عنوان مقاله [English]
The models based on thermal-time concept are useful tools for predicting germination in relation to time and temperature. In this study, conducted in 2016 at Ramin Agriculture and Natural Resources University, thermal-germination model was developed based on seven probability distribution function (Logistic, Weibull, Gumbel, Loglogistic, Inverse-Normal, Log-Normal and Gamma) and predicted germination time courses by these models for two spring oilseed rape cultivars (RGS003, Sarigol) were compared with the Normal thermal-germination outputs. Germination test were conducted at eleven constant temperature regimes of 8, 12, 16, 20, 24, 28, 32, 33, 34, 35 and 36 ºC. Results indicated that the Log-Normal thermal-germination model gave best fit to germination time courses of both cvs. RGS003 (AICc=-1173) and Sarigol (AICc=-1180). Based on the outputs of this model, base temperature for germination of cvs. RGS003 and Sarigol were estimated to be 5.85 and 5.60 ºC, respectively. The suboptimal thermal-time to initiate germination were predicted as 118.40 ºC h in cv. RGS003 and 120.00 ºC h in cv. Sarigol, While thermal-time required to complete germination at supra-optimal temperatures were estimated to be 29.07 ºC h in cv. RGS003 and 31.47 ºC h in cv. Sarigol. Also, both oilseed rape cultivars showed thermoinhibition beyond averaged temperature of 33.17 ºC. Estimated parameters in this study can be used in crop simulation models.
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. & 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. & Bradford, K. J. (2015). Molecular and hormonal regulation of thermoinhibition of seed germination. In J. V. Anderson (Ed), Advances in Plant Dormancy. (pp. 3-33). Springer International Publishing Switzerland.
13. Mesgaran, M. B., Mashhadi, H. R., Alizadeh, H., Hunt, J., Young, K. R. & Cousens, R. D. (2013). Importance of distribution function selection for hydrothermal time models of seed germination. Weed Research, 53(2), 89-101.
14. Mesgaran, M. B., Rahimian Mashhadi, H. R., Alizadeh, H., Ohadi, S. & 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 Farsi)
15. Soltani, E., Oveisi, M., Soltani, A., Galeshi, S., Ghaderifar, F. & Zeinali, E. (2014). Seed germination modeling of volunteer canola as affected by temperature and water potential: hydrothermal time model. Iranian Journal of Weed Research, 6(1), 23-38. (In Farsi)
16. Soltani, A., Robertson, M. J., Torabi, B., Yousefi-Daz, M. & Sarparast, R. (2006). Modelling seedling emergence in chickpea as influenced by temperature and sowing depth. Agricultural and Forest Meteorology, 138(1-4), 156-167.
17. Watt, M. S., Bloomberg, M., & Finch-Savage, W. E. (2011). Development of a hydrothermal time model that accurately characterises how thermoinhibition regulates seed germination. Plant, Cell & Environment, 34(5), 870–876.
18. Watt, M. S., Xu, V. & Bloomberg, M. (2010). Development of a hydrothermal time seed germination model which uses the Weibull distribution to describe base water potential. Ecological Modelling, 221(9), 1267–1272.