# Causal Modeling of Soil Processes for Improved Generalization

arXiv: https://arxiv.org/abs/2211.05675 · HTML: https://mlsanity.com/paper/2211.05675/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 10 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs](https://mlsanity.com/paper/1704.02901/) (2017)
- [D'ya like DAGs? A Survey on Structure Learning and Causal Discovery](https://mlsanity.com/paper/2103.02582/) (2021)
- [Simulation Intelligence: Towards a New Generation of Scientific Methods](https://mlsanity.com/paper/2112.03235/) (2021)
- [Predicting and Mapping of Soil Organic Carbon Using Machine Learning Algorithms in Northern Iran](https://mlsanity.com/paper/2007.12475/) (2020)
- [Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series](https://mlsanity.com/paper/2208.02349/) (2022)

## Built on by (most cited first, 1 of 1)

- [GPT-4 as an Agronomist Assistant? Answering Agriculture Exams Using Large Language Models](https://mlsanity.com/paper/2310.06225/) (2023)

## Abstract (arXiv)

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.