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<p>Efficient multi-trait selection across cropping cycles is essential for accelerating the genetic gain in the sugarcane breeding pipeline. However, limited studies have integrated REML/BLUP-based genetic parameters, and the multi-trait genotype ideotype distance index (MGIDI) approaches in sugarcane breeding for identification of candidate clones in terms of cane and sugar yield trait combination under diversified cropping cycles. The present study evaluated 28 sugarcane clones across three cropping cycles: Plant Crop I (2022–2023), Plant Crop II (2023–2024), and Ratoon (2023–2024) at RARS, Anakapalle, to identify high-yielding and stable clones. Except for cane length, all yield and sugar quality traits exhibited significant genetic variation and genotype × season interaction at the <i>p</i> ≤ 0.05 level. The REML/BLUP analysis revealed moderate heritability and high selection accuracy for CCS yield, identifying it as the most reliable trait for direct genetic improvement in sugarcane breeding. Principal component analysis explained 84.4% of the total variation and effectively grouped the cane yield and sugar quality traits. All agronomic traits were significantly and positively correlated with cane yield, while sugar quality traits showed non-significant associations. Path analysis indicated a strong relationship between cane yield and CCS yield (R<sup>2</sup> = 0.796), although multicollinearity was observed among sucrose %, brix %, and CCS %. MGIDI-based selection resulted in 84.41% cumulative genetic gain and identified clones 2018A 6, 2018A 157, 2019A 156, and 2019A 160 as closest to the ideotype (≥ 105 t ha⁻<sup>1</sup> cane yield and ≥ 14 t ha⁻<sup>1</sup> CCS yield). Factor analysis grouped the eight traits into three factors: FA1 (sugar quality traits and cane length), FA2 (CCS yield and NMC), and FA3 (cane yield and girth). Overall, the integration of REML/BLUP, multivariate analysis, and MGIDI across multiple cropping cycles provides a robust framework for identifying high-yielding, stable, and broadly adaptable sugarcane clones.</p>

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The Multi-trait Selection for Higher Cane Yield and Sugar Quality-Related Traits in Sugarcane (Saccharum spp.)

  • 期刊论文

  • 发表日期：

    2026年4月10日

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  • D. Adilakshmi,
  • P. V. Padmavathi,
  • B. N. V. S. R. Ravikumar,
  • D. Purushotama Rao

摘要

Efficient multi-trait selection across cropping cycles is essential for accelerating the genetic gain in the sugarcane breeding pipeline. However, limited studies have integrated REML/BLUP-based genetic parameters, and the multi-trait genotype ideotype distance index (MGIDI) approaches in sugarcane breeding for identification of candidate clones in terms of cane and sugar yield trait combination under diversified cropping cycles. The present study evaluated 28 sugarcane clones across three cropping cycles: Plant Crop I (2022–2023), Plant Crop II (2023–2024), and Ratoon (2023–2024) at RARS, Anakapalle, to identify high-yielding and stable clones. Except for cane length, all yield and sugar quality traits exhibited significant genetic variation and genotype × season interaction at the p ≤ 0.05 level. The REML/BLUP analysis revealed moderate heritability and high selection accuracy for CCS yield, identifying it as the most reliable trait for direct genetic improvement in sugarcane breeding. Principal component analysis explained 84.4% of the total variation and effectively grouped the cane yield and sugar quality traits. All agronomic traits were significantly and positively correlated with cane yield, while sugar quality traits showed non-significant associations. Path analysis indicated a strong relationship between cane yield and CCS yield (R2 = 0.796), although multicollinearity was observed among sucrose %, brix %, and CCS %. MGIDI-based selection resulted in 84.41% cumulative genetic gain and identified clones 2018A 6, 2018A 157, 2019A 156, and 2019A 160 as closest to the ideotype (≥ 105 t ha⁻1 cane yield and ≥ 14 t ha⁻1 CCS yield). Factor analysis grouped the eight traits into three factors: FA1 (sugar quality traits and cane length), FA2 (CCS yield and NMC), and FA3 (cane yield and girth). Overall, the integration of REML/BLUP, multivariate analysis, and MGIDI across multiple cropping cycles provides a robust framework for identifying high-yielding, stable, and broadly adaptable sugarcane clones.