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

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Non-linear regression techniques are used widely to fit weed field emergence patterns to soil microclimatic indices using S-type functions. Artificial neural networks present interesting and alternative features for such modeling purposes. In this work, a univariate hydrothermal-time based Weibull model and a bivariate (hydro-time and thermal-time) artificial neural network 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 to the non-linear regression approach due to the improved descriptive capacity of thermal-time and the hydro-time as independent explanatory variables. These outcomes suggest the potential applicability of the proposed modeling approach in the design of weed management decision support systems. Furthermore, due to specific ecological adaptations, the development of a single accurate predictive emergence model for A. fatua, applicable to a wide range of climatic environments, is difficult at the present time.
G. R. Chantre , A. M. Blanco , F. Forcella , R. C. Van Acker , M. R. Sabbatini , J. L. Gonzalez-Andujar
USDA Scientist Submission
Journal of agricultural science 2013
Journal Articles, USDA Authors, Peer-Reviewed
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