Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-17T15:22:04.396Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluating Phenological Indicators for Predicting Giant Foxtail (Setaria faberi) Emergence

Published online by Cambridge University Press:  20 January 2017

John Cardina ,
Catherine P. Herms ,
Daniel A. Herms  and
Frank Forcella
John Cardina*
Affiliation:
Department of Horticulture and Crop Science, The Ohio State University, Wooster, OH 44691
Catherine P. Herms
Affiliation:
Department of Horticulture and Crop Science, The Ohio State University, Wooster, OH 44691
Daniel A. Herms
Affiliation:
Department of Entomology, The Ohio State University, Wooster, OH 44691
Frank Forcella
Affiliation:
U.S. Department of Agriculture–Agriculture Research Service, North Central Soil Conservation Research Laboratory, Morris, MN 56267
*
Corresponding author's E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

We evaluated the use of ornamental plants as phenological indicators for predicting giant foxtail emergence and compared their performance with predictions based upon Julian day, cumulative growing degree–days (GDD), and the WeedCast program. From 1997 to 2001, we monitored giant foxtail emergence in a field experiment with and without fall and spring tillage to estimate the dates of 25, 50, and 80% emergence; we also recorded dates of first and full bloom of 23 ornamental plant species. Dates of weed emergence and ornamental blooming for 1997 to 2000 were compiled in a phenological calendar consisting of 54 phenological events for each year, and events were ordered by average (1997 to 2000) cumulative GDD (January 1 start date, 10 C base temperature). Bloom events occurring just before the giant foxtail emergence events were chosen as the phenological indicators for 2001. The Julian day method used the average (1997 to 2000) dates of foxtail emergence to predict 2001 emergence. The GDD model (October 1 start date, 0 C base temperature) was chosen by determining the combination of start date and base temperature that provided the lowest coefficient of variation for the 1997 to 2000 data. The WeedCast prediction was generated using local soil and environmental data from 2001. The rank order of the 54 phenological events in 2001 showed little deviation from the 4-yr (1997 to 2000) average rank order (R2 = 0.96). The phenological calendar indicated that, on average, 25% of giant foxtail seedlings had emerged when red chokeberry was in first bloom, and 80% of seedlings had emerged around the time multiflora rose was in full bloom. We compared the phenological calendar predictions for 25, 50, and 80% emergence with those based on Julian day, cumulative GDD, and WeedCast. The average deviation in predictions ranged from 4.4 d for the phenological calendar to 11.4 d for GDD. In addition to being generally more accurate, the phenological calendar approach also offers the advantage of providing information on the order of phenological events, thus helping to anticipate the progress of emergence and to plan and implement management strategies.

Keywords

Giant foxtailSetaria faberi Herrm. SETFASeedling emergencephenologyemergence predictiongrowing degree–days
Type
Weed Biology and Ecology
Information
Weed Science , Volume 55 , Issue 5 , October 2007 , pp. 455 - 464
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

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
Archer, D. W., Forcella, F., Eklund, J. J., and Gunsolus, J. 2001. WeedCast. Version 2.0. http://www.morris.ars.usda.gov.Google Scholar
Arnold, C. Y. 1959. The determination and significance of the base temperature in a linear heat unit system. Proc. Am. Soc. Hortic. Sci. 74:430–445.Google Scholar
Arnold, C. Y. 1960. Maximum–minimum temperatures as a basis for computing heat units. Proc. Am. Soc. Hortic. 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
Blackshaw, R. E., Stobbe, E. H., and Sturko, A. R. W. 1981. Effect of seeding dates and densities of green foxtail (Setaria viridis) on the growth and productivity of spring wheat (Triticum aestivum). Weed Sci. 29:212–217.Google Scholar
Carberry, P. S. and Campbell, L. C. 1989. Temperature parameters useful for modeling the germination and emergence of pearl millet. Crop Sci. 29:220–223.Google Scholar
Cardina, J. and Hook, J. E. 1989. Factors influencing germination and emergence of Florida beggarweed (Desmodium tortuosum). Weed Technol. 3:402–407.Google Scholar
Dekker, J., Atchison, B., and Jovaag, K. 2003. Setaria spp. seed pool formation and initial assembly in agro-communities. Asp. Appl. Biol. 69:247–259.Google Scholar
Dekker, J., Dekker, B. I., Hilhorst, H., and Karssen, C. 1996. Weedy adaptation in Setaria spp., IV: changes in the germinative capacity of S. faberi embryos with development from anthesis to after abscission. Am. J. Bot. 83:979–991.Google Scholar
Ekeleme, F., Forcella, F., Archer, D. W., Akobundu, I. O., and Chikoye, D. 2005. Seedling emergence model for tropic ageratum (Ageratum conyzoides). Weed Sci. 53:55–61.Google Scholar
Forcella, F. 1993. Seedling emergence model for velvetleaf. Agron. J. 85:929–933.Google Scholar
Forcella, F. 1998. Real-time assessment of seed dormancy and seedling growth for weed management. Seed Sci. Res. 8:201–209.Google Scholar
Forcella, F., Benech-Arnold, R. L., Sanchez, R., and Ghersa, C. M. 2000. Modeling seedling emergence. Field Crops Res. 67:123–139.Google Scholar
Forcella, F., Eradat-Oskoui, K., and Wagner, S. W. 1993. Application of weed seedbank ecology to low-input crop management. Ecol. Appl. 3:74–83.Google Scholar
Grundy, A. C. and Mead, A. 2000. Modeling weed emergence as a function of meteorological records. Weed Sci. 48:594–603.Google Scholar
Harker, K. N. and O'Sullivan, P. A. 1991. Effect of imazamethabenz on green foxtail, tartary buckwheat and wild oat at different growth stages. Can. J. Plant Sci. 71:821–829.Google Scholar
Harvey, S. and Forcella, F. 1993. Vernal seedling emergence model for common lambsquarters (Chenopodium album). Weed Sci. 41:309–316.Google Scholar
Herms, D. A. 1990. Biological clocks: using plant phenology to predict insect activity. Am. Nurseryman. 172:56–63.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., Chatfield, J.A. eds. Ornamental Plants: Annual Reports and Research Reviews, 1997. Columbus, OH Ohio Agriculture Research Development Center Special Circulation 157.Google Scholar
Herms, D. A. 1999. Plant and insect phenology in the year of El Niño: comparison with 1997. in Rose, M.A., Chatfield, J.A. eds. Ornamental Plants: Annual Reports and Research Reviews, 1998. Columbus, OH Ohio Agriculture Research Development Center Special Circulation 165. 136.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., Stone, A.K. eds. Ornamental Plants: Annual Reports and Research Reviews 2001. Columbus, OH Ohio Agriculture Research Development Center Special Circulation 186.Google Scholar
Herms, D. A. 2004. Using degree–days and plant phenology to predict pest activity. Pages 49–59. in Krischik, V., Davidson, J. eds. IPM (Integrated Pest Management) of Midwest Landscapes. St. Paul, MN Minnesota Agricultural Experiment Station Publication SB-07645.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
Huberman, M. A. 1941. Why phenology? J. Forestry. 39:1007–1013.Google Scholar
Kapler, J. E. 1966. Phenological events associated with the spring emergence of the smaller European elm bark beetle in Dubuque, Iowa. J. Econ. Entomol. 60:50–52.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. 42:561–567.Google Scholar
Leon, R. G., Knapp, A. D., and Owen, M. D. K. 2004. Effect of temperature on the germination of common waterhemp (Amaranthus tuberculatus), giant foxtail (Setaria faberi), and velvetleaf (Abutilon theophrasti). Weed Sci. 52:67–73.Google Scholar
Masin, R., Zuin, M. C., Archer, D. W., Forcella, F., and Zanin, G. 2005. WeedTurf: a predictive model to aid control of annual summer weeds in turf. Weed Sci. 53:193–201.Google Scholar
Mester, T. C. and Buhler, D. D. 1991. Effects of soil temperature, seed depth, and cyanazine on giant foxtail (Setaria faberi) and velvetleaf (Abutilon theophrasti) seedling development. Weed Sci. 39:204–209.Google Scholar
Moore, D. J. and Fletchall, O. H. 1963. Germination-regulating mechanisms of giant foxtail (Setaria faberi). Missouri Ag. Exp. Stn. Res. Bull. 829. 25.Google Scholar
Morrison, I. N. and Maurice, D. C. 1984. The relative response of two foxtail (Setaria) species to dicloop. Weed Sci. 32:686–690.Google Scholar
Mussey, G. J. and Potter, D. A. 1997. Phenological correlations between flowering plants and activity of urban landscape pests in Kentucky. J. Econ. Entomol. 90:1615–1627.Google Scholar
Norris, R. F., Caswell-Chen, E. P., and Kogan, M. 2003. Concepts in integrated pest management. Upper Saddle River, NJ Prentice Hall.Google Scholar
Ogg, A. G. and Dawson, J. H. 1984. Time of emergence of eight weed species. Weed Sci. 32:327–335.Google Scholar
Preuss, K. P. 1983. Day-degree methods for pest management. Environ. Entomol. 12:613–619.Google Scholar
Rathcke, B. and Lacey, E. L. 1985. Phenological patterns of terrestrial plants. Annu. Rev. Ecol. Syst. 16:179–214.Google Scholar
Roberts, H. A. and Feast, P. M. 1970. Seasonal distribution of emergence in some annual weeds. Exp. Hortic. 21:36–41.Google Scholar
Roberts, H. A. and Potter, M. E. 1980. Emergence patterns of weed seedlings in relation to cultivation and rainfall. Weed Res. 20:377–386.Google Scholar
Roman, E. S., Murphy, S. D., and Swanton, C. J. 2000. Simulation of Chenopodium album seedling emergence. Weed Sci. 48:217–224.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
Stanway, V. 1971. Laboratory germination of giant foxtail, Setaria faberii Herrm., at different stages of germination. Proc. Assoc. Off. Seed Anal. 61:85–90.Google Scholar
Stoller, E. W. and Wax, L. M. 1973. Periodicity of germination and emergence of some annual weeds. Weed Sci. 21:574–580.Google Scholar
Vleeshouwers, L. M. and Kropff, M. J. 2000. Modelling field emergence patterns in arable weeds. New Phytol. 148:445–457.Google Scholar
Webster, T. M., Cardina, J., and Norquay, H. M. 1998. Tillage and seed depth effects on velvetleaf (Abutilon theophrasti) emergence. Weed Sci. 46:76–82.Google Scholar