Allometric equations to estimate the leaf area of Psychotria colorata (Willd. Ex Schult.) Müll.Arg.

João Everthon da Silva  Ribeiro; Francisco  Romário Andrade Figueiredo; Ester  dos Santos Coêlho; Marlenildo Ferreira Melo

doi:10.14393/BJ-v37n0a2021-53711

Autores

João Everthon da Silva Ribeiro Universidade Federal da Paraíba https://orcid.org/0000-0002-1937-0066
Francisco Romário Andrade Figueiredo Universidade Federal Rual do Semi-Árido https://orcid.org/0000-0002-4506-7247
Ester dos Santos Coêlho Universidade Federal Rural do Semi-árido https://orcid.org/0000-0002-5541-1937
Marlenildo Ferreira Melo Universidade Federal Rural do Semi-árido https://orcid.org/0000-0001-7449-6916

DOI:

https://doi.org/10.14393/BJ-v37n0a2021-53711

Palavras-chave:

Allometric equations, Leaf blade, Modeling, Perpétua-do-mato, Rubiaceae

Resumo

Estimating leaf area using non-destructive methods from regression equations has become a more efficient, quick, and accurate way. Thus, this study aimed to propose an equation that significantly estimates the leaf area of Psychotria colorata (Rubiaceae) through linear leaf dimensions. For this purpose, 200 leaves of different shapes were collected, and length (L), width (W), product of length by width (L.W), and real leaf area (LA) of each leaf blade were determined. Then, equations were adjusted for predicting leaf area using simple linear, linear (0.0), quadratic, cubic, power, and exponential regression models. The proposed equation was selected according to the coefficient of determination (R²), Willmott's agreement index (d), Akaike's information criterion (AIC), mean absolute error (MAE), mean squared error (RMSE) and BIAS index. It was noted that the equations adjusted using L.W met the best criteria for estimating leaf area, but the equation LA = 0.59 * L.W from linear regression without intercept was the most suitable. This equation predicts that 59% of leaf area is explained by L.W. Concluding, the leaf area of P. colorata can be estimated using an allometric equation that uses linear leaf blade dimensions.

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Referências

AKAIKE, H. A new look at the statistical model identification. IEEE Transactions on Automatic Control. 1974, 19(6), 716-723. https://doi.org/10.1109/tac.1974.1100705

ASSIS, J.P., et al. Estimação da área foliar da “jitirana” (Merremia aegyptia (L.) Urban), através de modelos de regressão para Mossoró - RN. Revista Verde de Agroecologia e Desenvolvimento Sustentável. 2015, 10(2), 75-81. https://doi.org/10.18378/rvads.v10i4.3872

BENINCASA, M.M.P. Análise de crescimento de plantas: noções básicas. Jaboticabal: FUNEP, 2003.

BRAGA, N.S., et al. Mathematical models for teak foliar area estimation (Tectonas grandis L. f.). Nucleus. 2018, 15(2), 331-339. https://doi.org/10.3738/1982.2278.2910

CANDIDO, W.S., et al. Modelo para estimar a área foliar de Combretum leprosum Mart. Acta Agronómica. 2013, 62(1), 37-41.

CARVALHO, J.O., et al. Leaf area estimation from linear measurements in different ages of Crotalaria juncea plants. Anais da Academia Brasileira de Ciências. 2017a, 89(3), 1851-1868. https://doi.org/10.1590/0001-3765201720170077

CARVALHO, L.B., ALVES, E.A. and BIANCO, S. Modelo não destrutivo para estimar a área foliar de Commelina diffusa. Planta Daninha. 2017b, 35, 1-5. https://doi.org/10.1590/S0100-83582017350100088

COLLINS, T.J. ImageJ for microscopy. BioTechniques. 2007, 43(1), 25-30. https://doi.org/10.2144/000112517

Ferreira, M.L.A., et al. Determinação da área foliar da flor de seda (Calotropis procera) no sertão paraibano. Agropecuária Científica no Semiárido. 2015, 11(1), 53-55. https://doi.org/10.30969/acsa.v11i1.610

GAO, M., et al. Estimation of leaf area for large scale phenotyping and modeling of rose genotypes. Scientia Horticulturae. 2012, 138, 227-234. https://doi.org/10.1016/j.scienta.2012.02.014

HINNAH, F.D., et al. Estimativa da área foliar da berinjela em função das dimensões foliares. Bragantia. 2014, 73(3), 213-218. https://doi.org/10.1590/1678-4499.0083

JANSSEN, P.H.M. and HEUBERGER, P.S.C. Calibration of process-oriented models. Ecological Modelling. 1995, 83(1-2), 55-66. https://doi.org/10.1016/0304-3800(95)00084-9

LEITE, H.G. and ANDRADE, V.C.L. Um método para condução de inventários florestais sem o uso de equações volumétricas. Revista Árvore. 2002, 26(3), 321-328. https://doi.org/10.1590/S0100-67622002000300007

LEITE, M.L.M.V., et al. Leaf area estimate of Pennisetum glaucum by linear dimensions. Acta Scientiarum. Animal Sciences. 2019, 41(1), 1-7. https://doi.org/10.4025/actascianimsci.v41i1.42808

LEITE, M.L.M.V., et al. Estimativa da área foliar em Urochloa mosambicensis por dimensões lineares. Agropecuária Técnica. 2017, 38(1), 9-16. https://doi.org/10.25066/agrotec.v38i1.32041

LEVINE, D.M., STEPHAN, D.F. and SZABAT, K.A. Estatistic for managers using Microsoft Excel: Global edition. 8th ed. London: Person, 2017.

LUCENA, L.R.R., et al. Estimativa da área foliar em Urochloa mosambicensis por dimensões foliares e imagens digitais. Archivos de Zootecnia. 2018, 66(253), 141-150.

MALAGI, G., et al. Método não destrutivo para determinação da área foliar da videira, cultivar BRS-Violeta. Revista Brasileira de Fruticultura. 2010, 32(4), 1250-1254. https://doi.org/10.1590/S0100-29452011005000005

MATSUURA, H., PORTO, D. and FETT-NETO, A., 2013. Bioactive alkaloids from South American Psychotria and related Rubiaceae. In: K. RAMAWAT and J. MÉRILLON, eds. Natural Products: Phytochemistry, Botany and Metabolism of Alkaloids, Phenolics and Terpenes, Berlin: Springer-Verlag Berlin Heidelberg, pp. 119-147. https://doi.org/10.1007/978-3-642-22144-6

MOTA, C.S., LEITE, H.G. and CANO, M.A.O. Equações para estimar área foliar de folíolos de Acrocomia aculeta. Pesquisa Florestal Brasileira. 2014, 34(79), 217-224. https://doi.org/10.4336/2014.pfb.34.79.684

MOURA, L.T.S. and MARUO, V.M. Aspectos farmacológicos e toxicológicos de Psychotria colorata - Revisão. Revista Científica de Medicina Veterinária. 2014, 22(23), 1-16.

OLIVEIRA, R.F., et al. Utilização de modelos alométricos para estimar a área foliar de Talinum triangulare e Talinum paniculatum. Revista Brasileira de Agropecuária Sustentável. 2019, 9(1), 112-119. https://doi.org/10.21206/rbas.v9i1.3861

PADRÓN, R.A.R., et al. Non-destructive models to estimate leaf area on bell pepper crop. Ciência Rural. 2016, 46(11), 1938-1944. https://doi.org/10.1590/0103-8478cr20151324

PEEL, M.C., FINLAYSON, B.L. and MCMAHON, T.A. Updated world map of the Köppen-Geiger climate classification. Hydrology and Earth System Sciences. 2007, 11, 1633-1644.

PEZZINI, R.V., et al. Models for leaf area estimation in dwarf pigeon pea by leaf dimensions. Bragantia. 2018, 77(2), 221-229. https://doi.org/10.1590/1678-4499.2017106

POMPELLI, M.F., et al. Allometric models for non-destructive leaf area estimation of Jatropha curcas. Biomass and Bioenergy. 2012, 36, 77-85. https://doi.org/10.1016/j.biombioe.2011.10.010

RIBEIRO, J.E.S., BARBOSA, A.J.S. and ALBUQUERQUE, M.B. Leaf area estimate of Erythroxylum simonis Plowman by linear dimensions. Floresta e Ambiente. 2018a, 25(2), 1-7. https://doi.org/10.1590/2179-8087.010817

RIBEIRO, J.E.S., et al. Seasonal variation in gas exchange by plants of Erythroxylum simonis Plowman. Acta Botanica Brasilica. 2018b, 32(2), 287-296. https://doi.org/10.1590/0102-33062017abb0240

RIBEIRO, J.E.S., et al. Estimation of leaf area of Erythroxylum citrifolium from linear leaf dimensions. Bioscience Journal. 2019a, 35(6), 1923-1931. https://doi.org/10.14393/BJ-v35n6a2019-41743

RIBEIRO, J.E.S., et al. Leaf area estimation for Psychotria carthagenensis and Psychotria hoffmannseggiana as a function of linear leaf dimensions. Acta Scientiarum. Biological Sciences. 2019b, 41(1), 1-8. https://doi.org/10.4025/actascibiolsci.v41i1.43494

RIBEIRO, J.E.S., et al. Non-destructive method for estimating leaf area of Erythroxylum pauferrense (Erythroxylaceae) from linear dimensions of leaf blades. Acta Botanica Mexicana. 2020a, 127, 1-12. https://doi.org/10.21829/abm127.2020.1717

RIBEIRO, J.E.S., et al. Leaf area estimation of Palicourea racemosa (Aubl.) Borhidi from linear measurements. Floresta e Ambiente. 2020b, 27(4), 1-7. https://doi.org/10.1590/2179-8087.010518

RIBEIRO, J.E.S., et al. A non-destructive method for estimating leaf area of Ceiba glaziovii (Kuntze) K. Schum. Floresta. 2020c, 50(1), 1063-1070. https://doi.org/10.5380/rf.v50i1.61088

SARKER, S.K., et al. Developing allometric equations for estimating leaf area and leaf biomass of Artocarpus chaplasha in Raghunandan Hill Reserve, Bangladesh. Southern Forests. 2013, 75(1), 51-57. https://doi.org/10.2989/20702620.2013.773601

SCHMILDT, E.R., et al. Análise comparativa de equações para estimativa da área foliar em cafeeiros. Coffee Science. 2014, 9(2), 155-167.

SILVA, S.F., et al. Modelos alométricos para estimativa da área foliar de boldo pelo método não destrutivo. Revista Agrarian. 2017, 10(37), 193-198. https://doi.org/10.30612/agrarian.v10i37.2911

SKIRYCZ, A., et al. Medicinal Bioprospecting of the Amazon Rainforest: A Modern Eldorado? Trends in Biotechnology. 2016, 34(10), 781-790. https://doi.org/10.1016/j.tibtech.2016.03.006

SOUSA, L.F., et al. Método prático e eficiente para estimar a área foliar de gramíneas forrageiras tropicais. Archivos de Zootecnia. 2015, 64(245), 83-85. https://doi.org/10.21071/az.v64i245.380

SOUZA, R.K.D., MENDONÇA, A.C.A.M. and SILVA, M.A.P. Ethnobotanical, phytochemical and pharmacological aspects Rubiaceae species in Brazil. Revista Cubana de Plantas Medicinales. 2013, 18(1), 140-156.

SPANN, T.M. and HEEREMA, R.J. A simple method for non-destructive estimation of total shoot leaf area in tree fruit crops. Scientia Horticulturae. 2010, 125(3), 528-533. https://doi.org/10.1016/j.scienta.2010.04.033

TARTAGLIA, F.L., et al. Non-destructive models for leaf area determination in canola. Revista Brasileira de Engenharia Agricola e Ambiental. 2016, 20(6), 551-556. https://doi.org/10.1590/1807-1929/agriambi.v20n6p551-556

TAYLOR, C.M., CAMPOS, M.T.V.A. and ZAPPI, D. Flora Da Reserva Ducke, Amazonas, Brasil: Rubiaceae. Rodriguesia. 2007, 56(86), 189-204.

TOEBE, M., et al. Leaf area estimation in triticale by leaf dimensions. Revista Brasileira de Ciências Agrárias. 2019, 14(2), 1-9. https://doi.org/10.5039/agraria.v14i2a5656

VICENTINI, A.R., et al., 2016. Plantas da família Rubiaceae úteis ao tratamento de enfermidades veterinárias. In: U.R. VIANNA, et al., eds. Tópicos especiais em ciência animal V. Alegre: CAUFES, pp. 149-162.

WILLMOTT, C.J. On the validation of models. Physical Geography. 1981, 2(2), 184-194. https://doi.org/10.1080/02723646.1981.10642213

ZAPPI, D.C., et al. Growing knowledge: An overview of seed plant diversity in Brazil. Rodriguesia. 2015, 66(4), 1085-1113. https://doi.org/10.1590/2175-7860201566411

ZHANG, L. and PAN, L. Allometric models for leaf area estimation across different leaf-age groups of evergreen broadleaved trees in a subtropical forest. Photosynthetica. 2011, 49(2), 219-226. https://doi.org/10.1007/s11099-011-0027-x