Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-28T06:06:09.911Z Has data issue: false hasContentIssue false

Comparison of PRZM and GLEAMS Computer Model Predictions with Field Data for Fluometuron and Norflurazon Behavior in Soil

Published online by Cambridge University Press:  12 June 2017

William T. Willian
Affiliation:
Department of Plant and Soil Science, University of Tennessee, Knoxville, TN 37901-1071
Thomas C. Mueller
Affiliation:
Department of Plant and Soil Science, University of Tennessee, Knoxville, TN 37901-1071
Robert M. Hayes
Affiliation:
Department of Plant and Soil Science, University of Tennessee, Knoxville, TN 37901-1071
David C. Bridges
Affiliation:
University of Georgia, Griffin
Charles E. Snipes
Affiliation:
Mississippi State University, Stoneville, MS

Abstract

The ability of the pesticide root zone model (PRZM) and the groundwater-loading effects of agricultural management systems (GLEAMS) model to predict movement of two herbicides in soil was evaluated using site-specific environmental data from sites in three states. Predictions of herbicide movement with site-specific data were compared to predictions using more generalized database soil and pesticide data within each model. Field experiments examined fluometuron and norflurazon movement in three soils representative of the cotton-growing regions of the southeastern United States. In comparing the use of site-specific vs. database values, the small increase in accuracy using site-specific inputs would not justify the large cost to obtain the data. The databases for each model gave predictions similar to those using the site-specific numbers. Both the PRZM and the GLEAMS model had similar accuracy levels in predicting the presence of fluometuron or norflurazon present in three surface soils, although each model tended to overpredict movement and total herbicide concentration, especially at lower herbicide concentrations. At higher herbicide concentrations, prediction accuracy was less than that probably needed to predict agronomically relevant herbicide concentrations in surface soils.

Type
Research
Copyright
Copyright © 1999 by the 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

Ahrens, W. H., ed. 1994a. Herbicide Handbook. 7th ed. Champaign, IL: Weed Science Society of America. pp. 135137.Google Scholar
Ahrens, W. H., ed. 1994b. Herbicide Handbook. 7th ed. Champaign, IL: Weed Science Society of America. pp. 218220.Google Scholar
Allison, L. E., Bollen, W. B., and Moodie, C. D. 1965. Soil organic matter determination. In Black, C. A., ed. Methods of Soil Analysis. Part 1. Madison, WI: American Society of Agronomy. pp. 13531364.Google Scholar
Bouchard, D. C., Lavy, T. L., and Marx, D. B. 1982. Fate of metribuzin, metolachlor and fluometuron in soil. Weed Sci. 30:629632.CrossRefGoogle Scholar
Bush, P. B., Neary, D. G., Dowd, J. F., Allison, D. C., and Nutter, W. L. 1986. Role of models in environmental impact assessment. Proc. South. Weed Sci. Soc. 39:502516.Google Scholar
Carringer, R. D., Weber, J. B., and Monaco, T. J. 1975. Adsorption–desorption of selected pesticides by organic matter and montmorillonite. J. Agric. Food Chem. 23:568572.CrossRefGoogle ScholarPubMed
Carsel, R. F., Nixon, W. B., and Ballantine, L. G. 1986. Comparison of pesticide root zone model predictions with observed concentrations for the tobacco pesticide metalaxyl in unsaturated zone soils. Environ, Toxicol. Chem. 5:345353.CrossRefGoogle Scholar
Carsel, R. F., Smith, C. N., Mulkey, L. A., Dean, J. D., and Jowise, P. P. 1984. Users Manual for the Pesticide Root Zone Model (PRZM) Release 1. Athens, GA; U.S. Environmental Protection Agency Rep. EPA-600/3-84-109.Google Scholar
Hubbs, C. W., and Lavy, T. L. 1990. Dissipation of norflurazon and other persistent herbicides in soil. Weed Sci. 38:8188.CrossRefGoogle Scholar
Jones, R. L., Black, G. W., and Estes, T. L. 1986. Comparison of computer model predictions with unsaturated zone field data for aldicarb and aldoxycarb. Environ. Toxicol. Chem. 5:10271037.CrossRefGoogle Scholar
Keeling, J. W., Lloyd, R. W., and Abernathy, J. R. 1989. Rotational crop response to repeated applications of norflurazon. Weed Technol. 3:122125.CrossRefGoogle Scholar
Kilmer, V. J., and Alexander, L. T. 1949. Methods of making mechanical analysis of soils. Soil Sci. 68:112.CrossRefGoogle Scholar
Leonard, R. A., Knisel, W. G., and Still, D. A. 1987. GLEAMS: groundwater loading effects of agricultural management systems. Trans. Am. Soc. Agric. Eng. 30:14031418.CrossRefGoogle Scholar
Mueller, T. C., Jones, R. E., Bush, P. B., and Banks, P. A. 1992a. Comparison of PRZM and GLEAMS computer model predictions with field data for alachlor, metribuzin, and norflurazon leaching. Environ. Toxicol. Chem. 11:427436.CrossRefGoogle Scholar
Mueller, T. C., and Moorman, T. B. 1991. Liquid chromatographic determination of fluometuron and metabolites in soil. J. Assoc. Off. Anal. Chem. 74:671673.Google Scholar
Mueller, T. C., Moorman, T. B., and Snipes, C. E. 1992b. Effect of concentration, sorption, and microbial biomass on degradation of the herbicide fluometuron in surface and subsurface soils. J. Agric. Food Chem. 40:25172522.CrossRefGoogle Scholar
Parrish, R. S., and Smith, C. N. 1990. A method for testing whether model predictions fall within a prescribed factor of true values, with an application to pesticide leaching. Ecol. Model. 51:5972.CrossRefGoogle Scholar
Rogers, C. B., Talbert, R. E., Mattice, J. D., Lavy, T. L., and Frans, R. E. 1986. Residual fluometuron levels in three Arkansas soils under continuous cotton (Gossypium hirsutum) production. Weed Sci. 34:122130.CrossRefGoogle Scholar
Schroeder, J., and Banks, P. A. 1986. Persistence of norflurazon in five Georgia soils. Weed Sci. 34:595599.CrossRefGoogle Scholar
[SAS] Statistical Analysis Systems. 1996. Statistical Analysis Software User's Manual, Version 3.01. Cary, NC: Statistical Analysis Systems Institute. 389 p.Google Scholar
Talbert, R. E., and Fletchall, O. H. 1965. The adsorption of some s-triazines in soils. Weeds 13:4652.CrossRefGoogle Scholar
Wauchope, R.D., Buttler, T. M., Hornsby, A. G., Augustijn-Beckers, P.W.M., and Burt, J. P. 1992. The SCS/ARS/CES pesticide properties database for environmental decision-making. Rev. Environ. Contam. Toxicol. 123:1164.Google ScholarPubMed
Willian, W. T., and Mueller, T. C. 1994. Liquid chromatographic determination of norflurazon and its initial metabolite in soil. J. Assoc. Off. Anal. Chem. 77:752755.Google Scholar
Willian, W. T., Mueller, C., Hayes, R. M., Snipes, C. E., and Bridges, D. C. 1997a. Adsorption, dissipation and movement of fluometuron in three southeastern United States soils. Weed Sci. 45:183189.CrossRefGoogle Scholar
Willian, W. T., Mueller, T. C., Hayes, R. M., Snipes, C. E., and Bridges, D. C. 1997b. Norflurazon adsorption, dissipation and movement in three southern soils. Weed Sci. 45:301306.CrossRefGoogle Scholar
Zacharias, S., and Heatwole, C. D. 1994. Evaluation of GLEAMS and PRZM for predicting pesticide leaching under field conditions. Trans. Am. Soc. Agric. Eng. 37:439451.CrossRefGoogle Scholar
Zar, J. H. 1984. Biostatistical Analysis. Englewood Cliffs, NJ: Prentice Hall. 417 p.Google Scholar