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Coupling Geographic Information Systems and Models for Weed Control and Groundwater Protection

Published online by Cambridge University Press:  12 June 2017

John P. Wilson ,
William P. Inskeep ,
Paul R. Rubright ,
Diana Cooksey ,
Jeffrey S. Jacobsen  and
Robert D. Snyder
John P. Wilson
Affiliation:
Plant Soil Sci.; and GIS Tech., Geogr. Info. Anal. Cent., Montana State Univ., Bozeman, MT 59717
William P. Inskeep
Affiliation:
Plant Soil Sci.; and GIS Tech., Geogr. Info. Anal. Cent., Montana State Univ., Bozeman, MT 59717
Paul R. Rubright
Affiliation:
Plant Soil Sci.; and GIS Tech., Geogr. Info. Anal. Cent., Montana State Univ., Bozeman, MT 59717
Diana Cooksey
Affiliation:
Plant Soil Sci.; and GIS Tech., Geogr. Info. Anal. Cent., Montana State Univ., Bozeman, MT 59717
Jeffrey S. Jacobsen
Affiliation:
Plant Soil Sci.; and GIS Tech., Geogr. Info. Anal. Cent., Montana State Univ., Bozeman, MT 59717
Robert D. Snyder
Affiliation:
Plant Soil Sci.; and GIS Tech., Geogr. Info. Anal. Cent., Montana State Univ., Bozeman, MT 59717
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Abstract

The Chemical Movement through Layered Soils (CMLS) model was modified and combined with the USDA-SCS State Soil Geographic Data Base (STATSGO) and Montana Agricultural Potentials System (MAPS) digital databases to assess the likelihood of groundwater contamination from selected herbicides in Teton County, MT. The STATSGO and MAPS databases were overlaid to produce polygons with unique soil and climate characteristics and attribute tables containing only those data needed by the CMLS model. The Weather Generator (WGEN) computer simulation model was modified and used to generate daily precipitation and evapotranspiration values. A new algorithm was developed to estimate soil carbon as a function of soil depth. The depth of movement of the applied chemicals at the end of the growing season was estimated with CMLS for each of the soil series in the STATSGO soil mapping units and the results were entered into ARC/INFO to produce the final hazard maps showing best, weighted average, and worst case results for every unique combination (polygon) of soil mapping unit and climate. County weed infestation maps for leafy spurge and spotted knapweed were digitized and overlaid in ARC/INFO with the CMLS model results for picloram to illustrate how the results might be used to evaluate the threat to groundwater posed by current herbicide applications.

Keywords

Picloram (4-amino-3,5,6-trichloro-2-pyridinecarboxylic acid)leafy spurge, Euphorbia esula L. # EPHESspotted knapweed, Centaurea maculosa Lam. # CENMAGroundwater contaminationcomputer modelCentaurea maculosaEuphorbia esulaCENMAEPHES
Type
Symposium
Information
Weed Technology , Volume 7 , Issue 1 , March 1993 , pp. 255 - 264
Copyright
Copyright © 1993 by the Weed Science Society of America 

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References

Literature Cited

1. Bliss, N. B. and Reybold, W. U. 1989. Small-scale digital soil maps for interpreting natural resources. J. Soil Water Conserv. 44:30–34.Google Scholar
2. Boesten, J.J.T.I. and van der Linden, A.M.A. 1991. Modeling the influence of sorption and transformation on pesticide leaching and persistence. J. Environ. Qual. 20:425–435.Google Scholar
3. Caprio, J. M. 1971. The solar-thermal unit theory in relation to plant development and potential evapotranspiration. Mont. Agric. Exp. Stn. Circ. No. 251, Bozeman, MT.Google Scholar
4. DeLuca, T., Larson, J., Thorma, L., and Algard, G. 1989. A survey of pesticide residues in groundwater in Montana. Mont. Dep. Agnic., Environ. Manage. Div. Tech. Rep. No. 89-1, Helena, MT.Google Scholar
5. Hornsby, A. G., Pennell, K. D., Jessup, R. E., and Rao, P.S.C. 1988. Modeling environmental fate of toxic organic chemicals in soils. p. 7–39 in Final Rep. No. WH149, Fla. Dep. Environ. Regul., Tallahassee, FL.Google Scholar
6. Jersey, J. K. and Nielsen, G. A. 1992. Montana Soil Pedon Database Reference Manual. Mont. Agric. Exp. Stn. Rep. No. SR-043, Bozeman, MT.Google Scholar
7. Jury, W. A., Focht, D. D., and Farmer, W. J. 1987. Evaluation of pesticide groundwater pollution potential from standard indices of soil chemical adsorption and biodegradation. J. Environ. Qual. 16:422–428.Google Scholar
8. Moorman, T. B. and Harper, S. S. 1989. Transformation and mineralization of metribuzin in surface and subsurface horizons of a Mississippi Delta soil. J. Environ. Qual. 18:302–306.Google Scholar
9. Mulla, D. J., Cheng, H. H., Tuxhorn, G., and Sounhein, R. 1989. Management of ground water contamination in Washington's Columbia Basin. State of Washington Water Res. Cent. Rep. No. 72, Pullman, WA.Google Scholar
10. Nielsen, G. A., Caprio, J. M., McDaniel, P. A., Snyder, R. D., and Montagne, C. 1990. MAPS: A GIS for land resource management in Montana. J. Soil Water Conserv. 45:450–453.Google Scholar
11. Nofziger, D. L. and Hornsby, A. G. 1986. A microcomputer-based management tool for chemical movement in soil. Appl. Agric. Res. 1: 50–56.Google Scholar
12. Nofziger, D. L. and Hornsby, A. G. 1987. CMLS: Chemical Movement through Layered Soils Model Users Manual. Univ. Florida, Gainesville, FL.Google Scholar
13. Palmer, W. C. 1965. Meteorological drought. U.S. Dep. Commerce, Weather Bureau Res. Pap. No. 45, Washington, D.C. Google Scholar
14. Pothuluri, J. V., Moorman, T. B., Obenhuber, D. C., and Wauchope, R. D. 1990. Aerobic and anaerobic degradation of alachlor in samples from a surface to groundwater profile. J. Environ. Qual. 19:525–530.Google Scholar
15. Rao, P.S.C., Davidson, J. M., and Hammond, L. C. 1976. Estimation of nonreactive and reactive solute front locations in soils. p. 235–241 in Proc. Hazard. Wastes Res. Symp. EPA-600/19-76-015, Tucson, AZ.Google Scholar
16. Rawls, W. J. and Brakensiek, D. L. 1989. Estimation of soil water retention and hydraulic properties. p. 275–300 in Unsaturated Flow in Hydrologic Modelling—Theory and Practice, ed. Morel-Seytoux, H. J., Kluwer Academic Publ., Amsterdam.Google Scholar
17. Reybold, W. U. and TeSelle, G. W. 1989. Soil geographic data bases. J. Soil Water Conserv. 44:28–29.Google Scholar
18. Richardson, C. W. and Wright, D. A. 1984. WGEN: A model for generating daily weather variables. U.S. Dep. Agric., Agric. Res. Serv. Rep. No. ARS-8, Washington, D.C. Google Scholar
19. Sun, M. 1986. Groundwater ills: Many diagnoses, few remedies. Science 232:1490–1493.CrossRefGoogle Scholar
20. Wilson, J. P., Gerhart, K.E.S., Nielsen, G. A., and Ryan, C. M. 1992. Climate, soils, and crop yield relationships in Cascade County, Montana. Appl. Geogr., 12:261–279.Google Scholar