Apple Pollination: Demand Depends on Variety and Supply Depends on Pollinator Identity

Insect pollination underpins apple production but the extent to which different pollinator guilds supply this service, particularly across different apple varieties, is unknown. Such information is essential if appropriate orchard management practices are to be targeted and proportional to the potential benefits pollinator species may provide. Here we use a novel combination of pollinator effectiveness assays (floral visit effectiveness), orchard field surveys (flower visitation rate) and pollinator dependence manipulations (pollinator exclusion experiments) to quantify the supply of pollination services provided by four different pollinator guilds to the production of four commercial varieties of apple. We show that not all pollinators are equally effective at pollinating apples, with hoverflies being less effective than solitary bees and bumblebees, and the relative abundance of different pollinator guilds visiting apple flowers of different varieties varies significantly. Based on this, the taxa specific economic benefits to UK apple production have been established. The contribution of insect pollinators to the economic output in all varieties was estimated to be £92.1M across the UK, with contributions varying widely across taxa: solitary bees (£51.4M), honeybees (£21.4M), bumblebees (£18.6M) and hoverflies (£0.7M). This research highlights the differences in the economic benefits of four insect pollinator guilds to four major apple varieties in the UK. This information is essential to underpin appropriate investment in pollination services management and provides a model that can be used in other entomolophilous crops to improve our understanding of crop pollination ecology.

Responding to Fluctuating Demand for Healthcare Services

The level of demand for healthcare services can fluctuate quite strongly. Indeed, some parts of the healthcare service are renowned for having peaks of demand which challenge capacity. Dealing with fluctuations in demand is a common problem in many service industries. This article examines some of the strategies available for influencing the level of demand, including the use of price, communications and demand analysis. The article also outlines a wide variety of ways in which patients can be encouraged to be more tolerant of waiting to receive service from healthcare professionals. In particular, eight principles of waiting are discussed and illustrated in the context of healthcare services.

Job-demand for Learning, Job-related Learning and Need for Achievement

Individual learning is important, as it is both a precursor and an outcome of learning in organisations. Job-related learning is driven by external factors (e.g., the demands of the job) and internal factors (i.e., the personality of the individual). The study examined whether need for achievement moderates the relationship between job-demand for learning and job-related learning. Data were obtained from 153 full-time, white-collar employees from a range of industries. Hierarchical regression analysis using the product term revealed that need for achievement moderates the relationship between job-demand for learning and job-related learning. Specifically, although job-demand for learning is correlated positively to job-related learning for both the high and the low need for achievement groups, this correlation is stronger amongst the high group. The findings are discussed in terms of their implications for future research and practice.

Job-demand for Learning and Job-related Learning: the Mediating Effect of Job Performance Improvement Initiatives

This study examined whether job-performance-improvementinitiatives mediate the relationship between individuals’ job-demand for learning and job-related learning. Data were obtained from 115 full-time employees in a diverse range of occupations. A partial least squares analysis revealed that job-performance-improvement-initiatives mediate partially the effects of job-demand for learning on job-related learning. Several implications for future research and policy are drawn from the findings.