Skip to main content Accessibility help

A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence

  • G. R. CHANTRE (a1), A. M. BLANCO (a2), F. FORCELLA (a3), R. C. VAN ACKER (a4), M. R. SABBATINI (a1) and J. L. GONZALEZ-ANDUJAR (a5)...


Non-linear regression (NLR) techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks (ANNs) present interesting and alternative features for such modelling purposes. In the present work, a univariate hydrothermal-time based Weibull model and a bivariate (hydro-time and thermal-time) ANN were developed to study wild oat emergence under non-moisture restriction conditions using data from different locations worldwide. Results indicated a higher accuracy of the neural network in comparison with the NLR approach due to the improved descriptive capacity of thermal-time and the hydro-time as independent explanatory variables. The bivariate ANN model outperformed the conventional Weibull approach, in terms of RMSE of the test set, by 70·8%. These outcomes suggest the potential applicability of the proposed modelling approach in the design of weed management decision support systems.


Corresponding author

*To whom all correspondence should be addressed. Email:


Hide All
Alvarez, R. (2009). Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach. European Journal of Agronomy 30, 7077.
Baskin, C. C. & Baskin, J. M. (1998). Seeds: Ecology, Biogeography and Evolution of Dormancy and Germination. San Diego, CA: Academic Press.
Batlla, D. & Benech-Arnold, R. L. (2007). Predicting changes in dormancy level in weed seed soil bank: implications for weed management. Crop Protection 26, 189197.
Beale, M. H., Hagan, M. T. & Demuth, H. B. (2011). Neural Network Toolbox, User's Guide, MATLAB. Natick, MA: The MathWorks Inc.
Bouwmeester, H. J. (1990). The effect of environmental conditions on the seasonal dormancy pattern and germination of weed seeds. Ph.D. Thesis, Wageningen Agricultural University, The Netherlands.
Bradford, K. J. (1990). A water relations analysis of seed germination rates. Plant Physiology 94, 840849.
Bradford, K. J. (2002). Applications of hydrothermal time to quantifying and modelling seed germination and dormancy. Weed Science 50, 248260.
Brisson, N., Launay, M., Mary, B. & Beaudoin, N. (2008). Conceptual Basis, Formalizations and Parameterization of the STICS Crop Model. Paris, France: Quae Editions.
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, 886897.
Bullied, W. J., Van Acker, R. C. & Bullock, P. R. (2012). Hydrothermal modelling of seedling emergence timing across topography and soil depth. Agronomy Journal 104, 423436.
Burnham, K. P. & Anderson, D. R. (2002). Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach. New York: Springer-Verlag.
Cao, R., Francisco-Fernández, M., Anand, A., Bastida, F. & González-Andújar, J. L. (2011). Computing statistical indices for hydrothermal times using weed emergence data. Journal of Agricultural Science, Cambridge 149, 701712.
Chantre, G. R., Blanco, A. M., Lodovichi, M. V., Bandoni, J. A., Sabbatini, M. R., López, R. L., Vigna, M. R. & Gigón, R. (2012). Modelling Avena fatua seedling emergence dynamics: an artificial neural network approach. Computers and Electronics in Agriculture 88, 95102.
Chauhan, B. S., Gill, G. & Preston, C. (2006). Seedling recruitment pattern and depth of recruitment of 10 weed species in minimum tillage and no-till seeding systems. Weed Science 54, 658668.
Colbach, N. & Mézière, D. (2013). Using a sensitivity analysis of a weed dynamics model to develop sustainable cropping systems. I. Annual interactions between crop management techniques and biophysical field state variables. Journal of Agricultural Science, Cambridge 151, 229246.
Colbach, N., Chauvel, B., Darmency, H. & Tricault, Y. (2011). Sensitivity of weed emergence and dynamics to life-traits of annual spring-emerging weeds in contrasting cropping systems, using weed beet (Beta vulgaris ssp. vulgaris) as an example. Journal of Agricultural Science, Cambridge 149, 679700.
Cousens, R. D., Weaver, S. E., Martin, T. D., Blair, A. M. & Wilson, J. (1991). Dynamics of competition between wild oats (Avena fatua L.) and winter cereals. Weed Research 39, 203210.
Foley, M. E. (1994). Temperature and water status of seed affect after ripening in wild oat (Avena fatua). Weed Science 42, 200204.
Fennimore, S. A., Nyquist, W. E., Shaner, G. E., Doerge, R. W. & Foley, M. E. (1999). A genetic model and molecular markers for wild oat (Avena fatua L.) seed dormancy. Theoretical and Applied Genetics 99, 711718.
Forcella, F. (1998). Real-time assessment of seed dormancy and seedling growth for weed management. Seed Science Research 8, 201209.
Forcella, F., Benech-Arnold, R. L., Sánchez, R. & Ghersa, C. M. (2000). Modelling seedling emergence. Field Crops Research 67, 123139.
Gardarin, A., Durr, C. & Colbach, N. (2012). Modelling the dynamics and emergence of a multispecies weed seed bank with species traits. Ecological Modelling 240, 123138.
Gardarin, A., Guillemin, J. P., Munier-Jolain, N. M. & Colbach, N. (2010). Estimation of key parameters for weed population dynamics models: Base temperature and base water potential for germination. European Journal of Agronomy 32, 162168.
González-Andújar, J. L., Forcella, F., Kegode, G., Gallagher, R. & Van Acker, R. (2001). Modelizacion de la emergencia de plantulas de Avena loca (Avena fatua L.) usando tiempo hidrotermal. In Congreso 2001 de la Sociedad Española de Malherbologia (Eds Boto, J. A., Villarias, J. L. & Fernandez, J. C.), pp. 243246. Leon, Spain: Universidad de León.
Grundy, A. C. (2003). Predicting weed emergence: a review of approaches and future challenges. Weed Research 43, 111.
Grundy, A. C., Peters, N. C. B., Rasmussen, I. A., Hartmann, K. A., Sattin, M., Andersson, L., Mead, A., Murdoch, A. J. & Forcella, F. (2003) Emergence of Chenopodium album and Stellaria media of different origins under different climatic conditions. Weed Research 43, 163176.
Haj Seyed Hadi, M. R. & González-Andújar, J. L. (2009). Comparison of fitting weed seedling emergence models with nonlinear regression and genetic algorithm. Computers and Electronics in Agriculture 65, 1925.
Hammer, G. L., Carberry, P. S. & Muchow, R. C. (1993). Modelling genotypic and environmental control of leaf area dynamics in grain sorghum. I. Whole plant level. Field Crops Research 33, 293310.
Izquierdo, J., González-Andújar, J. L., Bastida, F., Lezaún, J. A. & Sánchez Del Arco, M. J. (2009). A thermal time model to predict corn poppy (Papaver rhoeas) emergence in cereal fields. Weed Science 57, 660664.
Leguizamón, E. S., Fernandez-Quintanilla, C., Barroso, J. & Gonzalez-Andujar, J. L. (2005). Using thermal and hydrothermal time to model seedling emergence of Avena sterilis ssp. ludoviciana in Spain. Weed Research 45, 149156.
Lek, S. & Guégan, J. F. (1999). Artificial neural networks as a tool in ecological modelling: an introduction. Ecological Modelling 120, 6573.
López, R. L. & Vigna, M. R. (1991). Patrón de emergencia de Avena fatua L. en el sudoeste de Buenos Aires. In Actas X Reunión Nacional CAPERAS (Ed. Universidad Nacional del Sur), pp. 135136. Bahía Blanca, Argentina: Universidad Nacional del Sur.
Martinson, K., Durgan, D., Forcella, F., Wiersma, J., Spokas, K. & Archer, D. (2007). An emergence model for wild oat (Avena fatua). Weed Science 55, 584591.
Masin, R., Loddo, D., Benvenuti, S., Otto, S. & Zanin, G. (2012). Modelling weed emergence in Italian maize fields. Weed Science 60, 254259.
Mickelson, J. A. & Grey, W. E. (2006). Effect of soil water content on wild oat (Avena fatua) seed mortality and seedling emergence. Weed Science 54, 255262.
Moschini, R. C., López, R. L., Vigna, M. R. & Damiano, F. (2009). Modelos basados en tiempo térmico e hidrotérmico para predecir la emergencia de Avena fatua en lotes con y sin labranza estival, en Argentina. In Herbología e Biodiversidade numa Agricultura Sustentável (Eds de Sousa, E., Calha, I. M., Moreira, I., Rodrigues, L., Portugal, J. & Vasconcelos, T.), pp. 239242. Lisboa, Portugal: ISA Press.
Onofri, A., Gresta, F. & Tei, F. (2010). A new method for the analysis of germination and emergence data of weed species. Weed Research 50, 187198.
Onofri, A., Mesgaran, M. B., Tei, F. & Cousens, R. D. (2011). The cure model: an improvement way to describe seed germination. Weed Research 51, 516524.
Page, E. R., Gallagher, R. S., Kemanian, A. R., Zhang, H. & Fuerst, E. P. (2006). Modelling site-specific wild oat (Avena fatua) emergence across a variable landscape. Weed Science 54, 838846.
Peters, N. C. B. (1982). The dormancy of wild oat seed (Avena fatua L.) from plants grown under various temperature and soil moisture conditions. Weed Research 22, 205212.
Schutte, B. J., Regnier, E. E., Harrison, S. K., Schmoll, J. T., Spokas, K. & Forcella, F. (2008). A hydrothermal seedling emergence model for giant ragweed (Ambrosia trifida). Weed Science 56, 555560.
Sharma, M. P., Mcbeath, D. K. & Vanden Born, W. H. (1976). Studies on the biology of wild oats. I. Dormancy, germination and emergence. Canadian Journal of Plant Science 56, 611618.
Spokas, K. & Forcella, F. (2009). Software tools for weed seed germination modelling. Weed Science 57, 216227.

A comparative study between non-linear regression and artificial neural network approaches for modelling wild oat (Avena fatua) field emergence

  • G. R. CHANTRE (a1), A. M. BLANCO (a2), F. FORCELLA (a3), R. C. VAN ACKER (a4), M. R. SABBATINI (a1) and J. L. GONZALEZ-ANDUJAR (a5)...


Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed