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ARS Home » Midwest Area » Urbana, Illinois » Global Change and Photosynthesis Research » Research » Publications at this Location » Publication #285978
Title: Seed burial physical environment explains departures from regional hydrothermal model of giant ragweed (Ambrosia trifida) seedling emergence in U.S. Midwest

Author
item Davis, Adam
item CLAY, SHARON - South Dakota State University
item CARDINA, JOHN - The Ohio State University
item DILLE, ANITA - Kansas State University
item Forcella, Frank
item LINDQUIST, JOHN - University Of Nebraska
item SPRAGUE, CHRISTY - Michigan State University

Submitted to: Weed Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/17/2013
Publication Date: 7/1/2013
Citation: Davis, A.S., Clay, S., Cardina, J., Dille, A., Forcella, F., Lindquist, J., Sprague, C. 2013. Seed burial physical environment explains departures from regional hydrothermal model of giant ragweed (Ambrosia trifida) seedling emergence in U.S. Midwest. Weed Science. 61(3):415-421.

Interpretive Summary: Improving the efficacy of early-season weed management, during the onset of weed seedling emergence, depends upon better scheduling of weed control operations. One means of achieving better management timing is to improve the accuracy of predictions of the timing of weed seedling emergence from the soil seedbank. We tested a new approach to modeling weed seedling emergence timing on a regional-scale data set. A common seed collection of giant ragweed from Illinois 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. We identified optimal base values for temperature (Tb = 4.4 C) and soil water potential (Psi-lower = -2500 kPa, Psi-upper = 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 germination progressed) and thermal time (base 10 C) during the seed burial period (r = -0.51, P = 0.03). 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.

Technical Abstract: Predicting weed emergence timing from the seed bank plays a critical role in scheduling early season postemergence weed management operations to achieve high efficacies. 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 (Psi-base and Psi-ceiling) for accumulation of hydrothermal time identified optima (Tb = 4.4 C, Psi-lower = -2500 kPa, Psi-upper = 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 germination progressed) and thermal time (base 10 C) during the seed burial period (r = -0.51, P = 0.03). 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.