Abstract
The cultivation of sweet corn is expanding in Brazil, but there are serious constraints about the availability of commercial cultivars. The selection of superior sweet corn genotypes can be performed based on selection indexes based on plant agronomic characteristics. Thus, the objective of this work was to compare selection indexes to select sweet corn genotypes aiming greater productions. The experiment was conducted at the Vegetable Experimental Station of the Federal University of Uberlândia, Campus Monte Carmelo. Eighteen sweet corn accessions (F3 generation) were evaluated. The selection indexes applied to the agronomic characteristics of sweet corn were: direct and indirect, the sum of ranks, desired gains and ideotype indexes. The characteristics evaluated presented significant differences among genotypes, except for stem diameter, prolificacy and grain number per corncob. The coefficients of variation were below 30%, and the genetic parameters were satisfactory for most of the characteristics. The greatest gains with direct selection were in production (21.23%) and productivity of commercial sweet corn ear (15.19%), however, the indirect gains are unsatisfactory for sweet corn selection, and the sum of ranks index provided a balanced distribution of gains. The sweet corn genotypes L2P11, L2P37, P45, L2L5P3, and L5P18 presented a superior performance for the set of characters evaluated.
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ABCSEM - Associação brasileira do comércio de sementes e mudas. Pesquisa de mercado. 2014. [accessed in: 2019 june 01]. http://www.abcsem.com.br
Azevedo AM, Andrade Júnior VC, Pedrosa CE, Oliveira CM, Dornas MFS, Cruz, CD, Valadares NR. 2015. Application of artificial neural networks in indirect selection: a case study on the breeding of lettuce. Bragantia, 74(4): 387–393, https://dx.doi.org/10.1590/1678-4499.0088
Baldissera JNC, Valentini G, Coan MMD, Guidolin AF, Coimbra JLM. 2014. Genetics factors related with the inheritance in autogamous plant populations. Revista de Ciências Agroveterinárias, 13(2): 181–189
Beck HE, Zimmermann NE, Mcvicar TR, Vergopolan N, Berg A, Wood EF. 2018. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data 1: 1–12, http://dx.doi.org/10.1038/sdata.2018.214
Bizari EH, Val BHP, Pereira E de M, Mauro AO Di, Unêda-Trevisoli SH. 2017. Selection indices for agronomic traits in segregating populations of soybean. Revista Ciência Agronômica, 48(1): 110–117, http://dx.doi.org/10.5935/1806-6690.20170012.
Camargo LKP, Resende JTV, Mógor AF, Camargo CK, Kurchaidt SM. 2016. Uso de índice de seleção na identificação de genótipos de batata doce com diferentes aptidões. Horticultura Brasileira, 34: 514–519, http://dx.doi.org/10.1590/s0102-053620160410
Cantelli DAV, Hamawaki OT, Rocha MR, Nogueira APO, Hamawaki RL, Sousa LB, Hamawaki CDL. 2016. Analysis of the genetic divergence of soybean lines through hierarchical and Tocher optimization methods. Genetics and molecular research, 15(4), http://dx.doi.org/10.4238/gmr.15048836
Carvalho ADF, Nogueira MTM, Silva GO, Luz JMQ, Maciel GM, Rabelo PG. 2017. Seleção de genótipos de cenoura para caracteres fenotípicos de raiz. Horticultura Brasileira, 35: 97–102, http://dx.doi.org/10.1590/s0102-053620170115
Cooxupé - Cooperativa Regional de Cafeicultores em Guaxupé. 2019. Estações meteorológicas - dados históricos; [accessed 2019 Oct 08] http://sismet.cooxupe.com.br:9000/dados/estacao/pesquisarDados/?cdEstacao=12
Cruz CD. 2013. Genes: a software package for analysis in experimental statistics and quantitative genetics. Acta Scientiarum. Agronomy, 35(3): 271–276, http://dx.doi.org/10.4025/actasciagron.v35i3.21251
Cruz CD, Regazzi AJ, Carneiro PCS. 2012. Modelos biométricos aplicados ao melhoramento genético. 4. ed. Viçosa: UFV
Cruz CD. 2006. Genes: biometria. Viçosa: UFV
Embrapa - Empresa Brasileira de Pesquisa Agropecuária. 2017. Manual de métodos de análise de solo. Embrapa: Rio de Janeiro, Brazil
Freire FM, França GE, Vasconcellos CA, Pereira Filho IA, Alves VMC, Pitta GVE. 1999. Milho verde. In: Ribeiro AC, Guimarães PTG, Alvarez VH. Recomendações para o uso de corretivos e fertilizantes em Minas Gerais. Viçosa (MG): UFV. p. 195–196
Freitas ILJ, Amaral Junior, AT, Viana AP, Pena GF, Cabral PS, Vittorazzi C, Silva TRC. 2013. Ganho genético avaliado com índices de seleção e com REML/Blup em milho‑pipoca. Pesquisa Agropecuária Brasileira, 48(11): 1464–1471, http://dx.doi.org/10.1590/S0100-204X2013001100007
Hamawaki OT, De Sousa LB, Romanato FN, Nogueira APO, Júnior CDS, Polizel AC. 2012. Genetic parameters and variability in soybean genotypes. Comunicata Scientiae, 3(2): 76–83
Hazel LN. 1943. The genetic basis for constructing selection indexes. Genetics, 28: 476–490, http://dx.doi.org/10.1590/S0100-204X2012000300012
Leite WS, Pavan BE, Matos Filho CHA, Alcantara Neto F, Oliveira CB, Feitosa FS. 2016. Genetic parameters estimation, correlations and selection indexes for six agronomic traits in soybean lines F8. Comunicata Scientiae, 7(3): 302–310, https://doi.org/10.14295/cs.v7i3.1176
Leite WS, Pavan BE, Matos Filho CHA, Feitosa FS, Oliveira CB. 2015. Estimativas de parâmetros genéticos e correlações entre caracteres agronômicos em genótipos de soja. Nativa, 3(4): 241–245, http://dx.doi.org/10.14583/2318-7670.v03n04a03
Luz JMQ, Camilo JS, Barbieri VHB, Rangel RM, Oliveira RC. 2014. Produtividade de genótipos de milho doce e milho verde em função de intervalos de colheita. Horticultura Brasileira, 32(2):163–167, http://dx.doi.org/10.1590/S0102-05362014000200007
Luz JMQ, Camilo JS, Barbieri VHB, Rangel RM, Oliveira RC. 2015. Produtividade de genótipos de milho doce e milho verde em intervalos de colheita. Revista Ceres, 62(1): 1–8, http://dx.doi.org/10.1590/S0102-05362014000200007
Mulamba NN, Mock JJ. 1978. Improvement of potential of the Eto Blanco maize (Zea mays L.) population by breeding for plant traits. Egyptian Journal Genetics and Cytology, 7: 40–51
Pereira Filho IA, Teixeira FF. 2016. O cultivo do milho-doce. Brasília, DF: Embrapa
Pesek J, Baker RJ. 1969. Desired improvement in relation to selected indices. Canadian Journal of Plant Science, 49: 803–804, http://dx.doi.org/10.4141/cjps69-137
Ramalho MAP, Abreu AFB, Santos JB, Nunes JAR. 2012. Aplicações da genética quantitativa no melhoramento de plantas autógamas. Lavras: Editora UFLA
Resende MAV, Freitas JA, Lanza MA, Resende MDV, Azevedo, CF. 2014. Divergência genética e índice de seleção via BLUP em acessos de algodoeiro para características tecnológicas da fibra. Pesquisa Agropecuária Tropical, 44(3): 334–340, http://dx.doi.org/10.1590/S1983-40632014000300006
Rosado LDS, Santos CEM, Bruckner CH, Nunes ES, Cruz CD. 2012. Simultaneous selection in progenies of yellow passion fruit using selection indices. Revista Ceres, 59: 95–101, http://dx.doi.org/10.1590/S0034-737X2012000100014
Smiderle ÉC, Furtini IV, Silva CSC, Botelho FBS, Resende MPM, Botelho RTC, Colombari Filho JM, Castro AP, Utumi MM. 2019. Index selection for multiple traits in upland rice progenies. Revista de Ciências Agrárias, 42(1): 4–12, http://dx.doi.org/10.19084/RCA18059
Smith HF. 1936. A discriminant function for plant selection. Annual Eugenics, 7: 240–250, https://doi.org/10.1111/j.1469-1809.1936.tb02143.x
Teixeira DHL, Oliveira MSP, Gonçalves FMA, Nunes JAR. 2012. Índices de seleção no aprimoramento simultâneo dos componentes da produção de frutos em açaizeiro. Pesquisa Agropecuária Brasileira, 47(2): 237–243, http://dx.doi.org/10.1590/S0100-204X2012000200012
Terres LR, Lenz E, Castro CM, Pereira, AS. 2015. Estimativas de ganhos genéticos por diferentes índices de seleção em três populações híbridas de batata. Horticultura Brasileira, 33: 305–310, http://dx.doi.org/10.1590/S0102-053620150000300005
Vasconcelos ES, Ferreira RP, Cruz DC; Moreira A, Rassini JB, Freitas AR. 2010. Estimativas de ganho genético por diferentes critérios de seleção em genótipos de alfafa. Revista Ceres, 57: 205–210, http://ainfo.cnptia.embrapa.br/digital/bitstream/item/38333/1/Estimativasganho-Rev.CERES-Reinaldo2010.pdf
Vendruscolo EP, Siqueira APS, Rodrigues AHA, Oliveira PR, Correia SR, Seleguini A. 2018. Viabilidade econômica do cultivo de milho doce submetido à inoculação com Azospirillum brasilense e soluções de tiamina. Ciências Agrarias, 61, http://dx.doi.org/10.22491/rca.2018.2674
Willians JS. 1962. The evaluation of a selection index. Biometrics, 18:375–393, http://dx.doi.org/10.2307/2527479
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da Silva, I.G., Castoldi, R., de Oliveira Charlo, H.C. et al. Prediction of Genetic Gain in Sweet Corn using Selection Indexes. J. Crop Sci. Biotechnol. 23, 191–196 (2020). https://doi.org/10.1007/s12892-019-0334-0
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DOI: https://doi.org/10.1007/s12892-019-0334-0