Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-23T07:10:58.090Z Has data issue: false hasContentIssue false
  • English
  • Français

Evaluating Multiple Rating Methods Utilized in Turfgrass Weed Science

Published online by Cambridge University Press:  20 January 2017

Jared A. Hoyle ,
Fred H. Yelverton  and
Travis W. Gannon
Jared A. Hoyle*
Affiliation:
Department of Crop and Soil Science, University of Georgia, 3111 Miller Plant Sciences Building, Athens, GA 30602
Fred H. Yelverton
Affiliation:
Department of Crop Science, North Carolina State University, Campus Box 7620, Raleigh, NC 27695
Travis W. Gannon
Affiliation:
Department of Crop Science, North Carolina State University, Campus Box 7620, Raleigh, NC 27695
*
Corresponding author's E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Turfgrass weed scientists commonly use visual ratings (VR) to assign a numerical value to a turfgrass or weed response. These ratings lack quantifiable numerical values and are considered subjective. Alternatives to VR, including line intersect analysis (LIA) and digital image analysis (DIA), have been used to varying extents in turfgrass research. Alternatives can be expensive, labor intensive, and can require extensive calibration and increased time for data acquisition. Minimal research has been conducted evaluating rating methods used in turfgrass weed science. Trials were conducted in 2007 and 2008 to evaluate ratings methods used to quantify large crabgrass populations as influenced by tall fescue mowing height (2.5, 5.1, 7.6, and 10.2 cm). Percent large crabgrass cover was assessed utilizing VR, LIA, and DIA to determine if differences existed among evaluation methods. Pairwise comparisons, Pearson's correlation, and linear regression were performed to compare evaluations. All rating methods were significantly correlated to one another. Differences of large crabgrass cover estimates existed between LIA and DIA data at all mowing heights and between VR and DIA data at the 7.6 and 10.2 cm mowing heights in 2007. Authors believe that shadows produced by the turf canopy at higher (≥ 7.6 cm) mowing heights increased DIA estimates of large crabgrass cover. At trial initiation in 2007, researchers did not capture calibration images because the methodology to eliminate a shadow influence using a standard digital image had not been published. Additional DIA calibration in 2008 corrected for canopy shadows, and no differences were observed in large crabgrass cover between all evaluation methods indicated by nonsignificance pairwise comparisons and estimated regression parameters. These data indicate VR are no different than LIA or DIA in estimating large crabgrass cover as affected by tall fescue mowing height.

Los científicos de malezas en céspedes usan estimaciones visuales (VR) para asignar un valor numérico a las respuestas del césped o de la maleza. Estas estimaciones carecen de valores numéricos cuantificables y son consideradas subjetivas. Las alternativas a VR incluyen el análisis de intersección de líneas y análisis digital de imágenes (DIA), que han sido usados en diferentes niveles en la investigación en céspedes. Las alternativas pueden ser costosas, intensivas en labor, y pueden requerir una calibración extensiva e incrementos en el tiempo de adquisición de datos. La investigación que se ha realizado ha sido mínima para evaluar los métodos de evaluación usados en la ciencia de malezas en céspedes. Se realizaron estudios en 2007 y 2008 para evaluar los métodos de evaluación usados para cuantificar poblaciones de Digitaria sanguinalis a su vez que la influencia de la altura de poda en Lolium arundinaceum.(2.5, 5.1, 7.6 y 10.2 cm). El porcentaje de cobertura de D. sanguinalis fue evaluado utilizando VR, LIA y DIA para determinar la existencia de diferencias entre estos métodos de evaluación. Comparaciones de pares, correlación Pearson, y regresión lineal fueron realizadas para comparar los diferentes métodos. Todos los métodos de evaluación correlacionaron entre ellos en forma significativa. Hubo diferencias en la cobertura de D. sanguinalis entre los datos de LIA y DIA en todas las alturas de poda y entre los datos de VR y DIA a alturas de 7.6 y 10.2 cm en 2007. Los autores creen que las sombras producidas por el dosel del césped a alturas de poda altas (≥7.6 cm) incrementó los estimados de DIA de la cobertura de D. sanguinalis. Al inicio del estudio en 2007, los investigadores no capturaron imágenes de calibración porque la metodología para eliminar la influencia de las sombras usando una imagen digital estándar no había sido publicada. La calibración adicional de DIA en 2008 corrigió por sombras del dosel, y no se observaron diferencias en la cobertura de D. sanguinalis entre los diferentes métodos de evaluación, lo cual fue indicado por la no-significancia de las comparaciones de pares y los parámetros de regresión estimados. Estos datos indican que VR no es diferente de LIA o DIA al estimar el porcentaje de cobertura de D. sanguinalis al ser influenciada por la altura de poda de L. arundinaceum.

Keywords

Tenacity (mesotrione)large crabgrass, Digitaria sanguinalis (L.) Scop. DIGSAtall fescue, Lolium arundinaceum (Schreb.) S. J. DarbyshireDigital image analysisrating methodsSigmaScanturfgrassvisual estimations
Type
Weed Management—Techniques
Information
Weed Technology , Volume 27 , Issue 2 , June 2013 , pp. 362 - 368
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

Askew, S. D. and Beam, J. B. 2002. Weed management in cool-season turf with mesotrione. Page 129 in Proceedings of the Northeastern Weed Science Society. Cambridge, MA Northeastern Weed Science Society.Google Scholar
Booth, D. T., Cox, S. E., Fifield, C., Phillips, M., and Williamson, N. 2005. Image analysis compared with other methods for measuring ground cover. Arid Land Res. Manag. 19:91–100.Google Scholar
Brosnan, J. T., Thoms, A. W., Breeden, G. K., and Sorochan, J. C. 2010. Effects of various plant growth regulators on the traffic tolerance of ‘Riviera' bermudagrass (Cynodon dactylon L.). HortScience. 45:966–970.Google Scholar
Butler, E. L., Tredway, L., Peacock, C., and Shew, D. 2004. Development of novel strategies for control of spring dead spot of bermudagrass. . Raleigh, NC North Carolina State University. 112 p.Google Scholar
Cook, C. W. and Stubbendieck, J. 1986. Range research: Basic Problems and Techniques. Denver. CO Society for Range Management. 317 p.Google Scholar
Dernoeden, P. H., Carroll, M. J., and Krouse, J. M. 1993. Weed management and tall fescue quality as influenced by mowing, nitrogen, and herbicides. Crop Sci. 33:1055–1061.Google Scholar
Ewing, R. P. and Horton, R. 1999. Quantitative color image analysis of agronomic images. Agron. J. 76:619–622.Google Scholar
Goddard, M.J.R., Sorochan, J. C., McElroy, J. S., Karcher, D. E., and Landreth, J. W. 2008. The effects of crumb rubber topdressing on hybrid kentucky bluegrass and bermudagrass athletic fields in the transition zone. Crop Sci. 48:2003–2009.Google Scholar
Horst, G. L., Engelke, M. C., and Meyers, W. 1984. Assessments of visual evaluation techniques. Agron. J. 76:619–622.Google Scholar
Ikemura, Y. 2003. Using Digital Image Analysis to Measure the Nitrogen Concentration of Turfgrasses. . Fayetteville, AR Univ. of Arkansas. 112 p.Google Scholar
[ITT] Interagency Technical Team. 1996. Sampling Vegetation Attributes, Interagency Technical Report 17. Fort Collins, CO U.S. Department of Agriculture, Forest Service. BLM/RS/ST-96/002+1730. 172 p.Google Scholar
Johnson, D. E., Vulfson, M., Louhaichi, M., and Harris, N. R. 2003. VegMeasure. Version 1.6, User's Manual. Corvallis, OR Department of Rangeland Resources.51 p.Google Scholar
Karcher, D. E. and Richardson, M. D. 2003. Quantifying turfgrass color using digital image analysis. Crop Sci. 43:943–951.Google Scholar
Karcher, D. E. and Richardson, M. D. 2005. Batch analysis of digital images to evaluate turfgrass characteristics. Crop Sci. 45:1536–1539.Google Scholar
Karcher, D. E., Richardson, M. D., Landreth, J. W., and McCalla, J. H. Jr. 2005a. Recovery of bermudagrass varieties from divot injury. Appl. Turf. Sci. DOI: /ATS-2005-0117-01-RS.Google Scholar
Karcher, D. E., Richardson, M. D., Landreth, J. W., and McCalla, J. H. Jr. 2005b. Recovery of zoysiagrass varieties from divot injury. Appl. Turf. Sci. DOI: /ATS-2005-0728-01-RS.Google Scholar
Laliberte, A. S., Rango, A., and Herrick, J. E. eds. Fredrickson, L. and Burkett, L. 2007. An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography. J. Arid Environ. 69:1–14.Google Scholar
Landschoot, P. J. and Mancino, C. F. 2000. A comparison of visual vs. instrumental measurement of color differences in bentgrass turf. HortScience. 35:914–916.Google Scholar
Louhaichi, M., Borman, M. M., and Johnson, D. E. 2001. Spatially coated platform and aerial photography for documentation of grazing impacts in wheat. Geocarto Int. 16:63–68.Google Scholar
Morris, K. N. 2002. A Guide to NTEP Turfgrass Rating. A Publication of the National Turfgrass Evaluation Program. Beltsville, MD: NTEP. 11:30–39.Google Scholar
Oosting, H. J. 1956. The Study of Plant Communities. San Francisco, CA W.H. Freeman, 398 p.Google Scholar
Patton, A. J. and Reicher, Z. J. 2007. Zoysiagrass species and genotypes differ in their winter injury and freeze tolerance. Crop Sci. 47:1619–1627.CrossRefGoogle Scholar
Purcell, L. C. 2000. Soybean canopy coverage and light interception measurements using digital imagery. Crop. Sci. 40:834–837.Google Scholar
Quinn, G. P. and Keough, M. J. 2002. Experimental Design and Data Analysis for Biologists. New York Cambridge. 350 p.Google Scholar
Richardson, M. D., Karcher, D. E., and Purcell, L. C. 2001. Quantifying turfgrass cover using digital image analysis. Crop Sci. 41:1884–1888.Google Scholar
Shaver, B. R., Richardson, M. D., McCalla, J. H., Karcher, D. E., and Berger, P. J. 2006. Dormant seeding bermudagrass cultivars in a transition-zone environment. Crop Sci. 46:1787–1792.Google Scholar
Skogley, C. R. and Sawyer, C. D. 1992. Field Research. Pages 589–614. in Waddington, D. V., Carrow, R. N., and Shearman, R. C., eds. American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America Publishers. Turfgrass. Agronomy Monograph 32.Google Scholar
Thoms, A. W., Sorochan, J. C., Brosnan, J. T., and Samples, T. J. 2010. Perennial ryegrass (Lolium perenne L.) and grooming affect bermudagrass traffic tolerance. Crop Sci. 51:2204–2211.Google Scholar
Vanini, J. T., Henderson, J. J., Sorochan, J. C., and Rogers, J. N. III. 2007. Evaluating traffic stress by the Brinkman Traffic Simulator and Cady Traffic Simulator on a kentucky bluegrass stand. Crop Sci. 47:782–786.Google Scholar