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A survey evaluating the spatial and temporal distribution of horseweed (Conyza canadensis) late season in Ohio soybean fields from 2013 to 2017

Published online by Cambridge University Press:  28 June 2021

Alyssa I. Essman*
Affiliation:
Research Associate, Department of Horticulture and Crop Science, Ohio State University, Columbus, OH, USA
Mark M. Loux
Affiliation:
Professor, Department of Horticulture and Crop Science, Ohio State University, Columbus, OH, USA
Alexander J. Lindsey
Affiliation:
Associate Professor, Department of Horticulture and Crop Science, Ohio State University, Columbus, OH, USA
Bruce A. Ackley
Affiliation:
Extension Program Specialist, Department of Horticulture and Crop Science, Ohio State University, Columbus, OH, USA
Emilie E. Regnier
Affiliation:
Associate Professor, Department of Horticulture and Crop Science, Ohio State University, Columbus, OH, USA
*
Author for correspondence: Alyssa I. Essman, Department of Horticulture and Crop Science, Ohio State University, 223 Kottman Hall, Columbus, OH 43210. (Email: essman.42@osu.edu)

Abstract

On-site surveys of weed populations provide information on the relative occurrence and density of weeds that can be useful to growers in that region. Data generated by weed surveys can aid in the management of weed issues by monitoring the movement of problem weeds and forecasting areas susceptible to infestations. Currently, on-site surveys are often performed on a small scale, within single fields or counties. Questionnaire surveys are helpful for assessing relative abundance but do not always provide detailed information on weed distribution in time or space. A survey was conducted annually in Ohio from 2013 through 2017 in 49 counties with soybean [Glycine max (L.) Merr.] production to assess the late-season occurrence of horseweed [Conyza canadensis (L.) Cronquist]. The objectives of this research were to: (1) determine the frequency, level of infestation, and distribution of C. canadensis in soybean fields in the primary soybean-producing Ohio counties over 5 yr; and (2) identify significant spatial clusters or movement trends over time. Conyza canadensis was encountered in each county from 2013 through 2017. Spatial cores of interest, or counties identified as having significant levels of C. canadensis infestations or a lack thereof relative to surrounding counties, were identified in all years except 2017. The lowest frequency of C. canadensis encountered at all rating levels occurred in 2017, which coincided with second-highest frequency of infestations (highest density level) among years. There was no distinct distribution or pattern of C. canadensis movement within the state from year to year, but there was an increase in counties with infestations over time compared with the early years of the survey when many counties had few to no infestations. These results suggest that C. canadensis persists as a common and troublesome threat to Ohio soybean producers and that growers should continue making C. canadensis management a priority when developing weed control programs.

Type
Research Article
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the Weed Science Society of America

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Footnotes

Associate Editor: Sharon Clay, South Dakota State University

References

Anselin, L (1995) Local indicators of spatial association—LISA. Geogr Anal 27:93115 CrossRefGoogle Scholar
Anselin, L (2017) Local spatial autocorrelation: univariate local statistics. GeoDa. https://geodacenter.github.io/workbook/6a_local_auto/lab6a.html. Accessed: March 1, 2018Google Scholar
Bathke, D (2017) United States Drought Monitor: Ohio. National Drought Mitigation Center. http://droughtmonitor.unl.edu/CurrentMap/StateDroughtMonitor.aspx?OH. Accessed: August 1, 2017Google Scholar
Bhowmik, P, Bekech, M (1993) Horseweed (Conyza canadensis) seed production, emergence, and distribution in no-tillage and conventional-tillage corn (Zea mays). Agron (Trends Agril Sci) 1:6771 Google Scholar
Bruce, J, Kells, J (1990) Horseweed (Conyza canadensis) control in no-tillage soybeans (Glycine max) with preplant and preemergence herbicides. Weed Technol 4:642647 CrossRefGoogle Scholar
Buhler, D, Owen, M (1997) Emergence and survival of horseweed (Conyza canadensis). Weed Sci 45:98101 Google Scholar
Cardina, J, Johnson, GA, Sparrow, DH (1997) The nature and consequence of weed spatial distribution. Weed Sci 45:364373 CrossRefGoogle Scholar
Colbach, N, Forcella, F, Johnson, GA (2000) Spatial and temporal stability of weed populations over five years. Weed Sci 48:366377 CrossRefGoogle Scholar
Dauer, J, Mortenson, D, VanGessel, M (2007) Temporal and spatial dynamics of long distance Conyza canadensis seed dispersal. J Appl Ecol 44:104114 Google Scholar
Dille, JA, Milner, M, Groeteke, JJ, Mortensen, DA, Williams, MM II (2002) How good is your weed map? A comparison of spatial interpolators. Weed Sci 51:4455 CrossRefGoogle Scholar
Efron, B, Hastie, T (2016) Large-scale hypothesis testing and FDRS. Pages 271–297 in Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs). Cambridge: Cambridge University PressGoogle Scholar
Fletcher, RS, Reddy, KN (2018) Geographic information system for pigweed distribution in the US southeast. Weed Technol 32:2026 CrossRefGoogle Scholar
Gassner, A, Schnug, E (2006) Geostatistics for soil science. Pages 760–764 in Lal R, ed. Encyclopedia of Soil Science. Volume 1. Milton Park, UK: Taylor & FrancisCrossRefGoogle Scholar
Hanzlik, K, Gerowitt, B (2016) Methods to conduct and analyse weed surveys in arable farming: a review. Agron Sustain Dev 36:118 CrossRefGoogle Scholar
Heap, I (2018) List of Herbicide Resistant Weeds by Country. http://www.weedscience.org/Summary/Country.aspx. Accessed: February 26, 2018Google Scholar
Hornsby, AG (1992) Site-specific pesticide recommendations: the final step in environmental impact prevention. Weed Technol 6:736742 CrossRefGoogle Scholar
Jarnevich, CS, Holcombe, TR, Barnett, DT, Stohlgren, TJ, Kartesz, JT (2010) Forecasting weed distributions using climate data: a GIS early warning tool. Invasive Plant Sci Manag 3:365375 CrossRefGoogle Scholar
Johnson, GA, Mortensen, DA, Gotway, CA (1996) Spatial and temporal analysis of weed seedling populations using geostatistics. Weed Sci 44:704710 CrossRefGoogle Scholar
Kalivas, DP, Vlachos, CE, Economou, G, Dimou, P (2012) Regional mapping of perennial weeds in cotton with the use of geostatistics. Weed Sci 60:233243 CrossRefGoogle Scholar
Korres, NE, Norsworthy, JK, Bagavathiannan, MV, Mauromoustakos, A (2015) Distribution of arable weed populations along eastern Arkansas–Mississippi delta roadsides: factors affecting weed occurrences. Weed Technol 29:596604 CrossRefGoogle Scholar
Loux, M, Berry, M (1991) Use of a grower survey for estimating weed problems. Weed Technol 5:460466 CrossRefGoogle Scholar
Loux, M, Doohan, D, Dobbles, T, Johnson, W, Young, B, Legleiter, T, Hager, A (2016) Weed Control Guide for Ohio, Indiana, and Illinois. Columbus, OH: Ohio State University Extension Bulletin 789. 224 pGoogle Scholar
Mitchell, KM, Pike, DR, Mitasova, H (1996) Using a geographic information system (GIS) for herbicide management. Weed Technol 10:856864 CrossRefGoogle Scholar
Mueller-Warrant, GW, Whittaker, GW, Young, WC III (2008) GIS analysis of spatial clustering and temporal change in weeds of grass seed crops. Weed Sci 56:647669 CrossRefGoogle Scholar
Rankins, A Jr, Byrd, JD Jr, Mask, DB, Barnett, JW, Gerard, PD (2005) Survey of soybean weeds in Mississippi. Weed Technol 19:492498 CrossRefGoogle Scholar
Sawada, M (2009) Global Spatial Autocorrelation Indices—Moran’s I, Geary’s C and the General Cross-Product Statistic. http://www.lpc.uottawa.ca/publications/moransi/moran.htm. Accessed: March 1, 2018Google Scholar
Unglesbee, E (2018) Picking Beans: A Look at the Many 2019 Herbicide-Tolerant Soybean Options. https://www.dtnpf.com/agriculture/web/ag/crops/article/2018/10/02/look-many-2019-herbicide-tolerant. Accessed: March 2, 2021Google Scholar
[USDA-NASS] U.S. Department of Agriculture–National Agricultural Statistics Service (2014) Glyphosate Effectiveness Declines. https://www.nass.usda.gov/Surveys/Guide_to_NASS_Surveys/Ag_Resource_Management/ARMS_Soybeans_Factsheet. Accessed: February 26, 2018Google Scholar
[USDA-NASS] U.S. Department of Agriculture–National Agricultural Statistics Service (2018) Ohio Agricultural Statistics 2017–2018 Annual Bulletin. https://www.nass.usda.gov/Statistics_by_State/Ohio/Publications/Annual_Statistical_Bulletin/Ohio%20bulletin%202017-2018.pdf. Accessed: May 11, 2021Google Scholar
[USDA-NASS] U.S. Department of Agriculture–National Agricultural Statistics Service (2021) Quick Stats: Soybean Planting Progress. https://quickstats.nass.usda.gov. Accessed: March 2, 2021Google Scholar
Vanderhoof, M, Holzman, BA, Rogers, C (2009) Predicting the distribution of perennial pepperweed (Lepidium latifolium), San Francisco Bay Area, California. Invasive Plant Sci Manag 2:260269 CrossRefGoogle Scholar
Van Wychen, L (2016) Survey of the most common and troublesome weeds in broadleaf crops, fruits and vegetables in the United Stated and Canada. Weed Science Society of America National Weed Survey Dataset. http://wssa.net/wp-content/uploads/2016_Weed_Survey_Final.xlsx. Accessed: June 7, 2017Google Scholar
Wiles, L, Schweizer, E (2002) Spatial dependence of weed seed banks and strategies for sampling. Weed Sci 50:595606 CrossRefGoogle Scholar
Wyse-Pester, DY, Wiles, LJ, Philip, W (2002) Infestation and spatial dependence of weed seedling and mature weed populations in corn. Weed Sci 50:5463 CrossRefGoogle Scholar
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