Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-22T20:25:22.889Z Has data issue: false hasContentIssue false
  • English
  • Français

Common Sunflower Seedling Emergence across the U.S. Midwest

Published online by Cambridge University Press:  20 January 2017

Sharon A. Clay ,
Adam Davis ,
Anita Dille ,
John Lindquist ,
Analiza H. M. Ramirez ,
Christy Sprague ,
Graig Reicks  and
Frank Forcella
Sharon A. Clay*
Affiliation:
South Dakota State University, Brookings, SD 57007
Adam Davis
Affiliation:
U.S. Department of Agriculture, Agricultural Research Service Urbana, Il 61801
Anita Dille
Affiliation:
Kansas State University, Manhattan, KS 66506
John Lindquist
Affiliation:
University of Nebraska, Lincoln, NE 68583
Analiza H. M. Ramirez
Affiliation:
Kansas State University, Manhattan, KS 66506
Christy Sprague
Affiliation:
Michigan State University, East Lansing, MI 48824
Graig Reicks
Affiliation:
South Dakota State University, Brookings, SD 57007
Frank Forcella
Affiliation:
USDA-ARS North Central Soil Conservation Research Lab, Morris, MN 56267
*
Corresponding author's email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Predictions of weed emergence can be used by practitioners to schedule POST weed management operations. Common sunflower seed from Kansas was used at six Midwestern U.S. sites to examine the variability that 16 climates had on common sunflower emergence. Nonlinear mixed effects models, using a flexible sigmoidal Weibull function that included thermal time, hydrothermal time, and a modified hydrothermal time (with accumulation starting from January 1 of each year), were developed to describe the emergence data. An iterative method was used to select an optimal base temperature (Tb) and base and ceiling soil matric potentials (ψb and ψc) that resulted in a best-fit regional model. The most parsimonious model, based on Akaike's information criterion (AIC), resulted when Tb = 4.4 C, and ψb = −20000 kPa. Deviations among model fits for individual site years indicated a negative relationship (r = −0.75; P < 0.001) between the duration of seedling emergence and growing degree days (Tb = 10 C) from October (fall planting) to March. Thus, seeds exposed to warmer conditions from fall burial to spring emergence had longer emergence periods.

Keywords

Common sunflower, Helianthus annuus LAbiotic influences on seed dormancyregional environmental variationseedling recruitment
Type
Weed Biology and Ecology
Information
Weed Science , Volume 62 , Issue 1 , March 2014 , pp. 63 - 70
Copyright
Copyright © Weed Science Society of America 

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

Literature Cited

Alan, LR, Wiese, AF (1985) Effects of degree days on weed emergence. Pages 448 p in Proceedings of Southern Weed Sci. Soc. 38th Annual Meeting. Raleigh, N0C SWSS Google Scholar
Archer, DW, Forcella, F, Korth, A, Kuhn, A, Eklund, J, Spokas, K (2006) WeedCast. http://www.ars.usda.gov/services/software/download.htm?softwareid=112). Accessed January 18, 2013Google Scholar
Baskin, JM, Baskin, CC (1987) Environmentally induced changes in the dormancy states of buried weed seeds. Pp 695–706 in Proceedings 1987 British Crop protection Conference Weeds. Brighton Google Scholar
Bazin, J, Batlla, D, Dussert, S, El-Maarouf-Bouteau, H, Bailly, C (2011) Role of relative humidity, temperature, and water status in dormancy alleviation of sunflower seeds during dry after-ripening. J Exp Bot. 62:627–640.Google Scholar
Bradford, KJ (2002) Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Sci. 50:248–260.Google Scholar
Brown, RF, Mayer, DG (1988) Representing cumulative germination. 2. The use of the Weibull function and other empirically derived curves. Ann Bot. 61:127–138.CrossRefGoogle Scholar
Burnham, KP, Anderson, DR (2002) Model selection and inference: a practical information-theoretic approach. 2nd edn. New York Springer Verlag. Pp 32–74 Google Scholar
Clay, S, Kleinjan, J, Clay, DE, Forcella, F, Batchelor, W (2005) Growth and fecundity of several weed species in corn and soybean. Agron J. 97:294–302.Google Scholar
Davis, A, Clay, S, Cardina, J, Dille, JA, Forcella, F, Lindquist, J, Sprague, C (2013) Overwinter burial environment explains departures from regional hydrothermal model of giant ragweed seedling emergence in the U.S. Midwest. Weed Sci. 61:415–421.CrossRefGoogle Scholar
Deines, SR, Dille, JA, Blinka, EL, Regehr, DL, Staggenborg, SA (2004) Common sunflower (Helianthus annuus) and shattercane (Sorghum bicolor) interference in corn. Weed Sci. 52:976–983.Google Scholar
Finch-Savage, WE, Leubner-Metzger, G (2006) Seed dormancy and the control of germination. New Phytol 171:501–523.Google Scholar
Forcella, F (1998) Real-time assessment of seed dormancy and seedling growth for weed management. Seed Sci Res 8:201–209.Google Scholar
Forcella, F, Benech-Arnold, RL, Sanchez, R, Ghersa, CM (2000) Modeling seedling emergence. Field Crop Res 67:123–139.Google Scholar
Gallandt, ER, Weiner, J (2007) Crop–Weed Competition. eLS. DOI: 10.1002/9780470015902.a0020477CrossRefGoogle Scholar
Gardarin, A, Guillemin, JP, Munier-Jolain, NM, Colbach, N (2010) Estimation of key parameters for weed population dynamics models: base temperature and base water potential for germination. Eur J Agron 32:162–168.Google Scholar
Geier, PW, Maddux, LD, Moshier, LJ, Stahlman, PW (1996) Common sunflower (Helianthus annuus) interference in soybean (Glycine max). Weed Technol 10:317–321.Google Scholar
Grundy, AC (2003) Predicting weed emergence: a review of approaches and future challenges. Weed Res 43:1–11.Google Scholar
Grundy, AC, Mead, A (2000) Modeling weed emergence as a function of meteorological records. Weed Sci. 48:594–603.Google Scholar
Grundy, AC, Peters, NCB, Rasmussen, IA, Hartmann, KM, Sattin, M, Andersson, L, Mead, A, Murdoch, AJ, Forcella, F (2003) Emergence of Chenopodium album and Stellaria media of different origins under different climatic conditions. Weed Res 43:163–176.Google Scholar
Hardegree, SP, Winstral, AH (2006) Predicting germination response to temperature. II. Three-dimensional regression, statistical gridding and iterative-probit optimization using measured and interpolated-subpopulation data. Ann Bot. 98:403–410.Google Scholar
Horak, MJ, Loughin, TM (2000) Growth analysis of four Amaranthus species. Weed Sci. 48:347–355.CrossRefGoogle Scholar
Larsen, SU, Bailly, C, Côme, D, Corbineau, F (2004) Use of the hydrothermal time model to analyze interacting effects of water and temperature on germination of three grass species. Seed Sci Res 14:35–50.Google Scholar
Leguizamón, ES, Fernandez-Quintanilla, C, Barroso, J, Gonzalez-Andujar, JL (2005) Using thermal and hydrothermal time to model seedling emergence of Avena sterilis ssp. ludoviciana in Spain. Weed Res 45:149–156.Google Scholar
Masin, R, Loddo, D, Benvenuti, S, Zuin, MC, Macchia, M, Zanin, G (2010) Temperature and water potential as parameters for modeling weed emergence in central-northern Italy. Weed Sci. 58:216–222.Google Scholar
McDonough, WT (1975) Water potentials of germinating seeds. Bot Gaz 136:106–108.Google Scholar
Meyer, SE, Debaene-Gill, SB, Allen, PS (2000) Using hydrothermal time concepts to model seed germination response to temperature, dormancy loss, and priming effects in Elymus elymoides . Seed Sci Res 10:213–223.Google Scholar
Moloney, KA, Holzapfel, C, Tielborger, K, Jeltsch, F, Schurr, FM (2009) Rethinking the common garden in invasion research. Persp Plant Ecol Evol Sys 11:311–320.Google Scholar
Myers, MM, Curran, WS, VanGessel, MJ, Calvin, DD, Mortensen, DA, Majek, BA, Karsten, HD, Roth, GW (2004) Predicting weed emergence for eight annual species in the northeastern United States. Weed Sci. 52:913–919.Google Scholar
North Dakota Ag Weather Network. http://ndawn.ndsu.nodak.edu/help-sunflower-growing-degree-days.html. Accessed January 18, 2013Google Scholar
Oriade, CA, Forcella, F (1999) Maximizing efficacy and economics of mechanical weed control in row crops through forecasts of weed emergence. J Crop Prod 2:189–205.CrossRefGoogle Scholar
Otto, S, Masin, R, Chistè, G, Zanin, G (2007) Modelling the correlation between plant phenology and weed emergence for improving weed control. Weed Res 47:488–498.Google Scholar
Oryokot, JOE, Hunt, LA, Murphy, SD, Swanton, CJ (1997a) Simulation of pigweed (Amaranthus spp.) seedling emergence in different tillage systems. Weed Sci. 45:684–690.Google Scholar
Oryokot, JOE, Murphy, SD, Swanton, CJ (1997b) Effect of tillage and corn on pigweed (Amaranthus spp.) seedling emergence and density. Weed Sci. 45:120–126.Google Scholar
Pinheiro, JC, Bates, DM (2004) Mixed-effects models in S and S-PLUS. New York Springer. 528 pGoogle Scholar
R Development Core Team. 2009. R: A language and environment for statistical computing. Last Accessed July 22, 2013Google Scholar
Ratkowski, DA (1983) Nonlinear Regression Modeling: a unified practical approach. New York Marcel Dekker. Pp 135–153 Google Scholar
Roman, ES, Murphy, SD, Swanton, CJ (2000) Simulation of Chenopodium album seedling emergence. Weed Sci. 48:217–224.Google Scholar
Rosemartin, AH, Crimmins, TM, USA National Phenology Network development team. 2012. An evaluation of observer engagement strategies for Nature's Notebook . DRAFT. USA-NPN Technical Series 2012-002. http://www.usanpn.org. Accessed January 14, 2013Google Scholar
Rowse, H, Finch-Savage, W (2003) Hydrothermal threshold models can describe the germination response of carrot (Daucus carota) and onion (Allium cepa) seed populations across both sub- and supra-optimal temperatures. New Phytol 158:101–108.Google Scholar
Sadras, VO, Hall, AJ (1988) Quantification of temperature, photoperiod and population effect on plant leaf area in sunflower crop. Crop Res J. 18:185–96.Google Scholar
Sattin, M, Berti, A, Zanin, G (1996) Crop yield loss in relation to weed time of emergence and removal: analysis of the variability with mixed infestations. Pp 67–72 in Proceedings of the Second International Weed Control Congress. Slagelse, Denmark Department of Weed Control and Pesticide Ecology Google Scholar
Schutte, BJ, Regnier, EE, Harrison, SK, Schmoll, JT, Spokas, K, Forcella, F (2008) A hydrothermal seedling emergence model for giant ragweed (Ambrosia trifida). Weed Sci. 56:555–560.Google Scholar
Spokas, K, Forcella, F (2009) Software tools for weed seed germination modeling. Weed Sci. 57:216–227.Google Scholar
Usganca-Mortera, E, Clay, SA, Forcella, F, Gunsolus, J (2007) Common waterhemp growth and fecundity as influenced by emergence date and competing crop. Agron J. 99:1265–1270.Google Scholar
Weibull, W (1951) A statistical distribution function of wide applicability. J Appl Mech 18:293–297.Google Scholar
Wortman, SE, Davis, AS, Schutte, BJ, Lindquist, JL, Cardina, J, Felix, J, Sprague, CL, Dille, JA, Ramirez, AHM, Reicks, G, Clay, SA (2012) Local conditions, not spatial gradients, drive demographic variation of Ambrosia trifida and Helianthus annuus across northern US maize belt. Weed Sci. 60:440–450.Google Scholar
Wolkovich, EM, Cook, BI, Allen, JM, Crimmins, TM, Betancourt, JL, Travers, SE, Pau, S, Regetz, J, Davies, TJ, Kraft, NJB, Ault, TR, Bolmgren, K, Mazer, SJ, McCabe, GJ, McGill, BJ, Parmesan, C, Salamin, N, Schwartz, MD, Cleland, EE (2012) Warming experiments underpredict plant phenological responses to climate change. Nature DOI:10.1038/nature11014Google Scholar