Volume 5, Issue 2 ((Autumn & Winter) 2019)                   Iranian J. Seed Res. 2019, 5(2): 1-13 | Back to browse issues page


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Porali F, Ghaderi-Far F, Soltani E, Palevani M H. Comparison of Different Models for Determining Time up to 50% Maximum Germination: A Case Study of Cottonseeds (Gossypium hirsutum). Iranian J. Seed Res.. 2019; 5 (2) :1-13
URL: http://yujs.yu.ac.ir/jisr/article-1-340-en.html
Gorgan University of Agricultural Science and Natural Resources , farshidghaderifar@yahoo.com
Abstract:   (4082 Views)
DOR: 98.1000/2383-1251.1397.5. 1.10.2.1605.41

Extended abstract
Introduction: Germination speed is one of the most important germination indices, used in most studies to compare the effects of different treatments on seed germination. Researchers use the reverse time up to 50% maximum germination (1/D50) to calculate the germination rate. One of the methods used for calculating the D50 is the utilization of nonlinear regression models such as Logestic, Gompertz, Richard, Weibull and Hill. In addition, for the purpose of calculating this parameter, simple empirical models such as the model presented by Farooq et al. and Ellis and Roberts are used. The question which arises is which of these methods has more precision predicting D50. The purpose of this study was to calculate D50, using different methods in seed germination of cotton.
Material and Methods: In this experiment, cottonseeds were placed at three temperatures of 15, 25 and 40°C with three replications, and germinated seeds were counted daily several times. To calculate D50, several nonlinear regression models including Gompertze, Logestic, Hill (the four-parameter), Richard and Weibull models were used. Moreover, for the purpose of calculating D50, the models presented by Farooq et al. and Ellis and Roberts were used.
Results: The results showed that all nonlinear regression models exhibited suitable fit to germination data. However, logestic, Hill and Weibull showed better predictability of D50, compared with other models. Besides, D50 calculated by the Farooq model was similar to that estimated by nonlinear regression models, whereas D50 estimated by the Ellis and Roberts model was higher than that estimated by other models.
Conclusions: The results of this study showed that both non-linear regression models and the model developed by Farooq could be used to calculate D50 of cottonseed. In general, the results of this study showed that nonlinear regression models could be used to calculate D50. In this research, Logestic, Hill, and Weibull showed good fit for cumulative seed germination data of cotton seeds versus time at different temperatures. These models have coefficients that have a biological concept that includes maximum germination percentage, time to 50% maximum germination and time to start germination. Moreover, when researchers only seek to measure D50 and are not familiar with the statistical software, they can use the empirical formula presented in this research.
 
Highlights:
  1. Calculating D50 in cottonseeds, using different methods.
  2. Using nonlinear regression models to calculate D50 in cottonseeds.
  3. Developing a proper method which is more accurate, and better lends itself to calculating D50 of cottonseeds.
Full-Text [PDF 500 kb]   (586 Downloads)    
Type of Study: Research | Subject: General
Received: 2018/04/28 | Accepted: 2018/10/28

References
1. Bailly, C., Benamar, A., Corbineau, F., and Côme, D. 2000. Antooxidant systems in sunflower (Helianthus annuus L.) seeds as affected by priming. Seed Science Research, 10: 35-42. [DOI:10.1017/S0960258500000040]
2. Cheng, C., and Gordon, I.L. 2000. The Richards function and quantitative analysis of germination and dormancy in meadow foam (Limnanthes alba). Seed Science Research, 10: 265-277. [DOI:10.1017/S0960258500000295]
3. Collbach, N., and Durr, C. 2003. Effects of seed production and storage conditions on black grass (Alopecurus myosuroides) germination and shoot elongation. Weed Science, 51(5): 708-718. [DOI:10.1614/P2002-051]
4. Dahlquist, R.M., Prather, T.S., and Stapleton, J.J. 2007. Time and temperature requirements for weed seed thermal death. Weed Science, 55(6): 619-655. [DOI:10.1614/WS-04-178.1]
5. El-Kassaby, Y.A., Moss, I., Kolotelo, D., and Stoehr, M. 2008. Seed germination: Mathematical representation and parameters extraction. Forest Science, 54: 220-227.
6. Ellis, R.H., and Roberts, E.H. 1980. Seed Production. Butterworths, London. 605-635 [PMID] [PMCID]
7. Farooq, M., Basra, S.M.A., Hafeez, K., and Warriach, E.A. 2004. Influence of high and low temperature treatments on the seed germination and seedling vigor of coarse and fine rice. International Rice Research Notes, 29: 69-71.
8. Galindez, G., Seal, C.E., Daws, M.I., Lindow, L., Ortega-Baes, P. and Pritchard, H.W. 2016. Alternative temperature combined with darkness resets base temperature for germination (Tb) in photoblastic seeds of Lippia and Aloysia (Verbenaceae). Plant Biology, 19: 41-45. [DOI:10.1111/plb.12449] [PMID]
9. Gan, Y., Stobbe, E.H., and Njue, C. 1996. Evaluation of selected nonlinear regression models in quantifying seedling emergence rate of spring wheat. Crop Science, 36: 165-168. [DOI:10.2135/cropsci1996.0011183X003600010029x]
10. Ghaderi-Far, F., Alimagham, S.M., Kameli, A.M., and Jamali, M. 2012. Isabgol (Plantago ovata Forsk) seed germination and emergence as affected by environmental factors and planting depth. International Journal of Plant Production, 6: 185-194.
11. Ghaderi-Far., F., and Soltani, E. 2015. Evaluation of seed germination in sesame genotypes in response to temperature: determination of cardinal temperatures and thermal tolerance. Iranian Journal of Field Crop Science, 46: 473-483 [In Persian with English Summary].
12. Hacisalihoglu, G., Taylor, A.G., Paine, D.H., Hilderbrand, M.B., and Khan, A.A. 1999. Embryo elongation and germination rates as sensitive indicators of lettuce seed quality: Priming and aging studies. Hortscience, 34(7): 1240-1243. [DOI:10.21273/HORTSCI.34.7.1240]
13. 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]
14. Jafar, M.Z., Farooq, M., Cheema, M.A., Afzal, I., Basra, S. M.A., Wahid, M. A., Aziz, T., and Shahid, M. 2012. Improving the performance of wheat by seed priming under saline conditions. Journal of Agronomy and Crop Science, 198(1): 38-45. [DOI:10.1111/j.1439-037X.2011.00485.x]
15. Joosen, R.V.L., Kodde, J., Willems, L.A.J., Ligterink, W., Van der Plas, H.W., and Hilhorst, H.W.M. 2010. Germinator: a software package for high-throughput scoring and curve fitting of Arabidopsis seed germination. The Plant Journal, 62(1): 148-159. [DOI:10.1111/j.1365-313X.2009.04116.x] [PMID]
16. Kibinza, S., Bazin, J., Bailly, C., Farrant, J.M., Francoise Corbineau, F., and El-Maarouf-Bouteau, H. 2011. Catalase is a key enzyme in seed recovery from ageing during priming. Plant Science, 181(3): 309-315. [DOI:10.1016/j.plantsci.2011.06.003] [PMID]
17. 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]
18. Loddo, D., Masin, R., Otto, S., and Zanin, G. 2012. Estimation of base temperature for Sorghum halepense rhizome sprouting. Weed Research, 52(1): 42-49. [DOI:10.1111/j.1365-3180.2011.00886.x]
19. 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(1): 60-66.
20. Lu, J.J., Zhou, Y.M., Tan, D.Y., Baskin, C.C., and Baskin, J.M. 2015. Seed dormancy in six cold desert Brassicaceae species with indehiscent fruits. Seed Science Research, 25(3): 276-285. [DOI:10.1017/S0960258515000215]
21. Maguire, J.D. 1962. Speed of germination-aid in selection and evaluation for seedling emergence and vigor. Crop Science, 2(2): 176-177. [DOI:10.2135/cropsci1962.0011183X000200020033x]
22. Masin, R. Onofri, A., Gasparini, V., and Zanin, G. 2017. Can alternating temperatures be used to estimate base temperature for seed germination?. Weed Research, 57(6): 390-398. [DOI:10.1111/wre.12270]
23. Mavi, K., Powell, A.A., and Matthews, S. 2016. Rate of radicle emergence and leakage of electrolytes provide quick predictions of percentage normal seedlings in standard germination tests of radish (Raphanus sativus). Seed Science and Technology, 17: 393-409. [DOI:10.15258/sst.2016.44.2.12]
24. Rebetzke, G.J., and Richards, R.A. 1999. Genetic improvement of early vigor in wheat. Australian Journal of Agricultural Research, 50: 291-301. [DOI:10.1071/A98125]
25. Shafii, B., Price, W.J., Swensen, J. B., and Murray, G. A. 1991. Nonlinear estimation of growth curve models for germination data analysis," in Proceedings of the 1991 Kansas State University Conference on Applied Statistics in Agriculture, Milliken, G.A., and Schwenke, J.R. (eds.), Manhattan, KS: Kansas State University, 19-42. [DOI:10.4148/2475-7772.1415]
26. Soltani, A. 2007. Application of SAS in statistical analysis. Second edition., Jehad-e-Daneshgahi Mashhad Press, Mashhad, Iran, 182 p. [In Persian].
27. Soltani, A., and Galeshi, S. 2002. Importance of rapid canopy closure for wheat production in a temperate sub-humid environment: experimentation and simulation. Field Crops Research, 77(1): 17-30. [DOI:10.1016/S0378-4290(02)00045-X]
28. Soltani, A., Galeshi, S., Zeinali, E., and Latifi, N. 2002. Germination, seed reserve utilization and seedling growth of chickpea as affected by salinity and seed size. Seed Science and Technology, 30(1): 51-60.
29. Soltani, A., Zeinali, E., Galeshi, S., and Latifi, N. 2001.Genetic variation for and interrelationships among seed vigor traits in wheat from the Caspian Sea coast of Iran. Seed Science and Technology, 29: 653-662.
30. Soltani, E., Ghaderi-Far, F., Baskin, C.C., and Baskin, J.M. 2015. Problems with using mean germination time to calculate rate of seed germination. Australian Journal of Botany, 63(8): 631-635. [DOI:10.1071/BT15133]
31. Soltani, E., Soltani, A., and Oveisi, M. 2013. Modeling seed aging effect on wheat seedling emergence in drought stress: Optimizing germin program to predict emergence pattern. Crops Improvement, 15: 147-160 [In Persian with English Summery].
32. 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(11): 2016-2021. [DOI:10.1590/0103-8478cr20131341]
33. Tahmasbi, B., Ghaderi-Far., F., Sadeghipour, H.R., and Galeshi, S. 2015. Impacts of accelerated aging on germination parameters, fatty acids and lipid hydroperoxides of sunflower (Helianthus annuus L.) seeds. Journal of Plant Process and Function, 4: 73-83 [In Persian with English Summary].
34. Tjørve, K.M.C., and Tjørve, E. 2017. A proposed family of Unified models for sigmoidal growth. Ecological Modeling, 359: 117-127. [DOI:10.1016/j.ecolmodel.2017.05.008]

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