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Performance of WeedSOFT for Predicting Soybean Yield Loss

Published online by Cambridge University Press:  20 January 2017

Shawn M. Hock,
Stevan Z. Knezevic,
Alex R. Martin and
John L. Lindquist
Shawn M. Hock
Affiliation:
University of Nebraska, Lincoln, NE 68583
Stevan Z. Knezevic*
Affiliation:
University of Nebraska, Concord, NE 68728
Alex R. Martin
Affiliation:
University of Nebraska, Lincoln, NE 68583
John L. Lindquist
Affiliation:
University of Nebraska, Lincoln, NE 68583
*
Corresponding author's E-mail: sknezevic2@unl.edu
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Abstract

Decision support systems (DSSs) have been developed to assist producers and consultants with weed management decisions. WeedSOFT is a DSS currently used in several states in the north-central region of the United States. Accurate estimates of crop yield loss due to weed interference are required for cost-effective weed management recommendations. WeedSOFT uses competitive indices (CIs) to predict crop yield loss under multiple weed species, weed densities, and relative times of weed emergence. Performance of several WeedSOFT versions to predict soybean yield loss from weed competition was evaluated using CI values in WeedSOFT version 9.0 compared to new CI values calculated from weed dry matter, weed volume, and soybean yield loss in two soybean row spacings (19 and 76 cm) and two relative weed emergence times (at soybean emergence and first trifoliate leaf stage). Overall, new CI values improved predictions of soybean yield loss by as high as 63%. It was especially true with using new CI values based on yield loss compared to those based on weed dry matter or weed volume. However, there were inconsistencies in predictions for most weed species, suggesting that additional modifications are needed to further improve soybean yield loss predictions.

Keywords

SoybeanGlycine max (L.) MerrCrop modelingcompetitiondecision support systemssimulationAbutilon theophrastiAmaranthus retroflexusAmaranthus rudisAmbrosia trifidaEchinochloa crus-galliHelianthus annuusSetaria faberiSetaria glaucaAE, average errorACI, adjusted competitive indexCI, competitive indexDM, WeedSOFT version based on weed dry matterDSS, decision support systemsRCBD, randomized complete block designTCL, total competitive loadTDM, total dry matterV1, soybean first trifoliate leaf stageVE, soybean emergenceVP, soybean plantingVOL, WeedSOFT version based on weed volumeWS, WeedSOFT version 9.0YL, WeedSOFT version based on yield loss

Information

Type
Research Article
Information
Weed Technology , Volume 20 , Issue 2 , June 2006, pp. 478 - 484
Copyright
Copyright © Weed Science Society of America 

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Footnotes

1 Published as University of Nebraska Agricultural Research Division Journal Series 14933.

References

Literature Cited

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. 17 p.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. 39:564–569.Google Scholar
Chikoye, D. and Swanton, C. J. 1995. Evaluation of three empirical models depicting Ambrosia artemisiifolia competition in white bean. Weed Res. 35:421–428.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
Cousens, R. 1985. A simple model relating yield loss to weed density. Ann. Appl. Biol. 2:239–252.Google Scholar
Cousens, R., Firbank, L. G., Mortimer, A. M., and Smith, R. G. R. 1988. Variability in the relationship between crop yield and weed density for winter wheat and Bromus sterilis . J. Appl. Ecol. 25:1033–1044.Google Scholar
Gonsolus, J. L. 1986. Reciprocal Interference Effects Between Weeds and Soybeans (Glycine max) measured by area of influence methodology. Ph.D. dissertation. North Carolina State University, Raleigh, NC. 67 p.Google Scholar
Hock, S. M., Knezevic, S. Z., Martin, A. R., and Lindquist, J. L. 2006. Soybean row spacing and weed emergence time influence weed competitiveness and competitive indices. Weed Sci. 54:38–46.Google Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1994. Interference of redroot pigweed (Amaranthus retroflexus) in corn (Zea mays). Weed Sci. 42:568–573.Google Scholar
Knezevic, S. Z., Weise, S. F., and Swanton, C. J. 1995. Comparison of empirical models depicting density of Amaranthus retroflexus L. and relative leaf area as predictors of yield loss in maize. Weed Res. 35:207–214.Google Scholar
Lindquist, J. L. and Mortensen, D. A. 1999. Ecophysiological characteristics of four maize hybrids and Abutilon theophrasti . Weed Res. 39:271–285.Google Scholar
Lindquist, J. L., Mortensen, D. A., Clay, S. A., Schmenk, R., Kells, J. J., Howatt, K., and Westra, P. 1996. Stability of corn (Zea mays)–velvetleaf (Abutilon theophrasti) interference relationships. Weed Sci. 44:309–313.Google Scholar
Lotz, L. A. P., Christensen, S., and 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
Mitchell, P. L. and Sheehy, J. E. 1997. Comparison of predictions and observations to assess model performance: a method of empirical validation. in Kropff, M. J., Teng, P. S., Aggarwad, P. K., Bouma, J., Bouman, B.A.M., Jones, J. W., and van Laar, H. H., eds. Applications of Systems Approaches at the Field Level. London: Kluwar Academic. Pp. 437–450.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. 10 p.Google Scholar
Neeser, C., Dille, J. A., Krishnan, G., Mortensen, D. A., Rawlinson, J. T., Martin, A. R., and Bills, L. B. 2004. WeedSOFT®: a weed management decision support system. Weed Sci. 52:115–122.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
Regnier, E. E. and Harrison, S. K. 1993. Compensatory responses of common cocklebur (Xanthium strumarium) and velvetleaf (Abutilon theophrasti) to partial shading. Weed Sci. 41:541–547.CrossRefGoogle Scholar
Regnier, E. E. and Stoller, E. W. 1989. The effects of soybean (Glycine max) interference on the canopy architecture of common cocklebur (Xanthium strumarium), jimsonweed (Datura stramonium), and velvetleaf (Abutilon theophrasti). Weed Sci. 37:187–195.Google Scholar
Ritchie, S. W., Hanway, J. J., and Benson, G. O. 1993. How a soybean plant develops. Spec. Rep. 53. Ames, IA: Iowa State University.Google Scholar
[SAS] Statistical Analysis System. 1999. SAS OnLine Doc, Version 8. Cary, NC: Statistical Analysis Systems Institute.Google Scholar
Sturgill, M. C., Wilkerson, G. G., Wilcut, J., Bennett, A. C., and Buol, G. S. 2001. HADSS 2001 User's Manual. Research Bulletin 192. Raleigh, NC: Crop Science Department, North Carolina State University.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., King, R. P., Schweizer, E. E., Lybecker, D. W., and Swinton, S. M. 1996. GWM: general weed management model. Agric. Syst. 50:335–376.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