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Genotype ✕ Environment Interactions for Cane and Sugar Yield and Their Implications in Sugarcane Breeding1

Crop Science (Impact Factor: 1.58). 01/1984; 24(3):435-440. DOI: 10.2135/cropsci1984.0011183X002400030002x
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    • "Tai et al. (1982) evaluated phenotypic stability of sugarcane cultivars by measuring regression coefficients (b values) and mean square deviations from regression (s d ) for several traits in Florida, USA. Kang and Miller (1984) evaluated three methods of partitioning G 9 E interaction into stability-variance components assigned to each cultivar in sugarcane. However, in these models, the parameters account for only a small proportion of the interaction sum of squares; while, the remaining variance is unexplained. "

    Full-text · Dataset · Jan 2015
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    • "There have been few studies on GEI in sugarcane in Punjab. In sugarcane, traits such as yield exhibit a large amount of GEI (Kang and Miller 1984; Milligan et al.1990), and tools such as GGE biplot can be used effectively to identify stable genotypes (Yan and Kang 2003). Thus, the objectives of this investigation were to explore GEI using sugarcane yield trial data from Punjab, evaluate the discriminating ability of the test locations, and assess the interaction of genotypes with crop seasons (spring and autumn) via GGE biplot analyses. "
    Sandhu · Brar · Singh · Bhagat · Kang
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    ABSTRACT: The performance of quantitative traits in sugarcane (Saccharum spp. complex) often varies across diverse environments because of significant genotype-by-environment interaction (GEI). Our objective was to assess performance stability of 20 advanced sugarcane genotypes across six environments, including two crop seasons in Punjab. Data were obtained on cane yield (t/ha), sucrose % juice, and commercial cane sugar % at harvest and subjected to GGE [genotype (G) plus genotype-environment (GE)] biplot analysis, which revealed high positive correlations between spring and autumn crop seasons at all locations for all measured traits. This implied that genotypes could be evaluated in either crop season, which should reduce testing cost and time. Test environment Faridkot (FDK) spring, being both discriminating and representative, was an ideal test environment for selecting generally adapted genotypes for cane yield. Similarly, Ludhiana (LDH) autumn was an ideal test environment for selecting generally adapted genotypes for quality traits. Co 0238 and CoPb 08214, having high mean performance and stability across environments for cane yield and quality traits, were identified as ideal genotypes. These genotypes can be exploited commercially for the entire state of Punjab. The GGE biplot helped identify a specifically adapted genotype, CoH 119, which was the best performer in Gurdaspur (GDSP) in both crop seasons.
    Full-text · Article · Sep 2014 · Journal of Crop Improvement
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    • "There have been few studies on GEI in sugarcane in Punjab. In sugarcane, traits such as yield exhibit a large amount of GEI (Kang and Miller 1984; Milligan et al.1990), and tools such as GGE biplot can be used effectively to identify stable genotypes (Yan and Kang 2003). Thus, the objectives of this investigation were to explore GEI using sugarcane yield trial data from Punjab, evaluate the discriminating ability of the test locations, and assess the interaction of genotypes with crop seasons (spring and autumn) via GGE biplot analyses. "

    Full-text · Article · Jan 2014 · Journal of Crop Improvement
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