Tomato families possessing resistance to late blight also display high-quality fruit
Abstract
In recent years, several efforts have been made to develop tomato cultivars displaying both late blight resistance and good organoleptic fruit quality. Selection indexes are considered the best option to perform genotype selection when many different traits are being considered to select genotypes as close to the desired ideotype as possible. Therefore, this study aimed at selecting late blight-resistant tomato families based on their fruit quality attributes using factor analysis and ideotype-design / best linear unbiased predictor (FAI-BLUP) index. For this purpose, we assessed the fruit quality parameters of 81 F3:5 tomato families previously selected as late blight resistant. The tomato cultivars Thaise, Argos, and Liberty were included in the trial as checks. The experimental arrangement consisted of complete randomized blocks with three replicates. Each plot was formed by five plants, three of which were used in the fruit quality assessment. The quality parameters assessed were fruit diameter, fruit length, fruit color (L, a*, C, and H), fruit firmness, titratable acidity, soluble solids content, hydrogen potential, and SS:TA ratio. Fruit quality data were analyzed using the mixed model methodology via REML/BLUP (restricted residual maximum likelihood / best linear unbiased prediction) to obtain BLUPs that were further subjected to the FAI-BLUP selection index. The FAI-BLUP was efficient in selecting late blight-resistant tomato genotypes based on their fruit quality attributes. Fourteen tomato families were classified as closest to the desirable ideotype for fruit quality. These genotypes should move on to the following stages of the tomato breeding program.
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References
Alvarenga, M. A. R. (2013). Tomate: Produção em campo, casa de vegetação e hidroponia (2. ed.). Lavras, MG: Editora UFLA.
Arivazhagan, S., Shebiah, R. N., Selva Nidhyanandhan, S., & Ganesan, L. (2010). Fruit Recognition using Color and Texture Features. Journal of Emerging Trends in Computing and Information Sciences, 1(2), 90-94.
Bartlett, M. S. (1978). Nearest neighbour models in the analysis of field experiments. Journal of the Royal Statistical Society, 40(2), 147-158. DOI: https://doi.org/10.1111/j.2517-6161.1978.tb01657.x
Beckles, D. M. (2012). Factors affecting the postharvest soluble solids and sugar content of tomato (Solanum lycopersicum L.) fruit. Postharvest Biology and Technology, 63(1), 129-140. DOI: https://doi.org/10.1016/j.postharvbio.2011.05.016
Beiragi, M. A., Ebrahimi, M., Mostafavi, K., Golbashy, M., & Saied, K. K. (2011). A study of morphological basis of corn ( Zea mays L .) yield under drought stress condition using correlation and path coefficient analysis. Journal of Cereals and Oilseeds, 2(2), 32-37.
Bertin, N., & Génard, M. (2018). Tomato quality as influenced by preharvest factors. Scientia Horticulturae, 15, 264-276. DOI: https://doi.org/10.1016/j.scienta.2018.01.056
Bojarian, M., Asadi-Gharneh, H. A., & Golabadi, M. (2019). Factor analysis, stepwise regression and path coefficient analyses of yield, yield-associated traits, and fruit quality in tomato. International Journal of Vegetable Science, 25(6), 542–553. DOI: https://doi.org/10.1080/19315260.2018.1551260
Campos, M. D., Félix, M. R., Patanita, M., Materatski, P., & Varanda, C. (2021). High throughput sequencing unravels tomato-pathogen interactions towards a sustainable plant breeding. Horticulture Research, 8(171), 1-12. DOI: https://doi.org/10.1038/S41438-021-00607-x
Causse, M., Friguet, C., Coiret, C., Lépicier, M., Navez, B., Lee, M., ... Grandillo, S. (2010). Consumer Preferences for fresh tomato at the European scale: A common segmentation on taste and firmness. Journal of Food Science, 75(9), 531-541. DOI: https://doi.org/10.1111/j.1750-3841.2010.01841.x
Commission Internationale de l'Eclairage [CIE]. (1978). Recommendations on uniform color spaces, color difference equations, psychometric color terms (Supplement nº. 2 of publication CIE nº. 15 (E-1.3.1). Paris, FR: Bureau Central de la CIE.
Copati, M. G. F., Alves, F. M., Dariva, F. D., Pessoa, H. P., Dias, F. O., Carneiro, P. C. S., ... Nick, C. (2019). Resistance of the wild tomato Solanum habrochaites to Phytophthora infestans is governed by a major gene and polygenes. Anais da Academia Brasiliera de Ciências, 91(4), 1-8. DOI: https://doi.org/10.1590/0001-3765201920190149
Copati, M. G. F., Dariva, F. D., Dias, F. O., Rocha, J. R. A. S. C., Pessoa, H. P., Almeida, G. Q., ... Nick, C. (2021). Spatial modeling increases accuracy of selection for Phytophthora infestans-resistant tomato genotypes. Crop Science, 61(6), 3919-3930. DOI: https://doi.org/10.1002/CSC2.20584
Dariva, F. D., Pessoa, H. P., Copati, M. G. F., Almeida, G. Q., Castro Filho, M. N., Picoli, E. A. T., ... Nick, C. (2021). Yield and fruit quality attributes of selected tomato introgression lines subjected to long-term deficit irrigation. Scientia Horticulturae, 289, 110426. DOI: https://doi.org/10.1016/j.scienta.2021.110426
Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carré, G., ... Lautenbach, S. (2013). A review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36(1), 27-46. DOI: https://doi.org/10.1111/j.1600-0587.2012.07348.x
Elston, R.C. (1963). A weight free index for the purpose of ranking of selection with respect to several traits at a time. Biometrics, 19, 85-87.
Food and Agriculture Organization of the United Nations [FAOSTAT]. (2021). Crops and livestock products. Retrieved on Jan. 10, 22 from http://www.fao.org/faostat/en/#data/QC
Foolad, M. R. (2007). Genome mapping and molecular breeding of tomato. International Journal Plant Genomics, 2007, 1-52. DOI: https://doi.org/10.1155/2007/64358
Gautier, H., Diakou-Verdin, V., Bénard, C., Reich, M., Buret, M., Bourgaud, F., ... Génard, M. (2008). How does tomato quality (sugar, acid, and nutritional quality) vary with ripening stage, temperature, and irradiance? Journal of Agricultural and Food Chemistry, 56(4), 1241-1250. DOI: https://doi.org/10.1021/jf072196t
Golbashy, M., Ebrahimi, M., Khorasani, S. K., & Choukan, R. (2010). Evaluation of drought tolerance of some corn (Zea mays L.) hybrids in Iran. African Journal of Agricultural Research, 5(19), 2714-2719.
Gómez, R., Costa, J., Amo, M., Alvarruiz, A., Picazo, M., & Pardo, J. E. (2001). Physicochemical and colorimetric evaluation of local varieties of tomato grown in SE Spain. Journal of the Science of Food and Agriculture, 81(11), 1101-1105. DOI: https://doi.org/10.1002/jsfa.915
Hashemi, M., Tabet, D., Sandroni, M., Benavent-Celma, C., Seematti, J., Andersen, C. B., & Grenville-Briggs, L. J. (2022). The hunt for sustainable biocontrol of oomycete plant pathogens, a case study of Phytophthora infestans. Fungal Biology Reviews, 40, 53-69. DOI: https://doi.org/10.1016/J.FBR.2021.11.003
Hazel, L. N. (1943). The genetic basis for constructing selection indexes. Genetics, 28, 476-490
Henderson, C. R. (1975). Best linear unbiased estimation and prediction under a selection model. Biometrics, 31(2), 423-447. DOI: https://doi.org/10.2307/2529430
Instituto Brasileiro de Geografia e Estatística [IBGE]. (2019). Levantamento sistemático da produção agrícola. Estatística da produção agrícola. Retrieved on Dec. 10, 2021 from https://biblioteca.ibge.gov.br/visualizacao/periodicos/2415/epag_2019_dez.pdf
Ilahy, R., Siddiqui, M. W., Tlili, I., Montefusco, A., Piro, G., Hdider, C., & Lenucci, M. S. (2018). When color really matters: horticultural performance and functional quality of high-lycopene tomatoes. Critical Reviews in Plant Science, 37(1), 15-53. DOI: https://doi.org/10.1080/07352689.2018.1465631
Kaiser, H. F. (1958). The varimax criterion for analytic rotation in factor analysis. Psychometrika, 23, 187-200. DOI: https://doi.org/10.1007/BF02289233
Kilonzi, J. M., Mafurah, J. J., & Nyongesa, M. W. (2023). Enhancing the efficacy of biocontrols and fungicide application for improved late blight management and yield of potato. East African Agricultural and Forestry Journal, 87(2), 11-11.
Kumar, D., Rani, A., Prajapati, J., Mahato, S., Pratap Verma, N., Vishwaraj, A., … Sanjay Pardhi, D. (2022). Breeding for biotic stresses resistance in tomato: A review. The Pharma Innovation Journal, 11(5), 316-321.
Lemos, R., & Santos, R. (1996). Manual de descrição e coleta de solo no campo da Sociedade Brasileira de Ciência do Solo (3. ed.). Campinas, SP: Sociedade Brasileira de Ciência do Solo; Centro Nacional de Pesquisa de Solos.
Li, C., Cao, Q., & Guo, F. (2009). A method for color classification of fruits based on machine vision. Wseas Transactions on Systems, 8(2), 312-321.
Luengwilai, K., Fiehn, O. E., & Beckles, D. M. (2010). Comparison of leaf and fruit metabolism in two tomato (Solanum lycopersicum L.) genotypes varying in total soluble solids. Journal of Agricultural and Food Chemistry, 58, 11790-11800. DOI: https://doi.org/10.1021/jf102562n
Maach, M., Boudouasar, K., Akodad, M., Skalli, A., Moumen, A., & Baghour, M. (2020). Application of biostimulants improves yield and fruit quality in tomato. International Journal of Vegetable Science, 27(3), 288-293. DOI: https://doi.org/10.1080/19315260.2020.1780536
Mulamba, N. N., & Mock, J. J. (1978). Improvement of yield potential of the Eto Blanco maize (Zea mays) population by breeding for plant traits. Egyptian Journal of Genetics and Cytology, 7, 40-57.
Nick, C., Laurindo, B. S., Almeida, V. S., Freitas, R. D., Aguilera, J. G., Silva, E. C. F., ... Silva, D. J. H., (2013). Seleção simultânea para qualidade do fruto e resistência à requeima em progênies de tomateiro. Pesquisa Agropecuária Brasileira, 48(1), 59-65. DOI: https://doi.org/10.1590/S0100-204X2013000100008
Oliveira, I. C. M., Marçal, T. S., Bernardino, K. C., Ribeiro, P. C. O., Parrella, R. A. C., Carneiro, P. C. S., ... Carneiro, J. E. S. (2019). Combining ability of biomass sorghum lines for agroindustrial characters and multitrait selection of photosensitive hybrids for energy cogeneration. Crop Science, 59(4), 1554-1566. DOI: https://doi.org/10.2135/cropsci2018.11.0693
Oliveira Dias, F., Magalhães Valente, D. S., Oliveira, C. T., Dariva, F. D., Copati, M. G. F., & Nick, C. (2023). Remote sensing and machine learning techniques for high throughput phenotyping of late blight-resistant tomato plants in open field trials. International Journal of Remote Sensing, 44(6), 1900-1921. DOI: https://doi.org/10.1080/01431161.2023.2192878
Ozores-Hampton, M., & Roberts, P. (2014). Late blight-resistant tomato varieties evaluation. The Florida Tomato Proceeding, 530, 11-14.
Patterson, H. D., & Thompson, R. (1971). Biometrika trust recovery of inter-block information when block sizes are unequal. Biometrika, 58(3), 545-554.
Pesek, J. & Baker, R. J. (1969). Desired improvement in relation to selected indices. Canadian Journal of Plant Science, 49, 803-804.
Pessoa, H. P., Rocha, J. R. A. S. C., Alves, F. M., Copati, M. G. F., Dariva, F. D., Silva, L. J. D., ... Gomes, C. N. (2022). Multi-trait selection of tomato introgression lines under drought-induced conditions at germination and seedling stages. Acta Scientiarum. Agronomy, 44(1), 1-12. DOI: https://doi.org/10.4025/actasciagron.v44i1.55876
Prunier, J. G., Colyn, M., Legendre, X., Nimon, K. F., & Flamand, M. C. (2015). Multicollinearity in spatial genetics: Separating the wheat from the chaff using commonality analyses. Molecular Ecology, 24(2), 263-283. DOI: https://doi.org/10.1111/mec.13029
Ribeiro, A. C., Guimarães, P. T. G., & Alvarez, V. H. (1999). Recomendações para o uso de corretivos e fertilizantes em Minas Gerais (5. ed.). Viçosa, MG: Sociedade Brasileira de Ciência do Solo.
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. GCB Bioenergy, 10(1), 52-60. DOI: 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(3), 1181-1189. DOI: https://doi.org/10.2134/agronj2018.12.0761
Seymour, G. B., (2002). Genetic identification and genomic organization of factors affecting fruit texture. Journal of Expimental Botany, 53(377), 2065-2071. DOI: https://doi.org/10.1093/jxb/erf087
Shibzukhov, Z. G., Bagov, A., Shibzukhova, Z., Khantsev, M., & Akbar, I. (2021). Tomato productivity depending on mineral nutrition and irrigation regimes in the conditions of film greenhouses in the mountain zone of the KBR. E3S Web of Conference, 262, 1-6. DOI: https://doi.org/10.1051/e3sconf/202126201032
Silva, M. J., Carneiro, P.C.S., Souza, J. E. S., Carneiro, Damasceno, C. M. B., Parrella, N. N. L. D., ... Parrella, R. A. C. (2018). Evaluation of the potential of lines and hybrids of biomass sorghum. Industrial Crops and Products, 125, 379–385. Doi: https://doi.org/10.1016/j.indcrop.2018.08.022
Smith, H. F. (1936). A discriminant function for plant selection. Annual Eugenics, 7, 240-250.
Socaci, S. A., Socaciu, C., Mureşan, C., Fărcaş, A., Tofană, M., Vicaş, S., & Pintea, A. (2014). Chemometric discrimination of different tomato cultivars based on their volatile fingerprint in relation to lycopene and total phenolics content. Phytochemical Analysis, 25(2), 161-169. DOI: https://doi.org/10.1002/pca.2483
Wan, P., Toudeshki, A., Tan, H., & Ehsani, R. (2018). A methodology for fresh tomato maturity detection using computer vision. Computers and Electronics in Agriculture, 146, 43-50. DOI: https://doi.org/10.1016/j.compag.2018.01.011
Weingerl, V., & Unuk, T. (2015). Chemical and fruit skin colour markers for simple quality control of tomato fruits. Croatian Journalof Food Science and Technology, 7(2), 76-85. DOI: https://doi.org/10.17508/cjfst.2015.7.2.03
Woyann, L. G., Meira, D., Zdziarski, A. D., Matei, G., Milioli, A. S., Rosa, A. C., ... Benin, G. (2019). Multiple-trait selection of soybean for biodiesel production in Brazil. Industrial Crops and Products, 140, 111721. DOI: https://doi.org/10.1016/j.indcrop.2019.111721
Wu, Y., Yan, S., Fan, J., Zhang, F., Xiang, Y., Zheng, J., & Guo, J. (2021). Responses of growth, fruit yield, quality and water productivity of greenhouse tomato to deficit drip irrigation. Scientia Horticulturae, 275, 109710. DOI: https://doi.org/10.1016/j.scienta.2020.109710
Zetouni, L., Henryon, M., Kargo, M., & Lassen, J. (2017). Direct multitrait selection realizes the highest genetic response for ratio traits1. Journal of Animal Science, 95(5), 1921-1925. DOI: https://doi.org/10.2527/jas.2016.1324
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