Hostname: page-component-788cddb947-55tpx Total loading time: 0 Render date: 2024-10-12T06:35:13.777Z Has data issue: false hasContentIssue false

Multi-Year Validation of a Decision Aid for Integrated Weed Management in Row Crops

Published online by Cambridge University Press:  12 June 2017

Frank Forcella
Affiliation:
U.S. Dep. Agric., Agric. Res. Ser., North Central Soil Cons. Res. Lab., Morris, MN 56267
Robert P. King
Affiliation:
Dep. Agric. and Appl. Econ., Univ. Minnesota, St. Paul, MN 55108
Scott M. Swinton
Affiliation:
Dep. Agric. Econ., Michigan State Univ., East Lansing, MI 48824
Douglas D. Buhler
Affiliation:
U.S. Dep. Agric., Agric. Res. Ser., Nat. Soil Tilth Lab., Ames, IA 50011
Jeffrey L. Gunsolus
Affiliation:
Dep. Agron. and Plant Genet., Univ. of Minnesota, St. Paul, MN 55108

Abstract

WEEDSIM is a bioeconomic decision aid for management of annual weeds in corn and soybean. It was field-tested for 4 yr in Minnesota. The decision aid has two categories of management recommendations: soil-applied plus postemergence (PRE+), based on estimated weed seedbank composition and density; and postemergence (POST), based upon observed weed seedling composition and density. Weed densities, weed control, herbicide use, environmental impact of herbicide use, weed management costs, crop yields, and economic returns that resulted from PRE+ and POST recommendations were compared to those associated with herbicide management systems (HERB) that were standard for the region. After 4 yr of applying WEEDSIM recommendations to the same plots, there were no increases in annual weed densities (seedbanks, seedlings, established plants, or seed production) or decreases in weed control or crop (soybean, rotation corn, and continuous corn) yields, compared to HERB. WEEDSIM recommendations resulted in average annual herbicide applications of 1.1 kg ai ha−1 for PRE+ and 1.0 kg ai ha−1 for POST, compared to 3.5 kg ai ha−1 for HERB. Environmental impact indices associated with PRE+, POST, and HERB were 0.75, 0.71, and 0.54, with the lowest value indicating greater environmental risk than the two higher values. Similarly, average weed management costs were $24, $33, and $77 ha−1 for PRE+, POST, and HERB, respectively. Based on crop prices of $94 Mg−1 for corn and $220 Mg-1 for soybean, the average gross margins over weed control costs were higher for PRE+ ($509 ha−1) and POST ($522 ha−1) than for HERB ($455 ha−1). In general, WEEDSIM appeared to make management recommendations that adequately controlled weeds, maintained crop yields, reduced herbicide use, decreased environmental risk, lowered weed management costs, and increased gross margins over weed control costs compared to the use of herbicides standard for the region.

Type
Weed Management
Copyright
Copyright © 1996 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. Anonymous. 1994. STATISTIX, Version 4.1. Analytical Software, Tallahassee, Florida.Google Scholar
2. Anonymous. 1995. Michigan soybean research report: Field evaluation of WEEDSIM weed management model in a corn-soybean rotation in Michigan. Michigan Farm News 72: 16.Google Scholar
3. Coble, H. D. 1994. Computer decision aids for reducing herbicide use. Proc. Northeast. Weed Sci. Soc. 48: 155159.Google Scholar
4. Durgan, B. R., Gunsolus, J. L., Becker, R. L., and Dexter, A. G. 1995. Cultural and chemical weed control in field crops. Minnesota Extension Service, University of Minnesota, St. Paul. BU-3157-S, 74 p.Google Scholar
5. Forcella, F. 1992. Prediction of weed seedling densities from buried seed reserves. Weed Res. 32: 2938.Google Scholar
6. Forcella, F., Westgate, M. E., and Warnes, D. D. 1992. Effect of row width on herbicide and cultivation requirements in row crops. Amer. J. Altern. Agric. 7: 161167.Google Scholar
7. Forcella, F., Buhler, D. D., Swinton, S. M., King, R. P., Gunsolus, J. L., and Maxwell, B. D. 1993. Field evaluation of a bioeconomic weed management model for the Corn Belt, U.S.A. In 8th EWRS Symposium, Quantitative Approaches in Weed and Herbicide Research and Their Practical Applications. Braunschweig, Germany. Pages 755760.Google Scholar
8. Forcella, F., Eradat Oskoui, K., Wagner, S. W. 1993. Application of weed seedbank ecology to low-input crop management. Ecol. Applic. 3: 7483.Google Scholar
9. Gunther, P., Pestemer, W., James, T. K., and Rahman, A. 1993. Testing the German herbicide advisory system HERBASYS under different edaphic and climatic conditions in New Zealand. 8th EWRS Symposium, Quantitative Approaches in Weed and Herbicide Research and Their Practical Applications. Braunschweig, Germany. Pages 777784.Google Scholar
10. Havlin, J. and Forcella, F. 1992. Criteria for evaluating sustainable agricultural systems. Proc. Participatory On-Farm Research and Education for Agricultural Sustainability. Univ. Illinois, Urbana. Pages 6376.Google Scholar
11. Kovach, J., Petzoldt, C., Degni, J., and Tette, J. 1992. A method to measure the environmental impact of pesticides. Food and Life Sci. Bulletin 139, New York State Agric. Exp. Stn. Geneva, 8 pages.Google Scholar
12. Lund, R. E. 1988. MSUSTAT Statistical Analysis Package 4.10. Research and Development Institute, Bozeman, Montana.Google Scholar
13. Lybecker, D. W., Schweizer, E. E., and King, R. P. 1991. Weed management decisions in corn based on bioeconomic modeling. Weed Sci. 39: 124129.Google Scholar
14. Lybecker, D. W., Schweizer, E. E., and Westra, P. 1994. WEEDCAM Manual. Progress Report. EPA Contact DW12934950-01-1, Transfer Weed Management Expert System Technology for Reduced Corn Herbicide Use to Farmers, Extension Agents, and Crop Consultants. Fort Collins, CO. 49 pages.Google Scholar
15. Medd, R. W. and Pandey, S. 1993. Compelling grounds for controlling seed production in Avena species (wild oats). 8th EWRS Symposium, Quantitative Approaches in Weed and Herbicide Research and Their Practical Applications. Braunschweig, Germany. Pages 769776.Google Scholar
16. Miller, D., Peterson, P., and Hoeffer, F. 1994. WEEDIR: a weed control decision aid program. Minnesota Extension Service, University of Minnesota, St. Paul. AG-CS-2163, Version 3.1.Google Scholar
17. Morrow, A. 1994. Weed predicting computers. The Farmer—Minnesota 112: 8.Google Scholar
18. Mortensen, D. A. and Coble, H. D. 1991. Two approaches to weed control decision aid software. Weed Technol. 5: 445452.Google Scholar
19. Schweizer, E., Wiles, L., Lybecker, D., and Westra, P. 1994. Bioeconomic modeling for weed management decisions in crops. Great Plains Agric. Council Bull. No. 150. Pages 135141.Google Scholar
20. Stigliani, L. and Resina, C. 1993. SELOMA: Expert system for weed management in herbicide-intensive crops. Weed Technol. 7: 550559.Google Scholar
21. Swinton, S. M. and King, R. P. 1994. The value of pest information in a dynamic setting: The case of weed control. Amer. J. Agr. Econ. 76: 3646.Google Scholar
22. Swinton, S. M. and King, R. P. 1994. A bioeconomic model for weed management in corn and soybean. Agric. Syst. 44: 313335.Google Scholar
23. Swinton, S. M., Buhler, D. D., Forcella, F., Gunsolus, J. L., and King, R. P. 1994. Estimation of crop yield loss due to interference by multiple weed species. Weed Sci. 42: 103109.Google Scholar
24. Teague, M. L., Mapp, H. P., and Bernardo, D. J. 1995. Risk indices for economic and water quality tradeoffs: An application to Great Plains agriculture. J. Prod. Agric. 8: In press.Google Scholar
25. Wiles, R., Cohen, B., Campbell, C., and Elderkin, S. 1994. Tap Water Blues. Environmental Working Group, Washington, D.C. 276 pages.Google Scholar
26. Wilkerson, G. A., Modena, S. A., and Coble, H. D. 1991. HERB: Decision model for postemergence weed control in soybean. Agron. J. 83: 413417.Google Scholar