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.
@article{arxiv.2211.05675,
title = {Causal Modeling of Soil Processes for Improved Generalization},
author = {Somya Sharma and Swati Sharma and Andy Neal and Sara Malvar and Eduardo Rodrigues and John Crawford and Emre Kiciman and Ranveer Chandra},
journal= {arXiv preprint arXiv:2211.05675},
year = {2022}
}
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