International Journal of Plant Production

, Volume 12, Issue 1, pp 53–60 | Cite as

Development and Validation of a Predictive Model for Seedling Emergence of Volunteer Canola (Brassica napus) Under Semi-Arid Climate

  • Elias Soltani
  • Jose L. Gonzalez-Andujar
  • Mostafa Oveisi
  • Nader Salehi
Original Paper


Volunteer canola (Brassica napus L.) can damage the production of subsequent canola crops and other crops. Timely and more accurate control could be developed if there is a better understanding of its temporal emergence patterns. The objectives of this study were to develop and validate a predictive model of emergence for B. napus under semi-arid conditions based on thermal time (TT). Experiments were conducted during 3 years to obtain cumulative seedling emergence data and used to develop and validate the model. A Weibull function was fitted to cumulative seedling emergence and TT. The model closely fitted the observed emergence patterns, accounting for 99% of the variation observed. According to this model, seedling emergence of B. napus started at 56.1 TT and increased to 50 and 95% of maximum seedling emergence at 86.3 and 105.4 TT, respectively. Validation was performed with the Weibull model and two logistics models (taken from the literature) developed under different climate conditions. The validation indicated that the Weibull model performed better than the logistic models. The Weibull model proposed is robust enough and could be useful as a predictive tool for effective control of B. napus under semi-arid climate.


Weibull model Logistic model Base temperature Soil depth Thermal time Degree days Weed emergence 



JLG-A was partially supported by FEDER (European Regional Development Fund) and the Spanish Ministry of Economy and Competitiveness Grant (AGL2015-64130-R).


  1. Baskin, C. C., & Baskin, J. M. (2014). Seeds: ecology, biogeography, and evolution of dormancy and germination (2nd ed.). San Diego: Elsevier/Academic Press.Google Scholar
  2. Benvenuti, S., Macchia, M., & Miele, S. (2001). Quantitative analysis of emergence of seedlings from buried weed seeds with increasing soil depth. Weed Science, 49, 528–535.CrossRefGoogle Scholar
  3. Bullied, W. J., Marginet, A. M., & Van Acker, R. C. (2003). Conventional and conservation-tillage systems influence emergence periodicity of annual weed species in canola. Weed Science, 51, 886–897.CrossRefGoogle Scholar
  4. Cameron, A. C., & Windmeijer, F. A. G. (1996). R -squared measures for count data regression models with applications to health-care utilization. Journal of Business and Economic Statistics, 14, 209–220.Google Scholar
  5. Cardina, J., Webster, T. M., & Herms, C. P. (1998). Long-term tillage and rotation effects on soil seedbank characteristics. Aspects of Applied Biology, 51, 213–220.Google Scholar
  6. Colbach, N., Dürr, C., Gruber, S., & Pekrun, C. (2008). Modelling the seed bank evolution and emergence of oilseed rapevolunteers for managing co-existence of GM and non-GM varieties. European Journal of Agronomy, 28, 19–32.CrossRefGoogle Scholar
  7. Farzaneh, S., Soltani, E., Zeinali, E., & Ghaderi-far, F. (2014). Screening oilseed rape germination for thermotolerance using a laboratory-based method. Seed Technology, 36, 15–27.Google Scholar
  8. Forcella, F. (1998). Real-time assessment of seed dormancy and seedling growth for weed management. Seed Science Research, 8, 201–209.CrossRefGoogle Scholar
  9. Forcella, F., Benech-Arnold, R. L., Sanchez, R., & Ghersa, C. M. (2000). Modeling weed emergence. Field Crops Research, 67, 123–139.CrossRefGoogle Scholar
  10. Gonzalez-Andujar, J. L., Chantre, G. R., Morvillo, C., Blanco, A., & Forcella, F. (2016). Predicting field weed emergence with empirical models and soft computing techniques. Weed Research, 56, 415–423.CrossRefGoogle Scholar
  11. Gruber, S., Bühler, A., Möhring, J., & Claupein, W. (2010). Sleepers in the soil—Vertical distribution by tillage and long-term survival of oilseed rape seeds compared with plastic pellets. European Journal of Agronomy, 33, 81–88.CrossRefGoogle Scholar
  12. Gruber, S., Pekrun, C., & Claupein, W. (2005). Life cycle and potential gene flow of volunteer oilseed rape in differenttillage systems. Weed Research, 45, 83–93.CrossRefGoogle Scholar
  13. Grundy, A. C. (2003). Predicting weed emergence: A review of approaches and future challenges. Weed Research, 43, 1–11.CrossRefGoogle Scholar
  14. Gulden, R. H., Shirtliffe, S. J., & Thomas, A. G. (2003). Harvest losses of canola (Brassica napus) cause large seedbank inputs. Weed Science, 51, 83–86.CrossRefGoogle Scholar
  15. Gulden, R. H., Thomas, A. G., & Shirtliffe, S. J. (2004). Secondary dormancy, temperature, and burial depth regulate seedbank dynamics in canola. Weed Science, 52, 382–388.CrossRefGoogle Scholar
  16. Izquierdo, J., Bastida, F., Lezaun, J. M., Sanchez Del Arco, M. J., & Gonzalez-Andujar, J. L. (2013). Development and evaluation of a model for predicting Lolium rigidum emergence in winter cereal crops in the Mediterranean area. Weed Research, 53, 269–278.CrossRefGoogle Scholar
  17. Izquierdo, J., Gonzalez-Andujar, J. L., Bastida, F., Lezaun, J. A., & Sanchez Del Arco, M. J. (2009). A thermal time model to predict corn poppy (Papaver rhoeas) emergence in cereal fields. Weed Science, 57, 660–664.CrossRefGoogle Scholar
  18. Lawson, A. N., Van Acker, R. C., & Friesen, L. F. (2006). Emergence timing of volunteer canola in spring wheat fields in Manitoba. Weed Science, 54, 873–882.CrossRefGoogle Scholar
  19. Legere, A., Simard, M.J., Thomas, A.G., Pageau, D., Lajeunesse, J., Warwick, S.I., Derksen, D.A., 2001. Presence and persistence of volunteer canola in Canadian cropping systems. In Pages 143–148 in British Crop Protection Council, ed. Proceedings of the Brighton Crop Protection Conference—Weeds 2001. Surrey, Great Britain: British Crop Protection Council.Google Scholar
  20. Leguizamon, E. S., Fernandez-Quintanilla, C., Barroso, J., & Gonzalez-Andujar, J. L. (2005). Using thermal and hydrothermal time to model seedling emergence of Avenasterilis ssp. ludoviciana in Spain. Weed Research, 45, 149–156.CrossRefGoogle Scholar
  21. Leguizamon, E. S., Rodriguez, N., Rainero, H., Perez, M., Perez, L., Zorza, E., et al. (2009). Modelling the emergence pattern of six summer annual weed grasses under no tillage systems in Argentina. Weed Research, 49, 98–106.CrossRefGoogle Scholar
  22. Momoh, E. J. J., Zhou, W. J., Kristiansson, B. (2002). Variation in the development of secondary dormancy in oilseed rape genotypes under conditions of stress. Weed Research, 42, 446–455.CrossRefGoogle Scholar
  23. Nowroozian, M. (2000). The list of permissive toxins in Iran. Tehran, Iran: Plant Protection Organization Press (in Persian).Google Scholar
  24. SAS Institute INC (2011) SAS/STAT 9.3 user’s guide, the PLSprocedure, SAS Campus Drive, Cary, North Carolina 27513.Google Scholar
  25. Sauermann, W. (1993). Einflüsse auf den Glucosinolatgehalt—Ergebnisse 2-jähriger Untersuchungenaus den Land-essortenversuchen. Raps, 11, 82–86.Google Scholar
  26. Saxton, K. E., Rawls, W. J., Romberger, J. S., & Papendick, R. I. (1986). Estimation generalized soil water characteristics from texture. Soil Science Society of America Journal, 50, 1031–1036.CrossRefGoogle Scholar
  27. Schlink, S., 1994. Ecology of germination and dormancy in oilseed rape (Brassica napus L.) and their importance forthe survival of the seeds in soil. 193 f. Dissertation (Master in Botany)—Faculty of Biology, University of Berlin, Berlin, 1994.Google Scholar
  28. Schwinghamet, T. D., & Van Acker, R. C. (2008). Emergence Timing and Persistence of Kochia (Kochia scoparia). Weed Science, 56, 37–41.CrossRefGoogle Scholar
  29. Simard, M. J., Légère, A., Pageau, D., Lajeunesse, J., & Warwick, S. (2002). The frequency and persistence of volunteer Canola (Brassica napus) in Québec cropping systems. Weed Technology, 16, 433–439.CrossRefGoogle Scholar
  30. Smith, E. P., & Rose, K. A. (1995). Model goodness-of-fit analysis using regression and related techniques. Ecological Modeling, 77, 49–64.CrossRefGoogle Scholar
  31. Soltani, E., Baskin, C. C., Baskin, J. M., Soltani, A., Galeshi, S., Ghaderi-far, F., et al. (2016). A quantitative analysis of seed dormancy and germination in the winter annual weed Sinapis arvensis (Brassicaceae). Botany, 94, 289–300.CrossRefGoogle Scholar
  32. Soltani, E., Gruber, S., Oveisi, M., Salehi, N., Alahdadi, I., Ghorbani-Javid, M. (2017). Water stress, temperature regimes, and light control secondary dormancy induction and loss in Brassica napus L. seeds. Seed Science Research, 27, 217–230. Scholar
  33. Soltani, A., & Sinclair, T. (2012). Modeling physiology of crop development, growth and yield. Wallingford: CABI Publication.CrossRefGoogle Scholar
  34. Soltani, E., Soltani, A., Galeshi, S., Ghaderi-far, F., & Zeinali, E. (2013). Seed bank modelling of volunteer oil seed rape: from seeds fate in the soil to seedling emergence. Planta Daninha, 31, 267–279.CrossRefGoogle Scholar
  35. Thomas, D. L., Raymer, P. L., & Breve, M. A. (1994). Seeding depth and packing wheel pressure effects on oilseed rape emergence. Journal of Production Agriculture, 7, 94–97.CrossRefGoogle Scholar
  36. Yousefi, A. R., Oveisi, M., & Gonzalez-Andujar, J. L. (2014). Prediction of annual weed seed emergence in garlic (Allium sativum L.) using soil thermal time. Scientia Horticulturae, 168, 189–192.CrossRefGoogle Scholar
  37. Zambrano-Navea, C., Bastida, F., & Gonzalez-Andujar, J. L. (2013). A hydrothermal seedling emergence model for Conyza bonariensis. Weed Research, 53, 213–220.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2017

Authors and Affiliations

  • Elias Soltani
    • 1
  • Jose L. Gonzalez-Andujar
    • 2
  • Mostafa Oveisi
    • 3
  • Nader Salehi
    • 1
  1. 1.Department of Agronomy and Plant Breeding Sciences, College of AburaihanUniversity of TehranPakdashtIran
  2. 2.Instituto de Agricultura Sostenible (CSIC)CórdobaSpain
  3. 3.Department of Agronomy and Plant Breeding, College of Agriculture and Natural ResourcesUniversity of TehranKarajIran

Personalised recommendations