Selection indexes based on genotypic values applied to Brazilian tropical wheat breeding
Resumo
Although Brazil is one of the main agricultural countries in the world, it is historically an importer of wheat. For this reason, strategies aimed at the expansion of wheat in the country, to areas that are not traditionally producing (warmer), are of paramount importance. In wheat breeding, phenotypic values are usually used in simultaneous selection, however, they do not always correspond with genetic superiority. Therefore, the objective of this work was to evaluate the efficiency of five selection indexes applied to the genotypic values of wheat, the coincidence between the indexes and to select the most promising lines. For this, we evaluated a panel with 41 genotypes of tropical wheat, for the traits: days for flowering, disease note, plant height, hectoliter weight and grain yield. Data were submitted to REML/BLUP analysis to estimate genetic parameters and genotypic values. We applied on the BLUPs the rank summation index, multiplicative index, genotype- ideotype distance index, additive index and FAI-BLUP index. There is a genotypic variation shown by analysis of deviance for all evaluated traits. We presented different estimates of gains from selection according to the selection index applied. We observed higher estimates of gains from selection for additive and genotype-ideotype distance indexes. High similarity was observed in the selection of genotypes through the coefficient of coincidence between the indexes. Eight lines were selected simultaneously by three or more indexes. Lines VI 14047, VI 14774 and VI 14980 showed the best performance among the eight lines evaluated by the Z index.
Downloads
Referências
Abeledo, L. G., Prado, S. A., Puhl, L.E., Zhou, Y., Costa, J. M., & Miralles, D. J. (2019). Phenotypic and genetic analysis to identify secondary physiological traits for improving grain yield in wheat considering anthesis time variability. Euphytica, 215, 171-189. https://doi.org/10.1007/s10681-019-2494-2
Candido, W. S., Silva, C. M., Costa, M. L., Silva, B. E. A., Almeida, P. H. S., Coelho, I. F., & Reis, E. F. (2020a). Selection of top cross hybrids for green maize yield via REML/BLUP method. Australian Journal of Crop Science, 14, 172-178. https://doi.org/10.21475/ajcs.20.14.01.p2061
Candido, W. S., Silva, C. M., Costa, M. L., Silva, B. E. A., Miranda, B. L., Pinto, J. F. N., & Reis, E. F. (2020b). Selection indexes in the simultaneous increment of yield components in topcross hybrids of green maize. Pesquisa Agropecuária Brasileira, 55, 1-8. https://doi.org/10.1590/S1678-3921.pab2020.v55.01206
Céron-Rojas, J. J., & Crossa, J. (2020). Expectation and variance of the estimator of the maximized selection response of linear selection indices with normal distribution. Theoretical and Applied Genetics,133, 2743-2758. https://doi.org/10.1007/s00122-020-03629-6
Christy, B., Riffkin, P., Richards, R., Partington, D., Acuña, T. B., Merry, A., Zhang, H., Trevaskis, B., & O’Leary, G. (2020). An allelic based phenological model to predict phasic development of wheat (Triticum aestivum L.), Field Crops Research, 249, 10772-10785. https://doi.org/10.1016/j.fcr.2020.107722
Companhia Nacional de Abastecimento - CONAB. (2020). Histórico mensal de trigo, Brasília, DF. https://www.conab.gov.br/info-agro/analises-do-mercado-agropecuario-e-extrativista/analises-do-mercado/historico-mensal-de-trigo/item/13512-trigo-analise-mensal-abril-2020
Cruz, C. D., Regazzi, A. J., & Carneiro, P. C. S. (2012). Modelos biométricos aplicados ao melhoramento genético. Viçosa, MG: Editora UFV.
Empresa Brasileira de Pesquisa Agropecuária - EMBRAPA. (2018). Informações técnicas para trigo e triticale, Brasília, DF. https://ainfo.cnptia.embrapa.br/digital/bitstream/item/196239/1/ID44570-2018InfTecTrigoTriticale2019
Fellahi, Z. E. A., Hannachi, A., & Bouzerzour, H. (2018). Analysis of direct and indirect selection and indices in bread wheat (Triticum aestivum L.) segregating progeny. International Journal of Agronomy, 2018, 1-12. https://doi.org/10.1155/2018/8312857
Fellahi, Z. E. A., Hannachi, A., & Bouzerzour, H. (2020). Expected genetic gains from mono trait and index-based selection in advanced bread wheat (Triticum aestivum L.) populations. Revista Facultad Nacional de Agronomía Medellín, 73, 9131-9141. Retrieved from https://doi.org/10.15446/rfnam.v73n2.77806
Fischer, R. A., & Rebetzke, G. J. (2018). Indirect selection for potential yield in early-generation, spaced plantings of wheat and other small-grain cereals: a review. Crop and Pasture Science, 69, 439-459. https://doi.org/10.1071/CP17409
Food and Agriculture Organization - FAO. (2020). World food situation. http://www.fao.org/worldfoodsituation/csdb/en/
Guendouz, A., Guessoum, S., & Hafsi. M. (2012). Investigation and selection index for drought stress in durum wheat (Triticum durum Desf.) under Mediterranean condition. Electronic Journal of Plan Breeding, 3, 733-740. Retrieved from https://www.indianjournals.com/ijor.aspx?target=ijor:ejpb&volume=3&issue=2&article=003
Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187-200. https://doi.org/10.1007/BF02289233
Mathew, I., Shimelis, H., Mwadzingeni, L., Zengeni, R., Mutema, M., & Chaplot, V. (2018). Variance components and heritability of traits related to root: shoot biomass allocation and drought tolerance in wheat. Euphytica, 214, 225-237. https://doi.org/10.1007/s10681-018-2302-4
Meier. C., Meira, D., Marchioro, V. S., Olivoto, T., Klein, L.A., & Souza, V. Q. (2019). Selection gain and interrelations between agronomic traits in wheat F5 genotypes. Revista Ceres, 66, 271-278. https://doi.org/10.1590/0034-737x201966040005
Michel, S., Löschenberger, F., Ametz, C., Pachler, B., Sparry, E., & Bürstmayr., H. (2019). Simultaneous selection for grain yield and protein content in genomics-assisted wheat breeding. Theoretical and Applied Genetics, 132, 1745-1760. https://doi.org/10.1007/s00122-019-03312-5
Mulamba, N. N., & Mock, J. J. (1978). Improvement of yield potential of the Eto Blanco maize (Zea mays L.) population by breeding for plant traits. Egypt Journal of Genetics and Cytology, 7, 40-51.
Mendes, F. F., Ramalho, M. A. P., & Abreu, A. F. B. (2009). Selection index for choosing segregating populations in common bean. Pesquisa Agropecuária Brasileira, 44, 1312-1318. https://doi.org/10.1590/S0100-204X2009001000015
Olivoto, T., & Lúcio, A. D. (2020). metan: an R package for multi-environment trial analysis. Methods on Ecology and Evolution, 11, 783-789. https://doi.org/10.1111/2041-210X.13384
Pasinato, A., Cunha, G. R., Fontana, D. C., Monteiro, J. E. B. A., Nakai, A. M., & Oliveira, A. F. (2018). Potential area and limitations for the expansion of rainfed wheat in the Cerrado biome of Central Brazil. Pesquisa Agropecuária Brasileira, 53, 779-790. http://dx.doi.org/10.1590/s0100-204x2018000700001
Pedrozo, C. A., Benites, F. R. G., Barbosa, M. H. P., Resende, M. D. V., & Silva, F. L. (2009). Efficiency of selection indexes using the REML/BLUP procedure in sugarcane breeding. Scientia Agraria, 10, 31-36. http://dx.doi.org/10.5380/rsa.v10i1.11711
Pereira, J. F., Cunha, G. R., & Moresco, E.R. (2019). Improved drought tolerance in wheat is required to unlock the production potential of the Brazilian Cerrado. Crop Breeding and Applied Biotechnology, 19, 217-225. https://doi.org/10.1590/1984-70332019v19n2r30
R Core Team. (2020). R: A language and environment for statistical computing (version 3.6.2) [Software]. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.
Rapp, M., Lein, V., Lacourde, F., Lafferty, J., Muller, E., Vida, G., Bozhanova, V., Ibraliu, A., Thorwarth, P., Piepho, H.P., Leiser, W. L., Wurschum, T., & Longin, C. F. H. (2018). Simultaneous improvement of grain yield and protein content in durum wheat by different phenotypic indices and genomic selection. Theoretical and Applied Genetics, 131, 1315-1329. https://doi.org/10.1007/s00122-018-3080-z
Resende, M. D. V., & Duarte, J. V. (2007). Precisão e controle de qualidade em experimentos de avaliação de cultivares. Pesquisa Agropecuária Tropical, 37, 182-194.
Resende, M. D. V. (2007). Matemática e estatística na análise de experimentos e no melhoramento genético. Brasília, DF: Embrapa Florestas.
Resende, M. D. V. (2016). Software Selegen-REML/BLUP: a useful tool for plant breeding. Crop Breeding and Applied Biotechnology, 16, 330-339. http://dx.doi.org/10.1590/1984-70332016v16n4a49
Ribeiro, G., Pimentel, A. J. B., Rocha, J. R. A. S. C., Oliveira, I. C. M., Souza, M. A. (2019). Potential of F3:4 segregating wheat populations for tolerance to heat stress. Journal of Neotropical Agriculture, 6, 19-25. https://doi.org/10.32404/rean.v6i2.3314
Rocha, J. R. A. S. C., Machado, J. C., & Carneiro, P. C. S. (2018). Multitrait index based on factor analysis and ideotype-design: proposal and application on elephant grass breeding for bioenergy. Global Change Biology Bioenergy, 10, 52-60. https://doi.org/10.1111/gcbb.12443
Rocha, J.R.A.S.C., Nunes, K.V., Carneiro, A.L.N., Marçal, T.S., Salvador, F.V., Carneiro, P.C.S., & Carneiro, J.E.S. (2019). Selection of superior inbred progenies toward the common bean ideotype. Agronomy Journal, 111, 1181-1189. https://doi.org/10.2134/agronj2018.12.0761
Scheeren, P. L., & Caierão, E. (2015). Cultivares. In A. Borém, P. L. Scheeren (Ed.). Trigo do plantio à colheita. (pp. 91-119). Viçosa, MG: Editora UFV.
Silva, M. J., Carneiro, P. C. S., Carneiro, J. E. S., Damasceno, C. M. B., Parrella, N. N. L. D., Pastina, M. M., Simeone, M. L. F., Schaffert, R. E., & Parrella, R. A. C. (2018). Evaluation of the potential of lines and hybrids of biomass sorghum. Induastrial Crops and Products, 125, 379-385. https://doi.org/10.1016/j.indcrop.2018.08.022
Subandi, W., Compton, A., & Empig, L. T. (1973). Comparison of the efficiencies of selection indices for three traits in two variety crosses of corn. Crop Science, 13, 184-186. https://doi.org/10.2135/cropsci1973.0011183X001300020011x
Tyagi, B. S., Foulkes, J., Singh, G., Sareen, S., Kumar, P., Broadley, M., Gupta, V., Krishnappa, G., Ojha, A., Khokhar, S. T., King, I. P., & Singh, G. P. (2020). Identification of wheat cultivars for low nitrogen tolerance using multivariable screening approaches. Agronomy, 10, 417-434. https://doi.org/10.3390/agronomy10030417
Woyann, L. G. Meira, D., Zdziarski, A. D., Matei, G., Milioli, A. S., Rosa, A. C., Madella, L. A., & Benin, G. (2019). Multiple-trait selection of soybean for biodiesel production in Brazil. Industrial Crops and Products, 140, 111721-111728. https://doi.org/10.1016/j.indcrop.2019.111721
Wricke, G., & Weber, E. (1986). Quantitative genetics and selection in plant breeding. New York: De Gruyter. https://doi.org/10.1515/9783110837520
Zadoks, J. C., Chang, T. T., & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. Weed Research, 14, 415-421. https://doi.org/10.1111/j.1365-3180.1974.tb01084.x
Copyright (c) 2022 ASB Journal
This work is licensed under a Creative Commons Attribution 4.0 International License.