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Predicting weed emergence for eight annual species in the northeastern United States

Published online by Cambridge University Press:  20 January 2017

William S. Curran
Department of Crop and Soil Sciences, The Pennsylvania State University, University Park, PA 16802
Mark J. VanGessel
Plant and Soil Sciences Department, University of Delaware, Research and Education Center, Georgetown, DE 19947
Dennis D. Calvin
Department of Entomology, The Pennsylvania State University, University Park, PA 16802
David A. Mortensen
Department of Crop and Soil Sciences, The Pennsylvania State University, University Park, PA 16802
Bradley A. Majek
Rutgers Agricultural Research and Extension Center, Rutgers University, Bridgeton, NJ 08032
Heather D. Karsten
Department of Crop and Soil Sciences, The Pennsylvania State University, University Park, PA 16802
Gregory W. Roth
Department of Crop and Soil Sciences, The Pennsylvania State University, University Park, PA 16802


A 2-yr experiment assessed the potential for using soil degree days (DD) to predict cumulative weed emergence. Emerged weeds, by species, were monitored every 2 wk in undisturbed plots. Soil DD were calculated at each location using a base temperature of 9 C. Weed emergence was fit with logistic regression for common ragweed, common lambsquarters, velvetleaf, giant foxtail, yellow foxtail, large crabgrass, smooth pigweed, and eastern black nightshade. Coefficients of determination for the logistic models fit to the field data ranged between 0.90 and 0.95 for the eight weed species. Common ragweed and common lambsquarters were among the earliest species to emerge, reaching 10% emergence before 150 DD. Velvetleaf, giant foxtail, and yellow foxtail were next, completing 10% emergence by 180 DD. The last weeds to emerge were large crabgrass, smooth pigweed, and eastern black nightshade, which emerged after 280 DD. The developed models were verified by predicting cumulative weed emergence in adjacent plots. The coefficients of determination for the model verification plots ranged from 0.66 to 0.99 and averaged 0.90 across all eight weed species. These results suggest that soil DD are good predictors for weed emergence. Forecasting weed emergence will help growers make better crop and weed management decisions.

Weed Biology and Ecology
Weed Science , Volume 52 , Issue 6 , December 2004 , pp. 913 - 919
Copyright © Weed Science Society of America 

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Literature Cited

Baskerville, G. L. and Emin, P. 1969. Rapid estimation of heat accumulation from maximum and minimum temperatures. Ecology 50:514517.CrossRefGoogle Scholar
Benech Arnold, R. L., Ghersa, C. M., Sanchez, R. A., and Insausti, P. 1990. A mathematical model to predict Sorghum halepense (L.) Pers. seedling emergence in relation to soil temperature. Weed Res 30:9199.CrossRefGoogle 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 113:839841.Google Scholar
Brown, R. F. and Mayer, D. G. 1988. Representing cumulative germination. 2. The use of the Weibull function and other empirically derived curves. Ann. Bot 61:127138.CrossRefGoogle Scholar
Buhler, D. D., Hartzler, R. G., Forcella, F., and Gunsolus, J. 1997. Relative Emergence Sequence for Weeds of Corn and Soybeans. Ames, IA: Iowa State University Publication on Pest Management, File 9 (SA-11).Google Scholar
Calvin, D. D., Higgins, R. A., and Knapp, M. C. et al. 1991. Similarities in developmental rates of geographically separate European Corn Borer (Lepidoptera: Pyralidae) populations. Environ. Entomol 20:441449.CrossRefGoogle Scholar
Fidanza, M. A., Dernoeden, P. H., and Zhang, M. 1996. Degree-days for predicting smooth crabgrass emergence in cool-season turfgrass. Crop Sci 36:990996.CrossRefGoogle Scholar
Forcella, F. 1998. Real-time assessment of seed dormancy and seedling growth for weed management. Seed Sci. Res 8:201209.CrossRefGoogle Scholar
Forcella, F., Benech Arnold, R. L., Sanchez, R., and Ghersa, C. M. 2000. Modeling seedling emergence. Field Crops Res 67:123139.CrossRefGoogle Scholar
Hartzler, R. G., Buhler, D. D., and Stoltenberg, D. E. 1999. Emergence characteristics of four annual weed species. Weed Sci 47:578584.Google Scholar
Hartzler, R. G. and Roth, G. W. 1993. Effect of prior year's weed control on herbicide effectiveness in corn (Zea mays). Weed Sci 7:611614.Google Scholar
King, C. A. and Oliver, L. R. 1994. A model for predicting large crabgrass (Digitaria sanguinalis) emergence as influenced by temperature and water potential. Weed Sci 42:561567.Google Scholar
Ogg, A. G. Jr. and Dawson, J. H. 1984. Time of emergence of eight weed species. Weed Sci 32:327335.Google Scholar
Roman, E. S., Murphy, S. D., and Swanton, C. J. 2000. Simulation of Chenopodium album seedling emergence. Weed Sci 48:217224.CrossRefGoogle Scholar
Schabenberger, O. and Pierce, F. J. 2002. Contemporary Statistical Models for the Plant and Soil Sciences. Boca Raton, FL: CRC. Pp. 185213.Google Scholar
Stoller, E. W. and Wax, L. M. 1973. Periodicity of germination and emergence of some annual weeds. Weed Sci 21:574580.Google Scholar
Vleeshouwers, L. 1997. Modelling Weed Emergence Patterns. Ph.D. dissertation. Wageningen Agricultural University, Wageningen, The Netherlands.Google Scholar