Monitoring of past and present status or trends in quantity and quality of a resource has often proven essential in making decisions regarding the management of that resource. As a very familiar example, a storeowner makes ordering decisions based on monitoring of inventory, and on factors such as past experience with consumer behavior, upcoming holidays, and time for the delivery of goods. Management abilities and predictive power for anticipating future needs improve with experience (or as data accumulate). Over time, changes in consumer preferences, and changes associated with technological advances in the delivery systems affect how storeowners manage their inventories. Models used to forecast climate, weather, or performance of stocks, are based on the sophisticated use of data on key parameters accumulated over time. Models improve as data series become longer, however the intrinsic degrees of uncertainty in these systems will never allow perfect predictions so monitoring will always be needed. In the economic world, productivity of workers, unemployment rates, stock indices, mortgage rates, and other variables are monitored and this information is used to predict and manage the local, regional, national and international economy. In contrast, we do not have reliable statistics or long-term monitoring indicators for most of the nations’ biological resources (NRC 2000a).

The charge to this committee as it pertains to environmental monitoring is to (1) evaluate the need for and approaches to environmental monitoring and validation processes and, if deemed necessary, to include recommendations for postcommercialization monitoring of transgenic plants and (2) provide guidance on the assessment of non-target effects, appropriate tests for environmental evaluation, and assessments of cumulative effects on agricultural and nonagricultural environments for transgenic plants.


Precommercial risk analysis has several inherent weaknesses. In general, small-scale precommercialization field experiments are not sensitive enough to detect anything but large effects. For any such experiment there will be some limit to what can be detected, and this limit will be rather high because the natural variability from one experimental replication to another is large. For example, in estimating the yield of a corn variety—a commercially important agronomic trait—it is necessary to run several hundred-yield trials to detect significant increases in yield. In the U.S. Corn Belt, corn yields may be greater than 150 bushels per acre. A variety that yields five additional bushels per acre is a significantly more

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