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Estimating Soil Carbon in Agricultural Systems using Ensemble Kalman Filter and DSSAT-CENTURY

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  Paper number  033041,  2003 ASAE Annual Meeting . (doi: 10.13031/2013.13847) @2003
Authors:   Jawoo Koo, W. McNair Bostick, James W. Jones, Arjan J. Gijsman, Jesse B. Naab
Keywords:   crop model, carbon sequestration, Kalman filtering, soil carbon model

Among various ways to sequester CO2 from the atmosphere, increasing plant productivity is an option that could also lead to increased agricultural productivity, especially in developing countries. To accept this option as a mechanism for reducing atmospheric CO2 levels, a reliable soil carbon accounting system needs to be developed. Soil carbon can be directly measured but measurements have a high error relative to annual soil carbon changes. To reduce such errors, the potential of using a biophysical model and a methodology to assimilate measurements with simulated outputs was studied. The DSSAT-CENTURY model and the Ensemble Kalman Filtering (EnKF) method were used to test if this approach can reduce the soil carbon measurement error. A base case simulation study was setup with maize farming system in Ghana for 50 years. A synthetic data set was created by introducing uncertainty in the DSSAT-CENTURY model and simulating crop growth and yield and soil carbon change for the 50-year time period. Furthermore, measurements were generated (one per year) as a random sequence, taking into account errors in measurements. Measurements of soil carbon and biomass were used to optimize simulated soil carbon, biomass estimates, and two model parameters. Results showed that EnKF reduced the soil carbon measurement errors in 41 years out of 50 years, but biomass measurement error was not significantly reduced. This new approach is expected to help develop a more reliable soil carbon accounting system and a way to aggregate estimates over space.

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