Soil Process

Soil processes are complex interactions within the soil environment, crucial for understanding agricultural productivity and environmental sustainability. Current research emphasizes developing improved predictive models for soil properties like organic carbon, focusing on causal modeling techniques to enhance the generalizability of predictions across diverse soil conditions and management practices. These models leverage both simulated and observed data, employing machine learning approaches to learn causal relationships between soil processes and improve the accuracy of predictions related to factors like fertilizer use and climate change. This improved understanding has significant implications for optimizing land management and mitigating environmental challenges.

Papers

June 15, 2023

Knowledge Guided Representation Learning and Causal Structure Learning in Soil Science
Somya Sharma, Swati Sharma, Licheng Liu, Rishabh Tushir, Andy Neal, Robert Ness, John Crawford, Emre Kiciman, Ranveer Chandra
Representation Learning Causal Discovery Method Causal Structure Learning Soil Process

November 10, 2022

Causal Modeling of Soil Processes for Improved Generalization
Somya Sharma, Swati Sharma, Andy Neal, Sara Malvar, Eduardo Rodrigues, John Crawford, Emre Kiciman, Ranveer Chandra
Causal Model Improved Generalization Cause Effect Soil Process Carbon Estimation

Soil Process

Papers

Knowledge Guided Representation Learning and Causal Structure Learning in Soil Science

Causal Modeling of Soil Processes for Improved Generalization