{"title":"A Richards growth model to predict fruit weight","authors":"Daniel Gerhard, Elena Moltchanova","doi":"10.1111/anzs.12380","DOIUrl":null,"url":null,"abstract":"<p>The Richards model comprises several popular sigmoidal and monomolecular growth curves. We illustrate fitting of a Bayesian Richards model by splitting the full growth model into several submodels, followed by a model selection procedure. The performance of the methodology is evaluated by Monte Carlo simulations. A double-sigmoidal version of the Richards model is applied to model grape bunch weight based on data from a New Zealand vineyard over a single growing period.</p><p>A Bayesian Richards growth model applied to grape size data. Representations of phenological processes are selected through multi-model inference.</p>","PeriodicalId":0,"journal":{"name":"","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/anzs.12380","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"","FirstCategoryId":"100","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/anzs.12380","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
The Richards model comprises several popular sigmoidal and monomolecular growth curves. We illustrate fitting of a Bayesian Richards model by splitting the full growth model into several submodels, followed by a model selection procedure. The performance of the methodology is evaluated by Monte Carlo simulations. A double-sigmoidal version of the Richards model is applied to model grape bunch weight based on data from a New Zealand vineyard over a single growing period.
A Bayesian Richards growth model applied to grape size data. Representations of phenological processes are selected through multi-model inference.
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