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Maximizing Herbicide Efficiency with Mixtures and Expert Systems

Published online by Cambridge University Press:  12 June 2017

Jerry M. Green
Jerry M. Green*
Affiliation:
Agric. Prod. Dep., Stine-Haskell Res. Cent., E. I. du Pont de Nemours & Co., Newark, DE 19714, U.S.A.
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Abstract

A practical and objective system is needed to determine the lowest rates of the most efficacious herbicides to meet each producer's specific weed control problems. Determining which method of weed control to utilize is difficult today with increasing product choices, the growing use and complexity of herbicide mixtures, regulatory pressures to reduce rates, and the closer integration of weed control with other crop decisions. Expert computer systems could improve current practices and use herbicide mixtures as a tool to increase herbicide efficiency. Such systems would account for herbicide dose and mixture responses; select most economical herbicides; optimize adjuvants; recommend control at economic thresholds; and vary rates according to weed spectrum, density, and local environmental conditions. An example using chlorimuron and thifensulfuron illustrates how these systems could use quantitative dose response and mixture information.

Keywords

Chlorimuron, 2-[[[[(4-chloro-6-methoxy-2-pyrimidinyl)amino]carbonyl]amino]-sulfonyl]benzoic acidthifensulfuron, 3-[[[[(4-methoxy-6-methyl-1,3,5-triazin-2-yl)amino]carbonyl]-amino]sulfonyl]-2-thiophenecarboxylic acidHerbicide interactiondose responseLISAenvironmentchlorimuronthifensulfuron
Type
Education
Information
Weed Technology , Volume 5 , Issue 4 , December 1991 , pp. 894 - 897
Copyright
Copyright © 1990 by the Weed Science Society of America 

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References

Literature Cited

1. Anonymous. 1989. An argument for being choosy. Agrichemical Age. 33(8):14–15.Google Scholar
2. Baandrup, M., and Ballegaard, T. 1989. Three years of field experience with an advisory computer system applying factor-adjusted doses. Proc. Brighton Crop. Prot. Conf. Weeds 4:555–563.Google Scholar
3. Barrett, M., and Witt, W. W. 1987. Maximizing pesticide use efficiency. p. 235–255 in Helsel, Z., ed. Energy in World Agriculture Handbook. Elsevier Science Publishers, Amsterdam, Netherlands.Google Scholar
4. Berkson, J. 1944. Application of the logistic function to bioassay. J. Am. Stat. Assoc. 39:357–365.Google Scholar
5. Boedeker, W. R., Altenburger, , Faust, M., and Grimme, L. H. 1990. Methods for the assessment of mixtures of plant protection substances (pesticides): Mathematical analysis of combination effects in phytopharmacology and ecotoxicology. Nachrichtenbl. Deut. Pflanzenschutzd. (Braunschweig) 42:70–78.Google Scholar
6. Combellack, J. H. 1990. Efficient utilization of herbicides. Proc. Australian Weeds Conf. 9:1–7.Google Scholar
7. DeFelice, M. S., Brown, W. B., Aldrich, R. J., Sims, B. D., Judy, D. T., and Guethle, D. R. 1989. Weed control in soybeans (Glycine max) with reduced rates of postemergence herbicides. Weed. Sci. 37:365–374.Google Scholar
8. Delvo, H. W. 1987. Herbicide use in wheat and soybean production. Proc. North Cent. Weed Control Conf. 42:28–29.Google Scholar
9. Fielding, R. J., and Stoller, E. W. 1989. Effects of additives on efficacy, uptake, and translocation of chlorimuron ethyl ester. Weed Technol. 4:254–271.Google Scholar
10. Green, J. M. 1988. Effect of inorganic salts on the efficacy of chlorimuron and DPX-M6316 on soybeans. Abstr. Weed Sci. Soc. Am. 28:16.Google Scholar
11. Green, J. M. 1989. Herbicide antagonism at the whole plant level. Weed Technol. 3:217–226.Google Scholar
12. Green, J. M., and Bailey, S. P. 1988. Herbicide interactions with herbicides and other agricultural chemicals. p. 37–61 in McWhorter, C. G. and Gebhardt, M. R., eds. Methods of Applying Herbicides. Weed Sci. Soc., Am., Champaign, IL.Google Scholar
13. Jones, J. W., Jones, P., and Everett, P. A. 1987. Combining expert systems and agricultural models: A case study. Transactions of the ASAE 30(5):1308–1314.Google Scholar
14. Kudsk, P. 1989. Experiences with reduced herbicide doses in Denmark and the development of the concept of factor-adjusted doses. Proc. Brighton Crop Prot. Conf. Weeds 4:545–554.Google Scholar
15. Linker, H. M., York, A. C., and Wilhite, D. R. Jr. 1990. WEEDS – A system for developing a computer-based herbicide recommendation program. Weed Technol. 4:380–385.Google Scholar
16. Martin, A. R. 1984. A computer program for herbicide selection. Abstr. Weed Sci. Soc. Am. 25:51.Google Scholar
17. Mueller, W. 1990. The congressional deluge: Part 2. Agrichemical Age. 34(2):12–28.Google Scholar
18. Reichenberger, L., and Russnogle, J. 1989. Farm by the foot. Farm J. 113(6):11–15.Google Scholar
19. Richardson, L. 1990. Back talk: Bucharest. Agrichemical Age. 34(2):30.Google Scholar
20. Willard, C. J. 1951. Where do we go from here? Weed 1:9–12.Google Scholar
21. Westberg, D. E., Oliver, L. R., and Frans, R. E. 1989. Weed control with clomazone alone and with other herbicides. Weed Technol. 3:678–685.CrossRefGoogle Scholar