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1 March 2005 WeedTurf: a predictive model to aid control of annual summer weeds in turf
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Abstract

Predicting weed emergence is useful for planning weed management programs. Unfortunately, our ability to anticipate initial emergence and subsequent levels of emergence from simple field observations or weather reports is often inadequate to achieve optimal control. Weed emergence models may provide predictive tools that help managers anticipate best management options and times and, thereby, improve weed control. In this study, the germination characteristics of four annual grass weeds (large crabgrass, goosegrass, green foxtail, and yellow foxtail) were investigated under different temperatures and water stresses to calculate base temperatures and base water potentials. These parameters were used to develop a mathematical model describing seedling emergence processes in terms of hydrothermal time. Hydrothermal time describes seed germination in a single equation by considering the interaction of soil water potential and soil temperature. The model, called WeedTurf, predicted emergence with some accuracy, especially for large crabgrass (lowest efficiency index [EF] value 0.95) and green foxtail (lowest EF value 0.91). These results suggest the possibility of developing interactive computer software to determine the critical timing of weed removal and provide improved recommendations for herbicide application timing.

Nomenclature: Goosegrass, Eleusine indica (L.) Gaertn. ELEIN; green foxtail, Setaria viridis (L.) Beauv. SETVI; large crabgrass, Digitaria sanguinalis (L.) Scop. DIGSA; yellow foxtail, Setaria glauca (L.) Beauv. SETLU.

Roberta Masin, Maria Clara Zuin, David W. Archer, Frank Forcella, and Giuseppe Zanin "WeedTurf: a predictive model to aid control of annual summer weeds in turf," Weed Science 53(2), 193-201, (1 March 2005). https://doi.org/10.1614/WS-04-066R1
Received: 17 March 2004; Accepted: 1 October 2004; Published: 1 March 2005
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