Integrating stochastic models and in situ sampling for monitoring soil carbon sequestration

• Published in: Agricultural systems 2007-04, Vol.94 (1), p.52-62
• Main Authors: Jones, J.W., Koo, J., Naab, J.B., Bostick, W.M., Traore, S., Graham, W.D.
• Format: Article
• Language: English
• Subjects: carbon sequestration; Data assimilation; Soil carbon; Stochastic model; stochastic processes; Uncertainty
• ISSN: 0308-521X; 1873-2267
• DOI: 10.1016/j.agsy.2005.06.023

Participation in carbon (C) markets could provide farmers in developing countries incentives for improving soil fertility. However carbon traders need assurances that contract levels of C are being achieved. Thus, methods are needed to monitor and verify soil C changes over time and space to determine whether target levels of C storage are being met. Because direct measurement over the large areas needed to sequester contract amounts of C in soil is not practical, other approaches are necessary. An integrated approach is described in which an Ensemble Kalman Filter (EnKF) is used to assimilate in situ soil carbon measurements into a stochastic soil C model to estimate soil C changes over time and space. The approach takes into account errors in in situ measurements and uncertainties in the model to estimate mean and variance of soil C for each land unit within a larger land area. The approach requires initial estimates of soil C over space along with uncertainties in these estimates. Model predictions are made to estimate soil C for the next year, in situ soil C measurements update these predictions using maximum likelihood methods, and the spatial pattern of soil C mean, variance, and covariance thus evolve over time. This approach can also be used to provide yearly estimates of the changes in soil C over multiple fields, the variance in those estimates, and aggregate soil carbon mean and variance values each year. In this paper, the use of the EnKF is shown for an area in Ghana with 12 fields, comparing numbers of fields sampled each year and ways of selecting which fields to sample each year. The model predicts soil C changes over time using first order decomposition of existing soil C and addition of C from plant residues. The lowest intensity sampling method (sampling only 1/4 of the fields per year) resulted in the highest level of uncertainty in aggregate soil C estimate. Rotating sample fields each year improved the performance of the EnKF. These results demonstrated a quantifiable tradeoff between field sampling intensity and uncertainty in aggregate soil C estimates. The framework could be modified to use more complex biophysical models and to assimilate remote sensing data.