95-7 Poster Title: The Contribution of Spectroscopic Measurements in Characterizing Variability in SOC and Scalability to Temporal Monitoring Regimes.

Poster Number 909

See more from this Division: S01 Soil Physics
See more from this Session: Soil Change: Characterization and Modeling Across Scales: II
Monday, November 1, 2010
Long Beach Convention Center, Exhibit Hall BC, Lower Level
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Elaine N. Viglione, The Rodale Institute, Kutztown, PA, Douglas Archibald, Pennsylvania State University, State College, PA and Alison Grantham, Rodale Institute, Kutztown, PA
Monitoring Soil Organic Carbon (SOC) quantity, temporal change, and spatial distribution in various farming practices is critical to understand the practices’ effects, and to develop recommendations to enhance carbon sequestration.  Developing methods to improve our understanding of the background SOC variation is especially critical for allowing rapid detection of small SOC changes. The relationship between background SOC variation and SOC change is incompletely understood and varies by site and by practice. The quantitative information that exists on horizontal variation of soil profiles is sparse and varies, as would be expected, depending on the location of the study.  We demonstrate an approach combining  traditional laboratory measurements of SOC with data from less expensive and more abundant handheld sensor-derived SOC estimates, developed as a more flexible and universal approach to addressing local spatial variability when setting up temporal monitoring regimes on previously unsampled sites.  Historical data from traditional sampling grids on 72 subplots in the 30-year-old Farming Systems Trial (FST) provided a suitable range of subplot variability, with coefficients of variation ranging from 0.02 to 0.36, and a third of them above 0.15.  By means of an efficient and portable measurement system that provides rapid, on-site SOC data, via hand-held spectroscopy at FST, we model how to supplement the sparser lab data, with more abundant sensor estimates to improve these statistics and our sensitivity to temporal change in paired comparisons.  We discuss how to incorporate into the model the uncertainty introduced by the sensor calibration model itself, and address considerations for implementation.  The availability of spatial covariate information such as elevation, soil types and texture, and their contribution to a generalization of the subplot findings, will be touched upon, particularly their potential usage as we expand the methods to gathering baseline data from farms launching participation in Pennsylvania’s Path to Organic program in the summer of 2010.
See more from this Division: S01 Soil Physics
See more from this Session: Soil Change: Characterization and Modeling Across Scales: II