Abstract

ABSTRACT Leaf area measurement is pivotal for plant physiologists. Hence, accurate measurement of their leaf area is incredibly relevant in agronomic terms. The plant Stevia rebaudiana is a sucrose-free plant species that is now vital to the global production of sucrose-free foods. Here, we estimated S. rebaudiana leaf area using a nondestructive methodology comprising allometric equations. Through leaf length (L), leaf width (W), and/or their product (LW) the leaf area was determined. One thousand leaves were sampled from four distinct S. rebaudiana genotypes for model construction. Linear or power models were generated, and the best equation was selected using a statistical criterion. The statistical criteria indicated that the linear models best suited all genotypes tested, included a function of LW, exhibited increased stability, and precisely estimated coefficients. ANOVA revealed that both generalized and combined equations were feasible. Nevertheless, grouping all genotypes into a single model was not possible as the genotype leaf architectures were very dissimilar.

Highlights

  • Modelos alométricos não destrutivos para estimação da área foliar de stevia: uma análise completa e profunda

  • Various stevia species contain sweetening compounds, such as diterpene glycosides but S. rebaudiana has the highest concentrations of these substances, whose are of considerable interest for sucrose-free food production

  • The large sample size used in this study (n = 1,000) revealed a diversity of leaf size ranging from expanding to fully expanded leaves

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Summary

Introduction

Modelos alométricos não destrutivos para estimação da área foliar de stevia: uma análise completa e profunda. Several studies (Antunes et al, 2008; Pompelli et al, 2012) reported that using small sample sizes to construct leaf area allometric models can generate biased equations. They satisfied the requirements of lower Fcalc values, higher sample-adjusted coefficients of determination, greater stability and standard errors of the estimate, and non-biased residual dispersion

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