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Response of Soybean Yield Components to 2,4-D

Published online by Cambridge University Press:  20 January 2017

Andrew P. Robinson
Affiliation:
Department of Botany and Plant Pathology, 915 West State Street, Purdue University, West Lafayette, IN 47907
Vince M. Davis
Affiliation:
Department of Agronomy, 1575 Linden Drive, Madison, WI 53706
David M. Simpson
Affiliation:
Dow AgroSciences, 9330 Zionsville Road, Indianapolis, IN 46268
William G. Johnson*
Affiliation:
Department of Botany and Plant Pathology, 915 West State Street, Purdue University, West Lafayette, IN 47907
*
Corresponding author's E-mail: [email protected]

Abstract

Soybean plants exposed POST to 2,4-D can have reduced seed yield depending on the dose and time of exposure, but it is unclear how 2,4-D affects specific yield components. Objectives were to quantify soybean injury, characterize changes in seed yield and yield components of soybean plants exposed to 2,4-D, and determine if seed-yield loss can be estimated from visual assessment of crop injury. Ten rates (0, 0.1, 1.1, 11.2, 35, 70, 140, 280, 560, and 2,240 g ae ha−1) of 2,4-D were applied to Becks brand 342 NRR soybean at three soybean growth stages (V2, V5, or R2). The soybeans were planted near Lafayette, IN and Urbana, IL in 2009 and 2010 and near Fowler, IN in 2009. Twenty percent visual soybean injury was caused by 29 to 109 g ha−1 2,4-D at 14 d after treatment (DAT) and 109 to 245 g ha−1 at 28 DAT. Nonlinear regression models were fit to describe the effect of 2,4-D on seed yield and yield components of soybean. Seed yield was reduced by 5% from 87 to 116 g ha−1 and a 10% reduction was caused by 149 to 202 g ha−1 2,4-D at all application timings. The number of seeds m−2, pods m−2, reproductive nodes m−2, and nodes m−2 were the most sensitive yield components. Path analysis indicated that seeds m−2, pods m−2, main stem reproductive nodes m−2, and main stem nodes m−2 were the most influential yield components in seed-yield formation. Seed-yield loss was significant (P < 0.0001) and highly correlated (R2 = 0.95 to 0.99) to visual soybean injury ratings. A 10% seed-yield loss was caused by 35% soybean injury observed at 14 DAT, whereas a 10% seed-yield loss was a result of 40, 19, and 15% soybean injury observed at 28 DAT when soybean was exposed to 2,4-D at the V2, V5, and R2 growth stages, respectively.

Type
Physiology, Chemistry, and Biochemistry
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Abel, S. and Theologis, A. 1996. Early genes and auxin action. Plant Physiol. 111: 917.Google Scholar
Al-Khatib, K., Parker, R., and Fuerst, E. P. 1993. Wine grape (Vitis vinifera L.) response to simulated herbicide drift. Weed Technol. 7: 97102.Google Scholar
Andersen, S. M. 2003. Analysis of soybean [Glycine max (L.) Merr.] response to simulated drift rates of PGR herbicides using a foliar residue test. . Brookings, SD: South Dakota State University. 72 p.Google Scholar
Andersen, S. M., Clay, S. A., Wrage, L. J., and Matthees, D. 2004. Soybean foliage residues of dicamba and 2,4-D and correlation to application rates and yield. Agron. J. 96: 750760.Google Scholar
Baskin, A. D. and Walker, E. A. 1953. The responses of tomato plants to vapors of 2,4-D and/or 2,4,5-T formulations at normal and higher temperatures. Weeds 2: 280287.Google Scholar
Board, J. E. and Modali, H. 2005. Dry matter accumulation predictors for optimal yield in soybean. Crop Sci. 45: 17901799.Google Scholar
De Bruin, J. L. and Pedersen, P. 2008. Soybean seed yield response to planting date and seeding rate in the Upper Midwest. Agron. J. 100: 696703.Google Scholar
Donald, W. W. and Khan, M. 1996. Canada thistle (Cirsium arvense) effects on yield components of spring wheat (Triticum aestivum). Weed Sci. 44: 114121.Google Scholar
Fehr, W. R. and Caviness, C. E. 1977. Stages of Soybean Development. Spec. Rep. 80. Ames, IA: Iowa Agric. Home Econ. Exp. Stn., Iowa State Univ. 11 p.Google Scholar
Fribourg, H. A. and Johnson, I. J. 1955. Response of soybean strains to 2,4-D and 2,4,5-T. Agron. J. 47: 171174.Google Scholar
Grossmann, K. 2003. Mediation of herbicide effects by hormone interactions. J. Plant Growth Regulation 22: 109122.Google Scholar
Grossmann, K. 2010. Auxin herbicides: current status of mechanism and mode of action. Pest Manag. Sci. 66: 113120.Google Scholar
Hatterman-Valenti, H. and Mayland, P. 2005. Annual flower injury from sublethal rates of dicamba, 2,4-D, and premixed 2,4-D + mecoprop plus dicamba. HortScience 40: 680684.Google Scholar
Hemphill, D. D. Jr. and Montgomery, M. L. 1981. Response of vegetable crops to sublethal application of 2,4-D. Weed Sci. 29: 632635.Google Scholar
Hillger, D. E., Havens, P. L., and Cryer, S. A. 2011. Modeling volatility of 2,4-D formulations. Proc. North Cent. Weed Sci. Soc. 66: 218.Google Scholar
Howell, R. W. and Cartter, J. L. 1958. Physiological factors aff ecting composition of soybeans: II. Response of oil and other constituents of soybeans to temperature under controlled conditions. Agron. J. 50: 664667.Google Scholar
Kahlon, C. S., Board, J. E., and Kang, M. S. 2011. An analysis of yield component changes for new vs. old soybean cultivars. Agron. J. 103: 1322.Google Scholar
Kang, M. S. 1994. Applied quantitative genetics. Baton Rouge, LA: M. S. Kang. 157 p.Google Scholar
Kelley, K. B., Lambert, K. N., Hager, A. G., and Riechers, D. E. 2004. Quantitative expression analysis of GH3, a gene induced by plant growth regulator herbicides in soybean. J. Agric. Food Chem. 52: 474478.Google Scholar
Kelley, K. B., Wax, L. M., Hager, A. G., and Riechers, D. E. 2005. Soybean response to plant growth regulator herbicides is affected by other postemergence herbicides. Weed Sci. 53: 101112.Google Scholar
Knezevic, S. Z., Sikkema, P. H., Tardif, F., Hamill, A. S., Chandler, K., and Swanton, C. J. 1998. Biologically effective dose and selectivity of RPA 201772 for preemergence weed control in corn (Zea mays). Weed Technol. 12: 670676.Google Scholar
Knezevic, S. Z., Streibig, J. C., and Ritz, C. 2007. Utilizing R software package for dose–response studies: the concept and data analysis. Weed Technol. 21: 840848.Google Scholar
Kniss, A. R. and Lyon, D. J. 2011. Winter wheat response to preplant applications of aminocyclopyrachlor. Weed Technol. 25: 5157.Google Scholar
Marple, M. E., Al-Khatib, K., and Peterson, D. E. 2008. Cotton injury and yield as affected by simulated drift of 2,4-D and dicamba. Weed Technol. 22: 609614.Google Scholar
Maybank, J., Yoshida, K., and Grover, R. 1978. Spray drift from agricultural pesticide applications. J. Air Pollut. Control Assoc. 28: 10091014.Google Scholar
Ritz, C. and Strebig, J. 2011. Package ‘drc’. Available at http://cran.r-project.org/web/packages/drc/drc.pdf. Accessed June 9, 2011.Google Scholar
Robinson, A. P., Conley, S. P., Volenec, J. J., and Santini, J. B. 2009. Analysis of high yielding, early-planted soybean in Indiana. Agron. J. 101: 131139.Google Scholar
Ruen, D. C., Hillger, D. E., and Scherder, E. F. 2011. Effect of nozzle type, spray droplet size and spray volume on crop tolerance and weed control with Enlist Duo. Proc. North Cent. Weed Sci. Soc. 66: 33.Google Scholar
Scherder, E. F., Schultz, M. E., Ellis, A. T., Spomer, N. A., Hamm, R. L., Richburg, J. S., Huff, J. A., Olsen, B. D., and Tofoli, G. R. 2010. Efficacy and crop tolerance of GF-2654 and GF-2726 in corn. Proc. North Cent. Weed Sci. Soc. 65: 129.Google Scholar
Sciumbato, A. S., Chandler, J. M., Senseman, S. A., Bovey, R. W., and Smith, K. L. 2004. Determining exposure to auxin-like herbicides. I. Quantifying injury to cotton and soybean. Weed Technol. 18: 11251134.Google Scholar
Seber, G.A.F. and Wild, C. J. 1989. Nonlinear regression. New York, NY: Wiley. 768 p.Google Scholar
Slife, F. W. 1956. The effect of 2,4-D and several other herbicides on weeds and soybeans when applied as post-emergence sprays. Weeds 4: 6168.Google Scholar
Spiess, A. and Neumeyer, N. 2010. An evaluation of R 2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach. BMC Pharmacol. 10: 617.Google Scholar
Wax, L. M., Knuth, L. A., and Slife, F. W. 1969. Response of soybeans to 2,4-D, dicamba, and picloram. Weed Sci. 17: 388393.Google Scholar
Weidenhamer, J. D., Triplett, G. B. Jr., and Sobotka, F. E. 1989. Dicamba injury to soybean. Agron. J. 81: 637643.Google Scholar
Wolf, T. M., Grover, R., Wallace, K., Shewchuk, S. R., and Maybank, J. 1993. Effect of protective shields on drift and deposition characteristics of field sprayers. Can. J. Plant Sci. 73: 12611273.Google Scholar
Wright, T. R., Shan, G., Walsh, T. A., Lira, J. M., Cui, C., Song, P., Zhuang, M., Arnold, N. L., Lin, G., Yau, K., Russell, S. M., Cicchillo, R. M., Peterson, M. A., Simpson, D. M., Zhou, N., Ponsamuel, J., and Zhang, Z. 2010. Robust crop resistance to broadleaf and grass herbicides provided by aryloxyalkanoate dioxygenase transgenes. Proc. Natl. Acad. Sci. U. S. A. 107: 20,24020,245.Google Scholar