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Leaf area estimation from linear measurements in different ages of Crotalaria juncea plants

Overview of attention for article published in Anais da Academia Brasileira de Ciências, September 2017
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Title
Leaf area estimation from linear measurements in different ages of Crotalaria juncea plants
Published in
Anais da Academia Brasileira de Ciências, September 2017
DOI 10.1590/0001-3765201720170077
Pubmed ID
Authors

Juliana O DE Carvalho, Marcos Toebe, Francieli L Tartaglia, Cirineu T Bandeira, André L Tambara

Abstract

The goal of this study was to estimate the leaf area of Crotalaria juncea according to the linear dimensions of leaves from different ages. Two experiments were conducted with C. juncea cultivar IAC-KR1, in the 2014/2015 sowing seasons. At 59, 82, 102, 129 days after sowing (DAS) of the first and 61, 80, 92, 104 DAS of the second experiment, 500 leaves were collected, totaling 4,000 leaves. In each leaf, the linear dimensions were measured (length, width, length/width ratio and length × width product) and the specific leaf area was determined through Digimizer and Sigma Scan Pro software, after scanning images. Then, 3,200 leaves were randomly separated to generate mathematical models of leaf area (Y) in function of linear dimension (x), and 800 leaves for the models validation. In C. juncea, the leaf areas determined by Digimizer and Sigma Scan Pro software are identical. The estimation models of leaf area as a function of length × width product showed superior adjustments to those obtained based on the evaluation of only one linear dimension. The linear model Ŷ=0.7390x (R2=0.9849) of the real leaf area (Y) as a function of length × width product (x) is adequate to estimate the C. juncea leaf area.

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The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 69 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 18 26%
Student > Ph. D. Student 16 23%
Student > Bachelor 8 12%
Researcher 5 7%
Student > Postgraduate 3 4%
Other 7 10%
Unknown 12 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 45%
Engineering 5 7%
Environmental Science 4 6%
Computer Science 3 4%
Mathematics 2 3%
Other 6 9%
Unknown 18 26%