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WeedSOFT®: a weed management decision support system

Published online by Cambridge University Press:  20 January 2017

Christophe Neeser
Affiliation:
Crop Diversification Centre, South SS4, Brooks, Alberta, Canada
J. Anita Dille
Affiliation:
Department of Agronomy, Kansas State University, Manhattan, KS 66506-5501
Gopal Krishnan
Affiliation:
Pioneer Hi-Bred Inc., Johnstown, IA 50131-0552
David A. Mortensen
Affiliation:
Department of Crop and Soil Sciences, Penn State University, University Park, PA 16802
Jeffery T. Rawlinson
Affiliation:
Nebraska Game and Parks Commission, 2200 North 33rd, Lincoln, NE 68503
Lynn B. Bills
Affiliation:
Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68583

Abstract

WeedSOFT® is a decision support system that was developed to help farmers and consultants in Nebraska with the selection of optimal weed management strategies. WeedSOFT® evolved from HERB, a bioeconomic model for soybean that was developed in North Carolina. The program is composed of four independent modules, namely, ADVISOR, EnviroFX, MapVIEW, and WeedVIEW. ADVISOR helps the user select a treatment based on maximum yield or maximum net gain. EnviroFX and MapVIEW provide environmentally relevant herbicide information and county soil maps that indicate vulnerability to groundwater contamination. WeedVIEW is a visual library of color images and line drawings of 46 common weed species. Over 500 farmers and consultants in Nebraska and adjacent states use WeedSOFT®. As a result of the current regionalization effort, the user base is expected to increase rapidly during the next 2 or 3 yr. This article explains the algorithms implemented in the current version of WeedSOFT®.

Type
Weed Management
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Adcock, T. E., Banks, P. A., and Bridges, D. C. 1990. Effects of preemergence herbicides on soybean (Glycine max): weed competition. Weed Sci 38:108112.CrossRefGoogle Scholar
Barrentine, W. L. and Oliver, L. R. 1977. Competition, threshold levels, and control of cocklebur in soybeans. Mississippi Agricultural and Forestry Experiment Station Technical Bulletin 83.Google Scholar
Bauer, T. A., Mortensen, D. A., Wicks, G. A., Hayden, T. A., and Martin, A. R. 1991. Environmental variability associated with economic thresholds for soybeans. Weed Sci 39:564569.CrossRefGoogle Scholar
Boerboom, C. M. and Lins, R. D. 2001. WeedSOFT: Effect of Soybean Row Spacing on Bioeconomical Predictions. N. Cent. Weed Sci. Soc. Abstr. 56. Champaign, IL: North Central Weed Science Society. [CD-ROM computer file.].Google Scholar
Coble, H. D. and Mortensen, D. A. 1992. The threshold concept and its application to weed science. Weed Technol 6:191195.CrossRefGoogle Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol 107:239252.CrossRefGoogle Scholar
Dieleman, A., Hamill, A. S., Weise, S. F., and Swanton, C. J. 1995. Emperical models of pigweed (Amaranthus spp.) interferences in soybean (Glycine max). Weed Sci 43:612618.CrossRefGoogle Scholar
Gonsolus, J. L. 1986. Reciprical Interference Effects Between Weeds and Soybeans (Glycine max) Measured by Area of Influence Methodology. Ph.D. dissertation. North Carolina State University, Raleigh, NC P. 67.Google Scholar
Johnson, W. G. and Schmidt, A. A. 2001. WeedSOFT: Effect of Total Competitive Load on Corn Yield Loss Predictions. N. Cent. Weed Sci. Soc. Abstr. 56. Champaign, IL: North Central Weed Science Society. [CD-ROM computer file.].Google Scholar
Knezevic, S. K., Weise, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus) in corn (Zea mays). Weed Sci 42:568573.CrossRefGoogle Scholar
Mortensen, D. A. and Coble, H. D. 1991. Two approaches to weed control decision-aid software. Weed Technol 5:445452.CrossRefGoogle Scholar
Mortensen, D. A., Martin, A. R., Roeth, F. W., and Harvill, T. E. 1992. NebraskaHERB—Computer-Aided Postemergence Weed Management. Version 1.0. Lincoln, NE: Department of Agronomy, University of Nebraska.Google Scholar
Mortensen, D. A., Martin, A. R., Roeth, F. W., Harvill, T. E., Klein, R. W., Wicks, G. A., Wilson, R. G., Holshouser, D. L., and McNamara, J. W. 1994. NebraskaHERB—Computer-Aided Postemergence Weed Management. Version 4.0. Lincoln, NE: Department of Agronomy, University of Nebraska.Google Scholar
Mortensen, D. A., Martin, A. R., and Roeth, F. W. et al. 2002. WeedSOFT®. Version 7.1. Lincoln, NE: Nebraska Cooperative Extension Publication CD5, University of Nebraska.Google Scholar
Sprague, C. L. 2001. WeedSOFT: Effect of Total Competitive Load on Soybean Yield Loss Predictions. N. Cent. Weed Sci. Soc. Abstr. 56. Champaign, IL: North Central Weed Science Society. [CD-ROM computer file.].Google Scholar
Stolpe, N. B., Kuzilla, M. S., and Shea, P. J. 1998. Importance of soil map detail in predicting pesticide mobility in terrace soils. Soil Sci 163:394403.CrossRefGoogle Scholar
Stubbendieck, J., Friisoe, G. Y., and Bolick, M. R. 1994. Weeds of Nebraska and the Great Plains. Lincoln, NE: Nebraska Department of Agriculture. 589 p.Google Scholar
Weaver, S. E. 1991. Size-dependent economic thresholds for three broadleaf weed species in soybeans. Weed Technol 5:674679.CrossRefGoogle Scholar
Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: decision model for postemergence weed control in soybean. Agron. J 83:413417.CrossRefGoogle Scholar