Monday, 7 November 2005 - 10:00 AM
37-5

Whole System Integration and Modeling Essential to Agricultural Science and Technology for the 21st Century.

Lajpat Ahuja, Liwang Ma, S. A. Saseendran, and Allan Andales. USDA-ARS, Great Plains Systems Research Unit, 2150 D, Centre Ave., Fort Collins, CO 80526

In the 21st century, agricultural research has more difficult and complex problems to solve. The continued increase in population in the developing countries requires continued increases in agricultural production. However, the increased use of fertilizers, pesticides, and water required for the new higher yielding crop varieties is already causing environmental problems. The quality of both the groundwater and surface waters is being seriously affected by excessive leaching and runoff of agricultural chemicals. Increase in soil salinity, decline in soil organic matter, and increase in soil erosion remain the major problems in intensively farmed areas. Even the air quality is being affected. At the same time, market-based global competition is challenging the economic viability of traditional agricultural systems. Global climate change will pose additional challenges. The solution or mitigation of these changing and multiple problems will require continual improvement or changes in management and selection of dynamic cropping systems using a whole-system approach. Therefore, synthesis and quantification of disciplinary knowledge at the whole system level is essential to meeting these challenges. The process-based models of agricultural systems provide such a synthesis and quantification for evaluating the effects of varying management practices, crops, soils, water, and climate on both the production and the environment. These system models will greatly enhance the efficiency of field research for developing sustainable agricultural systems, serve as guides for planning and management, and the transfer of new technologies to various conditions of the developing countries. Current state of the system models and their applications for these purposes will be reviewed, and advancements needed in models to improve and extend these applications will be presented.


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