Jay Ham
Professor, Department of Soil and Crop Sciences, Colorado State University
About Me:
Research Interests:
Environmental physics and micrometeorology; Effects of animal feeding operations on air and water quality; Long-term CO2 and H2O flux monitoring by eddy covariance; Global climate change and field-scale carbon budgets; Instrumentation development; Remote sensing; Soil-plant-water relations; Irrigation management
Current Research Projects:
(1) Water, Carbon, and Global Climate Change: Starting in 1997, I have operated a network of long-term eddy covariance sites as part of DOE’s Ameriflux program. These towers, which provided year-round hourly measurements of carbon flux and ET, have been deployed on prairies, a cedar forest, and at livestock operations. New research is aimed at characterizing spatial variations in ET and net carbon exchange so that research done at tower sites can be scaled up to make watershed- and regional-scale estimates of water and carbon cycles.
(2) Research on Animal Feeding Operations: My research group has developed instrumentation to measure ammonia/ammonium (NHx) fluxes from feedlots using a micrometeorological technique called relaxed eddy accumulation (REA). Ultimately, research could lead to improved practices for reducing NHx losses by allowing us to measure directly how a change in diet or waste management affects emissions. Anaerobic lagoons are widely used at AFOs to store and treat waste. My research team has been a leader in developing methods for measuring seepage losses and predicting the effects on groundwater quality. (3) Instrumentation Development
I am always looking to improve our measurement capabilities such as new sensor technologies, including sap flow gauges, soil moisture probes, various chamber designs for measuring whole-canopy gas exchange, techniques for measuring seepage and gas fluxes from animal waste lagoons, new micrometeorological techniques for measuring fluxes of NH3 and other trace gases from cattle feedlots, and more recently the development of low-cost unmanned aerial vehicles (UAVs) for remote sensing of vegetation