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

Authors

• Adam S. Davis, USDA Agricultural Research Service, Urbana, IllinoisFollow
• Sharon Clay, South Dakota State UniversityFollow
• John Cardina, The Ohio State University
• Anita Dille, Kansas State UniversityFollow
• Frank Forcella, USDA Agricultural Research Service, Morris, MinnesotaFollow
• John Lindquist, University of Nebraska-LincolnFollow
• Christy Sprague, University of Nebraska-LincolnFollow

Document Type

Article

Date of this Version

2013

Citation

Weed Science 61 (2013): 415-421

doi: 10.1614/WS-D-12-00139.1

Comments

U.S. government work. Public domain

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 (T_b) and base and ceiling soil matric potentials (ψ_b and ψ_c) for accumulation of hydrothermal time identified optima (T_b = 4.4 C, ψ_b = –2,500 kPa, ψ_c = 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.