A stochastic approach for a model of flowering in kiwifruit `Hayward'

Agostini, D.; Habib, Robert H.

doi:10.1080/14620316.1999.11511067

Cited by 5 publications

(6 citation statements)

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“…Stendiger and Shoemaker () extended the model to provide for the possibility of spatially variable developmental rates and allowing multinomial proportions to vary according to a Dirichlet distribution, with mean values following a Brownian motion model useful for describing populations in heterogeneous regions. The conceptual scheme has later been applied by Agostini, Habib, and Chadoeuf (), to kiwifruit flowering and by Normand and Habib (), on flowering of Psidium cattleianum . Yet, despite the advantages that a probabilistic model offers, such an approach has not been applied to detect overlapping of stage and generation transitions in moth species of economic significance and particularly C. pomonella .…”

Section: Discussionmentioning

confidence: 99%

“…Stendiger and Shoemaker (1985) extended the model to provide for the possibility of spatially variable developmental rates and allowing multinomial proportions to vary according to a Dirichlet distribution, with mean values following a Brownian motion model useful for describing populations in heterogeneous regions. The conceptual scheme has later been applied by Agostini, Habib, and Chadoeuf (1999), to kiwifruit flowering and by Normand and Habib (2001), on flowering of Psidium cattleianum.…”

Section: Discussion and Con Clus I On Smentioning

confidence: 99%

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Stage‐specific probabilistic phenology model of Cydia pomonella (Lepidoptera: Tortricidae) using laboratory maximum likelihood parameter estimates

Damos

Soulopoulou

2018

J Applied Entomology

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In this study, a probabilistic degree‐day phenology model has been developed for the codling moth, Cydia pomonella, and calibrated using data from laboratory growth studies. The model is further used to predict the succession and overlapping of certain biological events of C. pomonella in probabilistic‐physiological time scale in northern Greece fruit orchards. The model satisfactorily predicts the stage‐specific pest population dynamics, including egg laying and hatching, the occurrence of larvae and pupae stages and the emergence of adults. According to the model projections for the adult flights, there is a very high probability, p = 0.999, of observing adults of the first flight generation until 333 degree‐days (DD), but a very low probability of finding adults of the second flight generation. Moreover, at 575 DD, the probability of finding an individual to lay eggs is p = 0.15. However, there is nearly the same probability of egg hatch, p = 0.36, and larval completion p = 0.313, while at the same time, the probability of pupal completion is very low, p = 0.001. The above model predictions were validated using field data for the adult stage emergence as well as for the percentage of larval damage providing satisfactory results considering that larval emergence prediction was close to actual fruit damage observed in field. This information is very important considering that IPM programs rely on the use of biorational compounds, such as IGRs and bio‐toxins which are stage selective and often have a shorter residual activity than the preceding broad‐spectrum insecticides.

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Section: Discussionmentioning

confidence: 99%

Section: Discussion and Con Clus I On Smentioning

confidence: 99%

Stage‐specific probabilistic phenology model of Cydia pomonella (Lepidoptera: Tortricidae) using laboratory maximum likelihood parameter estimates

Damos

Soulopoulou

2018

J Applied Entomology

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“…The heart of the model was developed by Osawa et al (1983) to describe bud phenology of balsam ®r [Abies balsamea (L.) Mill.]. Dennis et al (1986) adapted it to insect development, and Agostini et al (1999) to kiwifruit ¯owering. We recall here its assumptions and formulation.…”

Section: Model Descriptionmentioning

confidence: 99%

“…Trees have the same ¯owering threshold a, and there is a parameter of variance relative to the ¯owering of the whole population: s 2 . This model, similar to that of Agostini et al (1999), has two parameters, a and s 2 , and is described by eqn (2).…”

Section: Model Descriptionmentioning

confidence: 99%

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A Stochastic Flowering Model Describing an Asynchronically Flowering Set of Trees

Normand¹

2002

Annals of Botany

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A general stochastic model is presented that simulates the time course of flowering of individual trees and populations, integrating the synchronization of flowering both between and within trees. Making some hypotheses, a simplified expression of the model, called the 'shoot' model, is proposed, in which the synchronization of flowering both between and within trees is characterized by specific parameters. Two derived models, the 'tree' model and the 'population' model, are presented. They neglect the asynchrony of flowering, respectively, within trees, and between and within trees. Models were fitted and tested using data on flowering of Psidium cattleianum observed at study sites at elevations of 200, 520 and 890 m in Reunion Island. The 'shoot' model fitted the data best and reproduced the strong irregularities in flowering shown by empirical data. The asynchrony of flowering in P. cattleianum was more pronounced within than between trees. Simulations showed that various flowering patterns can be reproduced by the 'shoot' model. The use of different levels of organization of the general model is discussed.

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Modelling Fruit Quality: Ecophysiological, Agronomical and Ecological Perspectives

Génard

Lescourret

Production Practices and Quality Assessment of Food Crops Volume 1

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A stochastic approach for a model of flowering in kiwifruit `Hayward'

Cited by 5 publications

References 1 publication

Stage‐specific probabilistic phenology model of Cydia pomonella (Lepidoptera: Tortricidae) using laboratory maximum likelihood parameter estimates

Stage‐specific probabilistic phenology model of Cydia pomonella (Lepidoptera: Tortricidae) using laboratory maximum likelihood parameter estimates

A Stochastic Flowering Model Describing an Asynchronically Flowering Set of Trees

Modelling Fruit Quality: Ecophysiological, Agronomical and Ecological Perspectives

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