Submitted to: Bioscience
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: July 20, 2001
Publication Date: N/A
Interpretive Summary: This paper presents how a Soil-Plant-Atmosphere-Research (SPAR) system can be used to collect data needed to understand various facets of growth and development in cotton, as an example crop, and shows how this understanding can be used to build process-level models and in aid in managing the cotton crop. As progress is gained in developing a systems understanding of plant response to environment, whether it be in support of global climate change research, the application of plants to remediate environmental conditions, or the increased application of precision agriculture technologies, the need for diagnostics and management decision aids will become more urgent. Mechanistic plant models and automated, user-friendly expert systems can facilitate selection of optimum solutions to problems with many variables. Essentially, all of the engineering and computing technologies needed to allow the use of variable site-specific technologies, such as precision agriculture, are now available. However, our understanding of plant eco-physiological response to environment as it relates to specific growth and developmental events requires further development. There are many aspects of plant growth; interactions with other plants, insects and diseases; and the responses of these organisms to their physical environment that are not properly understood. There are a variety of approaches and facilities to investigate plant responses to environment. Among these, the SPAR facilities are optimized for the measurement of plant and canopy-level physiological response to precisely controlled, but naturally lit, environmental conditions. The data sets that have been, and will be, obtained are unique and particularly instructive for applied and basic plant biologists.
As we approach the 21st century, there is a greater demand and need for process-level crop simulators that can be used in combination with other technological advances to make precision farming a reality. These advanced technologies include data handling and processing systems, geographical information systems, global positioning systems, decision support systems, and variable rate application systems. Better management becomes feasible by combining comprehensive, physiologically based forecasting crop models with other technologies. These models will be used to diagnose problem areas and prescribe alternative practices designed to eliminate specific production problems. Despite years of agronomic and crop science research, there are still knowledge gaps and a lack of quantitative information on crop responses to physical environment. A unique way to solve these problems is to build teams or laboratories specialized in this area to deliver essential information. In this paper, we present examples on how we have combined simulation modeling methods and a soil-plant-atmosphere-research (SPAR) facility to develop and deliver a process-level crop simulation model that works in the real world. First, we describe the SPAR facility, mastery of environmental controls and data acquisition on various biological processes and physical environmental conditions. Then, we present how the data generated in the controlled-environment facility can be used to develop several modules for a crop simulation model using cotton as an example crop.