Kashani, A. Parizi, H. and Mertins, K.H. 2018. Multi-step spray modelling of a flat fan atomizer. Computers and Electronics in Agriculture, Vol. 144, p. 58.
Blanchett, B.H. Grey, T.L. Prostko, E.P. Vencill, W.K. and Webster, T.M. 2017. The Effect of 2,4-Dichlorophenoxyacetic Acid (2,4-D) on Peanut when Applied During Vegetative Growth Stages. Peanut Science, Vol. 44, Issue. 1, p. 53.
Carter, O.W. Prostko, E.P. and Davis, J.W. 2017. The Influence of Nozzle Type on Peanut Weed Control Programs. Peanut Science, Vol. 44, Issue. 2, p. 93.
Sousa Alves, Guilherme Kruger, Greg R. da Cunha, João Paulo A. R. Vieira, Bruno C. Henry, Ryan S. Obradovic, Andjela and Grujic, Mica 2017. Spray Drift from Dicamba and Glyphosate Applications in a Wind Tunnel. Weed Technology, Vol. 31, Issue. 03, p. 387.
Schwartz-Lazaro, Lauren M. Miller, M. Ryan Norsworthy, Jason K. and Scott, Robert C. 2017. Comparison of Simulated Drift Rates of Common ALS-Inhibiting Rice Herbicides to Florpyrauxifen-Benzyl on Soybean. International Journal of Agronomy, Vol. 2017, p. 1.
Abiri, R. Maziah, M. Shaharuddin, N. A. Yusof, Z. N. B. Atabaki, N. Hanafi, M. M. Sahebi, M. Azizi, P. Kalhori, N. and Valdiani, A. 2017. Enhancing somatic embryogenesis of Malaysian rice cultivar MR219 using adjuvant materials in a high-efficiency protocol. International Journal of Environmental Science and Technology, Vol. 14, Issue. 5, p. 1091.
Byrd, Seth A. Collins, Guy D. Culpepper, A. Stanley Dodds, Darrin M. Edmisten, Keith L. Wright, David L. Morgan, Gaylon D. Baumann, Paul A. Dotray, Peter A. Manuchehri, Misha R. Jones, Andrea Grey, Timothy L. Webster, Theodore M. Davis, Jerry W. Whitaker, Jared R. Roberts, Phillip M. Snider, John L. and Porter, Wesley M. 2016. Cotton Stage of Growth Determines Sensitivity to 2,4-D. Weed Technology, Vol. 30, Issue. 03, p. 601.
Bohnenblust, Eric W. Vaudo, Anthony D. Egan, J. Franklin Mortensen, David A. and Tooker, John F. 2016. Effects of the herbicide dicamba on nontarget plants and pollinator visitation. Environmental Toxicology and Chemistry, Vol. 35, Issue. 1, p. 144.
Ding, Guanglong Guo, Dong Zhang, Wenbing Han, Ping Punyapitak, Darunee Guo, Mingcheng Zhang, Zhaopeng Wang, Baitao Li, Jianqiang and Cao, Yongsong 2016. Preparation of novel auxinic herbicide derivatives with high-activity and low-volatility by me-too method. Arabian Journal of Chemistry,
Bonny, Sylvie 2016. Genetically Modified Herbicide-Tolerant Crops, Weeds, and Herbicides: Overview and Impact. Environmental Management, Vol. 57, Issue. 1, p. 31.
Peterson, Mark A. McMaster, Steve A. Riechers, Dean E. Skelton, Josh and Stahlman, Phillip W. 2016. 2,4-D Past, Present, and Future: A Review. Weed Technology, Vol. 30, Issue. 02, p. 303.
Mohseni-Moghadam, Mohsen Wolfe, Scott Dami, Imed and Doohan, Douglas 2016. Response of Wine Grape Cultivars to Simulated Drift Rates of 2,4-D, Dicamba, and Glyphosate, and 2,4-D or Dicamba Plus Glyphosate. Weed Technology, Vol. 30, Issue. 03, p. 807.
Olszyk, David Pfleeger, Thomas Lee, E. Henry and Plocher, Milton 2015. Glyphosate and dicamba herbicide tank mixture effects on native plant and non-genetically engineered soybean seedlings. Ecotoxicology, Vol. 24, Issue. 5, p. 1014.
Mohseni-Moghadam, Mohsen and Doohan, Douglas 2015. Response of Bell Pepper and Broccoli to Simulated Drift Rates of 2,4-D and Dicamba. Weed Technology, Vol. 29, Issue. 02, p. 226.
Sosnoskie, Lynn M. Culpepper, A. Stanley Braxton, L. Bo and Richburg, John S. 2015. Evaluating the Volatility of Three Formulations of 2,4-D When Applied in the Field. Weed Technology, Vol. 29, Issue. 02, p. 177.
Blanchett, B.H. Grey, T.L. Prostko, E.P. and Webster, T.M. 2015. The Effect of Dicamba on Peanut When Applied during Vegetative Growth Stages. Peanut Science, Vol. 42, Issue. 2, p. 109.
Leon, Ramon G. Ferrell, Jason A. and Brecke, Barry J. 2014. Impact of Exposure to 2,4-D and Dicamba on Peanut Injury and Yield. Weed Technology, Vol. 28, Issue. 03, p. 465.
Commercial introduction of cultivars of soybean and cotton genetically modified with resistance to the synthetic auxin herbicides dicamba and 2,4-D will allow these compounds to be used with greater flexibility but may expose susceptible soybean and cotton cultivars to nontarget herbicide drift. From past experience, it is well known that soybean and cotton are both highly sensitive to low-dose exposures of dicamba and 2,4-D. In this study, a meta-analysis approach was used to synthesize data from over seven decades of simulated drift experiments in which investigators treated soybean and cotton with low doses of dicamba and 2,4-D and measured the resulting yields. These data were used to produce global dose–response curves for each crop and herbicide, with crop yield plotted against herbicide dose. The meta-analysis showed that soybean is more susceptible to dicamba in the flowering stage and relatively tolerant to 2,4-D at all growth stages. Conversely, cotton is tolerant to dicamba but extremely sensitive to 2,4-D, especially in the vegetative and preflowering squaring stages. Both crops are highly variable in their responses to synthetic auxin herbicide exposure, with soil moisture and air temperature at the time of exposure identified as key factors. Visual injury symptoms, especially during vegetative stages, are not predictive of final yield loss. Global dose–response curves generated by this meta-analysis can inform guidelines for herbicide applications and provide producers and agricultural professionals with a benchmark of the mean and range of crop yield loss that can be expected from drift or other nontarget exposures to 2,4-D or dicamba.
Email your librarian or administrator to recommend adding this journal to your organisation's collection.
Full text views reflects the number of PDF downloads, PDFs sent to Google Drive, Dropbox and Kindle and HTML full text views.
* Views captured on Cambridge Core between 20th January 2017 - 24th February 2018. This data will be updated every 24 hours.