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

Level of soil carbon in agricultural systems using ensemble Kalman filter and DSSAT-CENTURY was estimated. Soil carbon could be estimated from direct measurements, but measurements had higher variability than annual soil carbon changes due to the uncertain nature of in-situ sampling methods. A base-case simulation study was set up with a continuous maize farming system in Ghana for 20 years. The results showed that the method was not effective in estimating the model carbon decomposition rate parameter, because of the low correlations of that parameter with either soil carbon or crop biomass. Given that soil organic carbon (SOC) measurement uncertainty was reduced by 60%, it was concluded that the ensemble Kalman filtering (EnKF) method using the DSSAT-CENTURY model provided more reliable estimates of SOC over time.