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HADSS, Pocket HERB, and WebHADSS: Decision Aids for Field Crops

Published online by Cambridge University Press:  20 January 2017

Andrew C. Bennett,
Andrew J. Price,
Michael C. Sturgill,
Gregory S. Buol and
Gail G. Wilkerson
Andrew C. Bennett
Affiliation:
Crop Science Department, North Carolina State University, Raleigh, NC 27695-7620
Andrew J. Price
Affiliation:
Crop Science Department, North Carolina State University, Raleigh, NC 27695-7620
Michael C. Sturgill
Affiliation:
Crop Science Department, North Carolina State University, Raleigh, NC 27695-7620
Gregory S. Buol
Affiliation:
Crop Science Department, North Carolina State University, Raleigh, NC 27695-7620
Gail G. Wilkerson*
Affiliation:
Crop Science Department, North Carolina State University, Raleigh, NC 27695-7620
*
Corresponding author's E-mail: gail_wilkerson@ncsu.edu
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Abstract

Row crop weed management decisions can be complex due to the number of available herbicide treatment options, the multispecies nature of weed infestations within fields, and the effect of soil characteristics and soil-moisture conditions on herbicide efficacy. To assist weed managers in evaluating alternative strategies and tactics, three computer programs have been developed for corn, cotton, peanut, and soybean. The programs, called HADSS (Herbicide Application Decision Support System), Pocket HERB, and WebHADSS, utilize field-specific information to estimate yield loss that may occur if no control methods are used, to eliminate herbicide treatments that are inappropriate for the specified conditions, and to calculate expected yield loss after treatment and expected net return for each available herbicide treatment. Each program has a unique interactive interface that provides recommendations to three distinct kinds of usage: desktop usage (HADSS), internet usage (WebHADSS), and on-site usage (Pocket HERB). Using WeedEd, an editing program, cooperators in several southern U.S. states have created different versions of HADSS, WebHADSS, and Pocket HERB that are tailored to conditions and weed management systems in their locations.

Keywords

Corn, Zea mays L.cotton, Gossypium hirsutum L.peanut, Arachis hypogea L.soybean, Glycine max L.Bioeconomic modelscomputer decision aidsdecision support systemsweed managementHADSS, Herbicide Application Decision Support SystemPDS, postemergence-directedPOST, postemergencePPI, preplant-incorporatedPRE, preemergence

Information

Type
Review
Information
Weed Technology , Volume 17 , Issue 2 , June 2003, pp. 412 - 420
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Anonymous. 2000a. Dual Magnum product label. Greensboro, NC: Syngenta Crop Protection. 40 p.Google Scholar
Anonymous. 2000b. Canopy product label. Wilmington, DE: EI du Pont Nemours. 16 p.Google Scholar
Anonymous. 2001. AAtrex product label. Greensboro, NC: Syngenta Crop Protection. 16 p.Google Scholar
Askew, S. D. and Wilcut, J. W. 1999. Cost and weed management with herbicide programs in glyphosate-resistant cotton (Gossypium hirsutum). Weed Technol. 13: 308–313.Google Scholar
Bennett, A. C., Wilkerson, G. G., and Sturgill, M. C. 2001. Validating a decision support system for the southern US. Weed Sci. Soc. Am. Abst. 41: 134–135.Google Scholar
Bennett, A. C., Wilkerson, G. G., Sturgill, M. C., and Krueger, D. W. 1999. Using NCSU HADSS to teach site specific weed management. In Agronomy Abstracts. Madison, WI: ASA. 357 p.Google Scholar
Berti, A. and Zanin, G. 1994. Density equivalent: a method for forecasting yield loss caused by mixed weed populations. Weeds Res. 34: 327–332.Google Scholar
Berti, A. and Zanin, G. 1997. GESTINF: a decision model for post-emergence weed management in soybean (Glycine max (L.) Merr). Crop Prot. 16: 109–116.Google Scholar
Black, I. D. and Dyson, C. B. 1993. An economic thresholds model for spraying herbicides in cereals. Weed Res. 33: 279–290.Google Scholar
Bradley, P. R., Johnson, W. G., Hart, S. E., Buesinger, M. L., and Massey, R. E. 2000. Economics of weed management in glufosinate-resistant corn (Zea mays L). Weed Technol. 14: 495–501.Google Scholar
Bruce, J. A., Boyd, J., Penner, D., and Kells, J. J. 1996. Effect of growth stage and environment of foliar absorption, translocation, metabolism, and activity of nicosulfuron in quackgrass (Elytrigia repens). Weed Sci. 13: 447–454.Google Scholar
Buchanan, G. A. and Burns, E. R. 1970. Influence of weed competition in cotton. Weed Sci. 18: 149–154.Google Scholar
Clewis, S. B., Wilcut, J. W., Askew, S. D., and Hinton, J. D. 2000. Weed management in strip tillage roundup ready (glyphosate-resistant) cotton. Beltwide Cotton Conf. 1: 1476.Google Scholar
Coble, H. D. 1986. Development and implementation of economic thresholds for soybean. In Frisbie, R. E. and Adkisson, P. L., eds. CIPM: Integrated Pest Management on Major Agricultural Systems. College Station, TX: Texas A & M University. pp. 295–307.Google Scholar
Coble, H. D. and Mortensen, D. A. 1992. The threshold concept and its application to weed science. Weed Technol. 6: 191–195.Google Scholar
Culpepper, A. S. and York, A. C. 1998. Weed management in glyphosate-resistant cotton. J. Cotton Sci. 2: 174–185.Google Scholar
Economic Research Service. 2001. Agricultural Outlook. Washington, DC: USDA, ERS AGO 286.Google Scholar
Gold, H. J., Bay, J., and Wilkerson, G. G. 1996. Scouting for weeds, based on the negative binomial distribution. Weed Sci. 44: 504–510.Google Scholar
Hart, S. E., Wax, L. M., and Hager, A. G. 1997. Comparison of total postemergence weed control programs in soybeans. J. Prod. Agric. 10: 136–141.Google Scholar
Hagood, E. S., Swann, C. W., and Wilson, H. P. 2001. Chemical weed control in soybean. In 2000 Virginia Soybean Production Guide. Virginia Cooperative Extension Service publication. pp. 91–108.Google Scholar
Jensen, A. L., Boll, P. S., Thysen, I., and Pathak, B. K. 2000. PlnteInfo—a web-based system for personalised decision support in crop management. Comput. Electronics Agric. 25: 271–293.Google Scholar
Jordan, D. L. and York, A. C. 2002. Weed management in peanuts. In 2002 Peanut Information. North Carolina Cooperative Extension Service publication AG-331. pp. 23–51.Google Scholar
King, R. P., Lybecker, D. W., Schweizer, E. E., and Zimdahl, R. L. 1986. Bioeconomic modeling to simulate weed control strategies for continuous corn (Zea mays). Weed Sci. 34: 972–979.Google Scholar
Krishnan, G., Mortensen, D. A., Martin, A. R., Bills, L. B., Dieleman, A., and Nesser, C. 2001. WeedSOFT: a state of the art weed management decision support system. Weed Sci. Soc. Am. Abstr. 41: 41–42.Google Scholar
Krueger, D. W., Wilkerson, G. G., Coble, H. D., and Gold, H. J. 2000. An economic analysis of binomial sampling for weed scouting. Weed Sci. 48: 53–60.Google Scholar
Linker, H. M., York, A. C., and Wilhite, D. R. Jr. 1990. WEEDS—a system for developing a computer-based herbicide recommendation program. Weed Technol. 4: 380–385.Google Scholar
Loux, M. M., Liebl, R. A., and Slife, F. W. 1989. Availability and persistence of imazaquin, imazethapyr, and clomazone in soil. Weed Sci. 37: 259–267.Google Scholar
Lybecker, D. W., Schweizer, E. E., and King, R. P. 1991. Weed management decisions based on bioeconomic modeling. Weed Sci. 39: 124–129.Google Scholar
MacDonald, G. E., Bridges, D. C., and Brecke, B. J. 1998. Validation of HERB computer decision aid for peanuts. Proc. South. Weed Sci. Soc. 51: 216.Google Scholar
Martin, A. R., Mortensen, D. A., and Bills, L. 2001. Computerized weed management decision aids. Weed Sci. Soc. Am. Abstr. 41: 114–115.Google Scholar
Miller, D. K., Wilson, C. F., and Milligan, J. L. 1999. Total postemergence weed control in roundup ready cotton with combinations of roundup ultra and staple. Proc. South. Weed Sci. Soc. 52: 238–239.Google Scholar
Monks, C. D., Bridges, D. C., Woodruff, J. W., Murphy, T. R., and Berry, D. J. 1995. Expert system evaluation and implementation for soybean (Glycine max) weed management. Weed Technol. 535–540.CrossRefGoogle Scholar
Mortensen, D. A. and Coble, H. D. 1991. Two approaches to weed control decision-aid software. Weed Technol. 5: 445–452.Google Scholar
Mortensen, D. A., Martin, A. R., and Roeth, F. W. et al. 1999. WeedSOFT Version 4.0 User's Manual. Lincoln, NE: Department of Agronomy, University of Nebraska.Google Scholar
Murdock, S. W. and Murray, D. S. 2002. Obtaining weed populations for computerized decision support system (DSS) inputs: counts vs. estimations. Proc. South. Weed Sci. Soc. 55: 130.Google Scholar
Murphy, T. R., Bridges, D. C., MacDonald, G., Brecke, B. J., Wilkerson, G. G., and Coble, H. 1998. HERB User's Guide for Soybeans and Peanuts. Griffin, GA: The Georgia Station, Crop and Soil Sciences Department, The University of Georgia.Google Scholar
Obrigawitch, T., Hons, F. M., Abernathy, J. R., and Gipson, J. R. 1981. Adsorption, desorption, and mobility of metolachlor in soils. Weed Sci. 29: 332–336.CrossRefGoogle Scholar
Olson, B. L. S., Al-Khatib, K., Stahlman, P., and Isakson, P. J. 2000. Efficacy and metabolism of MON 37500 in Triticum aestivum and weedy grass species as affected by temperature and soil moisture. Weed Sci. 48: 541–548.Google Scholar
Pannell, D. J. 1990. An economic response model of herbicide application for weed control. Aust. J. Agric. Econ. 34: 223–241.Google Scholar
Price, A. J., Bennett, A. C., and Wilkerson, G. G. 2002. HADSS validation and evolution of regional HADSS programs. Abstr. Weed Sci. Soc. Am. 42: 55–56.Google Scholar
Rankins, A. Jr., Shaw, D. R., and Byrd, J. D. 1998. HERB and MSU-HERB field validation for soybean (Glycine max) weed control in Mississippi. Weed Technol. 12: 88–96.Google Scholar
Renner, K. A. and Black, J. R. 1991. SOYHERB–a computer program for soybean herbicide decision making. Agron. J. 83: 921–925.Google Scholar
Scott, G. H., Askew, S. D., Bennett, A. C., and Wilcut, J. W. 2001. Economic evaluation of HADSS computer program for weed management in non-transgenic and transgenic Gossypium hirsutum . Weed Sci. 49: 549–557.Google Scholar
Scott, G. H., Askew, S. D., Wilcut, J. W., and Bennett, A. C. 2002. Economic evaluation of HADSS computer program in North Carolina Arachis hypogaea . Weed Sci. 50: 91–100.Google Scholar
Shaw, D. R. and Murphy, G. P. 1997. Field persistence of bioavailable flumetsulam. Weed Sci. 45: 568–572.Google Scholar
Shaw, D. R., Rankins, A. Jr., Ruscoe, J. T., and Byrd, J. D. 1998. Field validation of weed control recommendations from HERB and SWC herbicide recommendation models. Weed Technol. 12: 78–87.Google Scholar
Stigliana, L. and Resina, C. 1993. SELOMA: expert system for weed management in herbicide-intensive crops. Weed Technol. 7: 550–559.Google Scholar
Swinton, S. M. and King, R. P. 1994. A bioeconomic model for weed management in corn and soybean. Agric. Syst. 44: 313–335.Google Scholar
Thomson, A. J. and Williamson, D. R. 1992. Formation and use of intermediate inferences in advisory systems: a herbicide example. AI Applic. 6: 29–37.Google Scholar
Weaver, S., Sturgill, M. C., Wilkerson, G. G., Coble, H. D., and Buol, G. S. 1999. HADSS User's Manual, Ontario Version 2.0. Harrow, ON Canada: Agriculture and Agri-Food Canada. 24 p.Google Scholar
White, A. D. and Coble, H. D. 1997. Validation of HERB for use in peanut (Arachis hypogaea). Weed 11: 573–579.Google Scholar
Wie, H. S., Hsiao, A. I., and Quick, W. A. 1997. Influence of drought on graminicide phytotoxicity in wild oat. (Avena fatua) grown under different temperature and humidity conditions. J. Plant Growth Regul. 16: 233–237.Google Scholar
Wiles, L. J., King, R. P., Schweizer, E. E., Lybecker, D. W., and Swinton, S. M. 1996. GWM: general weed management model. Agric. Syst. 50: 355–376.Google Scholar
Wiles, L. J., Wilkerson, G. G., Gold, H. J., and Coble, H. D. 1992. Modeling weed distribution for improved postemergence control decisions. Weed Sci. 40: 546–553.Google Scholar
Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: decision model for postemergence weed control in soybean. Agron. J. 83: 413–417.Google Scholar
Wilkerson, G. G., Wiles, L. J., and Bennett, A. C. 2002. Weed management decision models: pitfalls, perceptions, and possibilities of the economic threshold approach. Weed Sci. 50: 411–424.Google Scholar
York, A. C. and Culpepper, A. S. 2000. Weed management. In 2000 North Carolina Corn Production Guide. North Carolina Cooperative Extension Service publication. pp. 69–111.Google Scholar
York, A. C. and Culpepper, A. S. 2002. Weed management in cotton. In 2002 cotton information. North Carolina Cooperative Extension Service publication AG-417. pp. 76–125.Google Scholar