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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
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

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
Copyright
Copyright © Cambridge University Press 2013 

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