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Screening of sunflower populations for seed yield and its components through step-wise regression analysis

机译:通过逐步回归分析筛选向日葵种群的种子产量及其组成

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Improving achene yield is the principal breeding objective for sunflower improvement and its commercial acceptance. Therefore, selection was practiced in six sunflower populations for seed yield. Step-wise regression analysis revealed two variable model including head diameter and achene weight for the improvement of sunflower populations 2 and 3 accounting 48.7 and 73.0% variability in seed yield per plant, respectively. Sunflower population 5 accounted minimum variability 42.6% for improvement by including achene weight, leaves per plant and distance from head to soil surface. However, populations 1 and 4 accounted maximum variability of 73.0 and 74.3%, respectively for seed yield per plant. Four variable (head diameter, achene weight, bird damage and stem diameter) and five variable models (achene weight, head diameter, plant height, leaves per plant and internodal length) were best fitted for sunflower populations 4 and 1, respectively. From the study it is evident that selection of head diameter and 100-achene weight may be effective for seed yield improvement in sunflower.
机译:提高瘦果产量是向日葵改良及其商业认可的主要育种目标。因此,在六个向日葵种群中进行选择以获得种子产量。逐步回归分析揭示了两个变量模型,包括头直径和瘦果体重,用于改善向日葵种群2和3,分别占每株种子产量的48.7%和73.0%变异。向日葵种群5的最小变异为42.6%,其中包括瘦果重,单株叶片和从头到土壤表面的距离。但是,种群1和种群4的最大变异性分别为每株植物的73.0%和74.3%。四个变量(头直径,瘦果重,鸟类伤害和茎直径)和五个变量模型(烯重,头直径,植物高度,单株叶片和节间长度)分别最适合向日葵种群4和1。从研究中可以明显看出,选择头径和100烯重可以有效提高向日葵种子的产量。

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