Hostname: page-component-848d4c4894-jbqgn Total loading time: 0 Render date: 2024-07-04T09:29:36.052Z Has data issue: false hasContentIssue false
  • English
  • Français

Efficacy of Variable Rate Soil-applied Herbicides Based on Soil Electrical Conductivity and Organic Matter Differences

Published online by Cambridge University Press:  01 June 2017

G. J. Gundy ,
J. A. Dille  and
A. R. Asebedo
G. J. Gundy*
Affiliation:
Department of Agronomy, Kansas State University, Manhattan, Kansas, 66502, USA
J. A. Dille
Affiliation:
Department of Agronomy, Kansas State University, Manhattan, Kansas, 66502, USA
A. R. Asebedo
Affiliation:
Department of Agronomy, Kansas State University, Manhattan, Kansas, 66502, USA
*
† Email: ggundy@ksu.edu
Get access

Abstract

Soil application of herbicides for preemergence (PRE) weed control in grain sorghum is vital to control weeds. Efficacy of soil-applied herbicides is impacted by herbicide adsorption which is influenced by soil organic matter (SOM) and texture. With precision agriculture technologies, variable rate applications (VRA) can be utilized to maximize herbicide effectiveness. In 2016, algorithms were developed for two locations to use VRA of two tank mixed herbicides based on SOM and soil electrical conductivity (EC) collected by a Veris MSP3 system. Drone imagery provided an effective way to evaluate the efficacy of herbicide applications along with visual assessment. VRA applications of herbicide tank mixes provided equal weed control compared to flat rate applications.

Keywords

weed managementherbicide algorithmVerisweed mappingsoil variability
Type
Crop Protection
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 277 - 282
Copyright
© The Animal Consortium 2017 

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

Blackshaw, RE, Moyer, JR and Kozub, GC 1994. Efficacy of downy brome herbicides as influenced by soil properties. Canadian Journal of Plant Science 74, 177–183.Google Scholar
Doerge, T, Kitchen, NR and Lund, ED 1999. Soil electrical conductivity mapping. Crop Insights 9, 19.Google Scholar
Gerhards, R, Gutjahr, C, Weis, M, Keller, M, Sökefeld, M, Möhring, J and Piepho, HP 2012. Using precision farming technology to quantify yield effects attributed to weed competition and herbicide application. Weed Research 52, 6–15.Google Scholar
Jaynes, DB, Novak, JM, Moorman, TB and Cambardella, CA 1995. Estimating herbicide partition coefficients from electromagnetic induction measurements. Journal of Environmental Quality 24, 36–41.Google Scholar
Kerr, GW, Stahlman, PW and Dille, JA 2004. Soil pH and cation exchange capacity affects sunflower tolerance to sulfentrazone. Weed Technology 18, 243–247.Google Scholar
Kweon, G 2012. Toward the ultimate soil survey: sensing multiple soil and landscape properties in one pass. Agronomy Journal 104, 1547–1557.Google Scholar
Nordmeyer, H 2015. Herbicide application in precision farming based on soil organic matter. American Journal of Experimental Agriculture 8, 144–151.Google Scholar
Novak, JM, Moorman, TB and Cambardella, CA 1997. Atrazine sorption at the field scale in relation to soils and landscape position. Journal of Environmental Quality 26, 1271–1277.Google Scholar
Peña, JM, Torres-Sánchez, J, de Castro, AI, Kelly, M and López-Granados, F 2013. Weed mapping in early-season maize fields using object-based analysis of unmanned aerial vehicle (UAV) images. PLoS One 8, e77151.CrossRefGoogle ScholarPubMed
Price, OR, Oliver, MA, Walker, A and Wood, M 2009. Estimating the spatial scale of herbicide and soil interactions by nested sampling, hierarchical analysis of variance and residual maximum likelihood. Environmental Pollution 157, 1689–1696.Google Scholar
Shaner, DL, Henry, WB, Krutz, LJ and Hanson, B 2007. Rapid assay for detecting enhanced atrazine degradation in soil. Weed Science 55, 528–535.Google Scholar
Shaner, DL, Farahani, HJ and Buchleiter, GW 2008. Predicting and mapping herbicide soil partition coefficients for EPTC, metribuzin, and metolachlor on three Colorado fields. Weed Science 56, 133–139.CrossRefGoogle Scholar
Torres-Sánchez, J, López-Granados, F, De Castro, AI and Peña-Barragán, JM 2013. Configuration and specifications of an unmanned aerial vehicle (UAV) for early site specific weed management. PLoS One 8, e58210.Google Scholar
[USDA-NASS] U.S. Department of Agriculture National Agricultural Statistics Service 2015. Sorghum Acres in Kansas. Retrieved on 12 September 2016 from https://quickstats.nass.usda.gov.Google Scholar
Van Acker, RC 2005. The science of soil residual herbicides. In Canadian Weed Science Society-Société canadienne de malherbologie meeting symposium: Soil residual herbicides: Science and management, Winnipeg, Canada, November 2004. (pp. 3–22). Canadian Weed Science Society.Google Scholar
Wood, LS, Scott, HD, Marx, DB and Lavy, TL 1987. Variability in sorption coefficients of metolachlor on a Captina silt loam. Journal of Environmental Quality 16, 251–256.CrossRefGoogle Scholar