Genetic variance and stability assessment of sugarcane (Saccharum officinarum L.) clones using the multi-trait stability index across diverse cropping seasons

Main Article Content

D. Adilakshmi
P. V. Padmavathi
D. Purushotama Rao
Ch. Mukunda Rao

Abstract

The multi-trait stability index (MTSI) was employed in sugarcane (Saccharum officinarum L.) to identify superior clones that exhibit mean performance and stability across multiple trait combinations over crop seasons. Genetic variance analysis for thirteen yield and quality traits showed a significant effect (p < 0.001) for genotype, environment, and genotype-by-environment (G×E) interaction, except for stalk diameter over the crop seasons. Based on MTSI scores, two clones, 2017A 236 (G3) and 2017A 36 (G1) were identified as excellent variants with respect to mean performance and stability of cane yield and sugar quality traits across the three crop seasons. The MTSI index demonstrated selection differential for nine traits that exhibited positive selection gains, ranging from stalk length (5.21%) to number of tillers at 120 DAP (63.28%), while four traits showed negative gains for fiber (%) (-1.13%), brix (%) (-8.57%), jaggery yield (-14.32%) and stalk diameter (-23.69%). Correlation analysis showed a strong association among cane yield and yield-related traits across seasons, alongside high correlations observed among sucrose percent, brix percent and CCS percent over the crop seasons. The present results suggested that the selected clones are excellent candidates, showing superior performance for the evaluated traits across all crop seasons. Therefore, these promising clones hold potential for advancement in yield trials and inclusion in future breeding pipelines.

Downloads

Download data is not yet available.

Article Details

How to Cite
Adilakshmi, D. ., Padmavathi, P. V. ., Purushotama Rao, D. ., & Rao, C. M. . (2025). Genetic variance and stability assessment of sugarcane (Saccharum officinarum L.) clones using the multi-trait stability index across diverse cropping seasons. INDIAN JOURNAL OF GENETICS AND PLANT BREEDING, 85(03), 464–471. https://doi.org/10.31742/ISGPB.85.3.12
Section
Research Article

References

Alam Z. Akter S. Khan M. A. H. Rashid M. H. Hossain M. I.,Bashar A. and Sarker U. 2024. Multi trait stability indexing and trait correlation from a dataset of sweet potato (Ipomoea batatas L.). Data in Brief, 52: 109995.

Alarmelu S. and Kurup H. G. 2023. Principal component and cluster analyses based on morphological characterization in sugarcane. Journal of Sugarcane Research, 12(1): 16-31.

Appunu C., Hemaprabha G., Sreenivasa V., Durai A. A., Mohanraj K., Elayaraja K. and Ram B. 2024. Evaluation of sugarcane genotypes (Saccharum sp. hybrid) for multi-trait stability analysis across diverse environments. Industrial Crops and Products, 219: 118993.

Blakeney M. 2001. Protection of plant varieties and farmers’ rights. European Intellectual Property Review. 24: 9-19.

Carvalho I. R., Szareski V. J., Silva J. A. G. D., Nunes A. C. P., Rosa T. C. D., Barbosa M. H. and Souza V. Q. D. 2020. Multivariate best linear unbiased predictor as a tool to improve multi-trait selection in sugarcane. Pesquisa Agropecuária Brasileira, 55: e00518.

Durai A. A., Amaresh, Kumar R. A. and Hemaprabha G. 2025. Elucidating the GXE interaction using AMMI, AMMI stability parameters and GGE for cane yield and quality in sugarcane. Tropical Plant Biology, 18(1): 3.

Koundinya A. V. V., Ajeesh B. R., Hegde V., Sheela M. N., Mohan C. and Asha K. I. 2021. Genetic parameters, stability and selection of cassava genotypes between rainy and water stress conditions using AMMI, WAAS, BLUP and MTSI. Scientia Horticulturae, 281: 109949.

Mekonnen S. A., Azene T. F. and Tessema W. M. 2024. Yield and multivariate analysis among twelve sugarcane (Saccharum officinarum L.) genotypes at Mankusa, north western Ethiopia. South African Journal of Agricultural Extension, 52(2): 145-158.

Munda S., Paw M., Saikia S., Begum T., Baruah J. and Lal M. 2023. Stability and selection of trait specific genotypes of Curcuma caesia Roxb. using AMMI, BLUP, GGE, WAAS and MTSI model over three years evaluation. Journal of Applied Research on Medicinal and Aromatic Plants, 32: 100446.

Olivoto T. and Lúcio A. D. 2020. metan: An R package for multi‐environment trial analysis. Methods in Ecology and Evolution, 11(6): 783-789. Available at: https://doi.org/10.1111/2041-210x.13384.

Olivoto T. and Nardino M. 2021. MGIDI: Toward an effective multivariate selection in biological experiments. Bioinformatics, 37: 1383–1389.

Olivoto T., Nardino M., Meira D., Meier C., Follmann D. N., de Souza V. Q. and Baretta D. 2021. Multi‐trait selection for mean performance and stability in maize. Agronomy Journal, 113(5): 3968-3974.

Posit team. 2022. RStudio: Integrated development environment for R. Posit Software, PBC, Boston, MA. Available at: http://www.posit.co/.

Sellami M. H., Pulvento C. and Lavini A. 2021. Selection of suitable genotypes of lentil (Lens culinaris Medik.) under rainfed conditions in south Italy using multi-trait stability index (MTSI). Agronomy, 11(9): 1807.

Sharifi P., Erfani A., Abbasian A. and Mohaddesi A. 2020. Stability of some rice genotypes based on WAASB and MTSI indices. Iranian Journal of Genetics and Plant Breeding (IJGPB), 9(2).

Shiri M., Moharramnejad S., Estakhr A., Fareghi S., Najafinezhad H., Khavari Khorasani S. and Mohseni M. 2024. Determining the stability of new maize hybrids with WAASBY and MTSI indices. Journal of Crop Breeding, 16(2): 14-28.

Soni M., Shrivastava M. K., Amrate P. K., Sharma S., Singh Y. and Khare V. 2024. Assessing genetic variability and identifying stable soybean genotypes across rainfed and irrigated planting conditions using multi-trait stability index (MTSI). Indian Journal of Genetics and Plant Breeding, 84(3): 374-384.

Taleghani D., Rajabi A., Saremirad A. and Fasahat P. 2023. Stability analysis and selection of sugar beet (Beta vulgaris L.) genotypes using AMMI, BLUP, GGE biplot and MTSI. Scientific Reports, 13(1): 10019.

Tyagi S., Chandra S. and Tyagi G. 2023. Statistical modelling and forecasting annual sugarcane production in India: Using various time series models. Annals of Applied Biology, 182(3): 371-380.