Submitted to: Meeting Abstract
Publication Type: Abstract only
Publication Acceptance Date: 4/24/2009
Publication Date: N/A
Citation: Interpretive Summary:
Technical Abstract: Sunlit growth chambers known as Soil-Plant-Atmosphere-Research (SPAR) provide a unique environment for studying and quantifying the effects of environmental variables either alone or in combination on plant growth and development. SPAR chambers are appropriate for short-term or entire growing season experiments, have precise and repeatable environmental controls for CO2, temperature, water and nutrients, and allow canopy gas exchange and transpiration measurements. SPAR chambers with soil bins have the advantage of a realistic root volume and have the capabilities to monitor root growth and water use over the growing season. Because of their small size, SPAR chambers are not appropriate for estimating climate change effects crop yields or end of season biomass at the field scale. They are uniquely qualified, however for quantifying plant processes and plant responses to environmental variables over short time scales (minutes to days to months). The environmental conditions in SPAR chambers (other than light) can be controlled so the plant response will be affected by a minimum of variables. This allows one to develop equations and algorithms where responses are not confounded by varying temperatures, vapor pressure deficit or water. Canopy level photosynthesis, transpiration, and plant growth and development rates have been quantified for soybean, cotton, potato and corn under varying temperatures, nutrient levels, water regimes and CO2. This has provided a large database for the development of functional relationships of plant response to environmental variables. These algorithms have been incorporated into simulation models for cotton, maize, soybean and potato useful for evaluating the effects of elevated temperature and CO2 on plant growth, development and yield. Here, we will show several examples of research results from SPAR facilities that are crucial for developing process-level crop models for use in the real-world production environments.