Hostname: page-component-78c5997874-mlc7c Total loading time: 0 Render date: 2024-11-19T01:46:23.208Z Has data issue: false hasContentIssue false
  • English
  • Français

Between-Observer Differences in Relative Corn Yield vs. Rated Weed Control

Published online by Cambridge University Press:  20 January 2017

William W. Donald
William W. Donald*
Affiliation:
USDA-ARS, 269 Agricultural Engineering Building, UMC, Columbia, MO 65211
Get access Rights & Permissions [Opens in a new window]

Abstract

Crop yield and weed control rating have been used to measure weed and crop response to weed management treatments, eliminate unacceptable weed management treatments, and select “best” treatments for recommendation to farmers. However, the mathematical relationship between crop yield and rated weed control has not been reported before from such treated screening experiments. Likewise, differences have not been reported before in rated weed control among experienced observers (i.e., reliability) when rating the same experiments and for an experienced observer over time (i.e., repeatability). Data from published experiments on zone herbicide application in field corn in which weeds reduced yield to various amounts were reanalyzed to examine these issues. For this study, relative corn yield was calculated as a percentage of the 1× broadcast herbicide rate for two observers and either three experimental site-years or their average. For observer A, relative corn yield (%) increased linearly as rated total weed control (%) increased for all 3 site-yr and their average. For observer B, equations were curvilinear in 2 of 3 site-yr. For both observers, equations accounted for little data variability in relative corn yield (r2 = 0.25 and 0.25 in site-year 1, respectively, 0.38 and 0.36 in site-year 2, 0.58 and 0.57 in site-year 3, and 0.43 and 0.42 for their average). When rated total weed control by observer A was graphed against that of observer B, the relationship was a nearly ideal 1:1 linear relationship in only 1 of 3 site-yr. In two other site-years, equations were nonlinear, indicating that one observer distinguished smaller differences between treatments at lower rated control than the other observer. Between-row total weed cover and in-row total weed height influenced observer weed control rating.

Keywords

CornZea mays (L.) #3 ZEAMX ‘Pioneer 33G28’RatingweedSetaria faberii (L.) Beauv. # SETFAAmaranthus rudis Sauer. # AMATAIR, in-rowBR, between-rowZHA, zone herbicide application
Type
Research Article
Information
Weed Technology , Volume 20 , Issue 1 , March 2006 , pp. 41 - 51
Copyright
Copyright © Weed Science Society of America 

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

Literature Cited

Bedmar, F., Manetti, P., and Monterubbianesi, G. 1999. Determination of the critical period of weed control in corn using a thermal basis. Pesq. Agropec. Bras. Brasilia. 34:187–193.Google Scholar
Clement, R. T. 1996. Making Hard Decisions: An Introduction to Decision Analysis. 2nd ed. New York: Duxbury Press. 664 p.Google Scholar
Darwent, A. L., Cole, D., and Malik, N. 1997. Imazethapyr, alone or with other herbicides for weed control during alfalfa (Medicago sativa) establishment. Weed Technol. 11:346–353.Google Scholar
Donald, W. W., Archer, D., Johnson, W. G., and Nelson, K. 2004a. Zone herbicide application controls annual weeds and reduces residual herbicide use in corn. Weed Sci. 52:821–833.CrossRefGoogle Scholar
Donald, W. W., Johnson, W. G., and Nelson, K. A. 2004b. In-row and between-row interference by corn modifies annual weed control by preemergence residual herbicides. Weed Technol. 18:497–504.Google Scholar
Frans, R., Talbert, R., Marx, D., and Crowley, H. 1986. Experimental design and techniques for measuring and analyzing plant response to weed control practices. in Camper, N. D., ed. Research Methods in Weed Science. 3rd ed. Champaign, IL: Southern Weed Science Society. Pp. 29–46.Google Scholar
Hall, M. R., Swanton, C. J., and Anderson, G. W. 1992. The critical period of weed control in grain corn (Zea mays). Weed Sci. 40:441–447.Google Scholar
Hamill, A. S., Marriage, P. B., and Friesen, G. 1977. A method for assessing herbicide performance in small plot experiments. Weed Sci. 25:386–389.Google Scholar
Harvey, R. G. 1993. A simple technique for predicting future weed problems. Madison, WI: University of Wisconsin Extension Publications A3595 (1-10-93-10M-S). 6 p.Google Scholar
Hebert, T. T. 1982. The rationale for the Horsfall–Barratt plant disease assessment scale. Phytopathology 72:1269.CrossRefGoogle Scholar
Horsfall, J. G. and Cowling, E. B. 1978. Pathometry: the measurement of plant disease. in Horsfall, J. G. and Cowling, E. B., eds. Plant Disease. An Advanced Treatise. Volume 2. New York: Academic Press. Pp. 119–136.Google Scholar
Horst, G. L., Engelke, M. C., and Meyers, W. 1984. Assessment of visual evaluation techniques. Agron. J. 76:619–622.CrossRefGoogle Scholar
Hoshmand, A. R. 1994. Experimental Research Design and Analysis. A Practical Approach for Agricultural and Natural Sciences. Boca Raton, FL: CRC Press. Pp. 59–170.Google Scholar
James, W. C. 1971. An illustrated series of assessment keys for plant disease. Their preparation and usage. Can. Plant Dis. 51:39–65.Google Scholar
James, W. C. and Teng, P. S. 1979. The quantification of production constraints associated with plant diseases. Appl. Biol. 4:201–267.Google Scholar
Kennedy, K. A. and Addison, P. A. 1987. Some considerations for the use of visual estimates of plant cover in biomonitoring. J. Ecol. 75:151–157.Google Scholar
Lindquist, J. L. and Knezevic, S. Z. 2001. Quantifying crop yield response to weed populations: applications and limitations. in Peterson, R.K.D. and Higley, L. G., eds. Biotic Stress and Yield Loss. Chapter 12. Boca Raton, FL: CRC Press. Pp. 205–232.Google Scholar
Muir, P. S. and McCune, B. 1987. Index construction for foliar symptoms of air pollution injury. Plant Dis. 71:558–565.Google Scholar
Myers, R. H. and Montgomery, D. C. 2002. Response Surface Methodology. Process and Product Optimization Using Designed Experiments. 2nd ed. New York: John Wiley & Sons. Pp. 17–155.Google Scholar
Nilsson, H. E. 1995a. Remote sensing and image analysis in plant pathology. Annu. Rev. Phytopathol. 15:489–527.Google Scholar
Nilsson, H. E. 1995b. Remote sensing and image analysis in plant pathology. Can. J. Plant Pathol. 17:154–166.CrossRefGoogle Scholar
Nutter, F. W. Jr. and Schultz, P. M. 1995. Improving the accuracy and precision of disease assessments: selection of methods and use of computer-aided training programs. Can. J. Plant Pathol. 17:174–184.Google Scholar
Nutter, F. W. Jr., Gleason, M. L., Jenco, J. H., and Christians, N. C. 1993. Assessing the accuracy, intra-rater repeatability, and inter-rater reliability of disease assessment systems. Phytopathology 83:806–812.Google Scholar
Romero, C. and Rehman, T. 2003. Multiple Criteria Analysis for Agricultural Decision. New York: Elsevier. 186 p.Google Scholar
Sokal, R. R. and Rohlf, F. J. 1981. Biometry. 2nd ed. New York: W. H. Freeman & Co. p. 13.Google Scholar
Spomer, L. and Smith, M. A. L. 1988. Image analysis for biological research: camera influence on measurement accuracy. Intelligent Instrum. Comput. July/Aug:201–216.Google Scholar
SPSS, Inc. 2001. SPSS User's Guide. Version 11. 233 South Wacker Dr. 11th Floor, Chicago, IL 60606-6307.Google Scholar