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

Published online by Cambridge University Press:  23 January 2013

G. R. CHANTRE ,
A. M. BLANCO ,
F. FORCELLA ,
R. C. VAN ACKER ,
M. R. SABBATINI  and
J. L. GONZALEZ-ANDUJAR
G. R. CHANTRE
Affiliation:
Departamento de Agronomía/CERZOS, Universidad Nacional del Sur/CONICET, Bahía Blanca, Buenos Aires, 8000, Argentina
A. M. BLANCO
Affiliation:
Planta Piloto de Ingeniería Química, Universidad Nacional del Sur/CONICET, Bahía Blanca, Buenos Aires, 8000, Argentina
F. FORCELLA
Affiliation:
USDA-ARS North Central Soil Conservation Research Laboratory, Morris, MN 56267, USA
R. C. VAN ACKER
Affiliation:
Department of Plant Agriculture, University of Guelph, Guelph, ON, N1G 2W1, Canada
M. R. SABBATINI
Affiliation:
Departamento de Agronomía/CERZOS, Universidad Nacional del Sur/CONICET, Bahía Blanca, Buenos Aires, 8000, Argentina
J. L. GONZALEZ-ANDUJAR*
Affiliation:
Instituto de Agricultura Sostenible (CSIC), Aptdo. 4084, 14080 Córdoba, Spain
*
*To whom all correspondence should be addressed. Email: andujar@cica.es
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Summary

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.

Type
Crops and Soils Research Papers
Information
The Journal of Agricultural Science , Volume 152 , Issue 2 , April 2014 , pp. 254 - 262
Copyright
Copyright © Cambridge University Press 2013 

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