rYWAASB: An R package designed for the concurrent selection of stable and high-performing p lant genotypes

Ali Arminian; Hamid Hassaneian  Khoshro; Solmaz Amiri

doi:10.4025/actasciagron.v48.i1.75935

Autores

Ali Arminian Ilam University Autor https://orcid.org/0000-0003-4749-6085
Hamid Hassaneian Khoshro Agricultural Research, Education and Extension Organization Autor https://orcid.org/0000-0002-1251-0388
Solmaz Amiri Ilam University Autor

DOI:

https://doi.org/10.4025/actasciagron.v48.i1.75935

Palavras-chave:

average silhouette method; cluster; PCA; ranking; stability analysis.

Resumo

Various techniques have been employed to assess the stability and adaptability of crops, where mixed models or BLUP-based indexes like WAASB proving particularly more benefit. We introduce rYWAASB, an open-source R package designed to offer a novel index for quantifying both stability and performance of a biological trait. It quantifies the stability and performance of individuals/genotypes e.g. in plant breeding programs. It simultaneously considers the trait of interest, along with the WAASB index in a new perspective. The package then provides bar plots, PCA diagrams, and optimum cluster number estimate and cluster categorization by MCMC algorithms. For executing the package, a field experiment was conducted on chickpea (Cicer arietinum L.) from 2018 to 2020, evaluating the grain yield and days to maturity in rainfed conditions. The genotypes have put in 7 clusters for grain yield while genotypes 18, 69, and 5 were the most stable, exhibiting the highest grain yields, but genotypes 2, 103, 27, and 88 are identified as the earliest ripening varieties, exhibiting a higher degree of stability. The findings highlighted the effectiveness of the novel rYWAASB index in distinguishing observations in the experiment, suggesting its potential application in agronomic and plant breeding stability and adaptability programs.

Downloads

Os dados de download ainda não estão disponíveis.

Referências

Asadi Oskouei, E., Delsouz Khaki, B., Kouzegaran, S., Navidi, M. N., Haghighatd, M., Davatgar, N., & Lopez-Baeza, E. (2022). Mapping climate zones of Iran using hybrid interpolation methods. Remote Sensing, 14(11), 2632. https://doi.org/10.3390/rs14112632.

Bajpai, P. K., & Prabhakaran, V. T. (2000). A new procedure of simultaneous selection for high yielding and stable crop cultivars. Indian Journal of Genetics, 60, 141–146.

Becker, H. C., & Le´on, J. (1988). Stability analysis in plant breeding. Plant Breeding, 101, 1-23. https://doi.org/10.1111/j.1439-0523.1988.tb00261.x.

Cochran, W. G., & Cox, G. M. (1957). Experimental Designs. Wiley.

Crossa, J. (1990). Statistical analyses of multilocation trials. Advances in Agronomy, 44, 55-86. https://doi.org/10.1016/S0065-2113(08)60818-4.

Eberhart, S. A., & Russel, W. A. (1966). Stability parameters for comparing varieties. Crop Science, 6, 36-40. https://doi.org/ 10.2135/cropsci1966.0011183X000600010011x .

Farshadfar, E. (2008). Incorporation of AMMI stability value and grain yield in a single non-parametric index (GSI) in bread wheat. Pakistan Journal of Biological Sciences, 11:1791. https://doi.org/10.3923/pjbs.2008.1791.1796.

Finlay, K. W., & Wilkinson, G. N. (1963). The analysis of adaptation in a plant breeding programme. Australian Journal of Agricultural Research, 14, 742-754. https://doi.org/http://dx.doi.org/10.1071/AR9630742.

Fisher, R. A. (1919). The correlation between relatives on the supposition of Mendelian inheritance. Trans. R. Soc. Edinburgh, 52, 399-433. https://doi.org/10.1017/S0080456800012163.

Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver & Boyd. London.

Hatami Maleki, H., Hassaneian Khoshro, H., Kanouni, H., Shobeiri, S. S., & Moradi Ashour, B. (2024). Identifying dryland-resilient chickpea genotypes for autumn sowing, with a focus on multi-trait stability parameters and biochemical enzyme activity. BMC Plant Biology, 24, 750 https://doi.org/10.1186/s12870-024-05463-0.

Henderson, C. R. (1975). Best linear unbiased estimation and prediction under a selection model. Biometric, 31(2), 423-447. https://doi.org/10.2307/2529430.

ICARDA. (2008). Cereal program, annual report for 2007. Available online at: https://me.cgiar.org/.
Kang, M. S. (1988). A rank-sum method for selecting high-yielding, stable corn genotypes. Cereal Research Communications, 16(1/2), 113-115.

Kang, M. S. (1993). Simultaneous selection for yield and stability in crop performance trials: consequences for growers. Agronomy Journal, 85, 754-757. https://doi.org/10.2134/agronj1993.00021962008500030042x.

Kang, M. S., & Pham, H. N. (1991). Simultaneous selection for high yielding and stable crop genotypes. Agronomy Journal, 83, 161. https://doi.org/10.2134/agronj1991.00021962008300010037x.

LletÄ±, R., Ortiz, M. C., Sarabia, L. A., & Sánchez, M. S. (2004). Selecting variables for k-means cluster analysis by using a genetic algorithm that optimises the silhouettes. Analytica Chimica Acta, 515(1), 87-100. https://doi.org/10.1016/j.aca.2003.12.020.

Olivoto, T., & Lúcio, A. D. C. (2020). metan: an R package for multi-environment trial analysis. Methods in Ecology and Evolution, 11, 783-789. https://doi.org/10.1111/2041-210X.13384.

Olivoto, T., Lúcio, A. D. C., da Silva, J. A. G., Marchioro, V. S., de Souza, V. Q., & Jost, E. (2019a). Mean performance and stability in multi-environment trials I: combining features of AMMI and BLUP techniques. Agronomy Journal, 111(6), 2949–2960. https://doi.org/10.2134/agronj2019.03.0220.

Olivoto, T., Lúcio, A. D. C., da Silva, J. A. G., Sari, B. G., & Diel, M. I. (2019b). Mean performance and stability in multi-environment trials II: selection based on multiple traits. Agronomy Journal, 111, 2961–2969. https://doi.org/10.2134/agronj2019.03.0221.

Perkins, J. M., & Jinks, J. L. (1968). Environmental and genotype-environmental components of variability. III. Multiple lines and crosses. Heredity, 23(3), 339-356. https://doi.org/10.1038/hdy.1968.48.

Piepho, H. P. (1994). Best Linear Unbiased Prediction (BLUP) for regional yield trials: a comparison to additive main effects and multiplicative interaction (AMMI) analysis. Theoretical and Applied Genetics, 89(5), 647-654. https://doi.org/10.1007/BF00222462.

Purchase, J. L. (1997). Parametric analysis to describe genotype * environment interaction and yield stability in winter wheat University of the Orange Free State Bloemfontein.

Rao, A. R., & Prabhakaran, V. T. (2005). Use of AMMI in simultaneous selection of genotypes for yield and stability. Journal of the Indian Society of Agricultural Statistics, 59, 76–82.

Resende, M. D. V. d. (2004). Métodos estatísticos o'timos na análise de experimentos de campo. Ostra: INFOTECA-E. Colombo: Embrapa Florestas-Documentos.

Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal ofComputational and Applied Mathematics, 20, 53-65. https://doi.org/10.1016/0377-0427(87)90125-7.

Sharifi, P. (2020). Application of multivariate analysis methods in agriculural sciences. Rasht branch, Islamic Azad University Press (in Persian).

Shukla, G. K. (1972). Some statistical aspects of partitioning genotype-environmental components of variability. Heredity, 29, 238-245. https://doi.org/10.1038/hdy.1972.87.

Turbet Delof, M., Rivière, P., Dawson, J. C., Gauffreteau, A., Goldringer, I., Van Frank, G., & David, O. (2025). Bayesian joint-regression analysis of unbalanced series of on-farm trials. Peer Community Journal, 5. https://doi.org/10.24072/pcjournal.495.

Yates, F., & Cochran, W. G. (1938). The analysis of groups of experiments. Journal of Agricultural Sciences, 28(4), 556-580. https://doi.org/10.1017/S0021859600050978.