Samara K. A. de Sousa, Rodrigo G. M. Nascimento, Flavio Henrique S. Rodrigues, Rafael G. Viana, Lucas C. da Costa, Hugo A. Pinheiro
{"title":"Allometric models for non-destructive estimation of the leaflet area in acai (Euterpe oleracea Mart.)","authors":"Samara K. A. de Sousa, Rodrigo G. M. Nascimento, Flavio Henrique S. Rodrigues, Rafael G. Viana, Lucas C. da Costa, Hugo A. Pinheiro","doi":"10.1007/s00468-023-02474-6","DOIUrl":null,"url":null,"abstract":"<div><h3>Key message</h3><p>The leaflet area of acai (<i>Euterpe oleracea</i>) can be estimated by an exponential regression model adjusted by the relationship of leaflet maximum length and width.</p><h3>Abstract</h3><p>This work was carried out aiming to fit linear regression models for the non-invasive estimation of leaflet area (LA) in acai (<i>Euterpe oleracea</i> Mart.). Thus, 5010 leaflets were sampled from 403 fronds sampled on 100 acai seedlings. Maximum length (LL) and width (LW) of each leaflet were measured with a ruler and LA was determined using a leaf area meter. Half of the data set was used to adjust the models and the other half was used for model validation. The Jackknife re-sampling method was applied to reduce model bias. Two double-entry models (models A and B) were fitted using LL and LW simultaneously, while these linear dimensions of the leaves were separately considered in single-entry models (models C to F). The adjusted coefficients of determination varied between 0.9075 and 0.9785, with the highest values observed in models A and B, which also showed the lowest standard error of the estimate and Akaike's information criterion (AIC) score. All models were highly accurate in estimating LA, with values above 0.9156; however, the double-entry models A and B showed the best performance regarding the relationship between estimated and observed LA. Comparing the double-entry models, the lowest AIC score in model B indicates that this model is the most parsimonious for non-invasive estimation of acai leaflet area in relation to model A. Therefore, the equation <span>\\({\\text{LA}} = 1.0147 \\left[{{\\text{e}}}^{0.3685 + 0.8165 {\\text{ln}}\\left({\\text{LL}} \\times {\\text{LW}}\\right)}\\right]\\)</span>, deduced from model B, is the more precise model for the non-invasive determination of leaflet area in acai seedlings.</p></div>","PeriodicalId":805,"journal":{"name":"Trees","volume":"38 1","pages":"169 - 178"},"PeriodicalIF":2.1000,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trees","FirstCategoryId":"2","ListUrlMain":"https://link.springer.com/article/10.1007/s00468-023-02474-6","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"FORESTRY","Score":null,"Total":0}
引用次数: 0
Key message
The leaflet area of acai (Euterpe oleracea) can be estimated by an exponential regression model adjusted by the relationship of leaflet maximum length and width.
Abstract
This work was carried out aiming to fit linear regression models for the non-invasive estimation of leaflet area (LA) in acai (Euterpe oleracea Mart.). Thus, 5010 leaflets were sampled from 403 fronds sampled on 100 acai seedlings. Maximum length (LL) and width (LW) of each leaflet were measured with a ruler and LA was determined using a leaf area meter. Half of the data set was used to adjust the models and the other half was used for model validation. The Jackknife re-sampling method was applied to reduce model bias. Two double-entry models (models A and B) were fitted using LL and LW simultaneously, while these linear dimensions of the leaves were separately considered in single-entry models (models C to F). The adjusted coefficients of determination varied between 0.9075 and 0.9785, with the highest values observed in models A and B, which also showed the lowest standard error of the estimate and Akaike's information criterion (AIC) score. All models were highly accurate in estimating LA, with values above 0.9156; however, the double-entry models A and B showed the best performance regarding the relationship between estimated and observed LA. Comparing the double-entry models, the lowest AIC score in model B indicates that this model is the most parsimonious for non-invasive estimation of acai leaflet area in relation to model A. Therefore, the equation \({\text{LA}} = 1.0147 \left[{{\text{e}}}^{0.3685 + 0.8165 {\text{ln}}\left({\text{LL}} \times {\text{LW}}\right)}\right]\), deduced from model B, is the more precise model for the non-invasive determination of leaflet area in acai seedlings.
关键信息巴西莓（Euterpe oleracea）的小叶面积可通过指数回归模型估算，该模型根据小叶最大长度和宽度的关系进行调整。 摘要这项工作旨在拟合线性回归模型，以非侵入式估算巴西莓（Euterpe oleracea Mart.因此，从 100 株巴西莓幼苗的 403 个叶片中抽取了 5010 个小叶样本。用尺子测量了每片小叶的最大长度（LL）和宽度（LW），并用叶面积计测定了LA。数据集的一半用于调整模型，另一半用于模型验证。采用积刀再取样法减少模型偏差。使用 LL 和 LW 同时拟合了两个双入口模型（模型 A 和 B），而在单入口模型（模型 C 至 F）中分别考虑了叶片的这些线性尺寸。调整后的决定系数介于 0.9075 和 0.9785 之间，其中模型 A 和模型 B 的值最高，估计的标准误差和阿凯克信息准则（AIC）得分也最低。所有模型在估计 LA 方面的准确度都很高，其值都高于 0.9156；但是，在估计 LA 与观测 LA 之间的关系方面，双输入模型 A 和 B 的表现最好。比较双条目模型，模型 B 的 AIC 分数最低，这表明该模型相对于模型 A 最适合于无创估计巴西莓小叶面积。0147 \left[{{text/{e}}}^{0.3685+0.8165{text/{ln}}\left({text/{LL}} \times\{text/{LW}}\right)}\right]\)是从模型 B 中推导出来的，是无创测定巴西莓幼苗小叶面积的更精确的模型。
期刊介绍：
Trees - Structure and Function publishes original articles on the physiology, biochemistry, functional anatomy, structure and ecology of trees and other woody plants. Also presented are articles concerned with pathology and technological problems, when they contribute to the basic understanding of structure and function of trees. In addition to original articles and short communications, the journal publishes reviews on selected topics concerning the structure and function of trees.
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