Criterios de selección para el rendimiento en genotipos de cártamo (Charthamus tinctorius L.) en condiciones de secano

Autores/as

DOI:

https://doi.org/10.3989/gya.0449201

Palabras clave:

Cártamo, Correlación, GT (genotipo por rasgo)-biplot, Rendimiento, Selección

Resumen


Esta investigación se realizó con 20 genotipos de cártamo durante 3 años (2014-2016) en la región de Anatolia central de Turquía. Los experimentos se realizaron en bloques de diseño aleatorio con cuatro repeticiones. Se investigaron las relaciones de rendimiento con los otros rasgos y las relaciones genotipo-rasgo en plantas de cártamo. Como promedio de tres años, el mayor rendimiento de semillas (SY) se obtuvo del genotipo G5 (PI 451952) con 3156,3 kg·ha-1. Le siguieron los genotipos G4 (PI 525458) y G9 (PI 306686) con 3013,2 y 2977,1 kg·ha-1 respectivamente. Entre los cultivares estándar, el mayor rendimiento de semilla (2750,4 kg·ha-1) se obtuvo del cultivar Dinçer. El mayor contenido de aceite (OC) se obtuvo del genotipo G11 (PI 537665) con 36,5%. El contenido de aceite varió entre 29,1 - 36,5%. Las relaciones rendimiento-rasgo se evaluaron mediante análisis de correlación y análisis biplot GT (Genotipo por rasgo). Con base en los resultados de dos enfoques, la altura de la planta (PH), el número de ramas (NB), el número de cabezas (NH) y el peso de miles de semillas (TSW) se identificaron como los criterios de selección más importantes para el rendimiento en el cártamo. El uso combinado de análisis de correlación y biplot en la evaluación de las relaciones entre los rasgos mejoró la posibilidad de éxito.

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Citas

Akbar AA, Kamran M. 2006. Relationship among yield components and selection criteria for yield improvement in safflower (Cathamus tinctorious L.). J. Appl. Sci. 6, 2853-2855. https://doi.org/10.3923/jas.2006.2853.2855

Akçura M. 2011. The relationships of some traits in Turkish winter bread wheat landraces. Turk. J. Agric. Forestry 35 (2), 115-125.

Ali F, Yilmaz A, Chaudhary HJ, Nadeem MA, Rabbani MA, Arslan Y, Nawaz MA, Habyarımanas E, Baloch FS. 2020. Investigation of morphoagronomic performance and selection indices in the international safflower panel for breeding perspectives. Turk. J. Agric. Forestry. 44 (2), 103-120. https://doi.org/10.3906/tar-1902-49

Alizadeh K. 2005. Safflower as a new crop in the cold drylands of Iran. Sesame and Safflower Newslatter 20, 92-94.

Arslan B. 2007. The path analysis of yield and its components in safflower (Carthamus tinctorius L.). J. Biologic. Sci. 7, 668-672. https://doi.org/10.3923/jbs.2007.668.672

Baljani R, Shekari F, Sabaghnia N. 2015. Biplot analysis of trait relations of some safflower (Carthamus tinctorius L.) genotypes in Iran. Crop Res. 50, 63-73.

Camas N, Cirak C, Esendal E. 2007. Seed yield, oil content and fatty composition of safflower (Carthamus tinctorius L.) grown in northern Turkey conditions. J. Agric. Fac. Ondokuz Mayıs University 22, 98-104.

Coşge B, Kaya D. 2008. Performance of some safflower (Carthamus tinctorius L.) varieties sown in late-autumn and late-spring. SDU J. Nat. Appl. Sci. 12, 13-18.

Eslam BP, Monirifar H, Ghassemi MT. 2010. Evaluation of late season drought effects on seed and oil yields in spring safflower genotypes. Turk. J. Agric. Forestry 34, 373-380.

Flores F, Moreno MT, Cubero JI. 1998. A comparison of univariate and multivariate methods to analyze G× E interaction. Field Crops Res. 56, 271-286. https://doi.org/10.1016/S0378-4290(97)00095-6

Golkar P, Arzani A, Rezai AM. 2012. Genetic analysis of agronomic traits in safflower (Carthamus tinctorious L.). Notulae Botanicae Horti Agrobotanici Cluj-Napoca 40 (1), 276-281. https://doi.org/10.15835/nbha4017209

Hussein A, Ibrahim AE, Hussein I, Idris S. 2018. Assessment of genotype x environment interactions and stability for seed yield of selected safflower (Carthamus tinctorius L.) genotypes in central and northern Sudan. Gezira J. Agric. Sci. 16.

Kaya Y, Evci G, Pekcan V, Gucer T, Yilmaz Mİ. 2009. The Determination Relationships between Oil Yield and Some Yield Traits in Sunflower. J. Agric. Sci. 15, 310-318.

Koç H, Keles R, Ulker R, Gumuscü G, Ercan B, Akcacik AG, Gunes A, Ozdemir F, Ozer E, Uludag E. 2010. Determination of yield, yield components and quality properties of some safflower (Carthamus tinctorius L.) lines and relations between these properties. J. Crop Res. 2, 1-7.

Kose A. 2017. Agricultural performances of some safflower (Carthamus tinctorius L.) varieties under Eskisehir vonditions. Selcuk J. Agric. Food Sci. 31, 1-7. https://doi.org/10.15316/SJAFS.2017.28

Kroonenberg PM. 1995. Introduction to biplots for G × E tables. Department of Mathematics. Research Report 51. University of Queensland, Australia. [Online] Available: http://three-mode.leidenuniv.nl/document/biplot.pdf.

Mozaffari K, Asadi A. 2006. Relationships among traits using correlation principal components and path analysis in safflower mutants sown in irrigated and drought stress condition. Asian J. Plant Sci. 5, 977-983. https://doi.org/10.3923/ajps.2006.977.983

Nabloussi A, Fechtali ME, Lyagoubi S. 2008. Agronomic and technological evaluation of a world safflower collection in Moroccan Conditions. VII. International Safflower Conference. Wagga -Australia.

Ozturk O, Ada R, Akinerdem F. 2009. Determınatıon of yield and yield components of some safflower cultivars under irrigated and dry condıtıons. Selcuk J. Agric. Food Sci. 23,16-27.

Rad MN, Kadir MA, Rafii MY, Jaafar HZ, Naghavi MR, Ahmadi F. 2013. Genotype environment interaction by AMMI and GGE biplot analysis in three consecutive generations of wheat (Triticum aestivum) under normal and drought stress conditions. Australian J. Crop Sci. 7, 956.

Rubio J, Cubero JI, Martin LM, Suso MJ, Flores F. 2004. Biplot analysis of trait relations of white lupin in Spain. Euphytica 135, 217-224. https://doi.org/10.1023/B:EUPH.0000014911.70355.c9

Weiss EA. 2000. Oilseed Crops. Blackwell Publishing Limited, London, UK.

Yan W, Kang MS, Manjit B, Woods CS, Corneliusd PL. 2007. GGE Biplot vs. AMMI Analysis of Genotypeby-Environment Data. Crop Sci. 47, 643-653. https://doi.org/10.2135/cropsci2006.06.0374

Yan W, Kang MS. 2003. GGE biplot analysis. A graphical tool for breeders, geneticists and agronomists. CRC press. https://doi.org/10.1201/9781420040371

Yan W, Rajcan I. 2002. Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Sci. 42, 11-20. https://doi.org/10.2135/cropsci2002.1100 PMid:11756248

Yan W, Frégeau-Reid J. 2008. Breeding line selection based on multiple traits. Crop Sci. 48, 417-423. https://doi.org/10.2135/cropsci2007.05.0254

Yan W, Tinker NA, Bekele WA, Mitchell-Fetch J, Fregeau-Reid J. 2019a. Theoretical unification and practical Integration of conventional methods and genomic selection in plant breeding. Crop breed. Genetics and Genomics 1 (2).

Yan W, Frégeau-Reid J, Mountain N, Kobler J. 2019b. Genotype and management evaluation based on genotype by yield* trait (GYT) analysis. Crop Breed Genetics and Genomics 1 (2).

Yau SK. 1995. Regression and AMMI analyses of genotype x environment interactions, an empirical comparison. Agronomy J. 87, 121-126. https://doi.org/10.2134/agronj1995.00021962008700010021x

Publicado

2021-09-17

Cómo citar

1.
Koç H. Criterios de selección para el rendimiento en genotipos de cártamo (Charthamus tinctorius L.) en condiciones de secano. Grasas aceites [Internet]. 17 de septiembre de 2021 [citado 1 de mayo de 2025];72(3):e421. Disponible en: https://grasasyaceites.revistas.csic.es/index.php/grasasyaceites/article/view/1893

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Investigación