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United States Department of Agriculture

Agricultural Research Service

Title: Agricultural System Models in Field Research and Technology Transfer

Authors
item Ahuja, Lajpat
item Ma, Liwang
item Andales, Allan
item Saseendran, S - COLORADO STATE UNIVERSITY

Submitted to: International Agronomy Congress Abstracts
Publication Type: Proceedings
Publication Acceptance Date: April 15, 2004
Publication Date: July 24, 2004
Citation: Ahuja, L.R., Ma, L., Andales, A.A., Saseendran, S.A. 2004. Agricultural system models in field research and technology transfer. Second International Agronomy Congress Proceedings. Punjab Singh, I.P.S. Ahlawat and R.C. Gautam (eds.) Indian Society of Agronomy. pp. 226-233.

Interpretive Summary: Understanding real-world situations and solving significant agronomic, engineering and environmental problems require synthesis, integration and quantification of knowledge at the whole system level. 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 is requiring 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. Fortunately, the new electronic technologies can provide us a vast amount of real-time information about crop conditions and near-term weather via remote sensing by satellites or ground-based instruments, which can be utilized to develop a whole new level of management. However, we need the means to capture and make sense of this vast amount of site-specific data. Agricultural system models are essential to meeting all the above challenges and needs of the 21st century. Our customers, the agricultural producers, are asking for a quicker transfer of research results in an integrated, usable form for site-specific management. The system models are indeed the synthesis, integration, and quantification of current knowledge based on fundamental principles and laws. Models enhance understanding of data taken under certain conditions and help extrapolate their applications to other conditions and locations. Models facilitate better understanding of the interrelationships among management and various components in a system and can integrate numerous experimental results from different conditions. System modeling has been a vital step in many scientific disciplines. We would not have gone to the moon successfully without the combined use of models and some good data. Models have been used extensively in designing and managing water resource reservoirs and distribution systems, and in analyzing waste disposal sites. Although a lot more work is needed to bring models of agricultural systems to the level of physics and hydraulic system models, they have gone through a series of breakthroughs and, with some good data, can be used for practical applications. Integration of modeling with field research may be the best way to bring about further improvements.

Technical Abstract: Understanding real-world situations and solving significant agronomic, engineering and environmental problems require synthesis, integration and quantification of knowledge at the whole system level. 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 is requiring 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. Fortunately, the new electronic technologies can provide us a vast amount of real-time information about crop conditions and near-term weather via remote sensing by satellites or ground-based instruments, which can be utilized to develop a whole new level of management. However, we need the means to capture and make sense of this vast amount of site-specific data. Agricultural system models are essential to meeting all the above challenges and needs of the 21st century. Our customers, the agricultural producers, are asking for a quicker transfer of research results in an integrated, usable form for site-specific management. The system models are indeed the synthesis, integration, and quantification of current knowledge based on fundamental principles and laws. Models enhance understanding of data taken under certain conditions and help extrapolate their applications to other conditions and locations. Models facilitate better understanding of the interrelationships among management and various components in a system and can integrate numerous experimental results from different conditions. System modeling has been a vital step in many scientific disciplines. We would not have gone to the moon successfully without the combined use of models and some good data. Models have been used extensively in designing and managing water resource reservoirs and distribution systems, and in analyzing waste disposal sites. Although a lot more work is needed to bring models of agricultural systems to the level of physics and hydraulic system models, they have gone through a series of breakthroughs and, with some good data, can be used for practical applications. Integration of modeling with field research may be the best way to bring about further improvements.

Last Modified: 12/18/2014