ity from a variety of sources are being linked to satellite data. 20 For example, district-level data from the Brazilian population and agricultural censuses are being used to model the causes of deforestation, 21 and combinations of individual-, household-, and village-level longitudinal data are being joined to biophysical data at the village level to study how deforestation in Northeast Thailand is linked to household-level human activities, including migration.22 Each such research approach makes sense in the context of the region under examination and the substantive questions being addressed, but the diversity of social and economic data makes comparison and aggregation across regions difficult.

Data on agricultural inputs and management (e.g., tillage practices) are important to understanding such global change processes as carbon sequestration in soils. In the United States these data are reported at a county or state scale, so they cannot be reliably matched with spatially referenced soil and climate data in order to model the effects of agricultural practices on carbon sequestration or to examine the determinants of these agricultural practices. More spatially explicit data on the relevant human activities are collected in some European countries but are virtually nonexistent in developing countries.

Data on energy production and consumption have provided a critical input to the past decade of global change research. Detailed annual data on fossil fuel production for all of the countries of the world, developed primarily by the fossil fuel industries, are translated into carbon emissions based on measured and estimated values of energy content and carbon to energy ratios of each fuel. Uncertainty in global estimates of the total annual carbon emissions is estimated to be a few percent, while errors in year to year differences are much smaller.23

A similar key role is being played by data on the production of CFCs and nitrogen fertilizer and on nitrogen and sulfur oxide emissions in fossil fuel combustion. Many of the human source terms, however, are not well understood. Among these are carbon dioxide emissions associated with natural gas production, leakage rates of methane from the global natural gas production and distribution system, sources of methane arising from animal husbandry and rice cultivation, and sources of atmospheric nitrous oxide arising from managed ecosystems.

As the above examples and others in Chapter 7 indicate, efforts are increasing to link social, economic, and health data to biophysical data to improve understanding of the human dimensions of global change. However, a number of important observational issues must be addressed in the next decade. Some of these are similar to those in the biophysical sciences, and some are unique to human dimensions. This section focuses on those that are unique to human dimensions research, including the lack of involvement in USGCRP participation by key federal agencies, data comparability across political boundaries, georeferencing of social science data, and confidentiality issues that arise with human observations.

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