Relationship between morpho-agronomic traits in tomato hybrids

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Maria Inês Diel
Débora Turchetto Zamban
Tiago Olivoto
Dionatan Ketzer krysczun
Marcos Vinícius Marques Pinheiro
Bruno Giacomini Sari
Denise Schmidt
Alessandro Dal'Col Lúcio


The objective of this study was to identify and estimate the relationships between production component variables and fruit yields in tomatoes. This experiment was conducted in a randomized block design with a 2×3×3 factorial arrangement, with two tomato hybrids (Netuno and San Vito), three doses of boron (H3BO3 - 0, 2, 4 g pit1)  and three calcium floral applications (no application; application every 7 days; and application every 14 days), totaling 18 treatments with four replications and 20 plants per plot. Pearson correlation coefficients were estimated between the measured variables and, after that, those with greater significance were selected for productivity with the Stepwise method and verification of multicollinearity using the number of condition and inflation factor of the variance. The correlations of the selected variables were decomposed into direct and indirect effects on fruit productivity with path analysis. There was a strong correlation between the variables, excluding the variables height, diameter and average mass of the fruits. Thus, there were cause and effect relationships between the independent variable total mass of the fruits and the principal variable total fruit yield. The variables diameter and total number of fruits did not contribute to an increased tomato production.


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Alvares, C.A., J.L. Stape, P.C. Sentelhas, J.L. de Moraes Gonçalves, and G. Sparovek. 2013. Koppen’s climate classification map for Brazil. Meteorol. Z. 22(6), 711-728. Doi: 10.1127/0941-2948/2013/0507

Bastías, E., C. Alcaraz-López, I. Bonilla, M.C. Martínez-Ballesta, L. Bolaños, and M. Carvajal. 2010. Interactions between salinity and boron toxicity in tomato plants involve apoplastic calcium. J. Plant Physiol. 167(1), 54-60. Doi: 10.1016/J.JPLPH.2009.07.014

Cruz, C.D., A.J. Regazzi, and P.C.S. Carneiro. 2012. Modelos biométricos aplicados ao melhoramento genético. 4th ed. Editora UFV, Viçosa, Brazil.

Donazzolo, J., V.P. Salla, S.A.Z. Sasso, M.A. Danner, I. Citadin, and R.O. Nodari 2017. Path analysis for selection of feijoa with greater pulp weight. Ciênc. Rural 47(6), e20161062. Doi: 10.1590/0103-8478cr20161062

Edel, K.H., E. Marchadier, C. Brownlee, J. Kudla, and A.M. Hetherington. 2017. The evolution of calcium-based signalling in plants. Curr. Biol. 27(13), R667–R679. Doi: 10.1016/J.CUB.2017.05.020

Fallahi, H.-R., S.H.R. Ramazani, M. Ghorbany, and M. Aghhavani-Shajari. 2017. Path and factor analysis of roselle (Hibiscus sabdariffa L.) performance. J. Appl. Res. Med. Aromat. Plants 6, 119-125. Doi: 10.1016/J.JARMAP.2017.04.001

FAOSTAT, 2018. Crops - tomato. In:; consulted: May, 2018.

González-Fontes, A., M.T. Navarro-Gochicoa, C.J. Ceacero, M.B. Herrera-Rodríguez, J.J. Camacho-Cristóbal, and J. Rexach. 2017. Understanding calcium transport and signaling, and its use efficiency in vascular plants. pp. 165-180. In: Hossain, M.A., T. Kamiya, D.J. Burritt, L.-S.P. Tran, and T. Fujiwara (eds.). Plant macronutrient use efficiency: molecular and genomic perspectives in crop plants. Elsevier, Cambridge, MA. Doi: 10.1016/B978-0-12-811308-0.00009-0

Hocking, R.R. 1976. The analysis and selection of variables in linear regression. Biometric 32(1), 1-49. Doi: 10.2307/2529336

Hosmer, D.W., B. Jovanovic, and S. Lemeshow. 1989. Best subsets logistic regression. Biometric 45(4), 1265-1270. Doi: 10.2307/2531779

Kis, B.B. and C. Carvalho. 2017. Anuário brasileiro de hortaliças, 2017. Editora Gazeta, Santa Cruz do Sul, Brazil.

Kumar, D., R. Kumar, S. Kumar, M.L. Bhardwaj, M.C. Thakur, R. Kumar, K.S. Thakur, B.S. Dogra, A. Vikram, A. Thakur, and P. Kumar. 2013. Genetic variability, correlation and path coefficient analysis in tomato. Int. J. Veg. Sci. 19(4), 313-323. Doi: 10.1080/19315260.2012.726701

Lúcio, A.D., L. Storck, W. Krause, R.Q. Gonçalves, and A.H. Nied. 2013. Relações entre os caracteres de maracujazeiro-azedo. Ciênc. Rural 43(2), 225-232. Doi: 10.1590/S0103-84782013000200006

Moreira, S.O., L.S.A. Gonçalves, R. Rodrigues, C.P. Sudré, A.T. Amaral Júnior, and A.M. Medeiros. 2013. Correlações e análise de trilha sob multicolinearidade em linhas recombinadas de pimenta (Capsicum annuum L.). Rev. Bras. Ciênc. Agrar. 8(1), 15-20. Doi: 10.5039/agraria.v8i1a1726

Olivoto, T., V.Q. de Souza, V.Q., M. Nardino, I.R.Carvalho, M. Ferrari, A.J. de Pelegrin, V.J. Szareski, and D. Schmidt. 2017. Multicollinearity in path analysis: a simple method to reduce its effects. Agron. J. 109(1), 131-142. Doi: 10.2134/agronj2016.04.0196

Perica, S., P.H. Brown, J.H. Connell, A.M.S. Nyomora, C. Dordas, H. Hu, and J.Stangoulis. 2001. Foliar boron application improves flower fertility and fruit set of olive. HortScience 36(4), 714-716. Doi: 10.21273/HORTSCI.36.4.714

Plese, L.P.M., C.S. Tiritan, E.I. Yassuda, L.I. Prochnow, J.E. Corrente, and S.C. Mello. 1998. Efeitos das aplicações de cálcio e de boro na ocorrência de podridão apical e produção de tomate em estufa. Sci. Agric. 55(1), 144-148. Doi: 10.1590/S0103-90161998000100023

R Core Team, 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Cary, NC.

Rafiei, F. and G.H.A. Saeidi. 2005. Genotypic and phenotypic relationships among agronomic traits and yield components in safflower (Carthamus tinctorious L.). Sci. J. Agric. 28(1), 137-148.

Rios, S.A., A. Borém, P.E.O. Guimarães, and M.C.D. Paes. 2012. Análise de trilha para carotenoides em milho. Rev. Ceres 59(3), 368-373. Doi: 10.1590/S0034-737X2012000300011

Rodrigues, G.B., B.G. Marim, D.J.H. Silva, A.P. Mattedi, and V.S. Almeida. 2010. Análise de trilha de componentes de produção primários e secundários em tomateiro do grupo Salada. Pesq. Agropec. Bras. 45, 155-162. Doi: 10.1590/S0100-204X2010000200006

Sari, B.G., A.D. Lúcio, C.S. Santana, and S.J. Lopes. 2017. Linear relationships between cherry tomato traits. Ciênc. Rural 47(3), e20160666. Doi: 10.1590/0103-8478cr20160666

Toebe, M. and A.C. Filho. 2013. Não normalidade multivariada e multicolinearidade na análise de trilha em milho. Pesqui. Agropecu. Bras. 48(5), 466-477. Doi: 10.1590/S0100-204X2013000500002

Wright, S. 1923. The theory of path coefficients a reply to nile’s criticism. Genetics 8(3), 239-255.

Zhang, Z., 2016. Variable selection with stepwise and best subset approaches. Ann. Transl. Med. 4(7), 136-136. Doi: 10.21037/atm.2016.03.35


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