Hostname: page-component-cd9895bd7-lnqnp Total loading time: 0 Render date: 2024-12-27T07:52:11.090Z Has data issue: false hasContentIssue false
  • English
  • Français

Cover crop effects on horseweed (Erigeron canadensis) density and size inequality at the time of herbicide exposure

Published online by Cambridge University Press:  21 March 2019

John M. Wallace Open the ORCID record for John M. Wallace [Opens in a new window] ,
William S. Curran  and
David A. Mortensen
John M. Wallace*
Affiliation:
Assistant Professor, Plant Science Department, Pennsylvania State University, University Park, PA, USA
William S. Curran
Affiliation:
Professor Emeritus, Plant Science Department, Pennsylvania State University, University Park, PA, USA
David A. Mortensen
Affiliation:
Professor, Department of Agriculture, Nutrition, and Food Systems, University of New Hampshire, Durham, NH, USA
*
Author for correspondence: John M. Wallace, Email: jmw309@psu.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

Proactive integrated weed management (IWM) is critically needed in no-till production to reduce the intensity of selection pressure for herbicide-resistant weeds. Reducing the density of emerged weed populations and the number of larger individuals within the population at the time of herbicide application are two practical management objectives when integrating cover crops as a complementary tactic in herbicide-based production systems. We examined the following demographic questions related to the effects of alternative cover-cropping tactics following small grain harvest on preplant, burndown management of horseweed (Erigeron canadensis L.) in no-till commodity-grain production: (1) Do cover crops differentially affect E. canadensis density and size inequality at the time of herbicide exposure? (2) Which cover crop response traits are drivers of E. canadensis suppression at time of herbicide exposure? Interannual variation in growing conditions (study year) and intra-annual variation in soil fertility (low vs. high nitrogen) were the primary drivers of cover crop response traits and significantly affected E. canadensis density at the time of herbicide exposure. In comparison to the fallow control, cover crop treatments reduced E. canadensis density 52% to 86% at the time of a preplant, burndown application. Cereal rye (Secale cereale L.) alone or in combination with forage radish (Raphanus sativus L.) provided the most consistent E. canadensis suppression. Fall and spring cover crop biomass production was negatively correlated with E. canadensis density at the preplant burndown application timing. Our results also show that winter-hardy cover crops reduce the size inequality of E. canadensis populations at the time of herbicide exposure by reducing the number of large individuals within the population. Finally, we advocate for advancement in our understanding of complementarity between cover crop– and herbicide-based management tactics in no-till systems to facilitate development of proactive, herbicide-resistant management strategies.

Keywords

J. Anita Dille, Kansas State UniversityAgroecologydemographyeco-evolutionaryherbicide resistanceno-tillsize inequality
Type
Research Article
Information
Weed Science , Volume 67 , Issue 3 , May 2019 , pp. 327 - 338
Copyright
© Weed Science Society of America, 2019 

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

Baker, E, Hunt, GM (1981) Developmental changes in leaf epicuticular waxes in relation to foliar penetration. New Phytol 88:731–747CrossRefGoogle Scholar
Baraibar, B, Hunter, MC, Schipanski, ME, Hamilton, A, Mortensen, DA (2018) Weed suppression in cover crop monocultures and mixtures. Weed Sci 66:121–133CrossRefGoogle Scholar
Barberi, P, Bocci, G, Carlesi, S, Armengot, L, Blanco-Moreno, JM, Sans, FX (2018) Linking species traits to agroecosystems: a functional analysis of weed communities. Weed Res 58:76–88CrossRefGoogle Scholar
Bates, D, Maechler, M, Bolker, B, Walker, S, Christensen, RH, Singmann, H, Dai, B, Grothendieck, G, Green, P (2016) lme4: Linear Mixed-Effect Models. http://CRAN.R-project.org/package=lme4. Accessed: March 26, 2018Google Scholar
Björkman, T, Lowry, C, Shail, JW Jr, Brainard, DC, Anderson, DS, Masiunas, JB (2015) Mustard cover crops for biomass production and weed suppression in the Great Lakes region. Agron J 107:1235–1249CrossRefGoogle Scholar
Bolker, BM, Brooks, ME, Clark, CJ, Geange, SW, Poulsen, JR, Stevens, MH, White, JS (2008) Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol Evol 24:127–135CrossRefGoogle Scholar
Brainard, DC, Bellinder, RR, Kumar, V (2011) Grass-legume mixtures and soil fertility affect cover crop performance and weed seed production. Weed Technol 25:473–479CrossRefGoogle Scholar
Buckley, HL, Damgaard, C (2012) Lorenz R: R Code for Drawing Sample Lorenz Curves and to Calculate Gini Coefficients and Lorenz Asymmentry Coefficients. http://pure.au.dk/portal/en/publications/lorenzr(21dbe72-)f9c-4a3b-9a2c-f364828089d3).html. Accessed: March 26, 2018Google Scholar
Busi, R, Goggin, DE, Heap, IM, Horak, MJ, Jugulam, M, Masters, RA, Napier, RM, Riar, DS, Satchivi, NM, Torra, J, Westra, P, Wright, TR (2017) Weed resistance to synthetic auxin herbicides. Pest Manag Sci 74:2265–2276CrossRefGoogle Scholar
[CAST] Council for Agricultural Science and Technology (2012) Herbicide-Resistant Weeds Threaten Soil Conservation Gains: Finding a Balance for Soil and Farm Sustainability. Issue Paper 49. CAST, Ames, IA.Google Scholar
Cholette, TB, Soltani, N, Hooker, DC, Robinson, DE, Sikkema, PH (2018) Suppression of glyphosate-resistant Canada fleabane (Conyza canadensis) in corn with cover crops seeded after wheat harvest the previous year. Weed Technol 32:244–250CrossRefGoogle Scholar
Damgaard, C, Weiner, J (2000) Describing inequality in plant size or fecundity. Ecol 81:1139–1142CrossRefGoogle Scholar
Davis, AS, Liebman, M (2003) Cropping system effects on giant foxtail (Setaria faberi) demography: I. Green manure and tillage timing. Weed Sci 51:919–929CrossRefGoogle Scholar
Dinelli, G, Marotti, I, Bonetti, A, Minelli, M, Catizone, P, Barnes, J (2006) Physiological and molecular insight on the mechanisms of resistance to glyphosate in Conyza canadensis (L.) Cronq. biotypes. Pestic Biochem Physiol 86:30–41CrossRefGoogle Scholar
Evans, JA, Tranel, PJ, Hager, AG, Schutte, B, Wu, C, Chatham, LA, Davis, AS (2016) Managing the evolution of herbicide resistance. Pest Manage Sci 72:74–80CrossRefGoogle ScholarPubMed
Finney, DM, Kaye, JP (2016) Functional diversity in cover crop polycultures increases multifunctionality of an agricultural system. J Appl Ecol 54:509–517CrossRefGoogle Scholar
Finney, DM, White, CM, Kaye, JP (2015) Biomass production and carbon/nitrogen ratio influence ecosystem services from cover crop mixtures. Agron J 108:39–52CrossRefGoogle Scholar
Gallandt, ER, Molloy, T, Lynch, RP, Drummond, FA (2005) Effect of cover cropping systems on invertebrate seed predation. Weed Sci 53:69–76CrossRefGoogle Scholar
Grace, JB, Anderson, TM, Olff, H, Scheiner, SM (2010) On the specifications of structural equation models for ecological systems. Ecol Monogr 80:67–87CrossRefGoogle Scholar
Grace, JB, Bollen, KA (2005) Interpreting the results from multiple regression and structural equation models. Bull Ecol Soc Amer 86:283–313CrossRefGoogle Scholar
Heywood, JS (1986) The effect of plant size variation on genetic drift in populations of annuals. Amer Nat 137:851–861CrossRefGoogle Scholar
Holmes, AA, Thompson, AA, Wortman, SE (2017) Species-specific contributions to productivity and weed suppression in cover crop mixtures. Agron J 109:2808–2819CrossRefGoogle Scholar
Hothorn, T, Bretz, F, Westfall, P, Heiberger, RM, Schuetzenmeister, A, Scheibe, S (2008) multcomp: Simultaneous Inference in General Parametric Models. R Package v. 3.2.2.4. http://CRAN.R-project.org/package-multcomp. Accessed: March 26, 2018Google Scholar
Jaeger, B (2016) r2glmm: Computes R Squared for Mixed (Multilevel) Models. R Package v. 1.15.6. http://CRAN.R-project.org/package=r2glmm. Accessed: March 26, 2018Google Scholar
Jaeger, BC, Edwards, LJ, Das, K, Sen, PK (2017) An R2 statistic for fixed effects in the generalized linear model. J Appl Stat 44:1086–1105CrossRefGoogle Scholar
Lawley, YE, Teasdale, JR, Weil, RR (2012) The mechanism for weed suppression by a forage radish cover crop. Agron J 104:1–10CrossRefGoogle Scholar
Lefcheck, JS (2016) PiecewiseSEM: piecewise structural equation modeling in R for ecology, evolution, and systematics. Methods Ecol Evol 7:573–579CrossRefGoogle Scholar
Liebman, M, Baraibar, B, Buckley, Y, Childs, D, Christensen, S, Cousens, R, Eizenberg, H, Heijting, S, Loddo, D, Merotto, A, Renton, M, Riemens, M (2016) Ecologically sustainable weed management: how do we get from proof-of-concept to adoption? Ecol Appl 26:1352–1369CrossRefGoogle ScholarPubMed
Liebman, ML, Staver, CP (2001) Crop diversification and weed management. Pages 322–363 in Liebman, M Mohler, CL, Staver, CP, eds. Ecological Management of Agricultural Weeds. Cambridge University Press, CambridgeCrossRefGoogle Scholar
Liu, C, Bridges, ME, Kaundun, SS, Glasgow, L, Owen, MDK, Neve, P (2017) A generalized individual-based algorithm for modelling the evolution of quantitative herbicide resistance in arable weed populations. Pest Manag Sci 73:462–474CrossRefGoogle Scholar
Loux, MM, Dobbels, AF, Bradley, KW, Johnson, WG, Young, BG, Spaunhorst, DJ, Norsworthy, JK, Palhano, M, Steckel, LE (2017) Influence of cover crops on management of Amaranthus species in glyphosate- and glufosinate-resistant soybean. Weed Technol 31:487–495CrossRefGoogle Scholar
Maxwell, BD, Roush, ML, Radosevich, SR (1990) Predicting the evolution and dynamics of herbicide resistance in weed populations. Weed Technol 4:2–13CrossRefGoogle Scholar
Menalled, FD, Peterson, RK, Smith, RG, Curran, WS, Paez, DJ, Maxwell, BD (2016) The eco-evolutionary imperative: revisiting weed management in the midst of an herbicide resistance crisis. Sustainability 8:1297CrossRefGoogle Scholar
Montgomery, GB, McClure, AT, Hayes, RM, Walker, FR, Senseman, SA, Steckel, LE (2018) Dicamba-tolerant soybean combined cover crop to control Palmer amaranth. Weed Technol 32:109–115CrossRefGoogle Scholar
Mortensen, DA, Egan, JF, Maxwell, BD, Ryan, MR, Smith, RG (2012) Navigating a critical juncture for sustainable weed management. BioScience 62:75–84CrossRefGoogle Scholar
Nakagawa, S, Schielzeth, H (2012) A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol Evol 4:133–142CrossRefGoogle Scholar
Neve, P, Norsworthy, JK, Smith, KL, Zelaya, IA (2010) Modeling evolution and management of glyphosate resistance in Amaranthus palmeri. Weed Res 51:99–112CrossRefGoogle Scholar
Neve, P, Powles, S (2005) High survival frequencies at low herbicide use rates in populations of Lolium rigidum result in rapid evolution of herbicide resistance. Heredity (Edinb) 95:485–492CrossRefGoogle ScholarPubMed
Norsworthy, JK, Ward, SM, Shaw, DR, Llewellyn, RS, Nichols, RL, Webster, TM, Bradley, KW, Frisvold, G, Powles, SB, Burgos, NR (2012) Reducing the risks of herbicide resistance: best management practices and reccomendations. Weed Sci 60:31–62CrossRefGoogle Scholar
Pinheiro, J, Bates, D, DebRoy, S, Sarkar, D, R Core Team (2015) nlme: Linear and Nonlinear Mixed Effects Models. R Package v. 3.1.2.4. http://CRAN.R-project.org/package=nlme. Accessed: March 26, 2018Google Scholar
Powles, SB, Yu, Q (2010) Evolution in action: plants resistant to herbicides. Annu Rev Plant Biol 61:317–347CrossRefGoogle ScholarPubMed
R Core Team (2016) R: A language and environment for Statistical Computing, Vienna, Austria. https://www.R-project.org/. Accessed: 26 March 2018Google Scholar
Reberg-Horton, SC, Burton, JD, Danehower, DA (2005) Changes over time in the allelochemical content of ten cultivars of rye (Secale cereale L.) J Chem Ecol 31:179–193CrossRefGoogle Scholar
Renton, M, Busi, R, Neve, P, Thornby, D, Vila-Aiub, M (2014) Herbicide resistance modelling: past, present and future. Pest Manag Sci 70:1394–1404CrossRefGoogle ScholarPubMed
Rinella, MJ, James, JJ (2010) Invasive plant researchers should calculate effect sizes, not P-values. Invasive Plant Sci Manag 3:106–112CrossRefGoogle Scholar
Sarrantonio, M, Gallandt, E (2003) The role of cover crops in North American cropping systems. J Crop Prod 8:53–74CrossRefGoogle Scholar
Schappert, A, Messelhauser, MJH, Saile, M, Peteinatos, GG, Gerhards, R (2018) Weed suppressive ability of cover crop mixtures compared to repeated stubble tillage and glyphosate treatments. Agriculture 8:144CrossRefGoogle Scholar
Schipanski, ME, Barbercheck, M, Douglas, MR, Finney, DM, Haider, K, Kaye, JP, Kemanian, AR, Mortensen, DA, Ryan, MR, Tooker, J, White, C (2014) A framework for evaluating ecosystem services provided by cover crops in agroecosystems. Agric Syst 125:12–22. Accessed: March 26, 2018CrossRefGoogle Scholar
Smith, RG, Atwood, LW, Pollnac, FW, Warren, ND (2015) Cover crop species as distinct biotic filters in weed community assembly. Weed Sci 63:282–295CrossRefGoogle Scholar
Somerville, GJ, Powles, SB, Walsh, MJ, Renton, M (2016) Why was resistance to shorter-acting pre-emergence herbicides slower to evolve? Pest Manag Sci 73:844–851CrossRefGoogle Scholar
Storkey, J, Doring, T, Baddeley, J, Collins, R, Roderick, S, Jones, H, Watson, CA (2015a) Engineering a plant community to deliver multiple ecosystem services. Ecol Appl 25:1034–1043CrossRefGoogle Scholar
Storkey, J, Holst, N, Bojer, OQ, Bigongiali, F, Bocci, G, Colbach, N, Dorner, Z, Riemens, MM, Sartorato, I, Sonderskov, M, Verschwele, A (2015b) Combining a weed traits database with a population dynamics model predicts shifts in weed communities. Weed Res 55:206–218CrossRefGoogle ScholarPubMed
Teasdale, JR, Brandsaeter, LO, Calegari, A, Neto, FS (2007) Cover crops and weed management. Pages 49–64 in Upadhyaya, MK, Blackshaw, RE, eds. Non-chemical Weed Management. CABI, Wallingford, UKGoogle Scholar
Teasdale, JR, Mohler, CL (2000) The quantitative relationship between weed emergence and the physical properties of mulches. Weed Sci 48:385–392CrossRefGoogle Scholar
Vila-Aiub, M, Casas, C, Gundel, PE (2018) The role of plant size in the selection of glyphosate resistance in Sorghum halepense. Pest Manag Sci 74:2460–2467CrossRefGoogle ScholarPubMed
Wauchope, R, Sumner, H, Dowler, C (1997) A measurement of the total mass of spray and irrigation mixtures intercepted by small whole plants. Weed Technol 11:466–472CrossRefGoogle Scholar
Wayman, S, Kucek, LK, Mirsky, SB, Ackroyd, V, Cordeau, S, Ryan, MR (2016) Organic and conventional farmers differ in their perspectives on cover crop use and breeding. Renew Ag Food Syst 32:376–386CrossRefGoogle Scholar
Weiner, J (1985) Size hierarchies in experimental populations of annual plants. Ecology 66:743–752CrossRefGoogle Scholar
Weiner, J, Solbrig, OT (1984) The meaning and measurement of size hierarchies in plant populations. Oecologia 61:334–336CrossRefGoogle ScholarPubMed
Wells, MS, Reberg-Horton, SC, Smith, SN, Grossman, JM (2013) The reduction of plant-available nitrogen by cover crop mulches and subsequent effects on soybean performance and weed interference. Agron J 105:539–545CrossRefGoogle Scholar
Wiggins, MS, McClure, MA, Hayes, RM, Steckel, LE (2016) Integrating cover crops and POST herbicides for glyphosate-resistant Palmer amaranth (Amaranthus palmeri) control in corn. Weed Technol 29:412–418CrossRefGoogle Scholar
Williams, A, Wells, MS, Dickey, DA, Hu, S, Maul, J, Raskin, DT, Reberg-Horton, SC, Mirsky, SB (2018) Establishing the relationship of soil nitrogen immobilization to cereal rye residues in mulched system. Plant Soil 426:95–107CrossRefGoogle Scholar