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Weed management decision models: pitfalls, perceptions, and possibilities of the economic threshold approach

Published online by Cambridge University Press:  20 January 2017

Gail G. Wilkerson ,
Lori J. Wiles  and
Andrew C. Bennett
Lori J. Wiles
Affiliation:
USDA-ARS-WMU, AERC—Colorado State University, Fort Collins, CO 80523
Andrew C. Bennett
Affiliation:
Everglades Research and Education Center, 3200 East Palm Beach Road, Belle Glade, FL 33430-4702
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Abstract

The use of scouting and economic thresholds has not been accepted as readily for managing weeds as it has been for insects, but the economic threshold concept is the basis of most weed management decision models available to growers. A World Wide Web survey was conducted to investigate perceptions of weed science professionals regarding the value of these models. Over half of the 56 respondents were involved in model development or support, and 82% thought that decision models could be beneficial for managing weeds, although more as educational rather than as decision-making tools. Some respondents indicated that models are too simple because they do not include all factors that influence weed competition or all issues a grower considers when deciding how to manage weeds. Others stated that models are too complex because many users do not have time to obtain and enter the required information or are not necessary because growers use a zero threshold or because skilled decision makers can make better and quicker recommendations. Our view is that economic threshold–based models are, and will continue to be, valuable as a means of providing growers with the knowledge and experience of many experts for field-specific decisions. Weed management decision models must be evaluated from three perspectives: biological accuracy, quality of recommendations, and ease of use. Scientists developing and supporting decision models may have hindered wide-scale acceptance by overemphasizing the capacity to determine economic thresholds, and they need to explain more clearly to potential users the tasks for which models are and are not suitable. Future use depends on finding cost-effective methods to assess weed populations, demonstrating that models use results in better decision making, and finding stable, long-term funding for maintenance and support. New technologies, including herbicide-resistant crops, will likely increase rather than decrease the need for decision support.

Keywords

Bioeconomic modelsdecision support systemscomputer decision aids
Type
Research Article
Information
Weed Science , Volume 50 , Issue 4 , August 2002 , pp. 411 - 424
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Aarts, H.F.M. and de Visser, C.L.M. 1985. A management information system for weed control in winter wheat. Proc. 1985 Br. Crop Prot. Conf. Weeds 2:679–686.Google Scholar
Allen, W. A. and Rajotte, E. G. 1990. The changing role of extension entomology in the IPM era. Annu. Rev. Entomol. 35:379–397.CrossRefGoogle Scholar
Ambrosia, L., Dorado, J., and Del Monte, J. P. 1997. Assessment of the sample size to estimate the weed seedbank in soil. Weed Res. 37:129–137.Google Scholar
Anderson, J. M. and McWhorter, C. G. 1976. The economics of common cocklebur control in soybean production. Weed Sci. 24:397–400.Google Scholar
Andreasen, C., Rudemo, M., and Sevestre, S. 1997. Assessment of weed density at an early stage by use of image processing. Weed Res. 37:5–18.Google Scholar
Auld, B. A. and Tisdell, C. A. 1987. Economic thresholds and response to uncertainty in weed control. Agric. Syst. 25:219–227.Google Scholar
Barrentine, W. L. 1974. Common cocklebur competition in soybeans. Weed Sci. 22:600–603.Google Scholar
Beck, K. G., MacDonald, S. K., Nissen, S. J., and Westra, P. 2000. 2000 Colorado Weed Management Guide: Field and Vegetable Crops. Fort Collins, CO: Colorado State University Cooperative Extension Service.Google Scholar
Bennett, A. C., Wilkerson, G. G., and Sturgill, M. C. 2000. Implementation of a decision support system for the southern US. Weed Sci. Soc. Am. Abstr. 40:37.Google Scholar
Berti, A. and Zanin, G. 1994. Density equivalent: a method for forecasting yield loss caused by mixed weed populations. Weed 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 threshold model for spraying herbicides in cereals. Weed Res. 33:279–290.CrossRefGoogle Scholar
Blouch, R. and Fults, J. 1953. The influence of soil type on the selective action of chloro-IPC and sodium TCA. Weeds 2:119–124.Google Scholar
Buhler, D. D., King, R. P., Swinton, S. M., Gunsolus, J. L., and Forcella, F. 1996. Field evaluation of a bioeconomic model for weed management in corn (Zea mays). Weed Sci. 44:915–923.Google Scholar
Buhler, D. D., King, R. P., Swinton, S. M., Gunsolus, J. L., and Forcella, F. 1997. Field evaluation of a bioeconomic model for weed management in soybean (Glycine max). Weed Sci. 45:158–165.Google Scholar
Burrough, P. A. 1991. Sampling designs for quantifying map unit composition. Pages 89–125 In Mausbach, M. J. and Wilding, L. P., eds. Spatial Variabilities of Soils and Landforms. SSSA Special Publication no. 28. Madison, WI: Soil Science Society of America.Google Scholar
Coble, H. D. 1986. Development and implementation of economic thresholds for soybean. Pages 295–307 In Frisbie, R. E. and Adkisson, P. L., eds. CIPM: Integrated Pest Management on Major Agricultural Systems. College Station, TX: Texas A&M University.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
Coble, H. D. and Ritter, R. L. 1978. Pennsylvania smartweed (Polygonum pensylvanicum) interference in soybeans (Glycine max). Weed Sci. 26:556–559.Google Scholar
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 107:239–252.Google Scholar
Cousens, R., Brain, P., O'Donovan, J. T., and O'Sullivan, P. A. 1987a. The use of biologically realistic equations to describe the effects of weed density and relative time of emergence on crop yield. Weed Sci. 35:720–725.Google Scholar
Cousens, R., Moss, S. R., Cussans, G. W., and Wilson, B. J. 1987b. Modeling weed populations in cereals. Rev. Weed Sci. 3:93–112.Google Scholar
Doyle, C. J. 1991. Mathematical models in weed management. Crop Prot. 10:432–444.Google Scholar
Doyle, C. J. 1997. A review of the use of models of weed control in integrated crop protection. Agric. Ecosyst. Environ. 64:165–172.Google Scholar
Economic Research Service. 2001. Agricultural Outlook. Washington, DC: AGO 286, USDA, ERS.Google Scholar
El-Faki, M. S., Zhang, N., and Peterson, D. E. 2000. Weed detection using color machine vision. Trans. Am. Soc. Agric. Eng. 43:1969–1978.Google Scholar
Ennis, W. B. Jr. 1958. The challenges of modern weed control. Weeds 6:363–369.Google Scholar
Ennis, W. B. Jr. 1960. Use of herbicides, growth regulators, nematocides and fungicides. Pages 17–27 In The Nature and Fate of Chemicals Applied to Soils, Plants and Animals. Washington, DC: USDA-ARS.Google Scholar
Forcella, F., King, R. P., Swinton, S. M., Buhler, D. D., and Gunsolus, J. L. 1996. Multi-year validation of a decision aid for integrated weed management in row crops. Weed Sci. 44:650–661.Google Scholar
Foy, C. L. 1954. Effectiveness of isopropyl N-(3-chlorophenyl) carbamate as a selective preemergence herbicide in cotton. Weeds 3:282–291.Google Scholar
Freed, V. H. 1951. Some factors influencing the herbicidal efficacy of isopropyl N phenyl carbamate. Weeds 1:48–60.Google Scholar
Gerowitt, B. and Heitefuss, R. 1990. Weed economic thresholds in cereals in the Federal Republic of Germany. Crop Prot. 9:323–331.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
Gotway, C. A., Ferguson, R. B., and Hergert, G. W. 1996. The effects of mapping and scale on variable-rate fertilizer recommendations for corn. Pages 321–330 In Robert, P. C., Rust, R. H., and Larson, W. E., eds. Precision Agriculture. Minneapolis, MN: American Society of Agronomy.Google Scholar
Hall, J. C., Van Eerd, L. L., Miller, S. D., Owen, M.D.K., Prather, T. S., Shaner, D. L., Singh, M., Vaughn, K. C., and Weller, S. C. 2000. Future research directions for weed science. Weed Technol. 14:647–658.CrossRefGoogle Scholar
Harsh, S. B., Lloyd, J. W., and Borton, L. R. 1989. Models as an aid to decision making. Acta Hortic. 248:27–48.Google Scholar
Heap, I. 2000. The International Survey of Herbicide Resistant Weeds. Available at www.weedscience.com. Accessed: February 15, 2000.Google Scholar
Heap, I. 2002. The International Survey of Herbicide Resistant Weeds. Available at www.weedscience.com. Accessed: March 01, 2002.Google Scholar
Hoffman, M. L., Buhler, D. D., and Owen, M.D.K. 1999a. Weed populations and crop yield response to recommendations from a weed control decision aid. Agron. J. 91:386–392.Google Scholar
Hoffman, M. L., Buhler, D. D., and Owen, M.D.K. 1999b. Multi-year evaluations of model based weed control under variable crop and tillage conditions. J. Crop Prod. 2:207–224.CrossRefGoogle Scholar
Holstun, J. T. Jr., Wooten, O. B. Jr., McWhorter, C. G., and Crowe, G. B. 1960. Weed control practices, labor requirements and costs in cotton production. Weeds 8:232–243.Google Scholar
Indyk, H. W. 1957. Pre-emergence weed control in soybeans. Weeds 5:362–370.Google Scholar
Jensen, A. L., Boll, P. S., Thysen, I., and Pathak, B. K. 2000. Pl@ntInfo—a web-based system for personalised decision support in crop management. Comput. Electron. Agric. 25:271–293.CrossRefGoogle Scholar
Kamp, J.A.L.M. 1999. Knowledge based systems: from research to practical application: pitfalls and critical success factors. Comput. Electron. Agric. 22:243–250.Google Scholar
Keisling, T. C., Oliver, L. R., Crowley, R. H., and Baldwin, F. L. 1984. Potential use of response surface analyses for weed management in soybeans (Glycine max). Weed Sci. 32:552–557.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.CrossRefGoogle Scholar
King, R. P., Swinton, S. M., Lybecker, D. W., and Oriade, C. A. 1998. The economics of weed control and the value of weed management information. Pages 25–41 In Hatfield, J. L., Buhler, D. D., and Stewart, B. A., eds. Integrated Weed and Soil Management. Chelsea, MI: Ann Arbor Press.Google Scholar
Krishnan, G., Mortensen, D. A., Martin, A. R., and Bills, L. B. 2001a. Regionalizing a locally adapted weed management decision support system. Weed Sci. Soc. Am. Abstr. 41:41.Google Scholar
Krishnan, G., Mortensen, D. A., Martin, A. R., Bills, L. B., Dieleman, A., and Neeser, C. 2001b. WeedSOFT: a state of the art weed management decision support system. Weed Sci. Soc. Am. Abstr. 41:41.Google Scholar
Kropff, M. J. and Spitters, C.J.T. 1991. A simple model of crop loss by weed competition from early observations on relative leaf area of the weeds. Weed Res. 31:97–106.CrossRefGoogle 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
Kwon, T. J., Young, D. L., Young, F. L., and Boerboom, C. M. 1995. PALWEED:WHEAT: a bioeconomic decision model for postemergence weed management in winter wheat (Triticum aestivum). Weed Sci. 43:595–603.Google Scholar
Kwon, T. J., Young, D. L., Young, F. L., and Boerboom, C. M. 1998. PALWEED:WHEAT II: revision of a weed management decision model in response to field testing. Weed Sci. 46:205–213.Google Scholar
Lotz, L.A.P., Christensen, S., Cloutier, D., et al. 1996. Prediction of the competitive effects of weeds on crop yields based on the relative leaf area of weeds. Weed Res. 36:93–101.Google Scholar
Lundkvist, A. 1997. Weed management models: a literature review. Swed. J. Agric. Res. 27:155–166.Google Scholar
Lybecker, D. W., Schweizer, E. E., and King, R. P. 1991a. Weed management decisions based on bioeconomic modeling. Weed Sci. 39:124–129.Google Scholar
Lybecker, D. W., Schweizer, E. E., and Westra, P. 1991b. Computer aided decisions for weed management in corn. Pages 234–239 In Proceedings of the Western Agricultural Economics Association. Portland, OR: Western Agricultural Economics Association.Google Scholar
Lybecker, D. W., Schweizer, E. E., and Westra, P. 1993. Computer aid for managing weeds in corn. Proc. Conf. Agric. Res. Prot. Water Qual. 2:295–297.Google Scholar
Marra, M. C. and Carlson, G. A. 1983. An economic model for weeds in soybeans (Glycine max). Weed Sci. 31:604–639.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
Metcalf, R. L. 1980. Changing role of insecticides in crop protection. Annu. Rev. Entomol. 25:219–256.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.Google 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., Roeth, F. W., Harvill, T. E., Klein, R. W., Wicks, G. A., Wilson, R. G., Holshouser, D. L., and McNamara, J. W. 1993. NebraskaHERB Version 3.0 User's Manual. Lincoln, NE: Department of Agronomy, University of Nebraska.Google Scholar
Mortensen, D. A., Martin, A. R., Roeth, F. W., et al. 1999. WeedSOFT Version 4.0 User's Manual. Lincoln, NE: Department of Agronomy, University of Nebraska.Google Scholar
Murali, N. S., Secher, B.J.M., Rydahl, P., and Andreasen, F. M. 1999. Application of information technology in plant protection in Denmark: from vision to reality. Comput. Electron. Agric. 22:109–115.Google Scholar
National Agricultural Statistics Service. 2001. Agricultural Chemical Usage: 2000 Field Crops Summary. Washington, DC: Agricultural Statistics Board, USDA, NASS.Google Scholar
Norris, R. F. 1999. Ecological implications of using thresholds for weed management. J. Crop Prod. 2:31–58.Google Scholar
North Carolina State University. 2001. The 2001 North Carolina Agricultural Chemicals Manual. Raleigh, NC: College of Agriculture and Life Sciences, NCSU. 500 p.Google Scholar
Nyland, R. E., Nelson, D. C., and Dinkel, D. H. 1958. Comparative costs of weeding onions by hand or with monuron, CIPC, and CDAA. Weeds 6:304–309.Google Scholar
O'Donovan, J. T. 1996. Weed economic thresholds: useful agronomic tool or pipe dream? Phytoprotection 77:13–28.Google Scholar
O'Donovan, J. T., Newman, J. C., Harker, K. N., Blackshaw, R. E., and McAndrew, D. W. 1999. Effect of barley plant density on wild oat interference, shoot biomass and seed yield under zero tillage. Can. J. Plant Sci. 79:655–662.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
Pannell, D. J., Stewart, V., Bennett, A., Monjardino, M., Schmidt, C., and Powles, S. 2000. RIM: A bioeconomic Model for Integrated Weed Management. SEA Working Paper 00/10: Web page: http://www.general.uwa.edu.au/u/dpannell/dpap0010.htm. Nedlands, Australia: Agricultural and Resource Economics, University of Western Australia.Google Scholar
Powles, S. B., Lorraine-Colwill, D. F., Dellow, J. J., and Preston, C. 1998. Evolved resistance to glyphosate in rigid ryegrass (Lolium rigidum) in Australia. Weed Sci. 46:604–607.Google Scholar
Rankins, A., 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., Swinton, S. M., and Kells, J. J. 1999. Adaptation and evaluation of the WEEDSIM weed management model for Michigan. Weed Sci. 47:338–348.Google Scholar
Rydahl, P. 1999. Optimising mixtures of herbicides within a decision support system. Proc. 1999 Brighton Conf. Weeds 3:761–766.Google Scholar
Schribbs, J. M., Lybecker, D. W., and Schweizer, E. E. 1990. Bioeconomic weed management models for sugarbeet (Beta vulgaris) production. Weed Sci. 38:436–444.Google Scholar
Scott, G. H., Askew, S. D., Bennett, A. C., and Wilcut, J. W. 2001. Economic evaluation of HADDS™ computer program for weed management in nontransgenic and transgenic cotton. Weed Sci. 49:549–557.CrossRefGoogle Scholar
Scott, D. H., Shaw, W. C., and Ruppenthal, R. U. 1954. Evaluation of several chemicals for weed control in strawberry fields. Weeds 3:192–207.Google Scholar
Shadbolt, C. A. and Holm, L. G. 1956. Some quantitative aspects of weed competition in vegetable crops. Weeds 4:111–123.Google Scholar
Shaw, D. R., Rankins, A., 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
Slife, F. W. 1956. The effect of 2,4-D and several other herbicides on weeds and soybeans when applied as postemergence sprays. Weeds 4:61–68.Google Scholar
0 Stern, V. M., 1973. Economic thresholds. Annu. Rev. Entomol. 18:259–280.Google Scholar
Stern, V. M., Smith, R. F., van den Bosch, R., and Hagen, K. S. 1959. The integrated control concept. Hilgardia 29:81–99.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
Streibig, J. C. 1989. The herbicide dose-response curve and the economics of weed control. Proc. 1989 Br. Crop Prot. Conf. Weeds 3:927–935.Google Scholar
Sturgill, M. C., Buol, G. S., Wilkerson, G. G., Bennett, A. C., and D'mello, W. 2001a. HADSS: a family of herbicide decision support aids. C00-sturgill134016-D In ASA-CSSA-SSSA Abstracts CD-ROM. Madison, WI: American Society of Agronomy.Google Scholar
Sturgill, M. C., Wilkerson, G. G., and Buol, G. S. 1999. Pocket HERB: an in-the-field post emergence weed control decision aid. Page 35 In 1999 Abstracts. 91st Annual Meeting. Madison, WI: American Society of Agronomy.Google Scholar
Sturgill, M. C., Wilkerson, G. G., Wilcut, J., Bennett, A. C., and Buol, G. S. 2001b. HADSS 2001 User's Manual. Research Bulletin 192. Raleigh, NC: Crop Science Department, North Carolina State University.Google Scholar
Swanton, C. J., Weaver, S., Cowan, P., Van Acker, R., Deen, W., and Shreshta, A. 1999. Weed thresholds: theory and applicability. J. Crop Prod. 2:9–29.Google Scholar
Swinton, S. M., Buhler, D. D., Forcella, F., Gunsolus, J. L., and King, R. P. 1994. Estimation of crop yield loss due to interference by multiple weed species. Weed Sci. 42:103–109.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
Swinton, S. M. and Lyford, C. P. 1996. A test for choice between hyperbolic and sigmoidal models of crop yield response to weed density. J. Agric. Biol. Environ. Statist. 1:97–106.Google Scholar
VanGessel, M. J. 2001. Glyphosate-resistant horseweed from Delaware. Weed Sci. 49:703–705.Google Scholar
Wang, N., Zhang, N., Dowell, F. E., Sun, Y., and Peterson, D. E. 2001. Design of an optical weed sensor using plant spectral characteristics. Trans. Am. Soc. Agric. Eng. 44:409–419.Google Scholar
White, A. D. and Coble, H. D. 1997. Validation of HERB for use in peanut (Arachis hypogaea). Weed Technol. 11:573–579.Google Scholar
Wiles, L. J., Canner, S. R., and Bosley, D. B. 1998. Talking about weed pressure: an interview survey of farmer and crop consultant descriptions of weed density level. Proc. West. Weed Sci. Soc. 51:117.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., and Gold, H. J. 1992a. Value of information about weed distribution for improving postemergence control decisions. Crop Prot. 11:547–554.Google Scholar
Wiles, L. J., Wilkerson, G. G., Gold, H. J., and Coble, H. D. 1992b. Modeling weed distribution for improved postemergence control decisions. Weed Sci. 40:546–553.Google Scholar
Wilkerson, G. G., Buol, G. S., Sturgill, M. C., and Bennett, A. C. 2001. WeedED Version 4 User's Guide. Research Rep. 191. Raleigh, NC: Crop Science Department, North Carolina State University.Google Scholar
Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: decision model for postemergence weed control in soybeans. Agron. J. 83:413–417.Google Scholar
Wilkerson, G. G., Wilcut, J. W., Bennett, A. C., Sturgill, M. C., and Buol, G. S. 1999. Herb Version 9.0 User's Manual. Research Bulletin 178. Raleigh, NC: Crop Science Department, North Carolina State University.Google Scholar
Woebbecke, D. M., Meyer, G. E., Von Bargen, K., and Mortensen, D. A. 1995a. Color indices for weed identification under various soil, residue, and lighting conditions. Trans. Am. Soc. Agirc. Eng. 38:259–269.Google Scholar
Woebbecke, D. M., Meyer, G. E., Von Bargen, K., and Mortensen, D. A. 1995b. Shape features for identifying young weeds using image analysis. Trans. Am. Soc. Agric. Eng. 38:271–281.Google Scholar