Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-28T02:56:47.409Z Has data issue: false hasContentIssue false
  • English
  • Français

Phenological Indicators for Emergence of Large and Smooth Crabgrass (Digitaria sanguinalis and D. ischaemum)

Published online by Cambridge University Press:  20 January 2017

John Cardina ,
Catherine P. Herms  and
Daniel A. Herms
John Cardina*
Affiliation:
Ohio Agricultural Research and Development Center, The Ohio State University, 1680 Madison Avenue, Wooster, OH 44691
Catherine P. Herms
Affiliation:
Ohio Agricultural Research and Development Center, The Ohio State University, 1680 Madison Avenue, Wooster, OH 44691
Daniel A. Herms
Affiliation:
Ohio Agricultural Research and Development Center, The Ohio State University, 1680 Madison Avenue, Wooster, OH 44691
*
Corresponding author's E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

We studied the emergence phenology of large and smooth crabgrass in lawn and bare soil environments and identified ornamental plants as phenological indicators that predict the progress of emergence. From 2002 to 2004, we monitored emergence of large and smooth crabgrass in field plots to estimate the dates of first emergence, and 25, 50 and 80% emergence. Each year, we monitored 74 taxa of ornamental plants to determine dates of first and full bloom. We compiled dates of weed emergence and ornamental blooming to create a biological calendar of phenological events for each year, ordered by average cumulative degree days (DD) (January 1 start date, 10 C base temperature). Ornamental plant flowering events that occurred in a regular sequence before crabgrass emergence events were identified as the phenological indicators. We also evaluated DD and rule-based models for predicting crabgrass emergence and optimum time of PRE herbicide application. In general, smooth crabgrass reached each emergence stage earlier than large crabgrass. Differences in emergence between environments were not consistent over years for the two species. There was no consistent pattern in parameters for DD models predicting emergence events for either crabgrass species or environment. For published DD models, the deviation between observed and predicted emergence events ranged from 0 to > 60 d. Published rule-based predictions, though accurate in some cases, were sometimes difficult to implement. The order of ornamental plant blooming and crabgrass emergence events was generally consistent over years (R2 = 0.977). The biological calendar provided useful crabgrass emergence predictions using real-time field-based indicators of sequential biological events that can help managers plan and optimize management strategies.

Estudiamos la fenología de la emergencia de Digitaria sanguinalis y Digitaria ischaemum en ambientes de césped de jardín y del suelo desnudo e identificamos plantas ornamentales indicadoras que predicen la evolución de la emergencia. De 2002 a 2004 monitoreamos la emergencia de Digitaria sanguinalis y Digitaria ischaemum en parcelas de campo para estimar las fechas de la primera emergencia, y de 25, 50 y 80% de emergencia. Cada año, monitoreamos una muestra de 74 plantas ornamentales para determinar las fechas de la primera y de la floración total. Compilamos fechas de emergencia de las malezas y la floración de las plantas ornamentales para crear un calendario biológico de eventos fenológicos para cada año, ordenado por el promedio de grados-día acumulados (día de inicio: enero 1; temperatura base: 10 grados C). Los eventos de floración de las plantas ornamentales que ocurrieron en una secuencia regular antes de la emergencia de Digitaria ischaemum fueron identificados como indicadores fenológicos. También evaluamos modelos grado-día y modelos-norma basado en reglas para predecir la emergencia de Digitaria y el tiempo óptimo para la aplicación pre-emergente de herbicidas. En general, Digitaria ischaemum alcanzó cada etapa de emergencia más temprano que Digitaria sanguinalis. Las diferencias de emergencia entre los ambientes no fueron consistentes durante los años de estudio para las dos especies. No hubo un patrón consistente en los parámetros para de los modelos grado-día (DD) para predecir eventos de emergencia para ninguna de las especies de Digitaria o los dos ambientes. Para los modelos DD publicados, la desviación entre los eventos de emergencia observados y predichos, variaron entre 0 y > 60 días. Las predicciones publicadas basadas en modelos-norma, aunque bastante certeras en algunos casos, a veces fueron difíciles de implementar. El orden de floración de las plantas ornamentales y los eventos de emergencia de Digitaria fueron generalmente consistentes en los años del estudio (R2 = 0.977). El calendario biológico proporcionó predicciones útiles acerca de la emergencia de Digitaria, usando indicadores de los eventos de secuencia biológica, en tiempo real y basados en el campo, que pueden ayudar a los administradores a planear y optimizar las estrategias de manejo.

Keywords

Large crabgrass, Digitaria sanguinalis (L.) Scop. DIGSAsmooth crabgrass, Digitaria ischaemum (Schreb.) Schreb. ex Muhl. DIGISPhenologyemergence predictionindicator speciesdegree-day modelsbiological calendarPRE herbicide applicationsornamental plant floweringturfgrass
Type
Weed Biology and Competition
Information
Weed Technology , Volume 25 , Issue 1 , March 2011 , pp. 141 - 150
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Aguyoh, J. N. and Masiunas, J. B. 2003. Interference of large crabgrass (Digitaria sanguinalis) with snap beans. Weed Sci. 51:171–176.Google Scholar
Akers, R. C. and Nielsen, D. G. 1984. Predicting Agrilus anxius Gory (Coleoptera: Buprestidae) adult emergence by heat unit accumulation. J. Econ. Entomol 77:1459–1463.Google Scholar
Allen, J. C. 1976. A modified sine wave method for calculating degree-days. Environ. Entomol 5:388–396.Google Scholar
Arnold, C. Y. 1959. The determination and significance of the base temperature in a linear heat unit system. Proc. Am. Soc. Hort. Sci 74:430–445.Google Scholar
Arnold, C. Y. 1960. Maximum–minimum temperatures as a basis for computing heat units. Proc. Am. Soc. Hort. Sci 74:682–692.Google Scholar
Ascerno, M. E. and Moon, R. D. 1989. Forecaster: predicting biological phenomena based on daily temperatures. Minnesota Extension Service AG-CS-3029 Version 1.0.Google Scholar
Bridges, D. C. and Baumann, P. A. 1992. Weeds causing losses in the United States. Pages 75–147. In Bridges, D. C. ed. Crop Losses Due to Weeds in Canada and the United States. Champaign, IL Weed Science Society of America.Google Scholar
Calhoun, R. 2002. Crabgrass control in home lawns. Extension Bulletin E02TURF, Department of Crop and Soil Sciences, Michigan State University. www.turf.msu.edu. Accessed: March 1, 2010.Google Scholar
Cardina, J., Herms, C. P., Herms, D. A., and Forcella, F. 2007. Evaluating phenological indicators for predicting giant foxtail (Setaria faberi) emergence. Weed Sci. 55:455–464.Google Scholar
Derr, J. 2003. Crabgrass control. Grounds Maint 38:28–32.Google Scholar
Devore, J. and Peck, R. 1993. Statistics: The Exploration and Analysis of Data. Belmont, CA Wadsworth. Pp. 789–790.Google Scholar
Fidanza, M. A., Dernoeden, P. H., and Zhang, M. 1996. Degree-days for predicting smooth crabgrass emergence in cool-season turfgrass. Crop Sci 36:990–996.Google Scholar
Harker, K. N. and O'Sullivan, P. A. 1991. Synergistic mixtures of sethoxydim and fluazifop on annual grass weeds. Weed Technol. 5:310–316.Google Scholar
Hatterman-Valenti, H. 1992. Crabgrass control. ISU Entomology/Horticulture and Home Pest News/IC-463(7). http://www.ipm.iastate.edu/ipm/hortnews/1992/4-8-199. Accessed: July 7, 2010.Google Scholar
Herms, D. A. 1998. The flowering sequence of ornamental plants as a tool for predicting the phenology of insect pests. In Rose, M. A. and Chatfield, J. A. eds. Ornamental Plants: Annual Reports and Research Reviews, 1997. Ohio Agric. Res. Dev. Cen. Spec. Circ. 157.Google Scholar
Herms, D. A. 1999. Plant and insect phenology in the year of El Niño: comparison with 1997. In Rose, M. A. and Chatfield, J. A. eds. Ornamental Plants: Annual Reports and Research Reviews, 1998. Ohio Agric. Res. Dev. Cen. Spec. Circ. 165. 136. pp.Google Scholar
Herms, D. A. 2002. Biological clocks: a five-year calendar of plant and insect phenology in Secrest Arboretum. Pages 60–67. In Chatfield, J. A., Boggs, J. F., Draper, E. A., Mathers, H., and Stone, A. K. eds. Ornamental Plants: Annual Reports and Research Reviews 2001. Ohio Agric. Res. Dev. Cen. Spec. Circ. 186. 123. pp.Google Scholar
Herms, D. A. 2004. Using degree-days and plant phenology to predict pest activity. Pages 49–59. In Krischik, V. and Davidson, J. eds. IPM (Integrated Pest Management) of Midwest Landscapes. Minnesota Agricultural Experiment Station Publication SB-07645. 316. pp.Google Scholar
Higley, L. G., Pedigo, L. P., and Ostlie, K. R. 1986. DEGDAY: a program for calculating degree-days, and assumptions behind the degree-day approach. Environ. Entomol 15:999–1016.Google Scholar
Holm, L., Plucknett, D. L., Pancho, J. V., and Herberger, J. P. 1991. The World's Worst Weeds: Distribution and Biology. Malabar, FL Krieger. Pp. 8–125.Google Scholar
King, C. A. and Oliver, L. R. 1994. A model for predicting large crabgrass (Digitaria sanguinalis) emergence by temperature and water potential. Weed Sci. 42:561–567.Google Scholar
Landshoot 2009. Weed management in turf. The Pennsylvania State Univeristy, CODE # UC189. http://pubs.cas.psu.edu/FreePubs/pdfs/uc189.pdf. Accessed: July 7, 2010.Google Scholar
Masin, R., Zuin, M. C., Archer, D. W., and Zanin, G. 2005a. WeedTurf: a predictive model to aid control of annual summer weeds in turf. Weed Sci. 53:193–201.Google Scholar
Masin, R., Zuin, M. C., and Zanin, G. 2005b. Phenological observations on shrubs to predict weed emergence in turf. Int. J. Biometeorol 50:23–32.Google Scholar
McCarty, L. B., Everest, J. W., Hall, D. W., Murphy, T. R., and Yelverton, F. 2001. Color Atlas of Turfgrass Weeds. Chelsea, MI Ann Arbor Press. Pp. 38–42.Google Scholar
Morrison, I. N. and Maurice, D. C. 1984. The relative response of two foxtail (Setaria) species to diclofop. Weed Sci. 32:686–690.Google Scholar
Myers, M. W., Curran, W. S., VanGessel, M. J., Calvin, D. D., Mortensen, D. A., Majek, B. A., Karsten, H. D., and Roth, G. W. 2004. Predicting weed emergence for eight annual species in the Northeastern United States. Weed Sci. 52:913–919.Google Scholar
Muenscher, W. C. 1987. Weeds. Ithaca, NY Comstock.Google Scholar
Orton, D. A. 1996. Using plants to time pest control. Grounds Maint 1:14–19.Google Scholar
Preuss, K. P. 1983. Day-degree methods for pest management. Environ. Entomol 12:613–619.Google Scholar
Snyder, R. L., Spano, D., Cesaraccio, C., and Duce, P. 1999. Determining degree-day thresholds from field observations. Int. J. Biometeorol 42:177–182.Google Scholar
Sosnoskie, L. M., Herms, C. P., and Cardina, J. 2006. Weed seedbank community composition in a 35-year-old tillage and rotation experiment. Weed Sci. 54:263–273.Google Scholar
Sturgis, R. B. 2001. Landscape tips. Roger B. Sturgis & Associates, Inc. 1455 Concord St. Building #7 Framingham, MA 01701. http://www.rbsturgis.com/tips.html#anchor445000. Accessed: March 1, 2010.Google Scholar
Tae-Joon, K., Neal, J. C., DiTomaso, J. M., and Rossi, F. S. 2002. A survey of weed scientists' perceptions on the significance of crabgrasses (Digitaria spp.) in the United States. Weed Technol. 16:239–242.Google Scholar
Vleeshouwers, L. M. and Kropff, M. J. 2000. Modelling field emergence patterns in arable weeds. New Phytol 148:445–457.Google Scholar
Watschke, T. L., Dernoeden, P. H., and Shetlar, D. J. 1995. Managing turfgrass pests. Boca Raton, FL Lewis Publishers. Pp. 4–6.Google Scholar
Wiederholt, R. J. and Stoltenberg, D. E. 1995. Cross-resistance of a large crabgrass (Digitaria sanguinalis) accession to aryloxyphenoxypropionate and cyclohexanedione herbicides. Weed Technol. 9:518–524.Google Scholar