Unit Vision

Provide leadership in systems research for developing sustainable and adaptive integrated agricultural systems.

Unit Mission

Enhance economic and environmental sustainability of agricultural production systems by:

Synthesizing and quantifying biological, chemical and physical processes at the whole-system level.
Conducting cooperative field research to fill knowledge gaps.
Developing computer models of agricultural systems to support field research and analysis of major issues, emphasizing water quality and water conservation, production, precision farming, and climate change.
Providing farm-level computerized decision support technology and information system packages to farmers, ranchers, agricultural consultants and action agencies for evaluating sustainability of alternative farming/ranching options.
Creating an Object Modeling System with a library of science and related tools to assemble customized modular models.
Collaborating nationally and internationally to evaluate and improve knowledge and products.

Strategic Issues/Problem Areas

Whole System Integration and Modeling — Essential to Agricultural Science and Technology in the 21st Century
In the 20th Century, we made tremendous advances in discovering fundamental principles in different scientific disciplines that created major breakthroughs in management and technology for agricultural systems, mostly by empirical means. However, as we enter the 21st Century, agricultural research has more difficult and complex problems to solve. The environmental consciousness of the general public requires us to modify farm management to protect water, air, and soil quality, while staying economically profitable. At the same time, market-based global competition in agricultural products is challenging economic viability of the traditional agricultural systems, and requires the development of new and dynamic production systems. Frequency of extreme climatic events such as droughts has recently increased, possibly due to global climate change, requiring changes in management. Our customers, the agricultural communities, are asking for more timely transfer of research results in an integrated usable form to aid them in meeting these challenges through improved cropping and management. Fortunately, the new electronic technologies and remote sensing can provide us a vast amount of real-time information about crop conditions, near-term weather, and global markets, that can be utilized to develop a whole new level of management tools. However, we need the means to capture and make sense of this vast amount of site-specific data.

To address all the above needs requires us to better understand the whole system. Agricultural systems involve highly complex interactions among soil, plant, weather, and management components that are beyond a human brain to comprehend quantitatively. Modern computer technology can complement and assist the human brain in this process. The scientists need to improve on how to present their research results in the context of the whole agricultural system, this requires synthesis and quantification of experimental data based on fundamental principles and laws. The system models are indeed the product of this synthesis and quantification of current knowledge based on fundamental principles and laws. These models then form the basis of decision support systems for transferring research findings to producers under different soil and climatic conditions for making complex management decisions.

Integration of system models with field research has the potential to raise agricultural research to a higher plateau. It is also an essential first step to improve model usability and make a significant impact on the agricultural community. This integration will benefit field research and models in the following ways:

Promote a systems approach to field research.
Facilitate better understanding and quantification of research results.
Promote quick and accurate transfer of results to different soil and weather conditions, and different cropping and management systems outside of experimental plots.
Help research to focus on the identified fundamental knowledge gaps and make field research more efficient, i.e., get more out of research per dollar spent.
Provide the needed field tests of the models before delivery to other potential users—agricultural consultants, farmers/ranchers, state extension agencies, and federal action agencies (NRCS, EPA, and others).

The National Science and Technology Council, Committee on Environmental and Natural Resources, 1997 report entitled, “Integrating the Nations Environmental Monitoring and Research Network and Programs: A Proposed Framework”, recommended a national framework that links systematic observations and monitoring with predictive modeling and process research. The 1999 report of the National Academy of Sciences and Engineering on “New Strategies for America’s Watersheds” identified several critical support functions; one of these is the integration of theory, data, simulation models, and expert judgment to solve practical problems and provide a scientific basis for decision-making.

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