Hostname: page-component-745bb68f8f-s22k5 Total loading time: 0 Render date: 2025-01-15T19:20:11.419Z Has data issue: false hasContentIssue false
  • English
  • Français

Real-time assessment of seed dormancy and seedling growth for weed management

Published online by Cambridge University Press:  19 September 2008

Frank Forcella
Frank Forcella*
Affiliation:
USDA Agricultural Research Service, North Central Soil Conservation Research Laboratory, Morris, Minnesota 56267, USA
*
*+1-320-589-3787fforcella@mail.mrsars.usda.gov
Get access Rights & Permissions [Opens in a new window]

Abstract

Computer software called WeedCast was developed to simulate weed seed dormancy, timing of seedling emergence, and seedling height growth in crop environments in real-time and using actual or forecasted weather data. Weather data include daily rainfall and minimum and maximum air temperatures. Air temperatures are converted to average daily soil temperature at 5-cm soil depth using a series of equations that are specific for soil type, tillage system and previous year's crop-residue type. Daily rainfall and soil temperature estimates are combined to determine soil water potential (in megapascals) at 5-cm depth. Daily estimated soil water potential or soil temperatures are matched to empirically-derived threshold values that induce secondary dormancy in seeds of certain species. Soil growing degree days (GDD), calculated from soil temperatures, are used to project maximum emergence rates of weed seedlings. Emergence ceases on days when soil water potential falls below threshold values specific to each species. GDD based on air temperatures are used to estimate post-emergence seedling height growth. All three types of simulation provide information that allows users to answer important weed management questions in real-time. These types of questions include but are not limited to the following: (1) Are soil-applied treatments necessary? (2) How late can pre-emergence herbicides be applied? (3) When should mechanical control be implemented? (4) When should field-scouting commence and end? (5) When should post-emergence herbicides be applied?

Keywords

emergencegrowing degree daysseedlingsimulationsoftwaresoil temperaturesoil water potentialWeedCast
Type
Research Papers
Information
Seed Science Research , Volume 8 , Issue 2 , June 1998 , pp. 201 - 210
Copyright
Copyright © Cambridge University Press 1998

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

Buhler, D D and Gunsolus, J L (1996) Effect of date of preplant tillage and planting on weed populations and mechanical weed control in soybean (Glycine max). Weed Science 44, 373–379.Google Scholar
Buhler, D D, Gunsolus, J L and Ralston, D R (1992) Integrated weed management techniques to reduce herbicide inputs in soybean. Agronomy Journal 84, 973–978.Google Scholar
Dawson, J H (1963) Development of barnyardgrass seedlings and their response to EPTC. Weeds 11, 60–67.CrossRefGoogle Scholar
Forcella, F (1993) Seedling emergence model for velvetleaf. Agronomy Journal 85, 929–933.CrossRefGoogle Scholar
Forcella, F and Banken, K R (1996) Relationships among green foxtail seedling growth, growing degree-days, and time of nicosulfuron application. Weed Technology 10, 60–67.CrossRefGoogle Scholar
Forcella, F, Eradat-Oskoui, K and Wagner, S W (1993) Application of weed seedbank ecology to low-input crop management. Ecological Applications 3, 74–83.Google Scholar
Forcella, F, Durgan, B R and Buhler, D D (1996) Management of weed seedbanks. pp 21–26in Second International Weed Control Congress, International Weed Science Society, Copenhagen.Google Scholar
Forcella, F, Wilson, R G, Dekker, J, Kremer, R J, Cardina, J, Anderson, R L, Alm, D, Renner, K A, Harvey, R G, Clay, S and Buhler, D D (1997) Weed seedbank emergence across the Corn Belt. Weed Science 45, 67–76.CrossRefGoogle Scholar
Forcella, F, Buhler, D D, Anderson, R L, Kremer, R J, Wilson, R G, Cardina, J, Dekker, J and Olness, A E (1998) When weeds emerge: robust empirical models. Weed Science, in preparation.Google Scholar
Frazee, R W and Stoller, E W (1972) Differential growth of corn, soybean, and seven dicotyledonous weed seedlings. Weed Science 22, 336–339.CrossRefGoogle Scholar
Gunsolus, J L (1990). Mechanical and cultural weed control in corn and soybean. American Journal of Alternative Agriculture 5, 114–119.CrossRefGoogle Scholar
Gupta, S C, Larson, W E and Linden, D R (1983) Effect of tillage and surface residues on soil temperatures. I. Upper boundary temperature. Soil Science Society of America Journal 47, 1212–1218.CrossRefGoogle Scholar
Mohler, C L, Frisch, J C and Mt Pleasant, J (1997). Evaluation of mechanical weed management programs for corn (Zea mays). Weed Technology 11, 123–131.Google Scholar
Oriade, C and Forcella, F (1997) Maximizing efficacy and economics of mechanical weed control in row crops through forecasts of weed emergence. Journal of Crop Production 2, in press.Google Scholar
Swinton, S M and King, R P (1994) A bioeconomic model for weed management in corn and soybean. Agricultural Systems 44, 313–335.CrossRefGoogle Scholar
Wilson, R G, Jarvi, K J, Seymour, R C, Witkowski, J F, Danielson, S D and Wright, R F (1992) Annual weed growth across Nebraska. Research Bulletin 314-F. Institute of Agriculture and Natural Resources, University of Nebraska, Lincoln, Nebraska, 53 pp.Google Scholar