  Measuring and monitoring soil organic carbon is critical for agricultural
productivity and for addressing critical environmental problems. Soil organic
carbon not only enriches nutrition in soil, but also has a gamut of co-benefits
such as improving water storage and limiting physical erosion. Despite a litany
of work in soil organic carbon estimation, current approaches do not generalize
well across soil conditions and management practices. We empirically show that
explicit modeling of cause-and-effect relationships among the soil processes
improves the out-of-distribution generalizability of prediction models. We
provide a comparative analysis of soil organic carbon estimation models where
the skeleton is estimated using causal discovery methods. Our framework provide
an average improvement of 81% in test mean squared error and 52% in test mean
absolute error.
