1. Collbach, N. and Durr, C. 2003. Effects of seed production and storage conditions on blackgrass (Alopecurus myosuroides) germination and shoot elongation. Weed Science, 51: 708-718. [
DOI:10.1614/P2002-051]
2. Cristaudo, A., Catara, S., Mingo, A., Restuccia, A. and Onofri, A. 2019. Temperature and storage time strongly affect the germination success of perennial Euphorbia species in Mediterranean regions. Ecology and Evolution, 9: 10984-10999. [
DOI:10.1002/ece3.5535] [
PMID] [
PMCID]
3. El-Kassaby, Y.A., Moss, I., Kolotelo, D. and Stoehr, M. 2008. Seed germination: Mathematical representation and parameters extraction. Forest Science, 54(1): 220-227.
4. Gorzin, M., Ghaderi-Far, F., Sadeghipour, H.R. and Zeinali, E. 2020. Induced thermo-dormancy in rapeseed (Brassica napus L.) cultivars by sub-and supra-optimal temperatures. Journal of Plant Growth Regulation, 40(5): 1-14. [
DOI:10.1007/s00344-020-10266-2]
5. Gresta, F., Avola, G., Onofri, A., Anastasi, U. and Cristaudo, A. 2011. When does hard coat impose dormancy in legume seeds Lotus and Scorpiurus case study. Crop Science, 51(4): 1739-1747. [
DOI:10.2135/cropsci2010.12.0700]
6. Haj SeyedHadi, M.R., and Gonzalez-Andujar, J.L. 2009. Comparison of fitting weed seedling emergence models with nonlinear regression and genetic algorithm. Computers and Electronics in Agriculture, 65(1): 19-25. [
DOI:10.1016/j.compag.2008.07.005]
7. Humplík, J.F., Dostál, J., Ugena, L., Spíchal, L., De Diego, N., Vencálek, O. and Fürst, T. 2020. Bayesian approach for analysis of time-to-event data in plant biology. Plant Methods, 16(1): 1-7. [
DOI:10.1186/s13007-020-0554-1] [
PMID] [
PMCID]
8. Hunter, E. A., Glasbey, C. A., and Naylor, R. E. 1984. The analysis of data from germination tests. Journal of Agricultural Science, 102: 207- 213. [
DOI:10.1017/S0021859600041642]
9. Karami, H. 2016. An alternative model to quantifying corn seed germination to temperature and water potential. M.Sc. dissertation, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources, Iran. [In Persian with English Summary].
10. Lawson, A.N., Van Acker, R.C. and Friesen, L. F. 2006. Emergence timing of volunteer canola in spring wheat fields in Manitoba. Weed Science, 54: 873-882. [
DOI:10.1614/WS-05-169.I.1]
11. Loddo, D., Ghaderi-Far, F., Rastegar, Z. and Masin, R. 2018. Base temperatures for germination of selected weed species in Iran. Plant Protection Science, 54: 60-66. [
DOI:10.17221/92/2016-PPS]
12. McNair, J.N., Sunkara, A. and Frobish, D. 2012. How to analyze seed germination data using statistical time-to-event analysis: non-parametric and semi-parametric methods. Seed Science Research, 22(2): 77-95. [
DOI:10.1017/S0960258511000547]
13. Mostafalou, M. 2011. Effect of nitric oxide, cyanide and moist chilling on the alleviation of dormancy in Persian walnut kernels. M.Sc. dissertation, Faculty of Biology, Golestan University, Iran. [In Persian with English Summary].
14. Onofri, A., Benincasa, P., Mesgaran, M.B. and Ritz, C. 2018. Hydrothermal-time-to-event models for seed germination. European Journal of Agronomy, 101: 129-139. [
DOI:10.1016/j.eja.2018.08.011]
15. Onofri, A., Gresta, F. and Tei, F. 2010. A new method for the analysis of germination and emergence data of weed species. Weed Research, 50(3): 187-198. [
DOI:10.1111/j.1365-3180.2010.00776.x]
16. Onofri, A., Mesgaran, M.B., Neve, P. and Cousens, R.D. 2014. Experimental design and parameter estimation for threshold models in seed germination. Weed Research, 54(5): 425-435. [
DOI:10.1111/wre.12095]
17. Porlali, F. 2015. Modeling the effect of temperature on germination of cotton varieties: the different methods accuracy's comparison of germination rate calculation to determine cardinal temperatures. M.Sc. dissertation, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources, Iran. [In Persian with English Summary].
18. Porlali, F., Ghaderi_Far, F., Soltani, E., Pahlevani, M.H. 2019. Comparison of different models for determining time up to 50% maximum germination: a case study of cottonseeds (Gossypium hirsutum) . Iranian Journal of Seed Research, 5(2): 1-13. [In Persian with English Summary]. [
DOI:10.29252/yujs.5.2.1]
19. Pournik, S., Abbasi-Rostami, M., Sadeghipour, H.R. and Ghaderi-Far, F. 2019. True lipases beside phospholipases contribute to walnut kernel viability loss during controlled deterioration and natural aging. Environmental and Experimental Botany, 164: 71-83. [
DOI:10.1016/j.envexpbot.2019.04.016]
20. Rabani, R. 2013. Seed vigor tests for predicting seedling emergence of wheat seed lots in field. M.Sc. dissertation, Faculty of Plant Production, Gorgan University of Agricultural Sciences and Natural Resources, Iran. [In Persian with English Summary].
21. Ritz, C., Pipper, C., Yndgaard, F., Fredlund, K. and Steinrucken, G. 2010. Modelling flowering of plants using time-to-event methods. European Society for Agronomy, 32: 155-161. [
DOI:10.1016/j.eja.2009.10.002]
22. Ritz, C., Pipper, C.B. and Streibig, J.C. 2013. Analysis of germination data from agricultural experiments. European Society for Agronomy, 45: 1-6. [
DOI:10.1016/j.eja.2012.10.003]
23. Romano, A. and Stevanato, P. 2020. Germination data analysis by time-to-event approaches. Plants, 9(5): 617-617. [
DOI:10.3390/plants9050617] [
PMID] [
PMCID]
24. Rubinstein, R.Y. and Kroese, D.P. 2016. Simulation and the Monte Carlo method (Vol. 10). John Wiley & Sons. [
DOI:10.1002/9781118631980]
25. Scott, S.J. and Jones, R.A. 1982. Low temperature seed germination of Lycopersicon species evaluated by survival analysis. Euphytica, 31(3): 869-883. [
DOI:10.1007/BF00039227]
26. Sousa, I.F., Neto, J.E.K., Muniz, J.A., Guimarães, R.M., Savian, T.V. and Muniz, F.M. 2014. Fitting nonlinear autoregressive models to describe coffee seed germination. Ciência Rural, 44: 2016-2021. [
DOI:10.1590/0103-8478cr20131341]
27. Tjørve, K.M.C. and Tjørve, E. 2017. A proposed family of unified models for sigmoidal growth. Ecological Modelling, 359: 117-127. [
DOI:10.1016/j.ecolmodel.2017.05.008]