Morpho-agronomic variability in rice genotypes in the Central Pacific, Costa Rica




PCA, MCA, Oryza sativa, Morphological characters


Rice (Oryza sativa L.) is included in the daily diet of 75% of the world population. In Costa Rica, the rice sector is a fundamental axis of attention, particularly because it is the main product within the basic food basket. Studying the morpho-agronomic variability of this cereal is a key factor for planning breeding strategies. The objective of the present investigation was to determine which morpho-agronomic traits give the greater contribution to genetics in 54 rice lines. The evaluated lines come from a collection of the germplasm bank of CIAT in Colombia, these were sent to Costa Rica through the National Rice Corporation (CONARROZ). Seven quantitative and five qualitative morphological variables were measured. For the quantitative ones, the coefficient of variation was calculated and a Principal Component Analysis (PCA) was performed, as well as a Cluster Analysis. With the qualitative ones, a Multiple Correspondence Analysis (MCA) was performed. The most heterogeneous quantitative variables according to the values ​​of the coefficient of variation were: tons per hectare, fertile stems and full grains per panicle. The PCA revealed that all the variables, except plant height, contributed to the total variance, and the MCA suggests that the qualitative variables measured in this research are discriminatory to differentiate genotypes. No correlations were reached between the quantitative variables studied. In the cluster analysis, four groups were obtained, one of which comprised the majority of the genotypes. The genotypes of this last group are characterized by having high values ​​for the variable full grains per panicle and medium and high values ​​for the rest of the variables. The results obtained are useful to choose the appropriate variables for selection. The cluster analysis allowed to establish the phylogenetic relationship between the lines for the planning of future crosses.


Download data is not yet available.


Aguade A. (2013). Construction and analysis of augmented and modified augmented designs. Saarbrücken, Germany: Lambert Academic Publishing.

Aranzazu D.A., Rodríguez B.J., Zapata M.M., Bustamante J. y Restrepo L.F. (2007). Application of Multiple correspondence factor analysis to a heart valve study in pigs. Rev. Col. Cienc. Pec., 20: 129-140.

Baderinwa-Adejumo A.O. (2012). Potentials of agrobotanical characters of some local rice germplasm (Oryza sativa L.) for improved production in nigeria. Journal of Science and Science Education, 3(1): 111-117.

Berrio-Orozco L.E., Torres-Toro E.A., Barona-Valencia J. y Cuásquer-Sedano J. B. (2016). Diversidad genética de las variedades de arroz FLAR liberadas entre 2003-2014. Agron. Mesoam., 27(2): 217-231.

Díaz S.H., Cristo E., Morejón R., Castro R., Shiraishi M., Dhanappala M.P. y Keisuke A. (2013). Análisis de la estructura productiva y comportamiento de rendimiento de cuatro variedades de arroz (Oryza sativa L.) de diferentes orígenes en la prefectura de Ibaraki, Japón. Cultivos Tropicales. 34(1): 42-50.

Díaz S.H., Morejón R. y Pérez N.J. (2017). Behavior and selection of rice advanced lines (Oryza sativa L.) obtained by Breeding Program in Los Palacios. Cultivos Tropicales, 38(1): 81-88.

Díaz S.H., Morejón R., Castro R., Pérez N. y González M. (2004). Evaluación de variedades de arroz (Oryza sativa L.) para la época de primavera en Pinar del Río. Cultivos Tropicales, 25(4): 77-81.

Díaz S.H., Morejón R.; Onicka O. y Castro R. (2015). Evaluación de nuevas líneas de arroz (Oryza sativa L.) obtenidas por hibridaciones dentro del Programa de Mejoramiento Genético del cultivo en Cuba. Cultivos Tropicales, 36(3): 115-123.

Gámez N. (2012). Fundamentos y aplicaciones del análisis de correspondencias difuso. Comunicaciones en Estadística, 5(1): 7-32.

Garnica J.P., Rodríguez O.J., Jaramillo-Barrios C.I. y Vallejo F.A. (2020). Morphological Diversity and Selection Characters of Arracacha (Arracacha xanthorriza Bancr.) Germoplasm in Colombia. Ciencia y Agricultura, 17(3): 49-62. DOI:

González A.D., Pérez J., Sahagún J., Franco O., Morales E.J., Rubí M., Gutiérrez M.F. y Balbuena A. (2010). Aplicación y comparación de métodos univariados para evaluar la estabilidad en maíces del Valle Toluca-Atlacomulco. México. Rev. Agronomía Costarr, 34, 129-143.

Gour L., Maurya S.B, Koutu G.K, Singh S.K., Shukla S.S. y Mishra D.K. (2016). Characterization of rice (Oryza sativa L.) genotypes using principal component analysis including scree plot & rotated component matrix. International Journal of Chemical Studies, 5(4): 975-983.

GRiSP (Global Rice Science Partnership), 2013. Rice Almanac, 4th edition. International Rice Research Institute. Los Baños, Filipinas: IRRI Library.

Hossain M.S., Singh A.K. y Fasihuz-Zaman. (2009). Cooking and eating characteristics of some newly identified inter sub-specific (indica/japonica) rice hybrids. Science Asia, 35: 320-25. DOI:

Konate A., Zongo A., Kam H., Sanni A. y Audebert S. (2016). Genetic variability and correlation analysis of rice (Oryza sativa L.) inbred lines based on agromorphological traits. Afric. Jrnl. Agric. Res., 11(35): 3340-3346. DOI:

Kumbhar S.D., Kulwal P.L., Patil J.V., Sarawate C.D., Gaikwad A.P. y Jadhav A. S. (2015). Genetic Diversity and Population Structure in Landraces and Improved Rice Varieties from India. Rice Science, 22(3): 99-107. DOI:

Nidhi D., Raj S., Prasad M., Khatri N. y Raj B. (2019). Genetic Variability and Correlation Coefficients of Major Traits in Early Maturing Rice under Rainfed Lowland Environments of Nepal. Advances in Agriculture, 5975901. DOI:

Oko A., Ubi B., Efisue A., Efisue, A. y Nahemiah, D. (2012). Comparative analysis of the chemical nutrient composition of selected local and newly introduced rice varieties grown in Ebonyi state of Nigeria. Intern. Jrnl. Agric. Forestry, 2(2):16-23.

Rashid K., Kahliq I., Farooq M.O. y Ahsan M.Z. (2014) Correlation and Cluster Analysis of some Yield and Yield Related Traits in Rice (Oryza Sativa). Journal of Recent Advances in Agriculture, 2(8): 290-295.

Rojas-Martínez B. (2005). Bloques aumentados (Repaso: Federer, 1961). Agrociencia, 39(6): 693-695.

Sánchez Y. y Vega M.F. (2018). Situación del mercado del arroz en Costa Rica: una mirada a la realidad. Revista ABRA, 38(56): 1-22. DOI:

SES, (2002). Standar evaluation system for rice. International Rice Research Institute (IRRI). Los Baños, Filipinas: IRRI Library.

Singh P., Jain P.K. y Tiwari A. (2020). Principal Component Analysis Approach for Yield Attributing Traits in Chilli (Capsicum annum L.) Genotypes. Chemical Science Review and Letters, 9(33): 87-91.

Sohgaura N., Mishra D.K., Koutu G.K., Singh K., Kumar V. y Singh P. (2015). Evaluation of high yielding and better quality rice varieties using principal component analysis. Eco. Env. & Cons., (21):187-195.

Tinoco R. y Acuña A. (2009). Manual de recomendaciones técnicas en el cultivo de arroz. Instituto Nacional de Innovación y Transferencia en Tecnología Agropecuaria (INTA).

Uzzaman T., Sikder R.K., Asif M.I., Mehraj H. y Jamal-Uddin A.F.M. (2015). Growth and Yield Trial of Sixteen Rice Varieties under System of rice Intensification. Scientia Agriculturae, 11(2): 81-89. DOI:

Zhang B., Ye W., Ren D., Tian P., Peng Y., Gao Y., Ruan B., Wang L., Zhang G., Guo L., Qian Q. y Gao Z. (2015). Genetic analysis of flag leaf size and candidate genes determination of a major QTL for flag leaf width in rice. Rice, 8(2). DOI:

Zúñiga-Orozco A. y Carrodeguas-González A. (2021). Echeveria (Crassulaceae): Potencial para la mejora genética como ornamental. Avances en Investigación Agropecuaria. 25(3): 57-81.

Zúñiga-Orozco A., Carrodeguas-González A. y Chinchilla-Obando M. (2021). Variabilidad morfoagronómica de poblaciones F2 de pimiento (Capsicum annuum L.) en Cartago, Costa Rica. Avances en Investigación Agropecuaria. 25(2): 53-67.



How to Cite

Zuniga Orozco, A., & Carrodeguas González, A. (2022). Morpho-agronomic variability in rice genotypes in the Central Pacific, Costa Rica. Ciencia Y Agricultura, 19(1).