season in the same field and the other two coming from independent data from Southern Spain. The model also successfully described the emergence in different field management and tillage systems. This model may be useful for predicting ripgut brome emergence in winter cereal fields of semiarid Mediterranean regions.

Keywords

Ripgut brome, Bromus diandrus RothChapman functiongreat bromehydrothermal timesowing delaytillage systems
Type
Weed Management
Information
Weed Science , Volume 61 , Issue 1 , March 2013 , pp. 146 - 153
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Bair, N. B., Meyer, S. E., and Allen, P. S. 2006. A hydrothermal after-ripening time model for seed dormancy loss in Bromus tectorum L. Seed Sci. Res. 16: 17–28.Google Scholar
Cao, R., Francisco-Fernandez, M., Anand, A., Bastida, F., and Gonzalez-Andujar, J. L. 2011. Computing statistical indices for hydrothermal times using weed emergence data. J. of Agric. Sci. 149: 701–712.Google Scholar
Cirujeda, A., Recasens, J., Torra, J., and Taberner, A. 2008. A germination study of herbicide-resistant field poppies in Spain. Agronomy for Sustainable Development. 28: 207–220.Google Scholar
Colbach, N., Dürr, C., Roger-Estrade, J., and Caneill, J. 2005. How to model the effects of farming practices on weed emergence. Weed Res. 45: 2–17.Google Scholar
Del Monte, J. P. and Dorado, J. 2011. Effects of light conditions and after-ripening time on seed dormancy loss of Bromus diandrus Roth. Weed Res. 51: 581–590.Google Scholar
Ekeleme, F., Forcella, F., Archer, D. W., Akobundu, I. O., and Chikoye, D. 2005. Seedling emergence model for tropic ageratum (Ageratum conyzoides). Weed Sci. 53: 55–61.Google Scholar
Forcella, F., Benech Arnold, R. L., Sanchez, R., and Ghersa, C. M. 2000. Modeling seedling emergence. Field Crop. Res. 67: 123–139.Google Scholar
Gill, G. S. and Carstairs, S. A. 1988. Morphological, cytological and ecological discrimination of Bromus rigidus from Bromus diandrus . Weed Res. 28: 399–405.Google Scholar
Gleichsner, J. A. and Appleby, A. P. 1989. Effect of Depth and Duration of Seed Burial on Ripgut Brome (Bromus rigidus). Weed Sci. 37: 68–72.Google Scholar
Grundy, A. C., Phelps, K., Reader, R. J., and Burston, S. 2000. Modeling the germination of Stellaria media using the concept of hydrothermal time. New Phytol. 148: 433–444.Google Scholar
Haj Seyed Hadi, M. R. and Gonzalez-Andujar, J. L. 2009. Comparison of fitting weed seedling emergence models with nonlinear regression and genetic algorithm. Comput. Electron. Agri. 65: 19–25.Google Scholar
Kleemann, S. G. L. and Gill, G. S. 2006. Differences in the distribution and seed germination behaviour of populations of Bromus rigidus and Bromus diandrus in South Australia: Adaptations to habitat and implications for weed management. Aust. J. Agr. Res. 57: 213–219.Google Scholar
Kon, K. F. and Blacklow, W. M. 1989. Identification, distribution and population variability of great brome (Bromus diandrus Roth) and rigid brome (Bromus rigidus Roth). Australian J. Agric. Res. 39: 1039–1050.Google Scholar
Leblanc, M. L., Cloutier, D. C., Stewart, K. A., and Hamel, C. 2004. Calibration and validation of a common lambsquarters (Chenopodium album) seedling emergence model. Weed Sci. 52: 61–66.Google Scholar
Leguizamón, 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.Google Scholar
Martinson, K., Durgan, B., Forcella, F., Wiersma, J., Spokas, K., and Archer, D. 2007. An emergence model for wild oat (Avena fatua). Weed Sci. 55: 584–591.Google Scholar
Mayer, D. G. and Butler, D. G. 1993. Statistical validation. Ecol. Model. 68: 21–32.Google Scholar
Meyer, S. E. and Allen, P. S. 2009. Predicting seed dormancy loss and germination timing for Bromus tectorum in a semi-arid environment using hydrothermal time models. Seed Sci. Res. 19: 225–239.Google Scholar
Myers, M. W., Curran, W. S., VanGessel, M. J., Calvin, D. D., Mortensen, D. A., Majek, B. A., Karsten, H. D., and Roth, G. W. 2004. Predicting weed emergence for eight annual species in the northeastern United States. Weed Sci. 52: 913–919.Google Scholar
Peeper, T. F. 1984. Chemical and biological control of downy brome (Bromus tectorum) in wheat and alfalfa in North America. Weed Sci. 32: 18–24.Google Scholar
Riba, F. 1993. Demography and population dynamics of Bromus diandrus Roth in winter cereals. PhD dissertation. University of Lleida.117 p.Google Scholar
Riba, F. and Recasens, J. 1997. Bromus diandrus Roth en cereals de invierno. In: Sans, F. X. y. and Fernández-Quintanilla, C. (editores). La biología de las malas hierbas de España. Ed. Phytoma España-Sociedad Española de Malherbología. (25–35).Google Scholar
Roman, E. S., Murphy, S. D., and Swanton, C. J. 2000. Simulation of Chenopodium album seedling emergence. Weed Sci. 48: 217–224.Google Scholar
Royo-Esnal, A., Torra, J., Conesa, J. A., Forcella, F., and Recasens, J. 2010a. Modeling the emergence of three arable bedstraw (Galium) species. Weed Sci. 58: 10–15.Google Scholar
Royo-Esnal, A., Torra, J., Conesa, J. A., and Recasens, J. 2010b. Characterization of emergence of autumn and spring cohorts of Galium spp. in winter cereals. Weed Sci. 50: 572–585.Google Scholar
Schutte, B. J., Regnier, E. E., Harrison, S. K., Schmoll, J. T., Spokas, K., and Forcella, F. 2008. A hydrothermal seedling emergence model for giant ragweed (Ambrosia trifida). Weed Sci. 56: 555–560.Google Scholar
Spokas, K. and Forcella, F. 2009. Software tools for weed seed germination modeling. Weed Sci. 57: 216–227.Google Scholar