Leaf Area Estimation of <i>Palicourea racemosa</i> (Aubl.) Borhidi from Linear Measurements

Ribeiro, João Everthon da Silva; Figueiredo, Francisco Romário Andrade; Coêlho, Ester dos Santos; Pereira, Walter Esfrain; Albuquerque, Manoel Bandeira de

doi:10.1590/2179-8087.010518

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ORIGINAL ARTICLE, Forest Management • Floresta Ambient. 27 (4) • 2020 • https://doi.org/10.1590/2179-8087.010518 copy

Leaf Area Estimation of Palicourea racemosa (Aubl.) Borhidi from Linear Measurements

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

Estimating leaf area is essential to evaluate vegetal growth. Our study sought to obtain statistical models to allow the leaf area estimation of Palicourea racemosa, considering its length (L) and width (W). For such purpose, we collected 200 leaves of this species in the State Park Mata do Pau-Ferro, in the municipality of Areia, Paraíba, Northeast of Brazil. The regression models used were: linear, linear without intercept, power and exponential. The choice of the best equation was based on the values of the coefficient of determination (R²), root mean square error (RMSE), Akaike information criterion (AIC), Willmott concordance index (d) and BIAS ratio. All linear and power models may be used to measure the P. racemosa leaf area; however, the power model $LA = 0.609 * {(L . W)}^{0.995}$ is the most recommended to estimate this species’ leaf area.

Keywords:
biometry; modeling; non-destructive method; leaf dimension; Rubiaceae

Model	Formula
Linear	$LA = a + b * L$
Linear	$LA = a + b * W$
Linear	$LA = a + b * (L . W)$
Linear without intercept (0.0)	$LA = b * (L . W)$
Power	$LA = a * L^{b}$
Power	$LA = a * W^{b}$
Power	$LA = a * {(L . W)}^{b}$
Exponential	$LA = a * b^{L}$
Exponential	$LA = a * b^{W}$
Exponential	$LA = a * b^{(L . W)}$

	Length (cm)	Width (cm)	L.W (cm²)	Leaf area (cm²)
Minimum	0.73	0.26	0.19	0.13
Maximum	15.31	5.30	80.69	49.01
Mean	6.96	2.47	20.98	12.59
Median	6.86	2.37	15.92	9.50
Standard deviation	3.27	1.20	17.56	10.52
Standard error	0.23	0.08	1.24	0.74
CV (%)	47.06%	48.71%	83.70%	83.63%

Model	Estimated equation	R²	AIC	RMSE	d	BIAS
Linear	$LA = 8.754 + 3.065 * L$	0.9100	1,522.804	3.1514	0.9759	0.3151
Linear	$LA = - 8.235 + 8.425 * W$	0.9281	1,478.121	2.8183	0.9809	−0.2379
Linear	$LA = 0.035 + 0.598 * (L . W)$	0.9960	903.240	0.6696	0.9989	−0.0147
Linear (0.0)	$LA = 0.598 * (L . W)$	0.9960	901.236	0.6700	0.9989	−0.0148
Power	$LA = 0.219 * L^{1.9857}$	0.9749	843.539	1.9639	0.9910	−0.0111
Power	$LA = 1.941 * W^{1.8726}$	0.9809	807.169	1.7932	0.9925	0.0251
Power	$LA = 0.609 * {(L . W)}^{0.995}$	0.9975	412.820	0.6691	0.9990	0.0041
Exponential	$LA = 2.384 * 1 . 233^{L}$	0.7828	960.685	2.6321	0.9827	0.4151
Exponential	$LA = 2.586 * 1 . 749^{W}$	0.7976	952.750	2.5804	0.9833	0.4139
Exponential	$LA = 6.187 * 1 . 029^{(L . W)}$	0.8694	1,096.683	3.6979	0.9630	0.6021

Instituto de Florestas da Universidade Federal Rural do Rio de Janeiro Rodovia BR 465 Km 7, CEP 23897-000, Tel.: (21) 2682 0558 | (21) 3787-4033 - Seropédica - RJ - Brazil
E-mail: floram@ufrrj.br

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[1] CORRESPONDENCE TO João Everthon da Silva Ribeiro Universidade Federal da Paraíba (UFPB), Rodovia BR 079, km 12, Campus II, CEP 58397-000, Areia, PB, Brasil e-mail: j.everthon@hotmail.com