A comparative study between nonlinear regression and nonparametric approaches for modelling <i>Phalaris paradoxa</i> seedling emergence

González-Andújar, José L.; Francisco‐Fernández, Mario; Cao, Ricardo; Reyes, Miguel; Urbano, J. M.; Forcella, Frank; Bastida, Fernando

doi:10.1111/wre.12216

Cited by 12 publications

(10 citation statements)

References 31 publications

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“…The success of these kinds of models relies on ease of management and practicality; however, they have some statistical limitations, mainly due to a lack of flexibility in capturing complex features [19]. Censored observations are another statistical limitation, as knowing the exact seedling emergence moment between two sampling dates is impossible; thus, alternative non-parametric regressions could be more appropriate approaches to describe weed emergence [19] and have already been developed by other authors [19,20].…”

Section: Introductionmentioning

confidence: 99%

Predicting Seedling Emergence of Three Canarygrass (Phalaris) Species under Semi-Arid Conditions Using Parametric and Non-Parametric Models

2021

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The Phalaris genus includes annual weed species such as short-spiked canarygrass (Phalaris brachystachys Link.), little-seed canarygrass (Phalaris minor Retz.) and hood canarygrass (Phalaris paradoxa L.), which are especially problematic in Spain; as such, there is a need to develop models to predict the timing of their emergence. Field experiments were conducted at two different locations during two (2006/07 and 2007/08) and three (from 2005/06 to 2007/08) growing seasons. In both locations, 500 seeds of each Phalaris species were sown each growing season, simulating rain-fed cereal field conditions. In addition, the models were validated with three, four and eight independent experiments for P. brachystachys, P. minor and P. paradoxa, respectively. The emergence period of the three Phalaris species lasted between 31 and 48 days after sowing (DAS), showing two main flushes. The three cardinal points for parametric and non-parametric models were established to be between −1 °C and 1 °C for base temperature, between 9.8 °C and 11.8 °C for optimal temperature and between 21.2 °C and 23.4 °C for ceiling temperature; base water potential was estimated to be between −1 and −1.1 MPa. Both parametric and non-parametric models obtained similar results and were successfully validated in 12 out of 15 independent experiments.

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Section: Introductionmentioning

confidence: 99%

Predicting Seedling Emergence of Three Canarygrass (Phalaris) Species under Semi-Arid Conditions Using Parametric and Non-Parametric Models

2021

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“…Models that make fewer a priori assumptions about the shape of response might therefore be preferred (Hardegree and Winstral, 2006), as they can provide more flexibility as well as better prediction power. In this vein, some authors have recently attempted a totally non-parametric approach to seed germination modeling (Gonzalez-Andujar et al, 2016). Although flexible, the non-parametric methods resemble a black box with little biological underpinnings and are prone to overfitting.…”

Section: Discussionmentioning

confidence: 99%

Hydrothermal-time-to-event models for seed germination

Onofri

Benincasa

Mesgaran

et al. 2018

European Journal of Agronomy

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Time-to-event methods have been proposed in the agricultural sciences, as one of the most suitable options for the analysis of seed germination data. In contrast to traditional linear/nonlinear regression, time-to-event methods can easily account for all statistical peculiarities inherited in germination assays, such as censoring, and they can produce unbiased estimates of model parameters and their standard errors. So far, these methods have only been used in combination with empirical models of germination, which are lacking biological underpinnings. We bridge the gap between statistical requirements and biological understanding by developing a general method that formulates biologically-oriented hydro time (HT), thermal time (TT) and hydrothermal time (HTT) models into a time-to-event framework. HT, TT, and HTT models are widely used for describing seed germination and emergence of plants as affected by the environmental temperature and/or water potential. Owing to their simplicity and the direct biological interpretation of model parameters, these models have become one of the most common tools for both predicting germination as well as understanding the physiology of germination responses to environmental factors. However, these models are usually fitted by using nonlinear regression and, therefore, they fall short of statistical rigor when inference about model parameters is of interest. In this study, we develop HT-to-event, TT-to-event and HTT-to-event models and provide a readily available implementation relying on the package "drc" in the R statistical environment. Examples of usage are also provided and we highlight the possible advantages of this procedure, such as efficiency and flexibility.

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“…Germination percentage was calculated for a viable fraction of the total seed population. There are many models to predict weed emergence (Gonzalez‐Andujar et al ., ,b). In our experiment, functional three‐parameter logistic curves were fitted with the mean value to obtain the time needed to achieve 50% of the maximum germination of viable seeds ( T 50 ).…”

Section: Methodsmentioning

confidence: 99%

Germination behaviour of Cenchrus pauciflorus seeds across a range of salinities

2017

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Cenchrus pauciflorus (field sandbur) is an annual weed native to South America and Africa that has become an invasive plant in farmland and pastures around the globe. It forms two types of seeds that are different in shape and mass, defined as big seeds (M) and small seeds (P). This study examined effects of salinity and seed type on germination efficiency of C. pauciflorus. Increased concentration of salinity solutions (NaCl, Na 2 SO 4 and Na 2 CO 3 ) reduced germination rate and final germination percentage (FGP) of both seed types, while prolonging the mean final germination time (T FG ). M seeds tolerated salinity levels below 400 mmol (NaCl), 350 mmol (Na 2 SO 4 ) and 300 mmol (Na 2 CO 3 ), while P seeds tolerated salinity levels below 350 mmol (NaCl), 250 mmol (Na 2 SO 4 ) and 150 mmol (Na 2 CO 3 ). Furthermore, M seeds were less sensitive than P seeds, confirmed via increased germination rate (61%, 42% and 40%), FGP (36%, 29% and 26%), and shorter T FG (37%, 19% and 16%) in NaCl, Na 2 SO 4 and Na 2 CO 3 solutions respectively. Moreover, the germination threshold values of M and P seeds were 426 and 365 mmol for NaCl, 354 and 264 mmol for Na 2 SO 4 and 308 and 149 mmol for Na 2 CO 3 respectively. These results add to our understanding of the germination strategy of different seed types of C. pauciflorus. This information on germination ecology may indicate a potential to invade regions with little or no salt stress.

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

Cited by 12 publications

References 31 publications

Predicting Seedling Emergence of Three Canarygrass (Phalaris) Species under Semi-Arid Conditions Using Parametric and Non-Parametric Models

Predicting Seedling Emergence of Three Canarygrass (Phalaris) Species under Semi-Arid Conditions Using Parametric and Non-Parametric Models

Hydrothermal-time-to-event models for seed germination

Germination behaviour of Cenchrus pauciflorus seeds across a range of salinities

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