سامانه پشتیبانی خدمات اطلاعات

  اخبار و اعلانات

 آمار

تعداد نشریات161
تعداد شماره‌ها6,532
تعداد مقالات70,501
تعداد مشاهده مقاله124,099,223
تعداد دریافت فایل اصل مقاله97,206,795
Aboutalebian, Mohammad Ali, Nazari, Shahram, Golzardi, Farid. (1396). Prediction of emergence of Flixweed (Descurainia sophia) and Wild Oat (Avena fatua) using thermal time models in Winter Rapeseed (Brassica napus). , 48(ویژه نامه), 55-65. doi: 10.22059/ijfcs.2017.63653
Mohammad Ali Aboutalebian; Shahram Nazari; Farid Golzardi. "Prediction of emergence of Flixweed (Descurainia sophia) and Wild Oat (Avena fatua) using thermal time models in Winter Rapeseed (Brassica napus)". , 48, ویژه نامه, 1396, 55-65. doi: 10.22059/ijfcs.2017.63653
Aboutalebian, Mohammad Ali, Nazari, Shahram, Golzardi, Farid. (1396). 'Prediction of emergence of Flixweed (Descurainia sophia) and Wild Oat (Avena fatua) using thermal time models in Winter Rapeseed (Brassica napus)', , 48(ویژه نامه), pp. 55-65. doi: 10.22059/ijfcs.2017.63653
Aboutalebian, Mohammad Ali, Nazari, Shahram, Golzardi, Farid. Prediction of emergence of Flixweed (Descurainia sophia) and Wild Oat (Avena fatua) using thermal time models in Winter Rapeseed (Brassica napus). , 1396; 48(ویژه نامه): 55-65. doi: 10.22059/ijfcs.2017.63653

Prediction of emergence of Flixweed (Descurainia sophia) and Wild Oat (Avena fatua) using thermal time models in Winter Rapeseed (Brassica napus)

علوم گیاهان زراعی ایران
مقاله 6، دوره 48، ویژه نامه، مهر 1396، صفحه 55-65    اصل مقاله (285.78 K)
نوع مقاله: مقاله پژوهشی
شناسه دیجیتال (DOI): 10.22059/ijfcs.2017.63653
نویسندگان
Mohammad Ali Aboutalebian* 1؛ Shahram Nazari2؛ Farid Golzardi3
1Assistant Professor, Department of Agronomy and Plant Breeding, Faculty of Agriculture, Bu Ali Sina University, Hamedan, Iran
2Ph. D. Candidate, Department of Agronomy and Plant Breeding, Faculty of Agriculture, Bu Ali Sina University, Hamedan, Iran
3Assistant Professor, Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
چکیده
A thermal time (TT) model was developed to simulate field emergence of two weed species (flixweed and wild oat) in winter rapeseed. Practical predictive weed emergence models can provide information about timing of weed emergence. Non-linear regression models are usually able to accurately predict field emergence under specific environmental conditions. In the present study, cumulative seedling emergence in response to TT was described by the Weibull, Logistic, Gompertz, Sigmoid and Chapman functions. Some criteria were used to describe the goodness of fit of the models. These criteria includ coefficient of determination (r2adj), root mean square of error (RMSE) and Akaike index (AIC). In both species (Descurainia sophia: RMSE= 3.65, r2adj=0.97, AIC=88.87; Avena fatua: RMSE= 2.59, r2adj=0.99, AIC=45.83) the seedling emergence flushes were well described with the Weibull four-parameter function. To start emergence after sowing and to reach maximum emergence, lower TT requirements were observed in A. fatua than in D. sophia. Seedling emergence increased steadily and reached from 50% to 90% of total emergence at 315 and 543 TT, respectively for D. sophia and 70 and 286 TT, respectively for A. fatua.
کلیدواژه‌ها
Avena fatua؛ Descurania sophia؛ Soil temperature؛ Weibull model
عنوان مقاله [English]
Prediction of emergence of Flixweed (Descurainia sophia) and Wild Oat (Avena fatua) using thermal time models in Winter Rapeseed (Brassica napus)
نویسندگان [English]
Mohammad Ali Aboutalebian1؛ Shahram Nazari2؛ Farid Golzardi3
1Assistant Professor, Department of Agronomy and Plant Breeding, Faculty of Agriculture, Bu Ali Sina University, Hamedan, Iran
2Ph. D. Candidate, Department of Agronomy and Plant Breeding, Faculty of Agriculture, Bu Ali Sina University, Hamedan, Iran
3Assistant Professor, Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran
چکیده [English]
A thermal time (TT) model was developed to simulate field emergence of two weed species (flixweed and wild oat) in winter rapeseed. Practical predictive weed emergence models can provide information about timing of weed emergence. Non-linear regression models are usually able to accurately predict field emergence under specific environmental conditions. In the present study, cumulative seedling emergence in response to TT was described by the Weibull, Logistic, Gompertz, Sigmoid and Chapman functions. Some criteria were used to describe the goodness of fit of the models. These criteria includ coefficient of determination (r2adj), root mean square of error (RMSE) and Akaike index (AIC). In both species (Descurainia sophia: RMSE= 3.65, r2adj=0.97, AIC=88.87; Avena fatua: RMSE= 2.59, r2adj=0.99, AIC=45.83) the seedling emergence flushes were well described with the Weibull four-parameter function. To start emergence after sowing and to reach maximum emergence, lower TT requirements were observed in A. fatua than in D. sophia. Seedling emergence increased steadily and reached from 50% to 90% of total emergence at 315 and 543 TT, respectively for D. sophia and 70 and 286 TT, respectively for A. fatua.
کلیدواژه‌ها [English]
Avena fatua, Descurania sophia, Soil temperature, Weibull model
مراجع
  1. Baskin, C. C. & Baskin, J. M. (1998). Seeds: ecology, biogeography and evolution of dormancy and germination. San Diego, CA: Academic Press, 666 pp.
  2. Bradford, K. J. (1990). A water relation analysis of seed germination rates. Plant Physiology, 94, 840-849.
  3. Bradford, K. J. & Nonogaki, H. (2007). Seed development, dormancy and germination. Blackwell Publishing, Oxford, U.K, 392 pp.
  4. Brown, R. F. & Mayer, D. G. (1988). Representing cumulative germination. 2. The use of the Weibull function and other empirically derived curves. Annals of Botany, 61, 127-138.
  5. Cao, R., Francisco-Fernandez, M., Anand, A., Bastida, F. & Gonzalez-Andujar, J. L. (2011). Computing statistical indices for hydrothermal times using weed emergence data. Journal of Agricultural Science, 149, 701-712.
  6. Chantre, G. R., Blanco, A. M., Lodovichi, M. V., Bandoni, A. J., Sabbatini, M. R., Lopez, R. L., Vigna, M. R. & Gigon, R. (2012a). Modeling Avena fatua seedling emergence dynamics: An artificial neural network approach. Computers and Electronics in Agriculture, 88, 95-102.
  7. Chantre, G. R., Blanco, A. M., Forcella, F., Van Acker, R. C., Sabbatini, M. R. & Gonzalez-Andujar, J. L. (2013b). A comparative study between non-linear regression and artificial neural network approaches for modeling wild oat (Avena fatua) field emergence. Journal of Agricultural Science, 152(2), 1-9.
  8. Cousens, R. & Mortimer, M. (1995). Dynamics of weed populations. Cambridge University Press, New York, NY, 21-54 pp.
  9. Ellis, R. H., Covell, S., Roberts, E. H. & 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, 1503-1515.
  10. Finch-Savage, W. E., Phelps, K., Steckel, J. R. A., Whalley, W. R. & Rowse, H. R. (2001). Seed reserve-dependent and water potential in carrot (Daucus carota L). Journal of Experimental Botany, 52, 2187-2197.
  11. Forcella, F., Benech-Arnold, R. L., Sanchez, R. & Ghersa, C. M. (2000). Modeling of seedling emergence. Field Crops Research, 67, 123-139.
  12. France, J. & Thornley, J. H. M. (1984). Mathematical models in agriculture. Oxford University Press. Butterworths, London, 335 pp.
  13. Fry, K. E. (1983). Heat-unit calculations in cotton crop and insect models. United States Department of Agriculture, Oakland, California, 23 pp.
  14. Gompertz, B. (1825). On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. Philosophical Transactions of the Royal Society,  182, 513-585.
  15. Gonzalez-Diaz, L., Leguizamon, E., Forcella, F. & Gonzalez-Andujar, J. L. (2007). Short communication. Integration of emergence and population dynamic models for long term weed management using wild oat (Avena fatua L.) as an example. Spanish Journal of Agricultural Research, 5(2), 199-203.
  16. Hardegree, S. P. & Emmerich, W. E. (1990). Effect of polyethylene glycol exclusion on the water potential of the solution-saturated filter paper. Plant Physiology, 92, 462-466.
  17. Hardegree, S. P. (2006). Predicting germination response to temperature. I. Cardinal-temperature models and subpopulation-specific regression. Annals of Botany, 97, 1115-1125.
  18. Ibrahim, H. M., Kholosy, A. S., Zakran, M. K. & Hassanein, E. E. (1995). Study of wild oat (Avena fatua) competition with wheat. Annals of Agricultural Sciences, 40(2), 683-696.
  19. Karimmojeni, H., Taab, A., Rashidi, B. & Bazrafshan, A. M. (2014). Dormancy breaking and seed germination of the annual weeds Thlaspi arvense, Descurainia sophia and Malcolmia africana (Brassicaceae). Journal of Plant Protection Research, 54(2), 179-187.
  20. Kiemnce, G. L. & Mcinnis, M. L. (2002). Hoary cress (Cardaria draba) root extract reduces germination and root growth of five plant species. Weed Technology, 16, 231-234.
  21. Masin, R., Loddo, D., Benvenuti, S., Otto, S. & Zanin, G. (2012). Modelling weed emergence in Italian maize fields. Weed Science, 60(2), 254-259.
  22. Mayer, D. G. & Butler, D. G. (1993). Statistical validation. Ecological Modelling. 68, 21-32.
  23. Moschini, R. C., Damiano, F., Lopez, R. L., Vigna, M. R. & Gigon, R. (2011). Modelos no lineales basados en el tiempo termico e hidrotermico del suelo para simular la emergencia de plantulas de Avena fatua en Argentina. In: Proceedings of 10th Congreso de la Asociacion Latinoamericana de Malezas (ALAM). Vina del Mar, Chile, 146-153 pp.
  24. Norsworthy, J. K. & Oliveira, M. J. (2007). A Model for predicting common cocklebur (Xanthium strumarium) emergence in soybean. Weed Science, 55, 341-345.
  25. Parmoon, G., Moosavi, S. A., Akbari, H. & Ebadi, A. (2015). Quantifying cardinal temperatures and thermal time required for germination of Silybum marianum seed. The Crop Journal, 3, 145-151.
  26. Royo-Esnal, A., Torra, J., Conesa, J.A., Forcella, F. & Recasens, J. (2010). Modeling three emergences of three arable bedstraw (Galium) species. Weed Science, 58, 10-15.
  27. SAS, Institute. (2008). SAS User’s Guide: Statistics, Version 9.2. SAS Institute Inc. Cary. NC. USA.
  28. Schellenberg, M. P., Biligetu, B. & Wei, Y. (2013). Predicting seed germination of slender wheatgrass [Elymus trachycaulus (Link) Gould subsp. trachycaulus] using thermal and hydro time models. Canadian Journal of Plant Science, 93(5), 793-798.
  29. Schutte, B. J., Regnier, E. E., Harrison, S. K., Schmoll, J. T., Spokas, K. & Forcella, F. (2008). A hydrothermal emergence model for giant ragweed (Ambrosia trifida). Weed Science, 56, 555-560.
  30. Sharma, M. P. & Vanden Born, W. H. (1978). The biology of Canadian weeds. Avena fatua L. Canadian Journal of Plant Science, 58, 141-157.
  31. Vleeshouwers, L. M. & Kropff, M. J. (2000). Modelling field emergence patterns in arable weeds. New Phytologist, 148, 445-457.
  32. Wang, R. (2005). Modelling seed germination and seedling emergence in winterfat (Krascheninnikovia lanata (Pursh). Ph.D. thesis. University of Saskatchewan, Canada. 190 pp.
  33. Weibull, W. (1951). A statistical distribution function of wide applicability. Journal of Applied Mechanics, 18, 293-297.
  34. Windauer, L. B., Martinez, J., Rapoport, D., Wassner, D. & Benech-Arnold, R. (2012). Germination responses to temperature and water potential in Jatropha curcas seeds: a hydrotime model explains the difference between dormancy expression and dormancy induction at different incubation temperatures. Annals of Botany, 109, 265-273.
  35. Yousefi, A. R., Rastgoo, M., Ghanbari Motlagh, M. & Ebrahimi, M. (2013a). Predicting seedling emergence of Flixweed (Descurainia sophia (L.) Webb.) and Hoary cress (Cardaria draba (L.) Desv.) in rapeseed (Brassica napus)field in Zanjan conditions. Journal of Plant Protection, 27(1), 48-54.
  36. Yousefi, A. R., Oveisi, M. & Gonzalez Andujar, J. L. (2014b). Prediction of annual weed seed emergence in garlic (Allium sativum L.) using soil thermal time. Scientia Horticulturae, 168, 189-192.
آمار
تعداد مشاهده مقاله: 593
تعداد دریافت فایل اصل مقاله: 509


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب