A Hydrothermal Seedling Emergence Model for Giant Ragweed (Ambrosia trifida)
Authors:
Schutte, B.J. Regnier, E.E. Harrison, S.K. Schmoll, J.T. Spokas, K. Forcella, F. USDA, ARS
Source:
Weed science. 2008 July, v. 56, no. 4, p. 555-560.
NALT Subjects:
Ambrosia trifida seedling emergence model validation simulation models buried seeds soil water soil temperature spring prediction integrated weed management Ohio
Issue Date:
Jul-2008
Abstract:
Late-season giant ragweed emergence in Ohio crop fields complicates decisions concerning the optimum time to implement control measures. Our objectives were to develop a hydrothermal time emergence model for a late-emerging biotype and validate the model in a variety of locations and burial environments. To develop the model, giant ragweed seedlings were counted and removed weekly each growing season from 2000 to 2003 in a fallow field located in west central Ohio. Weather data, soil characteristics and geographic location were used to predict soil thermal and moisture conditions with the Soil Temperature and Moisture Model (STM2). Hydrothermal time (thetaHT) initiated March 1 and base values were extrapolated from the literature (Tb = 2 C, psi b = -10 MPa). Cumulative percent emergence initially increased rapidly and reached 60% of maximum by late April (approximately 400 thetaHT), leveled off for a period in May, and increased again at a lower rate before concluding in late July (approximately 2,300 θHT). The period in May when few seedlings emerged was not subject to soil temperatures or water potentials less than the thetaHT base values. The biphasic pattern of emergence was modeled with two successive Weibull models that were validated in 2005 in a tilled and a no-tillage environment and in 2006 at a separate location in a no-tillage environment. Root-mean-square values for comparing actual and model predicted cumulative emergence values ranged from 8.0 to 9.5%, indicating a high degree of accuracy. This experiment demonstrated an approach to emergence modeling that can be used to forecast emergence on a local basis according to weed biotype and easily obtainable soil and weather data.
