Poster Session # 5: GIS and Remote Sensing.

Monday, August 4 Presentation from 5:00 PM to 6:30 PM. SITCC Exhibit Hall B.

Grassland management and light use efficiency: Implications for remote sensing.

Conant, Richard1, Ngugi, Moffatt1, 1 Colorado State University, Fort Collins, CO, USA

ABSTRACT- Light-use-efficiency (LUE) - the efficiency with which absorbed photosynthetically active radiation is converted into plant biomass - is a key variable necessary for assessing aboveground net primary productivity (ANPP) in grasslands using remotely sensed data. Quantifying LUE is important not only for addressing scientific questions such as how management impacts grassland biogeochemistry (e.g., C fluxes), but to enable producers to make management decisions about livestock movement, forage stockpiling, and herd culling. If LUE was known or well constrained, remotely sensed data about the seasonality and amount of production could be used to answer scientific questions or make management decisions. But management directly impacts LUE, thus precluding direct assessment with remotely sensed estimates of productivity. One of the goals of this project is to investigate the degree to which management impacts LUE. We used two approaches. First, we reviewed the literature to examine how grassland LUE varies with management across different grassland physioclimatic regions. As one component of that we estimated LUE for grasslands for which ANPP data have been collected, thus generating independent estimates of LUE. Second, we evaluated the sensitivity of ecosystem model production estimates to variation in LUE in order to evaluate whether incorporating more information about LUE would lead to more accurate remotely sensed estimates of ANPP. Review of the grassland LUE literature immediately suggests that ignoring spatial, temporal, and management-induced variability in LUE will have dramatic impacts on accuracy of ANPP estimates. Our results, in which using season, -region-, and management-specific measures of LUE individually and in combination improve estimates of ANPP.

Key words: grassland, remote sensing, grazing, primary production