7-04: An integrated analysis tool to guide sustainable biomass production

Wednesday, April 21, 2010: 10:00 AM
Salon A-E (Hilton Clearwater Beach)
Robert P. Anex, Agricultural and Biosystems Engineering, Iowa State University, Ames, IA, David J. Muth Jr., Idaho National Laboratory, Idaho Falls, ID, J. Richard Hess, Biofuels & Renewable Energy Technologies, Idaho National Laboratory, Idaho Falls, ID, Tom L. Richard, Agricultural and Biological Engineering, Pennsylvania State University, University Park, PA and Douglas L. Karlen, Soil, Water and Air Resources Research Unit, USDA/ARS National Laboratory for Agriculture and the Environment, Ames, IA

The emerging cellulosic biofuels industry creates new sustainability challenges for agronomic systems.  For the first time, a commodity scale market will exist that consumes biomass residue and crop materials that have in the past maintained soil fertility and provided other environmental services.  To maintain soil and environmental quality we must quantify and manage the impact of biomass harvest.

This paper will describe an on-going project to develop an integrated and dynamic biomass removal analysis tool that facilitates the investigation of biomass production and harvest rates relative to six agronomic limiting factors. The six factors are: (1) Loss of soil organic matter or soil organic carbon, (2) Soil erosion, (3) Loss of plant nutrients, (4) Soil water and temperature dynamics, (5) Soil compaction, and (6) Environmental degradation. The field scale tool has been developed using existing modeling tools already adapted to specific agronomic limiting factor assessment in order to build an integrated and comprehensive toolset capable of investigating sustainable residue removal rates relative to all six limiting factors in parallel.

We discuss each of the limiting factors, describe and briefly explain the limitation, and suggest solutions, practices, and procedures to minimize the constraint to sustainable production and harvest of feedstock.  We discuss the development of the tool and demonstrate its application to two case studies based on specific field experiments.