Comparing Machine Learning Approaches for Predicting Spatially Explicit Life Cycle Global Warming and Eutrophication Impacts from Corn Production

Comparing Machine Learning Approaches for Predicting Spatially Explicit Life Cycle Global Warming and Eutrophication Impacts from Corn Production

Agriculture ranks as one of the top contributors to global warming and nutrient pollution. Quantifying life cycle environmental impacts from agricultural production serves as a scientific foundation for forming effective remediation strategies. However, methods capable of accurately and efficiently...

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Bibliographic Details
Main Authors: Xiaobo Xue Romeiko, Zhijian Guo, Yulei Pang, Eun Kyung Lee, Xuesong Zhang
Format: Article
Language:English
Published: MDPI AG 2020-02-01
Series:Sustainability
Subjects:
life cycle assessment
global warming
eutrophication
machine learning
spatial assessment
agriculture
Online Access:https://www.mdpi.com/2071-1050/12/4/1481

Internet

https://www.mdpi.com/2071-1050/12/4/1481

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