Grain yield components of pearl millet under optimum conditions can be used to identify germplasm with adaptation to and zones

There is evidence that high-tillering, small-panicled pearl millet landraces are better adapted to the severe, unpredictable drought stress of the and zones of NW India than are low-tillering, large-panicled modern varieties, which significantly outyield the landraces under favourable conditions. In this paper, we analyse the relationship of and zone adaptation with the expression, under optimum conditions, of yield components that determine either the potential sink size or the ability to realise this potential. The objective is to test whether selection under optimal conditions for yield components can identify germplasm with adaptation to and zones in NW India, as this could potentially improve the efficiency of pearl millet improvement programs targeting and zones. We use data from an evaluation of over 100 landraces from NW India, conducted for two seasons under both severely drought-stressed and favourable conditions in northwest and south India. Trial average grain yields ranged from 14 g m(-2) to 182 g m(-2). The landraces were grouped into clusters, based on their phenology and yield components as measured under well-watered conditions in south India. In environments without pre-flowering drought stress, tillering type had no effect on potential sink size, but low-tillering, large-panicled landraces yielded significantly more grain, as they were better able to realise their potential sink size. By contrast, in two low-yielding and zone environments which experienced pre-anthesis drought stress, low-fillering, large-panicled landraces yielded significantly less grain than high-tillering ones with comparable phenology, because of both a reduced potential sink size and a reduced ability to realise this potential. The results indicate that the high grain yield of low-tillering, large-panicled landraces under favourable conditions is due to improved partitioning, rather than resource capture. However, under severe stress with restricted assimilate supply, high-tillering, small-panicled landraces are better able to produce a reproductive sink than are large-panicled ones. Selection under optimum conditions for yield components representing a resource allocation pattern favouring high yield under severe drought stress, combined with a capability to increase grain yield if assimilates are available, was more effective than direct selection for grain yield in identifying germplasm adapted to and zones. Incorporating such selection in early generations of variety testing could reduce the reliance on random stress environments. This should improve the efficiency of millet breeding programs targeting and zones. (c) 2005 Elsevier B.V. All rights reserved.

Self-efficacy and task performance: An investigation from two levels of analysis and specificity

This research adopts a resource allocation theoretical framework to generate predictions regarding the relationship between self-efficacy and task performance from two levels of analysis and specificity. Participants were given multiple trials of practice on an air traffic control task. Measures of task-specific self-efficacy and performance were taken at repeated intervals. The authors used multilevel analysis to demonstrate dynamic main effects, dynamic mediation and dynamic moderation. As predicted, the positive effects of overall task specific self-efficacy and general self-efficacy on task performance strengthened throughout practice. In line with these dynamic main effects, the effect of general self-efficacy was mediated by overall task specific self-efficacy; however this pattern emerged over time. Finally, changes in task specific self-efficacy were negatively associated with changes in performance at the within-person level; however this effect only emerged towards the end of practice for individuals with high levels of overall task specific self-efficacy. These novel findings emphasise the importance of conceptualising self-efficacy within a multi-level and multi-specificity framework and make a significant contribution to understanding the way this construct relates to task performance.