Hostname: page-component-78c5997874-dh8gc Total loading time: 0 Render date: 2024-11-08T08:29:15.699Z Has data issue: false hasContentIssue false
  • English
  • Français

The Weather Factor: Incorporating Weather Variance Into Computer Simulation

Published online by Cambridge University Press:  12 June 2017

J. R. Williams ,
C. W. Richardson  and
R. H. Griggs
J. R. Williams
Affiliation:
U.S. Dep. Agric., Agric. Res. Serv., 808 East Blackland Road, Temple, TX 76502
C. W. Richardson
Affiliation:
U.S. Dep. Agric., Agric. Res. Serv., 808 East Blackland Road, Temple, TX 76502
R. H. Griggs
Affiliation:
Texas Agric. Exp. Stn., 808 East Blackland Road, Temple, TX 76502
Get access Rights & Permissions [Opens in a new window]

Abstract

The effect of weather variation on pesticide losses was estimated with the Erosion-Productivity Impact Calculator (EPIC) model. Weather variations had little effect on pesticide loss from a hypothetical site near Memphis, TN, but the effect was more dramatic and in the expected direction at Des Moines, IA. Atrazine losses at Des Moines were reduced by lowering relative humidity or rainfall intensity. Increasing the CO2 level from 300 to 660 ppm slightly increased atrazine losses. Results from these two sites are very limited and only serve to demonstrate modeling potential for addressing weather/pesticide problems. Further, more comprehensive studies are needed to better estimate pesticide loss sensitivity to weather variation.

Keywords

Atrazine, 6-chloro-N-ethyl-N′-(1-methylethyl)-1,3,5-triazine-2,4-diaminePesticidemodelrainfallhumidityCO2atrazinecyanazinedicambametolachlor
Type
Symposium
Information
Weed Technology , Volume 6 , Issue 3 , September 1992 , pp. 731 - 735
Copyright
Copyright © 1990 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

1. Leonard, R. A., Knisel, W. G., and Still, D. A. 1987. GLEAMS: Groundwater Loading Effects of Agricultural Management Systems. Trans. Am. Soc. Agric. Eng. 30:1403–1418.CrossRefGoogle Scholar
2. Monteith, J. L. 1965. Evaporation and Environment. Symp. Soc. Exp. Biol. 19:205–234.Google Scholar
3. Nicks, A. D. 1974. Stochastic generation of the occurrence, pattern, and location of maximum amount of daily rainfall. p. 154–171 in Proc. Symp. Statistical Hydrology (Tucson, AZ, Aug.-Sept. 1971).Google Scholar
4. Penman, H. L. 1948. Natural evaporation from open, bare soil and grass. Proc. R. Soc. (London) A193:120–145.Google Scholar
5. Richardson, C. W. 1981. Stochastic simulation of daily precipitation, temperature, and solar radiation. Water Resour. Res. 17:182–190.Google Scholar
6. Richardson, C. W. 1982. Dependence structure of daily temperature and solar radiation. Trans. Am. Soc. Agric. Eng. 25:735–739.Google Scholar
7. Richardson, C. W. 1982. A wind simulation model for wind erosion estimation. Am. Soc. Agric. Eng. Paper No. 82-2576.Google Scholar
8. Sharpley, A. N. and Williams, J. R., eds. 1990. EPIC-Erosion/Productivity Impact Calculator. 1. Documentation. USDA Tech. Bull. #1768. 235 p.Google Scholar
9. USDA, Soil Conservation Service. 1972. Hydrology. Chapters 4-10. In National Engineering Handbook. USA Government Printing Office, Washington, DC.Google Scholar
10. Williams, J. R., Jones, C. A., and Dyke, P. T. 1984. A modeling approach to determining the relationship between erosion and soil productivity. Trans. Am. Soc. Agric. Eng. 27:129–144.Google Scholar