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Using fine-scale panel data and an econometric model, we predict land use change in the Midwestern United States if a new bioenergy crop, Miscanthus × Giganteus (miscanthus), is introduced. To explain farmers' current crop choices, we use a local, limited dependent variable regression based on soil and weather characteristics. To this model, we add miscanthus as a new crop, based on its place dependent BioCro model-predicted yield. We find that the vast majority of land used to grow miscanthus will come from land now used for non-major crops, pasture, woodland, and other uses. This implies that miscanthus can help mitigate climate change by displacing oil usage without causing food conflict.

This article is from Journal of Circadian Rhythms , volume 9 . Abstract Background: Actigraphy provides a way to objectively measure activity in human subjects. This paper describes a novel family of statistical methods that can be used to analyze this data in a more comprehensive way. Methods: A statistical method for testing differences in activity patterns measured by actigraphy across subgroups using functional data analysis is described. For illustration this method is used to statistically assess the impact of apnea-hypopnea index (apnea) and body mass index (BMI) on circadian activity patterns measured using actigraphy in 395 participants from 18 to 80 years old, referred to the Washington University Sleep Medicine Center for general sleep medicine care. Mathematical descriptions of the methods and results from their application to real data are presented. Results: Activity patterns were recorded by an Actical device (Philips Respironics Inc.) every minute for at least seven days. Functional linear modeling was used to detect the association between circadian activity patterns and apnea and BMI. Results indicate that participants in high apnea group have statistically lower activity during the day, and that BMI in our study population does not significantly impact circadian patterns. Conclusions: Compared with analysis using summary measures (e.g., average activity over 24 hours, total sleep time), Functional Data Analysis (FDA) is a novel statistical framework that more efficiently analyzes information from actigraphy data. FDA has the potential to reposition the focus of actigraphy data from general sleep assessment to rigorous analyses of circadian activity rhythms.