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Economic comparison of broadcast and site-specific herbicide applications in nontransgenic and glyphosate-tolerant Glycine max

Published online by Cambridge University Press:  20 January 2017

Case R. Medlin
Affiliation:
Department of Plant and Soil Sciences, 117 Dorman Hall, Box 9555, Mississippi State University, Mississippi State, MS 39762

Abstract

Weed population estimates were collected from four Glycine max fields during the summers of 1997 and 1998. Seedling weed populations were sampled using a regular coordinate system on a grid either 50 by 50, 30 by 30, or 10 by 10 m. MSU-HERB and Mississippi Herbicide Application Decision Support System (HADSS) (yield loss prediction and herbicide recommendation models for G. max) were used to determine the estimated net gain resulting from simulated herbicide applications at each sample location in each field. When necessary, the appropriate data points from the 10- by 10-m grid were removed to form population data sets on grids 20 by 20, 40 by 40, and 80 by 80 m. The objectives of this research were to compare estimated economic returns of site-specific herbicide management and broadcast herbicide management in nontransgenic and glyphosate-tolerant G. max and to evaluate the effects of various weed sampling intensities on estimated economic returns from site-specific herbicide applications. Site-specific herbicide management was the compilation of simulated herbicide treatments giving the highest estimated net gains at each location within each field. Broadcast herbicide management was the simulated broadcast application giving the highest estimated net gain for each field. Sampling costs and the unattainable site-specific application costs were not included in the estimated net gain calculations. In nontransgenic G. max production, the estimated net gain for treating the four fields with site-specific technology was $104.76 ha−1 higher than when using the optimum broadcast herbicide. In glyphosate-tolerant G. max production, the average estimated net gain for site-specific treatment of the fields was $96.24 ha−1 higher than for treatment with the best broadcast herbicide application. In nontransgenic G. max, the estimated net gain resulting from site-specific applications on a 10-m grid was $77.17 ha−1 higher than from site-specific applications on a 20-m grid; however, in glyphosate-tolerant G. max, this difference was only $19.84 ha−1. Increased estimated net gain resulted primarily from the use of herbicides that maximized return for each field area and from the decrease of unnecessary herbicide applications because of below-threshold weed infestations.

Type
Research Article
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Auld, B. A. and Tisdell, C. A. 1988. Influence of spatial distribution of weeds on crop yield loss. Plant Prot. Q. 3:81.Google Scholar
Brain, P. and Cousens, R. 1990. The effect of weed distribution on predictions of yield loss. J. Appl. Ecol. 27:735742.Google Scholar
Cardina, J., Johnson, G. A., and Sparrow, D. H. 1997. The nature and consequence of weed spatial distribution. Weed Sci. 45:364373.CrossRefGoogle Scholar
Cousens, R. 1987. Theory and reality of weed control thresholds. Plant Prot. Q. 2:1320.Google Scholar
Delannay, X., Bauman, T. T., Beighley, D. H., et al. 1995. Yield evaluation of a glyphosate-tolerant G. max line after treatment with glyphosate. Crop Sci. 35:14611467.CrossRefGoogle Scholar
Dieleman, J. A., Mortensen, D. A., and Buhler, D. D. 1997. Multivariate approaches for linking field-scale variability of soil properties and weed populations. Weed Sci. Soc. Am. Abstr. 37:46.Google Scholar
Felton, W. L., Doss, A. F., Nash, P. G., and McCloy, K. R. 1991. To selectively spot spray weeds. Am. Soc. Agric. Eng. Symp. 11:427432.Google Scholar
Griffin, J. L., Reynolds, D. B., Jordan, D. L., Prochaska, L. M., and Rogers, R. L. 1994. Evaluation of Roundup Ready transgenic soybean in Louisiana. La. Agric. 37:23.Google Scholar
Johnson, G. A., Mortensen, D. A., and Gotway, C. A. 1996. Spatial and temporal analysis of weed seedling populations using geostatistics. Weed Sci. 44:704710.Google Scholar
Johnson, G. A., Mortensen, D. A., and Martin, A. R. 1995. A simulation of herbicide use based on weed spatial distribution. Weed Res. 35:197205.Google Scholar
Krueger, D. W., Coble, H. D., and Wilkerson, G. G. 1997. Mapping and analysis of weed spatial distribution. Weed Sci. Soc. Am. Abstr. 37:112.Google Scholar
Lindquist, J. L., Dieleman, J. A., Mortensen, D. A., Johnson, G. A., and Wyse-Pester, D. Y. 1998. Economic importance of managing spatially heterogeneous weed populations. Weed Technol. 12:713.CrossRefGoogle Scholar
Marshall, E.J.P. 1988. Field-scale estimates of grass weed populations in arable land. Weed Res. 28:191198.CrossRefGoogle Scholar
Maxwell, B. D. and Colliver, C. 1995. Expanding economic thresholds by including spatial and temporal weed dynamics. Proc. Brighton Crop Prot. Conf. Weeds 13:10691076.Google Scholar
Mississippi State University Agricultural Economics Department. 1996. Soybeans 1997 Planning Budgets. Mississippi State, MS: Mississippi State University Agricultural Economics Rep. 78. 112 p.Google Scholar
Mississippi State University Agricultural Economics Department. 1997. Soybeans 1998 Planning Budgets. Mississippi State, MS: Mississippi State University Agricultural Economics Rep. 87. 104 p.Google Scholar
Oriade, C. A., King, R. P., Forcella, F., and Gunsolus, J. L. 1996. A bioeconomic analysis of site-specific management for weed control. Rev. Agric. Econ. 18:523535.Google Scholar
Rankins, A. Jr., Shaw, D. R., and Byrd, J. D. Jr. 1998. HERB and MSUHERB field validation for soybean (Glycine max) weed control in Mississippi. Weed Technol. 12:8896.Google Scholar
Seelig, B. D., Richardson, J. L., and Knighton, R. E. 1991. Comparison of statistical and standard techniques to classify and deliniate sodic soils. Soil Sci. Soc. Am. J. 55:10421048.Google Scholar
Thomas, A. G., Stevenson, F. C., and Frick, B. 1997. Assessment of weed distributions on a field scale. Weed Sci. Soc. Am. Abstr. 37:133.Google Scholar
Thompson, J. F., Stafford, J. V., and Miller, P.C.H. 1991. Potential for automatic weed detection and selective herbicide application. Crop Prot. 10:254259.CrossRefGoogle Scholar
Thornton, P. K., Fawcett, R. H., Dent, J. B., and Perkins, T. J. 1990. Spatial weed distribution and economic thresholds for weed control. Crop Prot. 9:337342.Google Scholar
Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: decision model for postemergence weed control in soybean. Agron. J. 83:413417.CrossRefGoogle Scholar
Wollenhaupt, N. C., Wolkowski, R. P., and Clayton, M. K. 1994. Nutrient management: mapping soil test phosphorus and potassium for variable-rate fertilizer application. J. Prod. Agric. 7:441448.Google Scholar