Modeling Avena fatua seedling emergence dynamics: An artificial neural network approach

Chantre, Guillermo R.; Blanco, Anı́bal M.; Lodovichi, Mariela V.; Bandoni, Alberto; Sabbatini, Mario Ricardo; Lopez, Ricardo; Vigna, Mario R.; Gigón, Ramón

doi:10.1016/j.compag.2012.07.005

Cited by 24 publications

(39 citation statements)

References 35 publications

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“…However, these issues are not usually considered explicitly in the weed science literature, where fitting the model is the goal regardless of whether the statistical analysis is appropriate or not (Onofri et al ., , ; Cao et al ., ; Mesgaran et al ., ). In order to cope with such limitations, different modelling approaches have been proposed, including techniques that account for censoring (Onofri et al ., , ), genetic algorithms (Haj Seyed‐Hadi & Gonzalez‐Andujar, ; Blanco et al ., ) and artificial neural networks (Chantre et al ., ).…”

Section: Introductionmentioning

confidence: 97%

A comparative study between nonlinear regression and nonparametric approaches for modelling Phalaris paradoxa seedling emergence

et al. 2016

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Summary Parametric nonlinear regression (PNR) models are used widely to fit weed seedling emergence patterns to soil microclimatic indices. However, such approximation has been questioned, mainly due to several statistical limitations. Alternatively, nonparametric approaches can be used to overcome the problems presented by PNR models. Here, we used an emergence data set of Phalaris paradoxa to compare both approaches. Mean squared error and correlation results indicated higher accuracy for the descriptive ability but similar poor performance for predictive ability of the nonparametric approach in comparison with the PNR approach. These results suggest that our nonparametric cumulative distribution function approach is a valuable alternative to the classical parametric nonlinear regression models to describe complex emergence patterns for P. paradoxa, but not to predict them.

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Section: Introductionmentioning

confidence: 97%

A comparative study between nonlinear regression and nonparametric approaches for modelling Phalaris paradoxa seedling emergence

et al. 2016

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“…Our results partially agree with Yousefi et al (2013) in a comparison of three models (Gompertz, Logistic and Weibull models) on D. sophia, who found that the Weibull model gave the best fit for Iran climate. Chantre et al (2012) reported that the Weibull model was deemed to provide a better fit than the other models for A. fatua seedling emergence in Argentina. However, Gonzalez-Diaz et al (2007) reported that the Logistic model was deemed to provide a better fit than the other models for A. fatua seedling emergence in Spain.…”

Section: Discussionmentioning

confidence: 99%

“…However, Gonzalez-Diaz et al (2007) reported that the Logistic model was deemed to provide a better fit than the other models for A. fatua seedling emergence in Spain. The differences between A. fatua and D. sophia emergence patterns in semiarid conditions might be attributed mainly to a highly unpredictable precipitation regime, fuctuating thermal environment and seed dormancy level variations (Chantre et al, 2012). In addition to weather conditions, different soil management (such as cultivation operation and working depth) may affect the accuracy of the model by varying the vertical movement of the seeds within the soil profile (Grundy, 2003).…”

Section: Discussionmentioning

confidence: 99%

Short communication: Evaluation of a model for predicting Avena fatua and Descurainia sophia seed emergence in winter rapeseed

Aboutalebian

Nazari

González‐Andújar

2017

Span. j. agric. res.

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Avena fatua and Descurainia sophia are two important annual weeds throughout winter rapeseed (Brassica napus L.) production systems in the semiarid region of Iran. Timely and more accurate control of both species may be developed if there is a better understanding of its emergence patterns. Non-linear regression techniques are usually unable to accurately predict field emergence under such environmental conditions. The objectives of this research were to evaluate the emergence patterns of A. fatua and D. sophia and determine if emergence could be predicted using cumulative soil thermal time in degree days (CTT). In the present work, cumulative seedling emergence from a winter rapeseed field during 3 years data set was fitted to cumulative soil CTT using Weibull and Gompertz functions. The Weibull model provided a better fit, based on coefficient of determination (R

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“…Parametric regression models for emergence are usually employed in this framework. However, due to the limitations of this approach, different modeling approaches have been proposed, including techniques that account for censoring (Onofri, Gresta, & Tei, 2010;Onofri, Mesgaran, Tei, & Cousens, 2011;Onofri, Piepho, & Kozak, 2019), genetic algorithms (Blanco et al, 2014;Haj Seyed-Hadi & Gonzalez-Andujar, 2009), and artificial neural networks (Chantre et al, 2012). However, due to the limitations of this approach, different modeling approaches have been proposed, including techniques that account for censoring (Onofri, Gresta, & Tei, 2010;Onofri, Mesgaran, Tei, & Cousens, 2011;Onofri, Piepho, & Kozak, 2019), genetic algorithms (Blanco et al, 2014;Haj Seyed-Hadi & Gonzalez-Andujar, 2009), and artificial neural networks (Chantre et al, 2012).…”

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confidence: 99%

Analysis of interval‐grouped data in weed science: The binnednp Rcpp package

Barreiro-Ures

Francisco‐Fernández

Cao

et al. 2019

Ecology and Evolution

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Weed scientists are usually interested in the study of the distribution and density functions of the random variable that relates weed emergence with environmental indices like the hydrothermal time (HTT). However, in many situations, experimental data are presented in a grouped way and, therefore, the standard nonparametric kernel estimators cannot be computed. Kernel estimators for the density and distribution functions for interval‐grouped data, as well as bootstrap confidence bands for these functions, have been proposed and implemented in the binnednp package. Analysis with different treatments can also be performed using a bootstrap approach and a Cramér‐von Mises type distance. Several bandwidth selection procedures were also implemented. This package also allows to estimate different emergence indices that measure the shape of the data distribution. The values of these indices are useful for the selection of the soil depth at which HTT should be measured which, in turn, would maximize the predictive power of the proposed methods. This paper presents the functions of the package and provides an example using an emergence data set of Avena sterilis (wild oat). The binnednp package provides investigators with a unique set of tools allowing the weed science research community to analyze interval‐grouped data.

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Modeling Avena fatua seedling emergence dynamics: An artificial neural network approach

Cited by 24 publications

References 35 publications

A comparative study between nonlinear regression and nonparametric approaches for modelling Phalaris paradoxa seedling emergence

A comparative study between nonlinear regression and nonparametric approaches for modelling Phalaris paradoxa seedling emergence

Short communication: Evaluation of a model for predicting Avena fatua and Descurainia sophia seed emergence in winter rapeseed

Analysis of interval‐grouped data in weed science: The binnednp Rcpp package

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