Characterization of the vegetative vigor, and SPAD readings and vegetation index in a Coffea canephora population
DOI:
https://doi.org/10.33448/rsd-v11i15.37314Keywords:
Gene pool; Genetic parameters; Breeding.Abstract
Currently the most of the Coffea canephora cultivars, developed and recommended by the research, are propagated vegetatively, being popularly known as clonal varieties and/or cultivars. However, those propagated by seeds present a wide genetic base with greater variability of the plants in the crops. Thus, the objective was to characterize a population (genotypes) from C. canephora cultivar 'ES8152', of seminal propagation, from agronomic characteristics vegetative vigor and canopy height, SPAD readings and vegetation index, after the first harvest in 2022. The planting was carried out in 2019, in an augmented block design with three replications, 240 genotypes and four clonal witnesses (A1, LB1, V8 and V12), being possible to evaluate 199 genotypes, including the witnesses. Data were analyzed by the method of restricted maximum likelihood and best linear unbiased prediction, cluster analysis and genetic correlation networks. The conilon coffee population showed wide genetic variability. The characterization allowed the identification of promising ones, such as genotypes 16, 25, 82 and 173. The genetic correlations were positive, significant and higher between the pairs vegetative vigor x canopy height and SPAD readings x vegetation index. Most of the genotypes evaluated showed intermediate vegetative vigor, combined with low vegetative stress. Thus, the adopted strategies were efficient in the characterization of the genotypes, being able to assist in the taking of decisions in the process of breeding of conilon coffee.
References
Akpertey, A., Anim-Kwapong, E., Adu-Gyamfi, P. K. K., & Ofori, A. (2022). Genetic variability for vigor and yield of robusta coffee (Coffea canephora) clones in Ghana. Heliyon, 8(8), e10192. https://doi.org/10.1016/j.heliyon.2022.e10192.
Alkimim, E. R., Caixeta, E. T., Sousa, T. V., Gois, I .B., Silva, F. L., Sakiyama, N .S., Zambolim, L., Alves, R. S., & Resende, M. D. V. (2021). Designing the best breeding strategy for Coffea canephora: Genetic evaluation of pure and hybrid individuals aiming to select for productivity and disease resistance traits. PLoS One, 16, e0260997. https://doi.org/10.1371/journal.pone.0260997.
Bernado, W. P., Baroni, D. F., Ruas, K. F., Santos, A. R., Souza, S. B., Passos, L. C., Façanha, A. R., Ramalho, J. C., Campostrini, E., Rakocevic, M., Rodrigues, W. P. (2022). Ultraviolet radiation underlies metabolic energy reprograming in Coffea arabica and Coffea canephora genotypes. Scientia Horticulturae, 295, 110881. https://doi.org/10.1016/j.scienta.2022.110881.
Bhering, L. L. (2017). Rbio: A tool for biometric and statistical analysis using the R platform. Crop Breeding and Applied Biotechnology, 17, 187-190. 10.1590/1984-70332017v17n2s29.
Carias, C. M. O. M., Gravina, G. A., Ferrão, M. A. G., Fonseca, A. F. A., Ferrão, R. G., Vivas, M., & Viana, A. P. (2016). Predição de ganhos genéticos via modelos mistos em Progênies de café conilon. Coffee Science, 11, 39-45.
Carvalho, H. F., Silva, F. F., Resende, M. D. V., & Bhering, L. L. Selection and genetic parameters for interpopulation hybrids between kouilou and robusta coffee. (2019). Bragantia; 78(1), 52–59. https://doi.org/10.1590/ 1678-4499.2018124 20.
Companhia Nacional de Abastecimento (CONAB). (2022). Acompanhamento da safra brasileira de café, safra 2022, terceiro levantamento, 9(3), 1-65. Disponível em: https://www.conab.gov.br/info-agro/safras/cafe/boletim-da-safra-de-cafe. Acesso em: 03 de Out. de 2022.
Federer, W. T. Augmented (or hoonuiaku) designs. 1956. Hawaiian Planters’ Record, 55(2):191-208.
Ferrão, R. G., Ferrão, M. A. G., Fonseca, A. F. A., Volpi, P. S., Verdin Filho, A. C., Pacova, B. E. V., & Ferrão, L. F. V. (2019d). Coffea canephora Breeding. In Ferrão R. G., Fonseca A. F. A., Ferrão, M. A. G., DeMuner L.H. (eds). Conilon coffee. Incaper, Vitória, 145-201.
Ferrão, R.G., Fonseca, A.F.A., Ferrão, M.A.G., & Demuner, L.H. (2019b). Conilon Coffee. 3rd ed. Vitória, Incaper, 973.
Ferrão, R. G., Ferrão, M. A. G., Volpi, P. S., Verdin, A. C., Fonseca, A. F. A., Senra, J. F. B., Comério, M., Mendonça, R. F., & Ferrão, L. M. V. (2019a). ‘ES8152 – Conquista’: Cultivar de café conilon propagada por sementes para os produtores de base familiar e sua utilização no melhoramento genético para o estado do Espírito Santo. Multi-Science Research, 2(2):1-18.
Ferrão, R. G., Ferrão, M. A .G., Fonseca, A. F. A., Volpi, P. S., Verdin, A. C, Tóffano, J. L., Tragino, P. H., & Bragança, S. M. (2019c). Cultivars of Conilon Coffee. In Ferrão R. G.; Fonseca A. F. A.; Ferrão, M.A.G.; DeMuner L. H. (eds) Conilon coffee. Incaper, Vitória, 255-287.
Galvanin, E. A. S., Neves, S. M. A. S., Cruz, C. B. M., Neves, R. J., Jesus, P. H. H., & Kreitlow, J. P. (2014). Avaliação dos índices de vegetação NDVI, SR e TVI na discriminação de fitofisionomias dos ambientes do Pantanal de Cáceres/MT. Ciência Florestal, 24 (3), 707-715.
International Coffee Organization (ICO). (2020). Produção total de 2020. .
Maciel, D. A., Silva, V. A., Alves, H. M. R., Volpato, M. M. L., Barbosa, J. P. R. A., Souza, V. C. A., Santos, M. O., Silveira, H. R .O., Dantas, M. F., Freitas, A. F., Carvalho, G. R., & Santos, J. O. (2020). Leaf water potential of coffee estimated by landsat-8 images. PLoS One, 15(3), e0230013. https://doi.org/10.1371/journal.pone.0230013.
Martins, R. N., Pinto, F. A. C., Queiroz, D. M., Valente, D. S. M., & Rosas, J. T. F. A Novel Vegetation Index for Coffee Ripeness Monitoring Using Aerial Imagery. (2021). Remote Sensing, 263 (13). https://doi.org/ 10.3390/rs13020263.
Mota, F. M., Partelli, F. L., Santos, A. R., Ferrari, J. L, & Peluzio, J. B. E. (2020). Relação entre variáveis meteorológicas e Índices de Vegetação (IV’s) em lavouras cafeeiras. Braz. J. Anim. Environ. Res., 3(4), 4287-4209. https://doi.org/10.34188/bjaerv3n4-125.
Moura, W. M., Pedrosa, A. W., Oliveira, R. L., Cecon, P. R., Ferrão, R. G., Ferrão, M. A. G., & Verdin, A. C. (2022). Selection of Conilon coffee clones for the Zona da Mata region of Minas Gerais, Brazil. Coffee Science, 17, e171995. https://doi.org/10.25186/.v17i.1995.
Nogueira, S. M. C., Moreira, M. A., & Volpato, M. M. L. (2018). Relationship between coffee crop productivity and vegetation indexes derived from Oli / Landsat-8 sensor data with and without topographic correction. Engenharia Agrícola, 38, 387-394. https://doi.org/10.1590/1809-4430-Eng.Agric.v38n3p387-394/2018.
Prezotti, L. C., Oliveira, J. A., Gomes, J. A., & Dadalto, G. G. (2007). Manual de recomendação de calagem e adubação para o Estado do Espírito Santo: 5ª aproximação. Vitória: SEEA/Incaper/CEDAGRO, 305.
Putra, B. T. W., & Soni, P. (2018). Enhanced broadband greenness in assessing Chlorophyll a and b, Carotenoid, and Nitrogen in Robusta coffee plantations using a digital camera. Precision Agric, 19, 238–256. https://doi.org/10.1007/s11119-017-9513-x
R CORE TEAM - R: a language and environment for statistical computing. (2022). R Foundation for Statistical Computing, Vienna, Áustria.
Reis, A. R., Favarin, J. L., Malavolta, E., Júnior, J. L., & Moraes, M. F. (2009). Photosynthesis, Chlorophylls, and SPAD Readings in Coffee Leaves in Relation to Nitrogen Supply. Communications in Soil Science and Plant Analysis, 40, 1512-1528. https://doi.org/10.1080/00103620902820373.
Resende, M. D .V., & Alves, R. S. (2020). Linear, Generalized, Hierarchical, Bayesian and Random Regression Mixed Models in Genetics/Genomics in Plant Breeding. Functional Plant Breeding Journal, 2, 1–31. https://doi.org/http%3A//dx.doi.org/10.35418/2526-4117/v2n2a1.
Resende, M. D. V. (2007). Software SELEGEN-REML/BLUP: sistema estatístico e seleção genética computadorizada via modelos lineares mistos. Colombo: Embrapa Florestas, 359.
Resende, M. D. V. (2016). Software SELEGEN-REML/BLUP: a useful tool for plant breeding. Crop Breeding and Applied Biotechnology, 16, 330-339. https://doi.org/10.1590/1984-70332016v16n4a49.
Resende, M. D. V.; Duarte, J. B. (2007). Precisão e controle de qualidade em experimentos de avaliação de cultivares. Pesquisa Agropecuária Tropical, 37, 182-194.
Rissini, A. L. L., Kawakami, J., & Genú, A. M. (2015). Índice de vegetação por diferença normalizada e produtividade de cultivares de trigo submetidas a doses de nitrogênio. Revista Brasileira de Ciência do Solo, 39(1), 1703-1713. 10.1590/01000683rbcs20140686.
Senra, J. F. B., Silva, J. A., Ferrão, M. A. G., Esposti, M. D. D., Milheiros, I. S., & Fassarella, K. M. (2022). Genetic variability of conilon coffee population from cultivar ‘ES8152’ based on morphoagronomic variables. Coffee Science, e171986. https://doi.org/10.25186/.v17i.1986.
Severino, L. S, Sakiyama, N. S., Pereira, A. A., Miranda, G. V., Zambolim, L., & Barros, U. V. (2008). Associações da produtividade com outras características agronômicas de café (Coffea arabica L. “Catimor”). Acta Scientiarum Agronomy, Maringá, 24, 1467-1471. https://doi.org/10.4025/actasciagron.v24i0.2405.
Silva, J. A., Senra, J. F. B., Esposti, M. D. D., Mendonça, R. F., Silva, F. G, & Milheiros, I. S. (2021). Índice de vegetação por diferença normalizada em genótipos de cafeeiros cultivados em ambientes contrastantes de altitude. In: XXV Encontro Latino Americano de Iniciação Científica. Universidade do Vale do Paraíba, São José dos Campos, 1-6.
Souza, L. C., Ferrão, M. A. G., Carvalho, R. D., FerrãO, R. G., Fonseca, A. F. A., Pinheiro, P. F., & Soares, T. C. B. (2021). Molecular characterization of parents and hybrid progenies of conilon coffee. Annals of the Brazilian Academy of Sciences, 93, e20201649. 10.1590/0001-3765202120201649
Torres Netto, A.; Oliveira, E. C. J. G., & Bressan-Smith, R. E. (2005). Photosynthetic pigments, nitrogen, chlorophyll a fluorescence and SPAD-502 readings in coffee leaves. Scientia Horticulturae, 104(2), 199-209. https://doi.org/10.1016/j.scienta.2004.08.013.
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Copyright (c) 2022 Josimar Aleixo da Silva; João Felipe Brites Senra; Marlon Dutra Degli Esposti; Idalina Sturião Milheiros ; Uliana Ribeiro Silva; Amanda Oliveira da Conceição; Alex Justino Zacarias
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