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

Authors: Ahuja, Lajpat; Andales, Allan; Ma, Liwang; SASEENDRAN, S - COLORADO STATE UNIVERSITY

Submitted to: Journal of Crop Improvement
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/15/2007
Publication Date: 4/15/2007
Citation: Ahuja, L.R., Andales, A.A., Ma, L., Saseendran, S.A. 2007. Whole System Integration and Modeling Essential to Agricultural Science and Technology for the 21st Century. Journal of Crop Improvement. 19 (1-2).

Interpretive Summary: 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 has been causing environmental problems. Excessive leaching and runoff of agricultural chemicals are seriously affecting the quality of both the groundwater and surface waters. 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. Dealing with these problems will require continual improvement in the management of cropping systems. The interactions between various aspects of a cropping system must be considered in the context of the whole system. Computer simulation models of agricultural systems bring together information from different scientific disciplines to allow evaluation and prediction of the effects of varying management practices, crops, soils, water, and climate on both production and the environment. These system models can be used to evaluate management practices and environmental effects over a wide range of conditions and time, even beyond what can be tested in field experiments. They can help extend limited experimental results and, when they give unexpected predictions, can point to future research needs or new findings that can be tested in the field. Thus, the models can enhance the efficiency of field research for developing sustainable agricultural systems. The models can also serve as guides for planning and management, and help transfer new technologies to various conditions of developing countries. Examples of capabilities and applications of existing system models are given. Also, advancements needed in models to improve and extend their applications are presented.

Technical Abstract: 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 has been causing environmental problems. Excessive leaching and runoff of agricultural chemicals are seriously affecting the quality of both the groundwater and surface waters. 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 help transfer new technologies to various conditions of developing countries. Current state of the system models and their applications for these purposes are reviewed, and advancements needed in models to improve and extend these applications are presented.