Modeling Bromus diandrus Seedling Emergence Using Nonparametric Estimation

Cao, Ricardo; Francisco‐Fernández, Mario; Anand, A.; Bastida, Fernando; González‐Andújar, José Luis

doi:10.1007/s13253-012-0122-x

Cited by 14 publications

(11 citation statements)

References 28 publications

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“…(, , ) presented new developments that maintain the positive features of parametric approaches, and Cao et al . () described a nonparametric approach as a very flexible tool to model weed emergence.…”

Section: Introductionmentioning

confidence: 99%

Predicting field weed emergence with empirical models and soft computing techniques

et al. 2016

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Seedling emergence is one of the most important phenological processes that influence the success of weed species. Therefore, predicting weed emergence timing plays a critical role in scheduling weed management measures. Important efforts have been made in the attempt to develop models to predict seedling emergence patterns for weed species under field conditions. Empirical emergence models have been the most common tools used for this purpose. They are based mainly on the use of temperature, soil moisture and light. In this review, we present the more popular empirical models, highlight some statistical and biological limitations that could affect their predictive accuracy and, finally, we present a new generation of modelling approaches to tackle the problems of conventional empirical models, focusing mainly on soft computing techniques. We hope that this review will inspire weed modellers and that it will serve as a basis for discussion and as a frame of reference when we proceed to advance the modelling of field weed emergence.

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Section: Introductionmentioning

confidence: 99%

Predicting field weed emergence with empirical models and soft computing techniques

et al. 2016

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“…Alternatively, nonparametric approaches can be used to overcome the rigidity of parametric models. In this context, Cao et al (2013) propose a new methodology to model field weed emergence. This methodology does not impose a specific model (such as the normal distribution, or a linear relation) for the random variables under consideration.…”

Section: Introductionmentioning

confidence: 99%

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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“…As pointed out in the Introduction, nonparametric estimators (1) and (2) are novel approaches to model weed emergence, presenting some advantages over the parametric regression techniques traditionally used in this framework (Cao et al, 2013;González-Andújar, Francisco-Fernández, et al, 2016). These estimators, jointly with two types of bandwidth selectors, plug-in and bootstrap, have been implemented in the binnednp package.…”

Section: Density and Distribution Estimationmentioning

confidence: 99%

“…Similarly, to estimate the distribution function F, a kernel distribution estimator adapted for interval-grouped data, derived from (1), was proposed in Cao et al (2013),…”

Section: Density and Distribution Estimationmentioning

confidence: 99%

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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 Bromus diandrus Seedling Emergence Using Nonparametric Estimation

Cited by 14 publications

References 28 publications

Predicting field weed emergence with empirical models and soft computing techniques

Predicting field weed emergence with empirical models and soft computing techniques

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

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

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