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Seed Burial Physical Environment Explains Departures from Regional Hydrothermal Model of Giant Ragweed (Ambrosia trifida) Seedling Emergence in U.S. Midwest

Published online by Cambridge University Press:  20 January 2017

Adam S. Davis ,
Sharon Clay ,
John Cardina ,
Anita Dille ,
Frank Forcella ,
John Lindquist  and
Christy Sprague
Adam S. Davis*
Affiliation:
U.S. Department of Agriculture–Agricultural Research Service (USDA-ARS) Global Change and Photosynthesis Research Unit, Urbana, IL 61801
Sharon Clay
Affiliation:
South Dakota State University, Brookings, SD, 57007
John Cardina
Affiliation:
Ohio Agricultural Research and Development Center, The Ohio State University, Wooster, OH, 44691
Anita Dille
Affiliation:
Kansas State University, Manhattan, KS, 66506
Frank Forcella
Affiliation:
USDA-ARS North Central Soil Conservation Research Laboratory, Morris, MN, 56267
John Lindquist
Affiliation:
University of Nebraska, Lincoln, NE, 68583
Christy Sprague
Affiliation:
Michigan State University, East Lansing, MI, 48824
*
Corresponding author's E-mail: adam.davis@ars.usda.gov
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Abstract

Robust predictions of weed seedling emergence from the soil seedbank are needed to aid weed management. A common seed accession (Illinois) of giant ragweed was buried in replicate experimental gardens over 18 site years in Illinois, Michigan, Kansas, Nebraska, Ohio, and South Dakota to examine the importance of site and climate variability by year on seedling emergence. In a nonlinear mixed-effects modeling approach, we used a flexible sigmoidal function (Weibull) to model giant ragweed cumulative seedling emergence in relation to hydrothermal time accumulated in each site-year. An iterative search method across a range of base temperature (Tb ) and base and ceiling soil matric potentials (ψb and ψc) for accumulation of hydrothermal time identified optima (Tb = 4.4 C, ψb = −2,500 kPa, ψc = 0 kPa) that resulted in a parsimonious regional model. Deviations between the fits for individual site-years and the fixed effects regional model were characterized by a negative relationship between random effects for the shape parameter lrc (natural log of the rate constant, indicating the speed at which emergence progressed) and thermal time (base 10 C) during the seed burial period October through March (r = −0.51, P = 0.03). One possible implication of this result is that cold winter temperatures are required to break dormancy in giant ragweed seeds. By taking advantage of advances in statistical computing approaches, development of robust regional models now is possible for explaining arable weed seedling emergence progress across wide regions.

Keywords

Giant ragweed, Ambrosia trifida LAbiotic influences on seed dormancyhydrothermal timenonlinear mixed effects modelsregional environmental variationseedling recruitment phenology
Type
Weed Biology and Ecology
Information
Weed Science , Volume 61 , Issue 3 , September 2013 , pp. 415 - 421
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

[AOSA] Association of Official Seed Analysts. 2000. Tetrazolium testing handbook. Contribution No. 29 to the Handbook on Seed Testing.302 p.Google Scholar
Archer, D. W., Forcella, F., Korth, A., Kuhn, A., Eklund, J., and Spokas, K. 2006. WeedCast. http://www.ars.usda.gov/services/software/download.htm?softwareid=112. Accessed June 20, 2012.Google Scholar
Ballard, T. O., Foley, M. E., and Bauman, T. T. 1996. Germination, viability, and protein changes during cold stratification of giant ragweed (Ambrosia trifida L.) seed. J. Plant Physiol. 149:229–232.Google Scholar
Baskin, J. M. and Baskin, C. C. 1987. Environmentally induced changes in the dormancy states of buried weed seeds. Pages 695–706 in Proceedings of British Crop Protection Conference: Weeds. Brighton, UK British Crop Protection Council.Google Scholar
Benech-Arnold, R. L., Sanchez, R. A., Forcella, F., Kruck, B. C., and Ghersa, C. M. 2000. Environmental control of dormancy in weed seed banks in soil. Field Crops Res. 67:105–122.Google Scholar
Bewick, T. A., Binning, L. K., and Yandell, B. 1988. A degree day model for predicting the emergence of swamp dodder in cranberry. J. Am. Soc. Hortic. Sci. 113:839–841.CrossRefGoogle Scholar
Burnham, K. P. and Anderson, D. R. 2002. Model Selection and Inference: A Practical Information-Theoretic Approach. 2nd ed. New York Springer Verlag. Pp. 32–74.Google Scholar
Carey, J. B. and Kells, J. J. 1995. Timing of total postemergence herbicide applications to maximize weed control and corn yield. Weed Technol. 9:356–361.Google Scholar
Clay, S. A., Banken, K. R., Forcella, F., Ellsbury, M. M., Clay, D. E., and Olness, A. E. 2006. Influence of yellow foxtail on corn growth and yield. Commun. Soil Sci. Plant Anal. 37:1421–1435.Google Scholar
Dalley, C. D., Kells, J. J., and Renner, K. A. 2004. Effect of glyphosate application timing and row spacing on corn (Zea mays) and soybean (Glycine max) yields. Weed Technol. 18:165–176.CrossRefGoogle Scholar
Forcella, F. 1998. Real-time assessment of seed dormancy and seedling growth for weed management. Seed Sci. Res. 8:201–209.Google Scholar
Forcella, F., Benech-Arnold, R. L., Sanchez, R., and Ghersa, C. M. 2000. Modeling seedling emergence. Field Crops Res. 67:123–139.Google Scholar
Gardarin, A., Guillemin, J. P., Munier-Jolain, N. M., and Colbach, N. 2010. Estimation of key parameters for weed population dynamics models: base temperature and base water potential for germination. Eur. J. Agron. 32:162–168.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. and Mead, A. 2000. Modeling weed emergence as a function of meteorological records. Weed Sci. 48:594–603.CrossRefGoogle Scholar
Grundy, A. C., Peters, N.C.B., Rasmussen, I. A., Hartmann, K. M., Sattin, M., Andersson, L., Mead, A., Murdoch, A. J., and Forcella, F. 2003. Emergence of Chenopodium album and Stellaria media of different origins under different climatic conditions. Weed Res. 43:163–176.Google Scholar
Gummerson, R. J. 1986. The effect of constant temperature and osmotic potentials on the germination of sugar beet. J. Exp. Bot. 37:729–741.Google Scholar
Hardegree, S. P. and Winstral, A. H. 2006. Predicting germination response to temperature. II. Three-dimensional regression, statistical gridding and iterative-probit optimization using measured and interpolated-subpopulation data. Ann. Bot. 98:403–410.CrossRefGoogle ScholarPubMed
Johnson, B., Loux, M., Nordby, D., Sprague, C., Nice, G., Westhoven, A., and Stachler, J. 2007. Biology and Management of Giant Ragweed. GWC-12. Purdue University Extension. 16 p www.ces.purdue.edu/new. Accessed June 20, 2012.Google Scholar
Luschei, E. C. and Jackson, R. D. 2005. Research methodologies and statistical approaches for multitactic systems. Weed Sci. 53:393–403.Google Scholar
Masin, R., Loddo, D., Benvenuti, S., Zuin, M. C., Macchia, M., and Zanin, G. 2010. Temperature and water potential as parameters for modeling weed emergence in central-northern Italy. Weed Sci. 58:216–222.Google Scholar
Moriles, J., Hansen, S., Horvath, D. P., Reicks, G., Clay, D. E., and Clay, S. A. 2012. Microarray and growth analyses identify differences and similarities of early corn response to weeds, shade, and nitrogen stress. Weed Sci. 158–166.Google Scholar
Mt. Pleasant, J., Burt, R. F., and Frisch, J. C. 1994. Integrating mechanical and chemical weed management in corn (Zea mays). Weed Technol. 8:217–223.Google Scholar
Norsworthy, J. K. and Oliveira, M. J. 2004. Comparison of the critical period for weed control in wide- and narrow-row corn. Weed Sci. 52:802–807.Google Scholar
Oryokot, J.O.E., Hunt, L. A., Murphy, S. D., and Swanton, C. J. 1997a. Simulation of pigweed (Amaranthus spp.) seedling emergence in different tillage systems. Weed Sci. 45:684–690.Google Scholar
Oryokot, J.O.E., Murphy, S. D., and Swanton, C. J. 1997b. Effect of tillage and corn on pigweed (Amaranthus spp.) seedling emergence and density. Weed Sci. 45:120–126.Google Scholar
Pinheiro, J. C. and Bates, D. M. 2004. Mixed-Effects Models in S and S-PLUS. New York Springer. 528 p.Google Scholar
Ratkowski, D. A. 1983. Nonlinear Regression Modeling: A Unified Practical Approach. New York Marcel Dekker. Pp. 135–153.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
Schutte, B. J., Regnier, E. E., and Harrison, S. K. 2012. Seed dormancy and adaptive seedling emergence timing in giant ragweed (Ambrosia trifida). Weed Sci. 60:19–26.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
Steadman, K. J. and Pritchard, H. W. 2004. Germination of Aesculus hippocastanum seeds following cold-induced dormancy loss can be described in relation to a temperature-dependent reduction in base temperature (Tb) and thermal time. New Phytol. 161:415–425.CrossRefGoogle ScholarPubMed
Wortman, S. E., Davis, A. S., Schutte, B. J., Lindquist, J. L., Cardina, J., Felix, J., Sprague, C. L., Dille, J. A., Ramirez, A.H.M., Reicks, G., and Clay, S. A. 2012. Local conditions, not spatial gradients, drive demographic variation of Ambrosia trifida and Helianthus annuus across northern U.S. maize belt. Weed Sci. 60:440–450.Google Scholar