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

Weed Science

Volumn 52, Issue 6, 2004, Pages 913-919

  Myers, Matthew W ^a   Curran, William S ^a   VanGessel, Mark J ^b   Calvin, Dennis D ^a   Mortensen, David A ^a   Majek, Bradley A ^c   Karsten, Heather D ^a   Roth, Gregory W ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b UNIVERSITY OF DELAWARE   (United States)

^c Rutgers Agricultural Research and Extension Center   (United States)

Author keywords

Soil degree days; Weed competition; Weed emergence models

Indexed keywords

CROP IMPROVEMENT; EMERGENCE; EXPERIMENTAL STUDY; LOGISTICS; MODEL TEST; PLANT; PREDICTION; SOIL; TEMPERATURE; WEED; WEED CONTROL;

NORTH AMERICA; UNITED STATES;

ABUTILON THEOPHRASTI; ALOPECURUS PRATENSIS; AMARANTHUS HYBRIDUS; AMBROSIA; AMBROSIA ARTEMISIIFOLIA; CHENOPODIUM ALBUM; DIGITARIA SANGUINALIS; SETARIA FABERI; SOLANUM NIGRUM; SOLANUM PTYCHANTHUM;

EID: 33747704256     PISSN: 00431745     EISSN: None     Source Type: Journal    
DOI: 10.1614/WS-04-025R     Document Type: Article

References (17)

1. Baskerville, G. L. and P. Emin. 1969. Rapid estimation of heat accumulation from maximum and minimum temperatures. Ecology 50:514-517.
2. Benech Arnold, R. L., C. M. Ghersa, R. A. Sanchez, and P. Insausti. 1990. A mathematical model to predict Sorghum halefense (L.) Pers. seedling emergence in relation to soil temperature. Weed Res. 30:91-99.
3. Bewick, T. A., L. K. Binning, and B. Yandell. 1988. A degree day model for predicting the emergence of swamp dodder in cranberry. J. Am. Soc. Hortic. Sci. 113:839-841.
4. Brown, R. F. and D. G. Mayer. 1988. Representing cumulative germination. 2. The use of the Weibull function and other empirically derived curves. Ann. Bot. 61:127-138.
5. Buhler, D. D., R. G. Hartzier, F. Forcella, and J. Gunsolus. 1997. Relative Emergence Sequence for Weeds of Corn and Soybeans. Ames, IA: Iowa State University Publication on Pest Management, File 9 (SA-11).
6. Calvin, D. D., R. A. Higgins, M. C. Knapp, et al. 1991. Similarities in developmental rates of geographically separate European Corn Borer (Lepidoptera: Pyralidae) populations. Environ. Entomol. 20:441-449.
7. Fidanza, M. A., P. H. Dernoeden, and M. Zhang. 1996. Degree-days for predicting smooth crabgrass emergence in cool-season turfgrass. Crop Sci. 36:990-996.
8. Forcella, F. 1998. Real-time assessment of seed dormancy and seedling growth for weed management. Seed Sci. Res. 8:201-209.
9. Forcella, E, R. L. Benech Arnold, R. Sanchez, and C. M. Ghersa. 2000. Modeling seedling emergence. Field Crops Res. 67:123-139.
10. Hartzler, R. G., D. D. Buhler, and D. E. Stoltenberg. 1999. Emergence characteristics of four annual weed species. Weed Sci. 47:578-584.
11. Hartzler, R. G. and G. W. Roth. 1993. Effect of prior year's weed control on herbicide effectiveness in corn (Zea mays). Weed Sci. 7:611-614.
12. King, C. A. and L. R. Oliver. 1994. A model for predicting large crabgrass (Digitaria sanguinalis) emergence as influenced by temperature and water potential. Weed Sci. 42:561-567.
13. Ogg, A. G., Jr. and J. H. Dawson. 1984. Time of emergence of eight weed species. Weed Sci. 32:327-335.
14. Roman, E. S., S. D. Murphy, and C. J. Swanton. 2000. Simulation of Chenopodium album seedling emergence. Weed Sci. 48:217-224.
15. Schabenberger, O. and F. J. Pierce. 2002. Contemporary Statistical Models for the Plant and Soil Sciences. Boca Raton, FL: CRC. Pp. 185-213.
16. Stoller, E. W. and L. M. Wax. 1973. Periodicity of germination and emergence of some annual weeds. Weed Sci. 21:574-580.
17. Vleeshouwers, L. 1997. Modelling Weed Emergence Patterns. Ph.D. dissertation. Wageningen Agricultural University, Wageningen, The Netherlands.