Search National Agricultural Library Digital Collections

NALDC Record Details:

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

Permanent URL:
http://handle.nal.usda.gov/10113/58431
File:
Download [PDF File]
Abstract:
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.
Author(s):
G. R. Chantre , A. M. Blanco , F. Forcella , R. C. Van Acker , M. R. Sabbatini , J. L. Gonzalez-Andujar
Subject(s):
Avena fatua , accuracy , decision support systems , microclimate , neural networks , oats , regression analysis , seedling emergence , weed control , weeds
Source:
Journal of agricultural science 2013
Language:
English
Year:
2013
Collection:
Journal Articles, USDA Authors, Peer-Reviewed
Rights:
Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.