Causal Modeling of Soil Processes for Improved Generalization
- URL: http://arxiv.org/abs/2211.05675v1
- Date: Thu, 10 Nov 2022 16:26:13 GMT
- Title: Causal Modeling of Soil Processes for Improved Generalization
- Authors: Somya Sharma, Swati Sharma, Andy Neal, Sara Malvar, Eduardo Rodrigues,
John Crawford, Emre Kiciman, Ranveer Chandra
- Abstract summary: Soil organic carbon enriches nutrition in soil, but also has a gamut of co-benefits such as improving water storage and limiting physical erosion.
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.
- Score: 6.839859020308749
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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