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of decisions. The chief advantage of reduced-form approaches is that they greatly simplify the relationship between climate and biophysical outcomes. They are practical when data limitations are large. However, collinearity among explanatory climate variables is often large. Also, the climatic variations used to force the models are often outside the range of climatic observations from which the model coefficients were estimated. Spurious relationships between climate predictors and their predicted outcomes, and statistical overfitting of the data, are frequent (Katz, 1977). Because such models do not specify many variables that may affect the relationship between climatic variables and human outcomes, they are not useful for making predictions about what would happen if the missing variables (e.g., seed technology, forecasting skill) changed over time.

Deterministic models of plant growth and other ecological processes permit detailed estimates of the effects of climate variability to be made under a wide range of climate conditions. Examples include mathematical simulation models of forest growth and composition (Botkin et al., 1972; Shugart, 1984) and agricultural crop growth and yield (Williams et al., 1984; Jones and Kiniry, 1986). Such models realistically couple climatic determinants (e.g., temperature, precipitation, solar radiation, humidity, wind speed) with biophysical processes (e.g., plant water use, photosynthesis) that regulate biophysical outcomes (e.g., crop yields). For example, forest composition models have simulated the retreat of maple forests poleward in northeastern North America in response to climate change (Davis and Zabinski, 1992). They have also been used to estimate the impact of sustained drought on timber productivity in the central United States (Bowes and Sedjo, 1993). In the Missouri-Iowa-Nebraska-Kansas (MINK) study (Rosenberg et al., 1993; Easterling et al., 1993), a crop model simulated a contemporary crop response to a recurrence of the Dust Bowl droughts of the 1930s. MINK researchers found that such droughts, absent human intervention, would reduce current yields of maize, soybeans, and wheat by as much as 30 percent below current averages. The model revealed that crop development rates were abnormally increased by the high heat of the droughts, which led to premature termination of grainfill.

Deterministic approaches are richly detailed in causal explanation of biophysical impacts. They provide detailed diagnostic information on why a certain type of outcome was predicated. However, they require massive amounts of data and are highly location-specific, which requires the scaling of results to represent surrounding regions. Acquisition of the necessary data to run the models can be difficult, especially in nations and regions with less developed scientific infrastructure.

Modeling can be improved by joining together the strengths of reduced-form and deterministic models. Promising work on this front seeks



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