Hostname: page-component-586b7cd67f-t8hqh Total loading time: 0 Render date: 2024-11-24T06:07:46.908Z Has data issue: false hasContentIssue false
  • English
  • Français

ALS inhibitor–resistant smallflower umbrella sedge (Cyperus difformis) seed germination requires fewer growing degree days and lower soil moisture

Published online by Cambridge University Press:  09 October 2019

Rafael M. Pedroso Open the ORCID record for Rafael M. Pedroso [Opens in a new window] ,
Chris van Kessel ,
Durval Dourado Neto ,
Bruce A. Linquist ,
Louis G. Boddy ,
Kassim Al-Khatib  and
Albert J. Fischer
Rafael M. Pedroso*
Affiliation:
Graduate Student, Department of Plant Sciences, University of California at Davis, Davis, CA, USA
Chris van Kessel
Affiliation:
Professor, Department of Plant Sciences, University of California at Davis, Davis, CA, USA
Durval Dourado Neto
Affiliation:
Professor, Crop Science Department, University of Sao Paulo (ESALQ/USP), Piracicaba, Brazil
Bruce A. Linquist
Affiliation:
Project Scientist, Department of Plant Sciences, University of California at Davis, Davis, CA, USA
Louis G. Boddy
Affiliation:
Postdoctoral Fellow, Department of Plant Sciences, University of California at Davis, Davis, CA, USA
Kassim Al-Khatib
Affiliation:
Professor, Department of Plant Sciences, University of California at Davis, Davis, CA, USA
Albert J. Fischer
Affiliation:
Professor, Department of Plant Sciences, University of California at Davis, Davis, CA, USA
*
Author for correspondence: Rafael M. Pedroso, Crop Science Department, 11 Padua Dias Avenue, University of Sao Paulo (ESALQ/USP), Piracicaba, Sao Paulo, Brazil 13418-900. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The repetitive use of ALS inhibitors for smallflower umbrella sedge (Cyperus difformis L.) control has selected for herbicide-resistant (R) populations that threaten the sustainability of rice (Oryza sativa L.) production and demand alternative control measures be developed. A better understanding of seedling recruitment patterns at the field level is required to optimize the timing and efficacy of control measures. Therefore, a population-based threshold model was developed for optimizing germination prediction in multiple acetolactate synthase (ALS)-R and ALS-susceptible (ALS-S) C. difformis biotypes and applied to field-level emergence predictions. Estimated base temperatures (Tb) ranged from 16.5 to 17.6 C with no clear pattern between biotypes; such values are higher than Tb values of other important rice weeds, as well as for rice. Germination rates increased linearly from 16 to 33.7 C. ALS-R seeds germinate faster due to smaller median thermal times to germination (θT(50)) while also displaying lower germination synchronicity across water potentials. Interestingly, ALS-R biotypes were capable of germinating under lower moisture availability, as indicated by their lower (more negative) base water potential values (Ψb(50)) for seed germination; Ψb(50) values ranged from −0.24 to −1.13 MPa. In-field soil germination measurements found thermal times to emergence varied across three water regimes (daily water, flooded, or saturated). Seedling emergence under the daily water treatment was fastest; however, total seedling density was lower than for the other water regimes. In order to optimize springtime C. difformis seedling emergence, soil moisture should be kept around field capacity, as germination is hindered at lower moisture contents. By predicting when most of the seed population germinates, the thermal-time model can address issues regarding the optimal timing for herbicide applications, thereby allowing for improved C. difformis management in rice fields.

Keywords

Acetohydroxyacid synthaseherbicide resistancehydrotime modelOryza sativaricesmallflower umbrella sedgethermal time model
Type
Research Article
Information
Weed Science , Volume 68 , Issue 1 , January 2020 , pp. 51 - 62
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.)

Footnotes

Associate Editor: Dean Riechers, University of Illinois

References

Allen, PS, Benech-Arnold, RL, Batlla, D, Bradford, KJ (2007) Modeling of seed dormancy. Pages 72–112in Bradford, KJ, Nonogaki, H, eds. Seed Development, Dormancy and Germination (Annual Plant Reviews 27). Oxford: Wiley-BlackwellCrossRefGoogle Scholar
Alvarado, V, Bradford, KJ (2002) A hydrothermal time model explains the cardinal temperatures for seed germination. Plant Cell and Environ 25:1061–1069CrossRefGoogle Scholar
Baskin, CC, Baskin, JM (1998) Seeds. Ecology, Biogeography, and Evolution of Dormancy and Germination. San Diego: Academic Press. Pp 16–17, 57–70, 187Google Scholar
Boddy, LGK, Bradford, J, and Fischer, AJ (2012) Population-based threshold models describe weed germination and emergence patterns across varying temperature, moisture and oxygen conditions. J Applied Ecol 49:1225–1236CrossRefGoogle Scholar
Bradford, KJ (2002) Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Sci 50:248–260CrossRefGoogle Scholar
Chauhan, BS, Johnson, DE (2009) Ecological studies on Cyperus difformis, Cyperus iria and Fimbristylis miliacea: three troublesome annual sedge weeds of rice. Ann Appl Biol 155:103–112CrossRefGoogle Scholar
Cousens, R, Brain, P, Odonovan, JT, Osullivan, PA (1987) The use of biologically realistic equations to describe the effects of weed density and relative-time of emergence on crop yield. Weed Sci 35:720–725CrossRefGoogle Scholar
Covell, S, Ellis, RH, Roberts, EH, Summerfield, RJ (1986) The influence of temperature on seed-germination rate in grain legumes. 1. A comparison of chickpea, lentil, soybean and cowpea at constant temperatures. J Exp Bot 37:705–715CrossRefGoogle Scholar
Dahal, P, Bradford, KJ (1990) Effects of priming and endosperm integrity on seed germination rates of tomato genotypes II. Germination at reduced water potential. J Exp Bot 41:1441–1454CrossRefGoogle Scholar
Dahal, P, Bradford, KJ (1994) Hydrothermal time analysis of tomato seed germination at suboptimal temperature and reduced water potential. Seed Sci Res 4:71–80CrossRefGoogle Scholar
Dahal, P, Bradford, KJ, Jones, RA (1990) Effects of priming and endosperm integrity on seed-germination rates of tomato genotypes. I. Germination at suboptimal temperature. J Exp Bot 41:1431–1439CrossRefGoogle Scholar
Derakhshan, A, Gherekhloo, J (2013) Factors affecting Cyperus difformis seed germination and seedling emergence. Planta Daninha 31:823–832CrossRefGoogle Scholar
Dyer, WE, Chee, PW, Fay, PK (1993). Rapid germination of sulfonylurea-resistant Kochia scoparia L. accessions is associated with elevated seed levels of branched-chain amino-acids. Weed Sci 41:18–22CrossRefGoogle Scholar
Eberlein, CV, Guttieri, MJ, Berger, PH (1999) Physiological consequences of mutation for ALS-inhibitor resistance. Weed Sci 47:383–392CrossRefGoogle Scholar
Ellis, RH, Butcher, PD (1988) The effects of priming and natural differences in quality amongst onion seed lots on the response of the rate of germination to temperature and the identification of the characteristics under genotypic control. J Exp Bot 39:935–950CrossRefGoogle Scholar
Fischer, AJ, Moechnig, M, Mutters, RG, Hill, JE, Greer, C, Espino, L, Eckert, JW (2009) Managing herbicide resistance using alternative rice stand establishment techniques. Pages 459–464in Herbology and Biodiversity for a Sustainable Agriculture, XII Congress of the Spanish Weed Science Society (SEMh)–XIX Congress of the Latin American Weed Science Association (ALAM)–II Iberian Weed Science Congress (IBCM), Lisbon, 10–13 November 2009. Volume 2, Lisbon: ISA PressGoogle Scholar
Forcella, F, Arnold, RLB, Sanchez, R, Ghersa, CM (2000) Modeling seedling emergence. Field Crops Res 67:123–139CrossRefGoogle Scholar
Fox, TC, Kennedy, RA, Rumpho, ME (1994) Energetics of plant-growth under anoxia—metabolic adaptations of Oryza sativa and Echinochloa phyllopogon. Ann Bot 74:445–455CrossRefGoogle Scholar
Graziani, A, Steinmaus, SJ (2009) Hydrothermal and thermal time models for the invasive grass, Arundo donax. Aquat Bot 90:78–84CrossRefGoogle Scholar
Gummerson, RJ (1986) The effect of constant temperatures and osmotic potentials on the germination of sugar beet. J Exp Bot 37:729–741CrossRefGoogle Scholar
Heap, I (2019) The International Survey of Herbicide Resistant Weeds. www.weedscience.org/in.asp. Accessed: February 5, 2019Google Scholar
Huarte, HR, Benech-Arnold, RL (2010) Hormonal nature of seed responses to fluctuating temperatures in Cynara cardunculus (L.). Seed Sci Res 20:39–45CrossRefGoogle Scholar
Ismail, BS, Mansor, N, Rahman, MM (2007) Factors affecting germination and emergence of Cyperus difformis L. seeds. Malays Appl Biol 36:41–45Google Scholar
Kebreab, E, Murdoch, AJ (1999) Modelling the effects of water stress and temperature on germination rate of Orobanche aegyptiaca seeds. J Exp Bot 50:655–664CrossRefGoogle Scholar
Kuk, YI, Kim, KH, Kwon, OD, Lee, DJ, Burgos, NR, Jung, S, Guh, JO (2004) Cross-resistance pattern and alternative herbicides for Cyperus difformis resistant to sulfonylurea herbicides in Korea. Pest Manag Sci 60:85–94CrossRefGoogle ScholarPubMed
Legates, DR, McCabe, GJ (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resourc Res 35:233–241CrossRefGoogle Scholar
Linquist, BA, Fischer, AJ, Godfrey, L, Greer, C, Hill, J, Koffler, K, Moeching, M, Mutters, R, van Kessel, C (2008) Minimum tillage could benefit California rice farmers. Calif Agric 62:24–29CrossRefGoogle 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 58:216–222CrossRefGoogle Scholar
Mayer, DG, Butler, DG (1993). Statistical validation. Ecol Model 68:21–32CrossRefGoogle Scholar
Merotto, A, Jasieniuk, M, Fischer, AJ (2010). Distribution and cross-resistance patterns of ALS-inhibiting herbicide resistance in smallflower umbrella sedge (Cyperus difformis). Weed Sci 58:22–29CrossRefGoogle Scholar
Merotto, A, Jasieniuk, M, Osuna, MD, Vidotto, F, Ferrero, A, Fischer, AJ (2009). Cross-resistance to herbicides of five ALS-inhibiting groups and sequencing of the ALS gene in Cyperus difformis L. J Agric Food Chem 57:1389–1398CrossRefGoogle ScholarPubMed
Michel, BE (1983) Evaluation of the water potentials of solutions of polyethylene glycol 8000 both in the absence and presence of other solutes. Plant Physiol 72:66–70CrossRefGoogle ScholarPubMed
Park, KW, Mallory-Smith, CA, Ball, DA, Mueller-Warrant, GW (2004) Ecological fitness of acetolactate synthase inhibitor-resistant and -susceptible downy brome (Bromus tectorum) biotypes. Weed Sci 52:768–773CrossRefGoogle Scholar
Pedroso, RM, Al-Khatib, K, Alarcón-Reverte, R, Fischer, AJ (2016) A psbA mutation (Val219 to Ile) causes resistance to propanil and increased susceptibility to bentazon in Cyperus difformis. Pest Manag Sci 72:1673–1680CrossRefGoogle ScholarPubMed
Pedroso, RM, Al-Khatib, K, Hanson, BD, Fischer, AJ (2017) A high-throughput, modified ALS activity assay for Cyperus difformis and Schoenoplectus mucronatus seedlings. Pestic Biochem Physiol 135:78–81CrossRefGoogle ScholarPubMed
Pedroso, RM, Dourado Neto, D, Victoria Filho, R, Fischer, AJ, Al-Khatib, K (2019) Modeling germination of smallflower umbrella sedge (Cyperus difformis L.) seeds from rice fields in California across suboptimal temperatures. Weed Technol 33:733–738CrossRefGoogle Scholar
Purrington, CB, Bergelson, J (1999) Exploring the physiological basis of costs of herbicide resistance in Arabidopsis thaliana. Am Nat 154:s82–s91CrossRefGoogle ScholarPubMed
Ritchie, JT, NeSmith, DS (1991) Temperature and crop development. Pages 5–30 in Hanks, J, Ritchie, JT, eds. Modeling Plant and Soil Systems (Agronomy Monograph 31). Madison, WI: ASA, CSSA, SSSAGoogle Scholar
Roman, ES, Thomas, AG, Murphy, SD, Swanton, CJ (1999) Modeling germination and seedling elongation of common lambsquarters (Chenopodium album). Weed Sci 47:149–155CrossRefGoogle Scholar
Rundel, PW, Jarrell, WM (1989) Water in the environment. Pages 29–56in Pearcy, RW, Ehlringer, J, Mooney, HA, Rundel, PW, eds. Plant Physiological Ecology: Field Methods and Instrumentation. London: Chapman and HallCrossRefGoogle Scholar
Sanders, BA (1994) The life-cycle and ecology of Cyperus difformis (rice weed) in temperate Australia—a review. Aust J Exp Agric 34:1031–1038CrossRefGoogle Scholar
Singer, MJ, Munns, DN (2002) Soils: An Introduction. 5th ed. London, UK: Pearson International. 429 pGoogle Scholar
Spokas, K, Forcella, F (2006) Estimating hourly incoming solar radiation from limited meteorological data. Weed Sci 54:182–189CrossRefGoogle Scholar
Steinmaus, SJ, Prather, TS, Holt, JS (2000) Estimation of base temperatures for nine weed species. J Exp Bot 51:275–286CrossRefGoogle ScholarPubMed