Application of Thermal-time Concept to Modeling Oilseed Rape (Brassica napus L.) Seed Germination Response to Temperature

Document Type : Research Article

Authors

• A Derakhshan ¹
• A Bakhshandeh ¹
• S. A Siadat ¹
• M. R Moradi Telavat ¹
• B Andarzian ²

¹ Khuzestan Agricultural Sciences and Natural Resources University

² Research and Education Center of Agricultural and Natural Resources of Khuzestan

10.22067/gsc.v16i1.60457

Abstract

Introduction
In seed plants, seed germination is one of the important life history events, because it determines the time when a new life cycle is initiated. Temperature (T) is one of the most important environmental determinants of capacity and rate of germination. Base, optimum and ceiling T (cardinal temperatures) characterize the limit of this environmental factor over which the germination of a particular species can occur. The thermal-time approach has been successful in describing germination time courses in response to T, and most models predicting crop phenological development use a thermal-time scale to normalize for T variation over time. A clear understanding of the seed germination patterns is helpful in screening for tolerance of crops and cultivars to either low or high temperatures and in identifying geographical areas where a species or genotype can germinate and establish successfully by using the critical lower and upper temperatures for germination. Information on cardinal temperatures is lacking for germination of spring oilseed rape (Brassica napus L.), as one of the world’s major oilseed crops. The aim of the present work was to evaluate the relative accuracy of different thermal-time approaches for the description of germination in three cultivars of spring oilseed rape.
Materials and Methods
Germination responses of three spring oilseed rape cultivars were investigated at different constant temperatures. The seeds were incubated in the dark using germinators with controlled environments at eleven constant T regimes of 8, 12, 16, 20, 24, 28, 32, 33, 34, 35 and 36 ºC with a range of ±0.2 ºC over a 21-day period. These T regimes cover both the sub- and supra-optimal T ranges. The trial was replicated three times with 4 Petri-dishes in each replication, for a total of 12 Petri-dishes for each cultivar at each T regime. The germinated seeds (criterion, radicle protrusion of > 2 mm) were counted and removed at frequent time intervals (every 4-8 h). Germination counts at each replicate of each T regime were pooled by cultivar across trials for data analysis. Cumulative germination percentage was calculated for every cultivar and T regime for every count-hour. The time taken for cumulative germination to reach subpopulation percentiles of 10, 50 and 90% of maximum in each T regime were calculated by interpolation from the progress of germination (%) versus time curve. Experimentally obtained cumulative-germination curves were used to perform a non-linear regression procedure to assess the relative accuracy of different thermal-germination models in predicting germination response under constant incubation temperatures. Assessment of goodness-of-fit was performed by the Akaike information criterion (AIC).
Results and Discussion
The most accurate approach for simulating the thermal-germination response of all three spring oilseed rape cultivars achieved by assuming a normal distribution of both thermal-time required to complete the germination of each given seed fraction in sub-optimal T range (θT(g)) and maximum germination temperatures (Tm(g)), while base T (Tb) or supra-optimal thermal-time (θTm) were considered constant for the entire population. According to this model, the base T for different cultivars ranged from 5.66 (cv. Sarigol) to 7.13 ºC (cv. Dalgan). Estimated θTm varied between 31.62 to 34.55 ºC h for different spring oilseed rape cultivars. A θT(50) of 369.27 ºC h and a Tm(50) of 34.32 ºC were identified for seed population of cv. Sarigol. The θT(50) was estimated to be 378.76 ºC h for cv. Dalgan and 357.89 ºC h for cv. RGS003. The Tm(50) for germination of cv. Dalgan and cv. RGS003 was estimated to be 33.98 and 34.42 ºC, respectively. In all three cultivars, calculated values for optimum T (To) were not constant across subpopulations. The To(50) was estimated to be 31.85 ºC for cv. Sarigol, 31.78 ºC for cv. Dalgan and 32.06 ºC for cv. RGS003. Thermal-time analysis, although an empirical method, is considered by many researchers to have physiologically and ecologically relevant parameters and, in its standard form, provides several useful indices of seed germination behavior in response to T. Despite its popularity, the generality of its assumptions has not been examined systematically. If these assumptions do not hold, at least approximately, in a particular situation, misleading interpretations can easily arise.
Conclusions
The thermal thresholds for seed germination identified in this study explain the differences in seed germination detected among populations of different spring oilseed rape cultivars. The thermal-time model described here gave an acceptable explanation of the observed seed germination patterns.

Keywords

• Akaike information criterion
• Cardinal temperatures
• Cumulative distribution function
• Normal Distribution
• Thermal units

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References

1. Alvarado, V., and Bradford, K. J. 2002. A hydrothermal time model explains the cardinal temperatures for seed germination. Plant, Cell & Environment 25 (8): 1061-1069.

2. Baskin, C. C., and Baskin, J. M. 1998. Seeds: ecology, biogeography, and evolution of dormancy and germination. San Diego, CA: Academic Press.

3. Bradford, K. J. 2002. Applications of hydrothermal time to quantifying and modeling seed germination and dormancy. Weed Science 50 (2): 248-260.

4. Burnham, K. P., and Anderson, D. R. 2002. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. Springer, New York, USA.

5. Chantre, G. R., Batlla, D., Sabbatini, M. R., and Orioli, G. 2009. Germination parameterization and development of an after-ripening thermal-time model for primary dormancy release of Lithospermum arvense seeds. Annals of Botany 103 (8): 1291-1301.

6. Covell, S., Ellis, R. H., Roberts, E. H., and Summerfield, R. J. 1986.The influence of temperature on seed germination rate in grain legumes. I. A comparison of chickpea, lentil, soybean, and cowpea at constant temperatures. Journal of Experimental Botany 37 (5): 705-715.

7. Ellis, R. H., Covell, S., Roberts, E. H., and Summerfield, R. J. 1986. The influence of temperature on seed germination rate in grain legumes. II. Intraspecific variation in chickpea (Cicer arietinum L.) at constant temperatures. Journal of Experimental Botany 37 (10): 1503-1515.

8. Forcella, F., Benech-Arnold, R. L., Sanchez, R., and Ghersa, C. M. 2000.Modelling seedling emergence. Field Crops Research 67 (2): 123-139.

9. Garcia-Huidobro, J., Monteith, J. L., and Squire, G. R. 1982. Time, temperature and germination of pearl millet (Pennisetum typhoides S. & H.). I. Constant temperature. Journal of Experimental Botany 33 (2): 288-296.

10. Hardegree, S.P. 2006. Predicting germination response to temperature. III. Model validation under field-variable temperature conditions. Annals of Botany 98 (4): 827-834.

11. Hardegree, S. P., and Van Vactor, S. S. 2000.Germination and emergence of primed grass seeds under field and simulated-field temperature regimes. Annals of Botany 85 (3): 379-390.

12. Huo, H., and Bradford, K. J. 2015. Molecular and hormonal regulation of thermoinhibition of seed germination. PP 3-33 in J.V. Anderson ed. Advances in Plant Dormancy. Springer International Publishing Switzerland.

13. Jafari, N., Esfahani, M., and Sabouri, A. 2012. Assessment of non-linear regression models to evaluate response of seedling emergence rate to temperature in three oil seed rape seed cultivars. Iranian Journal of Field Crop Science 42 (4): 857-868. (in Persian with English abstract).

14. Mesgaran, M. B., Rahimian Mashhadi, H. R., Alizadeh, H., Ohadi, S., and Zare, A. 2014. Modeling the germination responses of wild barley (Hordeum spontaneum) and littleseed cannary grass (Phalaris minor) to temperature. Iranian Journal of Weed Science 9 (2): 105-118. (in Persian with English abstract).

15. Nascimento, W. M., Huber, D. J., and Cantliffe, D. J. 2013. Carrot seed germination and respiration at high temperature in response to seed maturity and priming. Seed Science and Technology 41 (1): 164-169.

16. Qiu, J., Bai, Y., Coulman, B., and Romo, J. T. 2006. Using thermal time models to predict seedling emergence of orchardgrass (Dactylis glomerata L.) under alternating temperature regimes. Seed Science Research 16 (4): 261-271.

17. Soltani, A., and Sinclair T. R. 2011. A simple model for chickpea development growth and yield. Field Crops Research 124 (2): 252-260.

18. Soltani, A., Robertson, M. J., Torabi, B., Yousefi-Daz, M., and Sarparast, R. 2006. Modelling seedling emergence in chickpea as influenced by temperature and sowing depth. Agricultural and Forest Meteorology 138 (1-4): 156-167.

19. Steadman, K. J. 2004. Dormancy release during hydrated storage in Lolium rigidum seeds is dependent on temperature, light quality, and hydration status. Journal of Experimental Botany 55 (398): 929-937.

20. Wang, R., Bai, Y., and Tanino, K. 2004. Effect of seed size and sub-zero imbibitions temperature on the thermal time model of winterfat (Eurotia lanata (Pursh) Moq.). Environmental and Experimental Botany 51 (3): 183-197.

21. Zhang, H., McGill, C. R., Irving, L. J., Kemp, P. D., and Zhou, D. 2012. A modified thermal time model to predict germination rate of ryegrass and tall fescue at constant temperatures. Crop Science 53 (1): 240-249.