Hostname: page-component-848d4c4894-sjtt6 Total loading time: 0 Render date: 2024-06-30T14:22:44.608Z Has data issue: false hasContentIssue false

Predicting seed dormancy loss and germination timing for Bromus tectorum in a semi-arid environment using hydrothermal time models

Published online by Cambridge University Press:  01 December 2009

Susan E. Meyer*
Affiliation:
US Forest Service, Rocky Mountain Research Station, Shrub Sciences Laboratory, Provo, Utah, USA
Phil S. Allen
Affiliation:
Department of Plant and Wildlife Sciences, Brigham Young University, Provo, Utah, USA
*
*Correspondence Fax: +1 801-375-6968 E-mail: smeyer@fs.fed.us

Abstract

A principal goal of seed germination modelling for wild species is to predict germination timing under fluctuating field conditions. We coupled our previously developed hydrothermal time, thermal and hydrothermal afterripening time, and hydration–dehydration models for dormancy loss and germination with field seed zone temperature and water potential measurements from early summer through autumn to develop predictions of germination timing for Bromus tectorum at a semi-arid site in north-central Utah, USA. Model predictions were tested with a validation dataset based on concomitant seed retrieval experiments in 2 years. Predictions were generally in agreement with observed field germination time courses, even though integration across multiple precipitation events was necessary. Success of the modelling effort hinged on two factors. First, we used a soil capacitance sensor that measured seed zone (5 mm soil depth) water content accurately over a wide range. Second, simulations were built using physiologically based threshold models that can incorporate differences in germination timing for multiple germination fractions and for multiple stages of dormancy loss. Our results suggest that simulation models using hydrothermal time concepts can predict field germination phenology accurately. Seeds in this study integrated their experiences in a widely fluctuating environment in a manner consistent with the assumptions of hydrothermal time. Such threshold-based models also have the advantage of generality, as these concepts can be applied to many different species, environments and weather scenarios.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2009. This is a work of the U.S. Government and is not subject to copyright protection in the United States 2009

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

Allen, P.S., White, D.B. and Markhart, A.H. (1993) Germination of perennial ryegrass and annual bluegrass seeds subjected to hydration–dehydration cycles. Crop Science 33, 10201025.CrossRefGoogle Scholar
Allen, P.S., Debaene-Gill, S. and Meyer, S.E. (1994) Regulation of germination timing in facultatively fall-emerging grasses. pp. 215219in Monsen, S.B.; Kitchen, S.G. (Eds) Symposium on Ecology, Management, and Restoration of Intermountain Annual Rangelands, Boise, Idaho, May 18–22, 1992. Ogden, UT, USDA, Forest Service, Intermountain Research Station, Gen. Tech. Rep. INT-GTR-313.Google Scholar
Allen, P.S., Benech-Arnold, R.L., Batlla, D. and Bradford, K.J. (2007) Modeling of seed dormancy. pp. 72112in Bradford, K.J.; Nonogaki, H. (Eds) Seed development, dormancy, and germinaton. Oxford, UK, Blackwell Publishing.CrossRefGoogle Scholar
Alvarado, V. and Bradford, K.J. (2002) A hydrothermal time model explains the cardinal temperatures for seed germination. Plant, Cell and Environment 25, 10611069.CrossRefGoogle Scholar
Bair, N.B., Meyer, S.E. and Allen, P.S. (2006) A hydrothermal after-ripening time model for seed dormancy loss in Bromus tectorum L. Seed Science Research 16, 1728.CrossRefGoogle Scholar
Bauer, M.C., Meyer, S.E. and Allen, P.S. (1998) A simulation model to predict seed dormancy loss in the field for Bromus tectorum L. Journal of Experimental Botany 49, 12351244.Google Scholar
Bewley, D.J. and Black, M. (1994) Seeds: physiology of development and germination (2nd edition). New York, Plenum Press.CrossRefGoogle Scholar
Bradford, K.J. (1990) A water relations analysis of seed germination rates. Plant Physiology 94, 840849.CrossRefGoogle ScholarPubMed
Bradford, K.J. (1995) Water relations in seed germination. pp. 351396in Kigel, J.; Galili, G. (Eds) Seed development and germination. New York, Marcel Dekker Inc.Google Scholar
Bradford, K.J. and Haigh, A.M. (1994) Relationship between accumulated hydrothermal time during priming and subsequent seed germination rates. Seed Science Research 4, 110.CrossRefGoogle Scholar
Bradford, K.J., Côme, D. and Corbineau, F. (2007) Quantifying the oxygen sensitivity of seed germination using a population-based threshold model. Seed Science Research 17, 3343.CrossRefGoogle Scholar
Cheng, Z. and Bradford, K.J. (1999) Hydrothermal time analysis of tomato seed germination responses to priming treatments. Journal of Experimental Botany 50, 8999.CrossRefGoogle Scholar
Christensen, M., Meyer, S.E. and Allen, P.S. (1996) A hydrothermal time model of seed after-ripening in Bromus tectorum L. Seed Science Research 6, 155163.CrossRefGoogle Scholar
Debaene, S.B.G., Allen, P.S. and White, D.B. (1994) Dehydration of germinating perennial ryegrass seeds can alter rate of subsequent radicle emergence. Journal of Experimental Botany 45, 13011307.CrossRefGoogle Scholar
Dutta, S. and Bradford, K.J. (1994) Water relations of lettuce seed thermoinhibition. II. Ethylene and endosperm effects on base water potential. Seed Science Research 4, 1118.CrossRefGoogle Scholar
Finch-Savage, W.E., Rowse, H.R. and Dent, K.C. (2005) Development of combined imbibition and hydrothermal threshold models to simulate maize (Zea mays) and chickpea (Cicer arientinum) seed germination in variable environments. New Phytologist 165, 825838.CrossRefGoogle Scholar
Forcella, F. (1998) Real-time assessment of seed dormancy and seedling growth for weed management. Seed Science Research 8, 201209.CrossRefGoogle Scholar
Forcella, F., Benech-Arnold, R.L., Sanchez, R.A. and Ghersa, C.M. (2000) Modeling seedling emergence. Field Crops Research 67, 123139.CrossRefGoogle Scholar
Gianinetti, A. and Cohn, M.A. (2007) Seed dormancy in red rice. XII. Population-based analysis of dry-afterripening with a hydrotime model. Seed Science Research 17, 253271.CrossRefGoogle Scholar
Gummerson, R.J. (1986) The effect of constant temperatures and osmotic potentials on the germination of sugar beet. Journal of Experimental Botany 37, 729741.CrossRefGoogle Scholar
Hanks, R.J. (1992) Applied soil physics. New York, Springer-Verlag.CrossRefGoogle Scholar
Kebreab, E. and Murdoch, A.J. (1999) Modeling the effects of water stress and temperature on germination rate of Orobanche aegyptica seeds. Journal of Experimental Botany 50, 655664.CrossRefGoogle Scholar
Meyer, S.E. and Allen, P.S. (1999) Ecological genetics of seed germination regulation in Bromus tectorum L. I. Phenotypic variance among and within populations. Oecologia 120, 2734.CrossRefGoogle Scholar
Meyer, S.E., Debaene-Gill, S.B. and Allen, P.S. (2000) Using hydrothermal time concepts to model seed germination response to temperature, dormancy loss, and priming effects in Elymus elymoides. Seed Science Research 10, 213223.CrossRefGoogle Scholar
Ni, B. and Bradford, K.J. (1992) Quantitative models characterizing seed germination responses to abscisic acid and osmoticum. Plant Physiology 98, 10571068.CrossRefGoogle ScholarPubMed
Roundy, B.A., Hardegree, S.P., Chambers, J.C. and Whittaker, A. (2007) Prediction of cheatgrass field germination potential using wet thermal accumulation. Rangeland Ecology and Management 60, 613623.CrossRefGoogle Scholar
Rowse, H.R. and Finch-Savage, W.E. (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 Phytologist 158, 101108.CrossRefGoogle Scholar
Steadman, K.J. and Pritchard, H.W. (2004) Germination of Aesculus hippocastanum seeds following cold-induced dormancy loss can be described in relation to a temperature-dependent reduction in base temperature (b) and thermal time. New Phytologist 161, 415425.CrossRefGoogle ScholarPubMed
Taylor, J.R., Roundy, B.A., Allen, P.S., Meyer, S.E. and Eggett, D.L. (2007) Soil water sensor accuracy for predicting seedling emergence using a hydrothermal time model. Arid Land Research and Management 21, 229243.CrossRefGoogle Scholar
Vleeshouwers, L.M. and Bouwmeester, H.J. (2001) A simulation model for seasonal changes in dormancy and germination of seeds. Seed Science Research 11, 135148.CrossRefGoogle Scholar