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Weed-sensing technology modifies fallow control of rush skeletonweed (Chondrilla juncea)

Published online by Cambridge University Press:  09 July 2020

Jacob W. Fischer
Affiliation:
Graduate Student, Department of Crop and Soil Sciences, Washington State University, Pullman, WA, USA
Mark E. Thorne
Affiliation:
Research Associate, Department of Crop and Soil Sciences, Washington State University, Pullman, WA, USA
Drew J. Lyon*
Affiliation:
Professor, Department of Crop and Soil Sciences, Washington State University, Pullman, WA, USA
*
Author for correspondence: Drew J. Lyon, Professor, Washington State University, P.O. Box 646420, Pullman, WA99164-6420 Email: drew.lyon@wsu.edu

Abstract

Rush skeletonweed is an aggressive perennial weed that establishes itself on land in the Conservation Reserve Program (CRP), and persists during cropping following contract expiration. It depletes critical soil moisture required for yield potential of winter wheat. In a winter wheat/fallow cropping system, weed control is maintained with glyphosate and tillage during conventional fallow, and with herbicides only in no-till fallow. Research was conducted for control of rush skeletonweed at two sites in eastern Washington, Lacrosse and Hay, to compare the effectiveness of a weed-sensing sprayer and broadcast applications of four herbicides (aminopyralid, chlorsulfuron + metsulfuron, clopyralid, and glyphosate). Experimental design was a split-plot with herbicide and application type as main and subplot factors, respectively. Herbicides were applied in the fall at either broadcast or spot-spraying rates depending on sprayer type. Rush skeletonweed density in May was reduced with use of aminopyralid (1.1 plants m−2), glyphosate (1.4 plants m−2), clopyralid (1.7 plants m−2), and chlorsulfuron + metsulfuron (1.8 plants m−2) compared with the nontreated check (2.6 plants m−2). No treatment differences were observed after May 2019. There was no interaction between herbicide and application system. Area covered using the weed-sensing sprayer was, on average, 52% (P < 0.001) less than the broadcast application at the Lacrosse location but only 20% (P = 0.01) at the Hay location. Spray reduction is dependent on foliar cover in relation to weed density and size. At Lacrosse, the weed-sensing sprayer reduced costs for all herbicide treatments except aminopyralid, with savings up to US$6.80 per hectare. At Hay, the weed-sensing sprayer resulted in economic loss for all products because of higher rush skeletonweed density. The weed-sensing sprayer is a viable fallow weed control tool when weed densities are low or patchy.

Type
Research Article
Copyright
© Weed Science Society of America, 2020

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Footnotes

Associate Editor: Prashant Jha, Iowa State University

References

Anonymous (2020) NuFarm CRUCIAL advanced technology herbicide product label. NuFarm publication No. 86761/121622. Laverton North, Victoria, Australia: NuFarm. 27 pGoogle Scholar
Ahrens, WH (1994) Relative Costs of a Weed-Activated Versus Conventional Sprayer in Northern Great Plains Fallow. Weed Technol 8:5057 CrossRefGoogle Scholar
Belles, WS, Wattenarger, DW, Lee, GA (1980) Rush skeletonweed herbicide trial applied in the spring of 1978. Pages 32–33 in Western Society of Weed Science Research Progress Report. Salt Lake City, UT: Western Society of Weed ScienceGoogle Scholar
Biller, RH (1998) Reduced input of herbicides by use of optoelectronic sensors. J Agric Eng Res 71:357362 CrossRefGoogle Scholar
Blackshaw, RE, Molnar, LJ, Lindwall, CW (1998) Merits of a weed-sensing sprayer to control weeds in conservation fallow and cropping systems. Weed Sci 46:120126 CrossRefGoogle Scholar
Brewster, BD, Appleby, AP (1990) Effect of Rate, Carrier Volume, and Surfactant on Imazamethabenz Efficacy. Weed Technol 4:291293 CrossRefGoogle Scholar
Chancellor, WJ, Goronea, MA (1994) Effects of spatial variability of nitrogen, moisture, and weeds on the advantages of site-specific applications for wheat. T ASAE 37:717724 CrossRefGoogle Scholar
Cheney, TM, Belles, WS, Lee, GA (1980) Herbicidal control of rush skeletonweed. Pages 52–53 in Western Society of Weed Science Proceedings 33. Salt Lake City, UT: Western Society of Weed ScienceGoogle Scholar
Donaldson, E, Schillinger, WF, Dofing, SM (2001) Straw production and grain yield relationships in winter wheat. Crop Sci 41:100106 CrossRefGoogle Scholar
Felton, WL, Alston, CL, Haigh, BM, Nash, PG, Wicks, GA, Hanson, GE (2002) Using reflectance sensors in agronomy and weed science. Weed Technol 16:520527 CrossRefGoogle Scholar
Felton, WL, Doss, AF, Nash, G, McCloy, KR (1991) A microprocessor controlled technology to selectively spot spray weeds. Pages 427–431 in Automated Agriculture for the 21st Century Symposium. Fargo, NDGoogle Scholar
Heap, JW (1993) Control of rush skeletonweed (Chondrilla juncea) with herbicides. Weed Technol 7:954959 CrossRefGoogle Scholar
Huang, Y, Reddy, KN, Fletcher, RS, Pennington, D (2018) UAV low-altitude remote sensing for precision weed management. Weed Technol 32:26 CrossRefGoogle Scholar
Jenks, B (2019) North Dakota Weed Control Guide. Fargo: North Dakota State University. 136 pGoogle Scholar
Knoche, M (1994) Effect of droplet size and carrier volume on performance of foliage-applied herbicides. Crop Prot 13:63178 CrossRefGoogle Scholar
Lamb, DW, Brown, RB (2001) Remote-sensing and mapping of weeds in crops. J Agr Eng Res 78:117125 CrossRefGoogle Scholar
Legleiter, TR, Johnson, WG (2016) Herbicide coverage in narrow row soybean as influenced by spray nozzle design and carrier volume. Crop Prot 83:18 CrossRefGoogle Scholar
Riar, DS, Ball, DA, Yenish, JP, Burke, IC (2011) Light-activated, sensor-controlled sprayer provides effective postemergence control of broadleaf weeds in fallow. Weed Technol 25:447453 CrossRefGoogle Scholar
SAS Institute. 2019. SAS OnlineDoc. Version 9.4. Cary, NC: SAS InstituteGoogle Scholar
Spring, J, Thorne, M, Burke, I, Lyon, D (2018) Rush skeletonweed (Chondrilla juncea) control in Pacific Northwest winter wheat. Weed Technol 32:360363 CrossRefGoogle Scholar
Stougaard, RN (1999) Carrier volume adjustments improve imazamethabenz efficacy. Weed Technol 13:227232 CrossRefGoogle Scholar
Stroup, WW (2015) Rethinking the analysis of non-normal data in plant and soil science. Agron J 107:811827 CrossRefGoogle Scholar
Verhulst, N, Govaerts, B, Sayre, KD, Deckers, J, François, IM, Dendooven, L (2009) Using NDVI and soil quality analysis to assess influence of agronomic management on within-plot spatial variability and factors limiting production. Plant Soil 317:4159 CrossRefGoogle Scholar
Wallace, J, Prather, T (2010) Rush skeletonweed control with aminopyralid on Idaho rangeland. Pages 22–23 in Western Society of Weed Science 2010 Research Progress Report. Waikola, HI: Western Society of Weed ScienceGoogle Scholar
Wicks, GA, Felton, WL, Murison, RD, Hanson, GE, Nash, PG (1998) Efficiency of an Optically Controlled Sprayer for Controlling Weeds in Fallow. Weed Technol 12:638645 CrossRefGoogle Scholar
Woebbecke, DM, Meyer, GE, Von Bargen, K, Mortensen, DA (1995) Color indices for weed identification under various soil, residue, and lighting conditions. T ASAE 38:259269 CrossRefGoogle Scholar
Young, FL, Yenish, JP, Launchbaugh, GK, McGrew, LL, Alldredge, JR (2008) Postharvest control of Russian thistle (Salsola tragus) with a reduced herbicide applicator in the Pacific Northwest. Weed Technol 22:156159 CrossRefGoogle Scholar