Sentence examples for variability in productivity from inspiring English sources

The influence of product variety on the assembly process is first examined using an etic approach by applying ANOVA methods to production data to test the impact of product type (268 unique assemblies) and product platform (8 unique product platforms) on the variability in productivity.

To assess the possible impact of interannual variability in productivity on future management change, we assumed that farmers adapt their livestock density to the simulated minimum productivity in the past 10 years (i.e., 10-year minimum NPP managed ).

An interesting point to note is that higher variability in productivity in LDCs could be the consequence of higher regulations to begin with: firms in sectors with a comparative advantage could have higher worker productivities, while firms in protected sectors lower productivities (even considering government regulations to protect them).

The six factors we examined were: (1) regional habitat productivity; (2) inter-annual variability in productivity (e.g. silver-spoon effects); (3) habitat quality; (4) human footprint and activity; (5) rate of landscape change; and (6) density dependence.

After accounting for intrinsic biological factors (age, sex), we examined how body size, measured in mass, length and body condition, was influenced by: (a) population density; (b) regional habitat productivity; (c) inter-annual variability in productivity (including silver spoon effects); (d) local habitat quality; (e) human footprint (disturbances); and (f) landscape change.

Instead of more productive stands transpiring more water, the greatest variability in both productivity and transpiration was determined by site conditions and to a lesser degree, species composition.

We use the standard deviation (SD) to diagnose the magnitude of the interannual variability in modeled productivity during different time periods.

We found that underlying variability in primary productivity across the study area had a substantial effect on conversion thresholds required to trigger land use change when compared to results from NPV analysis.

However, due to the broad variability in field productivity and the difficulty in predicting material demands by field activities, it is more challenging to achieve efficiency in supply chain management in the construction industry than in the manufacturing industry (Dubois and Gadde 2002; Tommelein et al. 2003).

The reason for this resides on the large variability in primary productivity that occurs for any given producer nutrient content both across aquatic and terrestrial ecosystems [10], [11].

Factors that influence the variability in forest productivity among the sites or the response to thinning were included as random effects, but not specifically as predictive variables.