基因型与环境(GEI)之间的相互作用显着影响植物的性能,对于育种计划和最终提高作物生产力至关重要。在GEI旁边,育种者在寻求提高产量的过程中遇到了另一个障碍,关键性状之间的显著负相关和负相关。本研究探讨了白糖产量(WSY)的稳定性,根系产量(RY),含糖量(SC),糖提取系数(ECS),以及包括RY在内的基本特征之间的相互作用,SC,α氨基氮(N),钠(Na+),15个甜菜杂种和三个对照品种的钾(K)。调查连续两年(2022-2023年)跨越两个地点,采用随机完整的区组设计,重复四次,全面分析这些因素。方差分析强调了环境的显著影响,基因型,和GEI在1%的概率水平。值得注意的是,GEI的AMMI分析揭示了第一成分对WSY的重要性,RY,SC,前两个组件对ECS具有重要意义。在线性混合模型(LMM)中,WSY,RY,SC,ECS显示出基因型和GEI的显着影响。在WAASB双图中,基因型10、8、17、6、13、14、15、7、12和16在WSY中表现出稳定性,而基因型9、10、6、14、7、8、13、12、18和15在RY中表现出稳定性。此外,基因型10、15、12、13、16、17、6和14对于SC是稳定的,基因型8、10、7、6、13、12、16、17、15、14和18在ECS中显示出稳定性,拥有高于平均水平的产量值。在按产量×性状(GYT)双plot的基因型中,基因型15、18和16在结合RY和SC时表现最好,Na+,N,K+,表明它们有可能被纳入育种计划。
The interaction between genotype and environment (GEI) significantly influences plant performance, crucial for breeding programs and ultimately boosting crop productivity. Alongside GEI, breeders encounter another hurdle in their quest for yield improvement, notably adverse and negative correlations among pivotal traits. This study delved into the stability of white sugar yield (WSY), root yield (RY), sugar content (SC), extraction coefficient of sugar (ECS), and the interplay among essential traits including RY, SC, alpha amino nitrogen (N), sodium (Na+), and potassium (K+) across 15 sugar beet hybrids and three control varieties. The investigation spanned two locations over two consecutive years (2022-2023), employing a randomized complete block design with four replications to comprehensively analyze these factors. The analysis of variance highlighted the significant effects of environment, genotype, and GEI at the 1% probability level. Notably, the AMMI analysis of GEI revealed the significance of the first component for WSY, RY, and SC, with the first two components proving significant for ECS. Within the linear mixed model (LMM), WSY, RY, SC, and ECS demonstrated significant effects from both genotype and GEI. In the WAASB biplot, genotypes 10, 8, 17, 6, 13, 14, 15, 7, 12, and 16 exhibited stability in WSY, while genotypes 9, 10, 6, 14, 7, 8, 13, 12, 18, and 15 displayed stability in RY. Additionally, genotypes 10, 15, 12, 13, 16, 17, 6, and 14 were stable for SC, and genotypes 8, 10, 7, 6, 13, 12, 16, 17, 15, 14, and 18 showcased stability in ECS, boasting above-average yield values. In the genotype by yield × trait (GYT) biplot, genotypes 15, 18, and 16 emerged as top performers when combining RY with SC, Na+, N, and K+, suggesting their potential for inclusion in breeding programs.